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Molecular landscape and biomarker discovery in adrenocortical carcinoma: An integrative review of bioinformatics and translational insights
Javad Omidi
Department of Chemical Engineering, Columbia University, New York, NY 10027, USA
| ARTICLE INFO | ABSTRACT |
|---|---|
| Keywords: Adrenocortical carcinoma Biomarkers Transcriptomics Drug repositioning Systems biology | Adrenocortical carcinoma (ACC) is a rare and aggressive endocrine malignancy with limited therapeutic options and poor prognosis. Recent advances in high-throughput sequencing and integrative bioinformatics have unraveled the complex molecular landscape of ACC, highlighting critical genomic, epigenomic, transcriptomic, and immune-related alterations. This review synthesizes current evidence to provide a comprehensive overview of the key molecular mechanisms driving ACC pathogenesis. The role of recurrent mutations (e.g., TP53, CTNNB1), dysregulated cell cycle genes (e.g., CDK1, CCNB1, AURKA), non-coding RNAs, and epigenetic mod- ifications in shaping tumor behavior is discussed. Multi-omics integration and systems biology approaches have enabled the identification of robust prognostic gene signatures and protein biomarkers, offering novel tools for risk stratification. Furthermore, the tumor immune microenvironment is examined, with hypoxia, immune suppression, and checkpoint pathways highlighted as emerging targets. Finally, computational drug reposi- tioning strategies that nominate repurposed agents such as IGF1R inhibitors and BCLAF1 modulators for ther- apeutic intervention are explored. Together, these insights pave the way for precision oncology in ACC, while emphasizing the need for rigorous multi-layered validation and standardized clinical integration to enable real- world translational impact. |
1. Introduction
Adrenocortical carcinoma (ACC) is a rare but highly aggressive malignancy originating from the adrenal cortex, with an estimated incidence of 0.7-2.0 cases per million annually worldwide [1]. Despite being uncommon, ACC carries a disproportionately high mortality rate, with 5-year survival rates ranging from 10 % to 47 % depending on stage at diagnosis [2].
Due to its rarity, delayed diagnosis, and limited treatment options, ACC poses significant challenges for clinicians and researchers alike. Surgical resection remains the only potentially curative treatment, yet recurrence rates remain high even in patients undergoing complete resection [3]. Systemic therapies such as mitotane or platinum-based chemotherapy offer limited benefit, and there is a pressing need for improved diagnostic, prognostic, and therapeutic tools [4].
In the past two decades, high-throughput sequencing and integrative bioinformatics have improved our understanding of ACC’s molecular landscape. Several studies have explored genetic and epigenetic alter- ations, transcriptomic signatures, and immune-related features, identi- fying key genes and pathways such as TP53, IGF2, Wnt/ß-catenin, and
the cell cycle machinery [5-7]. Notably, integrative analyses have revealed distinct gene expression profiles distinguishing ACC from benign adrenal tumors, enabling the identification of diagnostic and prognostic biomarkers [8,9]. Moreover, multi-omics studies and pro- tein-protein interaction (PPI) networks have facilitated the recognition of hub genes with prognostic value, including CDK1, CCNB1, TOP2A, BUB1B, and CEP55 [2].
The tumor microenvironment (TME) in ACC has also gained atten- tion, particularly in relation to immune infiltration, hypoxia, and im- mune checkpoint molecules such as PD-L1 and CTLA-4. These insights offer potential avenues for immunotherapy and personalized medicine [4]. Additionally, recent efforts in computational drug repositioning have identified candidate therapeutics such as Cosyntropin and IGF1R inhibitors as potential treatments for ACC, circumventing the limitations of de novo drug discovery [10].
This review synthesizes recent bioinformatics-driven studies on ACC, highlighting key molecular mechanisms, biomarkers, immune landscape features, and therapeutic opportunities. It also identifies major knowl- edge gaps and proposes future research directions. By systematically integrating and contrasting findings across a broad range of peer-
reviewed studies [1-39], this review establishes a unified and up-to-date framework for understanding the molecular and translational landscape of ACC, something not previously available in the literature. A graphical overview of the review framework and thematic structure is presented in Fig. 1, which visually maps the key molecular and clinical domains addressed in the subsequent sections.
2. Molecular pathogenesis of ACC
2.1. Genetic and epigenetic alterations
ACC displays a heterogeneous spectrum of genetic and epigenetic alterations contributing to its aggressive clinical course. One of the most recurrent genetic events involves TP53, frequently mutated in pediatric cases and linked to Li-Fraumeni syndrome in adults [5]. The oncogenic role of AURKA in destabilizing TP53 has been further highlighted as a targetable vulnerability in ACC. Recent integrative analyses also revealed a survival-associated proliferative gene module in ACC, in which AURKA, POLD1, RECQL4, CDC7, FANCI, and DTL form a TP53-centered protein interaction network. This mitotic control hub cooperates with TP53 dysfunction to drive tumor aggressiveness (Fig. 2a).
Alterations in the Wnt/ß-catenin pathway are common, particularly CTNNB1 mutations and ZNRF3 deletions. These changes promote tumor proliferation and invasiveness [5]. Additionally, overexpression of IGF2 and its embedded miR-483, along with suppression of H19/miR-675, are repeatedly observed, reinforcing the role of epigenetically modulated growth signaling. Consistent with this, Giordano et al. reported pro- nounced IGF2 overexpression in 10 of 11 ACC tumors, underscoring IGF2 as a dominant growth driver in ACC pathogenesis [9].
Emerging genomic analyses have revealed novel mutated genes such as KIF23, POLD1, and TPX2, several of which exhibit interactions with TP53 [11]. The functional importance of kinesin family genes in ACC progression and immune evasion mechanisms is now being recognized.
At the epigenetic level, DNA methylation is a major contributor to transcriptional deregulation in ACC. Guan et al. [12] categorized tumors into three methylation-based clusters with distinct survival outcomes (Fig. 2b). These subtypes demonstrate different enrichment in immune and metabolic pathways and diverge in DNA repair and mismatch repair
Multi-Omics Integration and Systems Biology in ACC
1
Molecular Pathogenesis of ACC
6
o Genetic and Epigenetic Alterations
o Transcriptomic Signatures
2
Therapeutic Targets &
ADRENCORTICAL CARCINOMA (ACC)
Prognostic & Predictive Biomarkers
Drug Repositioning
o Gene-based Signatures o Protein Markers and Immunohistochemistry
5
Pediatric vs. Adult ACC: Comparative Genomics
Tumor Microenvironment & Immune Landscape
3
4
profiles.
Long non-coding RNAs (lncRNAs) play central roles in the epigenetic regulation of ACC. For instance, LINC00271 is significantly down- regulated in ACC and associated with chromosome segregation and Wnt signaling dysregulation [13]. Moreover, PAX8-AS1, KCNQ1OT1, and NEAT1 participate in competitive endogenous RNA networks, particu- larly through the BIRC5-miR-335-5p-PAX8-AS1 axis strongly corre- lated with poor prognosis [14]. MicroRNAs (miRNAs) also represent critical regulatory elements in ACC. Tömböl et al. [15] identified dif- ferential expression of miR-184, miR-503, and miR-511 distinguishing ACC from benign tumors. These miRs influence pathways such as G2/M checkpoint damage, indicating potential biomarker roles.
Transcriptomic profiling by Di Dalmazi et al. [16] identified novel fusion transcripts including EXOSC10-MTOR and AKAP13-PDE8A, which may impact cAMP and mTOR signaling. Such gene fusions pro- vide further insight into ACC heterogeneity and therapeutic resistance (Fig. 2c). Epigenetic modifiers such as DNMT3B, EZH2, and UHRF1 were found to be overexpressed in ACC compared to normal adrenal glands. These regulators influence chromatin remodeling and DNA methylation, underlining their oncogenic potential [7]. Telomere maintenance mechanisms (TMMs) have also emerged as relevant in ACC, where ALT (alternative lengthening of telomeres) and telomerase activation coexist, correlating with poor prognosis in specific subgroups [17]. Finally, dysregulation of lipid metabolism genes, particularly within sphingolipid and steroid synthesis pathways, has been linked to progression and worse prognosis in ACC, suggesting a potential meta- bolic vulnerability [18].
The molecular pathogenesis of ACC is strongly influenced by both genetic and epigenetic alterations, which drive tumor initiation, pro- gression, and metastatic potential. Several recent studies have employed integrative bioinformatics approaches to uncover these aberrations, of- fering valuable insights into their prognostic and therapeutic implications.
Recent mechanistic studies further demonstrate that non-coding RNA-mediated regulation plays a central role in shaping tumor signaling, immune evasion, and therapy resistance in ACC. For instance, evidence from glioma and other cancer models has shown that circRNAs modulate treatment response through Ras/Raf/ERK and PI3K/AKT cascades, influencing proliferation and resistance phenotypes [45,46]. Long non-coding RNAs similarly regulate oncogenic pathways such as Wnt, STAT3, and EZH2, driving cell cycle progression and metastatic behavior [47]. Moreover, natural antisense transcripts exert transcrip- tional and epigenetic control across multiple cancer settings, providing additional layers of regulatory complexity [48]. Key oncogenic signaling modules, particularly the PI3K-AKT-mTOR axis, have emerged as convergence points linking non-coding RNA activity with metabolic reprogramming and proliferative signaling [49-51]. Collectively, these mechanistic insights highlight ncRNA-centered regulatory networks as critical determinants of tumor behavior and suggest that similar RNA-mediated signaling architectures may drive pathogenesis, hetero- geneity, and therapeutic vulnerabilities in ACC [52].
One of the most significant epigenetic modifications implicated in ACC is N6-methyladenosine (m6A) RNA methylation, which represents the most prevalent internal modification of mRNA and plays a critical role in regulating mRNA stability, splicing, and translation. Xu et al. [19] comprehensively profiled m6A regulators in ACC and identified a prognostic signature composed of RBM15 and HNRNPC, two key genes involved in the m6A pathway. Notably, HNRNPC promotes prolifera- tion, migration, and invasion in ACC cells, supporting its oncogenic function. Moreover, patients with high m6A risk scores exhibited sup- pressed immune-related pathways and reduced expression of immune checkpoint molecules such as PD-L1 and CTLA4, indicating a link be- tween epigenetic regulation and immune evasion in ACC progression.
Another key regulatory mechanism involved in ACC is alternative splicing (AS). Xu et al. [20] conducted a genome-wide analysis of AS events using TCGA SpliceSeq data and revealed 3919 events
Enrichment Score
Subtype
Enrichment Score
1
Subtype Laterality
1
TACC3
NEK2
Laterality
0.5
Gender
0
Gender
0.5
0
a
(b)
Age
-
Age
-0.5
Stage
-0.5
Stage
-1
N-Glycan Biosynthesis
-1
APICAL JUNCTION
Cher Types of OGlycan Biosynthesis
RRM2
EPITHELIAL MESENCHYMAL_TRANSITION
MYOGENESIS
Glycosphingolipid Biosynthesis
AURKA
APICAL SURFACE
Glycogen Biosynthesis
Hexcsammine Biosynthesis
HELLS
INTERFERON_ALPHA_RESPONSE
Edekaphase Metabolism
HAYANATUA NO RESPONSE
Shingolipid Metabolism
19 STATS SIGNALING
Fattone
ALLOGRAFT REJECTION
Retinoid Metabolism
16 JAK STATS SIGNALING
Mucin Type O Glycan Biosynthesis
Glycogen Degradation
INFLAMMATORY_RESPONSE
Vitamin k
EXO
FANCI
Glycosaminoglycan Biosynthesis
THEA SAMMA
INFA SIGNALING_VIA_NFKB
Bon Metabolism
BILE ACID METABOLISM
Lipoic Acid Metabolism
DPYD
OXIDATIVE PHOSPHORYLATION
Polyamine Biosynthese
DTL
PEROKISOME ADIPOGENESIS
-Glutamine and D-Glutamate Metabolism Thiamine Metabolism
FATTY ACID METABOLISM
Transsuturation
TP53
KRAS SIGNALING ON
Taurine and Hypotaurine Metabolism
ESTROGEN RESPONSE EARLY
Cartoon boende Metabolism
RESPONSE- TATE
XENOBIOTIC METABOLISM
Other Glycan Degradation
FANCD2
REACTIVE_OKYGEN_SPECIES_PATHWAY
Porphyrin and Chlorophyll Metabolism
GLYCOLYSIS
Peritose and Glucuronste Interconversions
UV-RESPOROT HOMEOSTASIS
Ascorbate and Aldrate Metabolism
WTORCT SIGNALING
SPERMATOGENESIS
Brug Metabolism by other enzymes
G2M CHECKPOINT
Nicotinamide Adenine Dinucleotide Biosynthesis
Nicotinate and Nicotinamide Metabolism
Metabolism of Xenobiotics by Cytochrome P450
CDC7
RFC5
UNFOLDED PROTEIN_RESPONSE
ONA REPAR
Metabolism
MYC TARGETS V2
Steroid Hormone Biosynthesis
WNT BETA_CATENIN_SIGNALING HEDGEHOG SIGNALING
Phenylalanine Metabolism
NOTCH SIGNALING
Cardiolipin Metabolism
RECQL4
TGF BETA SIGNALING
Codative Phosphorylation
Nitrogen Metabo st Metabolism
POLD1
POK AKT MTOR SIGNALING
ANDROGEN RESPONSE
Pantothenate and CoA Biosynthesis
(c)
Dopamine Biosynthesis
PROTEIN SECRETION
HEME_METABOLISM
Epinephrine Bicisynthesis
Norepinephone Biosynthesis
Samples in TCGA-ACC cohort
AKAP13-PDE8A
Enrichment Score
her Led Megom
Subtype Laterality Gender
1
alpha- Linoleie Ack Metabolism
Cyclooxygenase Arachidonic Acid Metabolism
0.5
Štostanoid Biosynthesis
Neomycin, Kanimysin and Gentamicin Biosynthesis
40
0
TGATACATTGGAGACCATTGCTCCTGAAAACCTCAACAAAGACTAAGCC
50
60
70
90
Stage
WP_ONA_MISMATCH_REPAIR
-0.5
Starch and Suclose Metabolism
Alachiconic Add Metabolism
REACTOME_MISMATCH_REPAIR
-1
Retinoic Acid Metabolism
Prostaglandin Biosynthesis
KEGG_MIŞMATCH_REPAIR
Glutathione Metabolism
KEGG_P53_SIGNALING_PATHWAY
Fructose and Mannose Metabolism
gluconeogenesis
BIOCARTA_RB_PATHWAY
BIOCARTA_P53_PATHWAY
Allinine, Aspartate and Glutamate Metabolism
ST_WNT_BETA_CATENIN_PATHWAY KEGG_WNT_SIGNALING_PATHWWWY
Urea Cycle
Arginine Bigsynthesis
Fatty Acid Elongation
WNT_SIGNALING
sosynthesis of Unsaturated Faty Acids
GO_CHROMATIN_REMODELING
Valine, Leucine and Isoleucine Degradation
REACTOME_CHROMATIN_MODIFYING_ENZYMES
Citric Acid Cycle
GO_NOTCH_SIGNALING_PATHWAY
Pycocylate and Dicarboxylate Metabolism
Nicoanamide Adenine Me-poism
chromosome 15
chromosome 15
L
REACTOME_SIGNALING_BY_NOTCH
PID_NOTCH_PATHWAY
Ketone Biosynthesis and Metabolism
q25.3
925.3
Samples in TOGA-ACC cohort
Pyruvate
Sulfur Metabolism
Selenocompound Metabolism
Vitamin B6 Metabolism
breakpoint
breakpoint 15.85526367
Subtype
Stage
Glycine, Serine and Threonine Metabolism
15.86064806
pane, Leucine and Isoleucine Biosynthesis
Pentose Phosphate
Coverage
ACC1
Ubiquinone and other Terpenoid-Quinone Biosynthesis
ACC2
Stage I
atty Acid Biosynthesis
Pherylalanine Tyrosine and Tryptophan Biosynthesis
0-
ACC3
Stage II
?
2
Stage III
Stage IV
Arginine and Proline Metabolism Esdine Metabolism
AKCAP13
PRESA
Age
unknown
Tryptophan Metabolism
80
Homocysteine Biosynthesis Merthion ine Cype
ENST00000561343-2
ENST00000310296.4
olate One Carbon Metabolism
60
Pyrimidine Biosynthesis
Purine Metabolism
AGTTCTGATACATTGGAGACCATTGCTOCTGAAAACCTCAACAAAGACTAAGCCCAGAAAC
5. kbp
40
Laterality
Gender
Pyrimidine Metabolism
introns not to scale
erpenod Backbone Biosynthesis
20
Left
FEMALE
herold Bios ynthesis
0
Right
MALE
Aldosterone Biosynthesis
Cortisol Biosynthesis stradiol Blosynthesis
Samples in TCGA-ACC cohort
Testosterone Biosynthesis
(d)
si NC si Bclaf1
EV
FL
si NC
si Bclaf1
EV
FL
1.5
2.0
*
*
*
*
*
145kDa
Bclaf1
Relative protein levels
*
Relative protein levels
1.0
1.5
34kDa
CDK1
1.0
58kDa
Cyclin B1
0.5
0.5
ß-actin
0.0
Bclaf1
CDK1
Cyclin B1
0.0
Bclaf1
CDK1
Cyclin B1
NCI-H295R
si NC si Bclaf1
sh NC sh Bclaf1
si NC
si Bclaf1
sh NC
sh Bclaf1
145kDa
Bclaf1
Relative protein levels
1.5
**
*
*
Relative protein levels
1.5
**
*
*
34kDa
CDK1
1.0
1.0
T
T
58kDa
Cyclin B1
₡0.5
0.5
ß-actin
₾0.0
CDK1
0.0
Bclaf1
Cyclin B1
Bclaf1
CDK1
Cyclin B1
SW-13
significantly associated with overall survival. Among these, exon skip- ping (ES) was the most prevalent, while mutually exclusive exons (ME) were rare. Prognostic AS signatures, especially those involving alter- native acceptor (AA) and retained intron (RI) events, showed high predictive accuracy (AUC > 0.9). A correlation network between splicing factors (SFs) and survival associated AS events identified EIF3A and HNRNPR as hub SFs, suggesting a tight interplay between tran- scriptional regulation and tumor aggressiveness.
At the genomic level, Zhou et al. [3] highlighted the oncogenic role of Bcl-2-associated transcription factor 1 (Bclaf1) in ACC. Using RNA-seq and experimental validation, the authors demonstrated that Bclaf1 upregulates CDK1 and Cyclin B1 (CCNB1), which are essential drivers of G2/M cell cycle progression. Silencing Bclaf1 significantly impaired ACC cell proliferation and altered cell cycle dynamics, positioning it as a potential therapeutic target (Fig. 2d).
Additional genetic aberrations were identified by Zhang et al. [21], who performed integrative bioinformatics analysis across multiple GEO datasets. The study identified a panel of hub genes, including MYC, MMP9, CDC20, and KIF2C associated with metastasis and poor survival. These genes are involved in key pathways such as cell cycle regulation, immune evasion, and extracellular matrix remodeling, highlighting the multifactorial nature of ACC progression at the molecular level. Com- plementing these findings, Ye et al. [22] constructed a competing endogenous RNA (ceRNA) network involving interactions among mRNAs, miRNAs, and lncRNAs. Notably, the HOX- A11-AS/miR-24-3p/CDK1 axis emerged as a key regulatory module, linking non-coding RNA-mediated control with cell cycle dysregulation.
This ceRNA sub-network suggests novel post-transcriptional mecha- nisms contributing to ACC tumorigenesis and prognosis.
2.2. Transcriptomic signatures
Multiple transcriptomic studies consistently show that mitotic dys- regulation is a key molecular feature of ACC. A landmark microarray study by Giordano et al. demonstrated that global transcriptional profiling robustly distinguishes ACC from adrenocortical adenomas and normal cortex, with marked overexpression of IGF2 and significantly elevated proliferation-associated markers such as TOP2A and Ki-67 identified as defining molecular features of malignancy [9]. While the methodologies employed, ranging from traditional Differentially expressed genes (DEG) screening to co-expression analysis, hypoxia modeling, and miRNA integration differ in scope and scale, they remarkably converge on a core proliferative signature that underpins tumor progression.
A foundational study by Xing et al. [2] reported 228 DEGs in ACC, enriched in cell cycle processes and chromosomal dynamics. Among the 14 hub genes identified, AURKA, EZH2, and RRM2 stood out for their association with poor prognosis. However, when the lens is shifted to pediatric ACC, Kulshrestha et al. [6] arrived at a nearly parallel conclusion. They pinpointed CDK1, CDC20, and BUB1B as critical hubs across both PPI and GGI networks.
Taking a different angle, Chen et al. [4] integrated hypoxia into transcriptomic stratification and identified a three-gene signature (CCNA2, COL5A1, EFNA3) that correlated with both immune
(a)
stage
3
CCNA2
EFNA3
COL5A1
15
0.093
0.65
0.28
2
0.0001
0.17
0.0015
EFNA3*
0.014
0.049
0.03
1
0.0035
0.021
0.045
0
0.065
0.0065
0.25
Gene expression
10
0.83
0.18
0.93
-1
-2
CCNA2 ***
-3
5
Stage I
Stage II
COL5A1 **
Stage III
Stage IV
Stage I
Stage II
Stage III
Stage IV
Stage I
Stage II
Stage III
Stage IV
Stage I
Stage II
Stage III
Stage IV
(b)
Cell cycle
(c)
(d)
30
Cellular senescence
3
ACC-UMAP1
ACC-UMAP2
25
Progesterone-mediated oocyte maturation
2
20
Oocyte meiosis
Count
1
GSE10927
-LogFDR
2
0
3
p53 signalling pathway
UMAPZ
15
4
-1
5
Antifolate resistance
6
-2
10
p.adjust
-3
DNA replication
5
0.04
-4
Folate biosynthesis
0.03
0.02
-8
-6
-4
-2
0
UMAP1
2
4
6
8
0
0.01
One carbon pool by folate
4
2
0
N
T
0.10
0.15
0.20
Log,FC O2
GeneRatio
suppression and proliferation (Fig. 3a). Rather than focusing on indi- vidual genes, Yuan et al. [23] applied Weighted Gene Co-expression Network Analysis (WGCNA) to identify co-expressed gene modules linked to clinical traits. Guo et al. [24] approached the question through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrich- ment. Their analysis revealed pathways such as G2/M checkpoint regulation and DNA replication to be dominantly enriched, with central roles played by CDK1, CCNB1, and TOP2A (Fig. 3b).
The prognostic relevance of these transcriptomic alterations is underscored by Yin et al. [25], who demonstrated that overexpression of AURKA, TOP2A, and CDK1 correlated with reduced overall survival and higher tumor grade. Notably, not all transcriptomic changes reflect activation. Xu et al. [26] identified downregulated hub genes including EFEMP2, MFAP4, and CSRP1, associated with extracellular matrix or- ganization and differentiation. While their expression patterns were not strong predictors of overall survival, their silencing suggests a shift to- ward de-differentiation and tissue disintegration, offering a comple- mentary view to the proliferation-focused findings.
A novel subtype of ACC was proposed by Knott and Leidenheimer [27], who found that tumors overexpressing ABAT and GABRB2, involved in GABA metabolism, were associated with favorable prog- nosis. This metabolic signature, though outside the mitotic axis, suggests a biologically distinct subclass that may warrant different therapeutic strategies. Moreover, Tömböl et al. [15] provided a regulatory perspective by integrating mRNA and miRNA profiles. They identified miR-503 and miR-511 as miRNAs that post-transcriptionally target key mitotic regulators, including CDK1 and TOP2A. These findings point to a multi-layered regulatory framework, where both transcriptional and post-transcriptional mechanisms converge on a common proliferative program.
The studies indicate that ACC transcriptomic profiles are dominated by mitotic and cell cycle dysregulation. The recurring identification of CDK1, CCNB1, AURKA, and CCNA2 across methods, datasets, and tumor subtypes confirms their role as central drivers of tumor biology. Beyond classical DEG and pathway-based studies, several recent investigations have broadened the understanding of ACC transcriptomics through pan- cancer comparisons, multi-omics integration, and machine learning — driven biomarker discovery, highlighting previously underappreciated layers of transcriptomic complexity.
Crona et al. [28] approached ACC from a pan-cancer perspective, analyzing over 3300 tumors across 35 cancer types using TCGA and TARGET datasets. Their unsupervised clustering revealed that ACC forms a distinct and homogeneous transcriptomic cluster, aligning more closely with neuroendocrine tumors such as gliomas and neuroblas- tomas than with epithelial malignancies. These findings challenge con- ventional clinicopathological classifications and suggest a shared developmental origin may underlie transcriptomic similarities among neural crest-related tumors. Notably, one sarcomatoid ACC outlier clustered with soft tissue sarcomas, highlighting potential transcrip- tional reprogramming in histologically variant subtypes.
In contrast, Marquardt et al. [29] employed unsupervised clustering and random forest-based machine learning to reclassify ACC into survival-associated subgroups using only mRNA data (Fig. 3c). Their method reidentified the known C1A (poor prognosis) and C1B (better prognosis) clusters but further refined the subgrouping by identifying novel prognostic biomarkers, such as SOAT1 and EIF2A1, absent from traditional DEG lists. Interestingly, one cluster (UMAP1) aligned more closely with benign adrenal tumors, suggesting that transcriptomic distance from normal tissue may reflect malignancy grade and outcome more accurately than histology alone.
Building on multi-omics, Li et al. [30] integrated gene expression data from both GEO and TCGA to identify 490 DEGs (28 upregulated, 462 downregulated), with CCNB1 and NDC80 emerging as robust hub genes through multiple ranking algorithms (Fig. 3d). These genes, confirmed across KEGG, Reactome, and STRING analyses are central to mitotic spindle assembly and chromosomal segregation. While CCNB1
has been widely reported, NDC80 represents a less frequently empha- sized but potentially powerful biomarker, especially given its consistent upregulation and prognostic association in ACC. Their approach exem- plifies how cross-validation across platforms strengthens biomarker confidence.
Transcriptomic profiling studies in ACC consistently demonstrate coordinated dysregulation of proliferative and metabolic signaling programs. Omidi [31] reported that CKS2 (cell-cycle progression) and ACAT2 (lipid metabolic flux) are co-upregulated in tumors relative to normal adrenal tissue, reflecting a reprogrammed transcriptional state that supports accelerated growth and metabolic sustenance. This coor- dinated expression shift provides an early indication of a synergistic oncogenic transcriptional signature that becomes more pronounced at the biomarker level.
Complementing this, Di Dalmazi et al. [16] focused on genotype — transcriptome correlations, leveraging RNA-seq and mutational data from 59 adrenocortical tumors. Their analyses uncovered two tran- scriptomic clusters: one enriched in CTNNB1 and PRKACA mutations, associated with cortisol-producing adenomas; and another with wild-type status or GNAS mutations. Interestingly, several cases without known drivers still showed transcriptomic profiles close to ACC, sug- gesting undiscovered regulatory alterations, possibly involving lncRNAs or alternative splicing. Indeed, their study was among the first to sys- tematically investigate lncRNA and gene fusion landscapes in adrenal tumors, expanding the definition of transcriptomic dysregulation beyond coding genes.
These studies reveal that transcriptomic classification of ACC is no longer a one-dimensional exercise. Dimensionality reduction, cross- platform gene validation, integration of lncRNA and fusion data, and machine learning-based stratification have each provided new angles of insight. When taken with previous DEG and pathway-based findings, they illustrate that transcriptomic heterogeneity in ACC is both biolog- ically significant and clinically relevant and that unified models must accommodate molecular diversity ranging from mitotic activation to metabolic or neuroendocrine deviation.
Table 1 summarizes the key genes, their functional roles, supporting evidence, and prognostic value. The genes summarized in Table 1 converge on key biological themes that underlie ACC aggressiveness. A large subset, including CDK1, CCNB1, CDC20, BUB1B, TOP2A, and CKS2 reflects pervasive dysregulation of mitotic control, supporting the view that aberrant cell cycle progression is a defining hallmark of ACC. Additional regulators such as EZH2 and HNRNPC underscore the contribution of epigenetic remodeling and RNA-processing pathways. Meanwhile, alterations in ECM remodeling (COL5A1, EFEMP2), im- mune modulation (EFNA3), and metabolic reprogramming (ACAT2, SOAT1) indicate that tumor invasion and metabolic adaptation also shape disease progression. Notably, several of these genes demonstrate strong prognostic value, reinforcing their potential utility in risk strati- fication and the development of targeted therapeutic strategies in ACC.
2.3. Methodological overview of multi-omics analyses
Multi-omics analyses in ACC research often rely on system-level network approaches to contextualize gene expression patterns within broader regulatory architectures. Weighted Gene Co-expression Network Analysis (WGCNA) identifies sets of genes that exhibit coor- dinated expression across samples, thereby capturing shared regulatory control rather than isolated transcriptional changes. By transforming pairwise expression correlations into a network topology, WGCNA al- lows the detection of gene modules that can be associated with clinical traits, molecular subtypes, or disease outcomes. Hub genes within these modules represent central regulatory nodes that may serve as potential biomarkers or therapeutic targets by virtue of their influence on module- level behavior.
In parallel, ceRNA network modeling elucidates interactions among coding and non-coding transcripts based on shared miRNA binding
| Gene | Function | Evidence Source | Prognostic Value |
|---|---|---|---|
| CDK1 | Cell cycle regulation, mitosis | Xing et al. (2019) [2] | High |
| Kulshrestha et al. (2016) [6] | |||
| Guo et al. (2020) [24] | |||
| Yin et al. (2023) | |||
| [25] | |||
| CCNB1 | G2/M checkpoint control | Kulshrestha et al. (2016) [6] | High |
| Guo et al. (2020) [24] | |||
| Yin et al. (2023) | |||
| [25] | |||
| AURKA | Mitotic spindle formation, proliferation | Xing et al. (2019) [2] | High |
| Yin et al. (2023) [25] | |||
| EZH2 | Epigenetic regulation (PRC2 complex) | Xing et al. (2019) [2] | Moderate |
| RRM2 | DNA synthesis and repair | Xing et al. (2019) [2] | High |
| CCNA2 | Cell cycle, hypoxia signature | Chen et al. (2021) | High |
| component | [4] | ||
| Guo et al. (2020) [24] | |||
| Yin et al. (2023) [25] | |||
| COL5A1 | ECM remodeling, hypoxia- associated gene | Chen et al. (2021) [4] | Moderate |
| EFNA3 | Cell signaling, immune infiltration | Chen et al. (2021) [24] | Moderate |
| CDC20 | Chromosome segregation, mitosis | Kulshrestha et al. (2016) [6] | High |
| BUB1B | Spindle checkpoint regulation | Kulshrestha et al. (2016) [6] | High |
| TOP2A | DNA topology during mitosis | Guo et al. (2020) [24] | High |
| Yin et al. (2023) [25] | |||
| ABAT | GABA metabolism, | Knott and | Favorable |
| mitochondrial function | Leidenheimer (2020) [27] | (High) | |
| GABRB2 | GABA receptor subunit, neuroendocrine role | Knott and Leidenheimer (2020) [27] | Moderate |
| EFEMP2 | ECM structure, cell adhesion | Xu et al. (2020) | Low (Disease- |
| [26] | free survival) | ||
| MFAP4 | Microfibril-associated protein | Xu et al. (2020) [26] | Low |
| NDC80 | Kinetochore function, chromosomal segregation | Li et al. (2022) [30] | High |
| SOAT1 | Sterol O-acyltransferase activity, lipid metabolism | Marquardt et al. (2021) [29] | Moderate |
| EIF2A1 | Translation initiation factor, stress response | Marquardt et al. (2021) [29] | Moderate |
| AKAP13- | Fusion gene, cAMP signaling | Di Dalmazi et al. (2020) [16] | Unknown |
| PDE8A | disruption | ||
| CTNNB1 | Wnt/ß-catenin pathway activation | Di Dalmazi et al. (2020) [16] | Moderate |
| PRKACA | Protein kinase A catalytic subunit, cAMP pathway | Di Dalmazi et al. (2020) [16] | Moderate |
| GNAS | G-protein alpha subunit, cAMP pathway | Di Dalmazi et al. (2020) [16] | Moderate |
| IGF2 | Growth factor signaling; drives autocrine proliferative loops | Giordano et al. (2003) [9] | High |
| MKI67 (Ki-67) | Proliferation index; reflects mitotic activity and tumor grade | Giordano et al. (2003) [9] | High |
| CKS2 | Cell-cycle progression; mitotic checkpoint control | Omidi (2025) [31] | High |
| ACAT2 | Lipid and fatty-acid metabolic reprogramming; supports tumor energy flux | Omidi (2025) [31] | High |
relationships. Within this framework, transcripts that compete for the same miRNAs can modulate each other’s abundance, forming interde- pendent regulatory axes across mRNAs, lncRNAs, and other RNA spe- cies. Incorporating expression correlations and miRNA-target interaction data enables the reconstruction of ceRNA networks that reflect post-transcriptional regulatory dynamics. These inferred net- works help identify regulatory hubs that contribute to ACC-specific transcriptional states and may underlie tumor progression or thera- peutic response.
To further contextualize biologically relevant gene sets, PPI analysis is used to map functional connectivity among candidate genes, high- lighting densely interconnected clusters and putative driver nodes. When these network features are integrated with survival modeling approaches such as Kaplan-Meier or Cox proportional-hazards (Cox) regression, specific hub genes can be evaluated for prognostic value in clinical cohorts. Subsequent pathway enrichment using Gene Ontology (GO) and KEGG frameworks enables functional interpretation of these gene clusters, linking molecular signatures to defined cellular pathways and oncogenic processes. This combined analytical strategy facilitates the translation of multi-omic signals into mechanistically grounded biomarkers and stratification tools relevant to precision oncology in ACC.
3. Prognostic and predictive biomarkers
3.1. Gene-based signatures
Integrative transcriptomic analyses have provided fertile ground for developing gene-based prognostic signatures in ACC, revealing both converging biological principles and methodological diversity. A comprehensive summary of these gene-based prognostic models is provided in Table 2.
Hypoxia-associated gene signatures have shown particular promise. Chen et al. [4] developed a three-gene model, CCNA2, COL5A1, and EFNA3 demonstrating that high-risk ACC patients exhibit poorer overall survival and altered immune landscapes. Lipid metabolism-related signatures were described by Subramanian et al. [18], where several genes involved in cholesterol and phospholipid metabolism (e.g., SGPL1, FDFT1, SQLE, PIK3C2B, DGAT1, PLD1) showed stage-dependent expression patterns and significant survival associations in ACC (Fig. 4a). Similarly, Guo et al. [24] identified nine hub genes, including CCNB1 and CDK1, strongly associated with poor prognosis via KEGG and PPI analyses.
A comprehensive multi-omics approach by Li et al. [30] confirmed CCNB1 and NDC80 as key prognostic genes across datasets, validated by multiple algorithms. Meanwhile, Li et al. [32] explored the TME and used immune/stromal scores to uncover 18 hub genes linked to prog- nosis, providing a broader transcriptomic context. GABAergic signaling genes were also found to have prognostic significance. Knott and Lei- denheimer [27] showed that high expression of ABAT, GABRB2, and GABRD is associated with favorable outcomes in some ACC patients, suggesting metabolic heterogeneity (Fig. 4b). Li et al. [11] focused on the kinesin family genes (KIFs) and identified KIF4A, KIF11, and PLK1 as genes whose overexpression was correlated with tumor stage and im- mune infiltration.
Unsupervised ML-based transcriptomic clustering, as demonstrated by Marquardt et al. [29], identified two prognostic ACC subtypes (UMAP1/C1B and UMAP2/C1A) with distinct expression profiles. SOAT1 and EIF2A1 emerged as novel markers in this analysis. The role of lncRNAs in prognosis was investigated by Buishand et al. [13], who found that downregulation of LINC00271 was significantly associated with poor survival and dysregulation of cell cycle pathways. Finally, integrated mRNA-miRNA-IncRNA regulatory networks were explored by Subramanian et al. [14], highlighting a prognostic axis involving BIRC5, miR-335-5p, and PAX8-AS1 as a candidate biomarker triplet.
A comprehensive genomic analysis by Sun and Zhang [5] revealed a
| Model/Study | Genes Included | Method | Prognostic Value |
|---|---|---|---|
| Buishand et al. (2020) [13] | LINC00271, HOTTIP, CHL1, HOXA11-AS1, CRNDE, FAM211A-AS1, TBXAS1 SGPL1, FDFT1, SQLE, PIK3C2B, PIK3CD, SYNJ2, DGAT1, PLA2G16, PLD1, GPD1, CHPT1 | Human LncRNA/mRNA Microarray, TaqMan qRT- PCR, TCGA survival analysis | Only LINC00271: High |
| Subramanian et al. (2021) [18] | Integrated bioinformatics analysis using GEO datasets (GSE19750, GSE10927, GSE12368), DAVID, STRING, Cytoscape (MCODE), TCGA validation, survival analysis with R2 and Cox regression | High expression of SGPL1, FDFT1 and SQLE, and low expression of PIK3C2B, PIK3CD, SYNJ2, DGAT1, PLA2G16, PLD1, GPD1, CHPT1 associated with poor survival | |
| Subramanian et al. (2023) [14] Sun-Zhang et al. (2024) [5] Ye et al. (2020) [22] | CDK1, CCNB1, PRC1, BIRC5, GINS1, TPX2 (upregulated), RSPO3, NR2F1, TLR4, USP53, HOXA5, SLC16A9 (downregulated) A2ML1, ABCC2, ABCC9, ALDH5A1, ANGPT2, ARTN, AURKA, BMP1, C7, CCNB2, CCDC150, CDC27, CDC7, CDK1, CDKN3, CDCA7, CHAF1B, CRNDE, DIAPH3, DPYD, DTL, EML1, ETV5, EXO1, FANCD2, FANCI, GAS2L3, GINS1, HELLS, HOOK1, HTRA3, IQGAP3, KIF23, KIF2C, KNTC1, LTBP1, NEK2, PHF19, PHACTR2, PNLIPRP3, POLD1, POLQ, RECQL4, RFC5, RRM2, SDC4, SH3D19, SLC7A14, SLC9A9, TACC3, TMEM150C, TPX2, USP53 mRNAs: CDK1, CCNB1, MCM4, MAD2L1, AURKA, NCAPG, RRM2, TYMS, TPX2; miRNAs: miR-212-3p, miR-24-3p, let-7a-5p, miR-196a- 5p; lncRNA: HOXA11-AS | Integrated analysis of 8 GEO datasets (GSE143385, GSE22816, GSE49279, GSE143383, GSE19750, GSE12368, GSE90713, GSE14922) using GEO2R, miRNet, DAVID, R2 Kaplan-Meier survival analysis Integrated analysis of TCGA, GEO (GSE10927), and YKD cohorts using RNA-seq, WES, differential expression analysis, Cox regression, and protein-protein interaction analysis Differential expression from GEO (GSE12368, GSE19750), GO/KEGG via DAVID, PPI via STRING, Hub gene detection via Cytoscape, miRNA prediction via miRTarBase, lncRNA prediction via miRNet, survival analysis via cBioPortal, OncomiR, and GEPIA | All 12 hub genes significantly associated with poor overall survival in TCGA (P < 0.01) |
| All 45 genes in the signature were significantly associated with poor survival, even after adjusting for stage and age (P < 0.001); patients with mutations in these genes had median survival of 18.1 months vs. 80.2 months All 9 key mRNAs (CDK1, CCNB1, MCM4, MAD2L1, AURKA, NCAPG, RRM2, TYMS, TPX2), 4 miRNAs (miR-212-3p, miR-24-3p, let-7a-5p, miR-196a-5p), and the lncRNA HOXA11-AS were significantly associated with poor prognosis (P < 0.05) | |||
| Li et al. (2023) [11] | KIF4A, KIF11, KIF20A, KIF22 (prognostic); PRC1, PLK1, KIF23, KIFC1, KIF5A (interaction network) | Gene expression comparison using TCGA and GTEx, survival analysis via GEPIA and R packages (survival, survminer), functional enrichment via clusterProfiler, protein-protein interaction via STRING and GeneMANIA, immune infiltration analysis via TIMER2.0 | High expression of KIF4A, KIF11, KIF20A, and KIF22 was significantly associated with poor overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in ACC patients (P < 0.001) |
| Zhang et al. (2023) [21] | CDC20, DLGAP5, KIF2C, MMP9, MYC, CCL2; Hub genes: GAPDH, MYC, VEGFA, CDC20, CCL2, MMP9, ITGAM, DLGAP5, KIF2C, FCGR3A; DEmiRs: miR-21-3p (targets DLGAP5, CCL2), miR-21-5p (targets VEGFA, GAPDH, MYC, MMP9), miR-451a (targets MYC, MMP9), let-7e-5p, let-7a-5p (both target MYC, MMP9) | Integrated analysis of GEO datasets (GSE90713, GSE143383, GSE19750, GSE22816), differential expression via limma, functional enrichment via Enrichr (GO, KEGG), PPI network via STRING and Cytoscape (CytoHubba), miRNA-hub gene network via multiMiR, survival analysis via GEPIA and TCGA | High expression of CCL2 was associated with better survival; low expression of CDC20, DLGAP5, KIF2C, MMP9, and MYC also associated with better survival (P < 0.05); expression of VEGFA, GAPDH, ITGAM, and FCGR3A was not significantly associated with prognosis |
| Xu et al. (2020) [12] | Top AS events included UQCR11 (upregulated AT: 46528, downregulated AT: 46527), NASP (ES: 2754), EIF3A, HNRNPR, and additional genes listed in Table 2 (e.g., WASH4P, ZNF692, RHOC, CIRBP, IKBKB, NOP2, PRKAG1, USP4, METTL15, STOML1, GGCX, PSEN2, etc.) | Analysis of TCGA SpliceSeq data for 92 ACC patients; identification of survival-related AS events via univariate and multivariate Cox regression and Lasso analysis; development of eight prognostic models (based on seven AS types and all AS); GO and KEGG enrichment analyses for splicing factor (SF) genes; construction of AS-SF interaction network using Cytoscape | 3919 alternative splicing (AS) events were significantly associated with overall survival (OS) in ACC; prognostic models based on four AS types (AA, AD, AP, RI) showed strong predictive value (AUC > 0.9); multivariate analysis confirmed AS- based risk scores as independent prognostic factors |
| Xu et al. (2021) [19] | RBM15, HNRNPC (prognostic m6A regulators); other analyzed m6A genes: METTL3, METTL14, WTAP, KIAA1429, ZC3H13, FTO, ALKBH5, YTHDC1, YTHDC2, YTHDF1, YTHDF2 | Lasso regression on TCGA data to construct m6A- based risk signature (RBM15 and HNRNPC); validation in GEO dataset (GSE33371); survival analysis via Kaplan-Meier, Cox regression, ROC curves, PCA, t-SNE; immune correlation via CIBERSORT, ssGSEA, TISIDB | High expression of RBM15 and HNRNPC was significantly associated with poor survival in both TCGA and GEO validation cohorts; m6A-related risk score (based on RBM15 and HNRNPC) was identified as an independent prognostic factor with AUC > 0.7; high-risk group had worse overall survival across multiple clinical subgroups |
| Li et al. (2020) [32] | CD4, HLA-DRA, HCK, CD53, RAC2, HLA-DRB5, RAB37, CD93, FOLR2, GRAP2, HLA-DOA, HLA- DPA1, HPGDS, LAIR1, PTPRB, TACR1, TBXAS1, WAS | Transcriptome data and clinical characteristics from TCGA; immune and stromal scores calculated using ESTIMATE algorithm; DEGs identified via limma; intersect genes from immune and stromal DEGs; hub genes identified by cytoHubba (12 topological algorithms); survival analysis via Kaplan-Meier and Cox regression; immune infiltration analysis via TIMER database | All 18 hub genes were significantly associated with poor overall survival (OS); patients in the high-risk group (based on multivariate Cox regression models using these genes) had significantly worse survival outcomes (P < 0.0001); AUC for the prognostic model was 0.887 |
| Marquardt et al. (2021) [29] | Top genes identified by RF and associated with poor prognosis cluster ACC-UMAP2 include SLC2A1 (GLUT1), SOAT1, EIF2S1, MYC, SMAD2, BUB3, ASB4, MED27, FSCN1, GNAI3, CBX3; total top-100 genes are listed in the study | UMAP-based unsupervised clustering using mRNA expression (FPKM) data from TCGA-ACC (n = 79); random forest classifier trained on cluster labels to identify key discriminative genes; validation on ENSAT dataset; survival analysis via Kaplan-Meier and Cox regression; mutation correlation via cBioPortal | UMAP-based clustering of TCGA-ACC transcriptomic data reproduced known C1A/C1B clusters associated with prognosis; ACC-UMAP1 correlated with better survival (median OS: 12.46 years) vs. ACC-UMAP2 (median OS: 7.38 years, HR = 6.27, p = 0.000029); random forest classifier identified 100 genes with the highest discriminatory power, most overexpressed in the poor prognosis cluster ACC-UMAP2 |
| Li et al. (2022) [30] | CCNB1, CDC20, CENPU, FOXM1, KIF4A, KIF11, KIF20A, MAD2L1, NCAPG, NDC80, NUF2, PBK, | Differential expression analysis using TCGA and GEO datasets (GSE10927, GSE19750); functional enrichment (GO, KEGG, PANTHER, BIOCYC, | 16 of the 17 hub genes were significantly associated with poor prognosis in ACC (log-rank P < 0.001); only C3AR1 was not significantly (continued on next page) |
| Model/Study | Genes Included | Method | Prognostic Value |
|---|---|---|---|
| RACGAP1, RRM2, TOP2A, TPX2 (prognostic); C3AR1 (excluded from prognostic model) | REACTOME) via WebGestalt and KOBAS; PPI network and hub gene selection via STRING and Cytoscape (cytoHubba: Degree, MCC, MNC); survival analysis via GEPIA and cBioPortal | correlated with survival; CCNB1 and NDC80 were identified as core prognostic genes | |
| Tömböl et al. (2009) [15] | Differentially expressed miRs: miR-184, miR- 503, miR-511, miR-214, miR-210, miR-375; Target genes inferred from expression correlation include CCNB2, CDC2, CDC25C, TOP2A, CDKN1C, IGF2 along with further genes reported in the study | Integrated miRNA and mRNA expression profiling (TLDA microarray and Agilent whole-genome microarrays), tissue-specific target prediction using combined data from TargetScan, PicTar, miRBase, and expression correlation; pathway analysis via GSEA and Ingenuity Pathway Analysis (IPA) | Differential expression of miR-184, miR-503 (upregulated), and miR-511, miR-214, miR-375 (downregulated) distinguished ACC from benign adrenocortical tumors with high diagnostic power; ROC analysis for dCTmiR-511-dCTmiR- 503 achieved 100 % sensitivity and 97 % specificity; prognostic relevance inferred from expression patterns, though survival data not directly analyzed |
| Xu et al. (2020) [26] | CNN1, MYLK, CSRP1, MYH11, EFEMP2, FBLN1, MFAP4, FBLN5 | Differential expression analysis from GEO datasets (GSE19750, GSE12368, GSE14922), PPI network via STRING, hub gene selection via Cytoscape (MCODE), enrichment via DAVID, survival analysis via cBioPortal, gene expression verification via Oncomine, clinical correlation via UCSC Cancer Browser | Expression changes of all eight hub genes did not affect overall survival; however, downregulation of EFEMP2 was significantly associated with decreased disease-free survival; CSRP1 and MFAP4 expression levels were also associated with adverse clinicopathological features (capsular invasion, histological grade, vascular invasion) |
| Yan et al. (2021) [36] | ASPM, AURKA, CCNB2, CDC20, CENPA, EXO1, FBXO5, HJURP, KIF2C, MKI67, NUF2, PARPBP, TACC3, TROAP | Multi-step bioinformatics analysis including WGCNA on GSE76019, regression modeling (Lasso, Ridge, Elastic Net) on TCGA-ACC, survival analysis (OS, DFS), ROC/AUC validation, CNV and mutation analysis via cBioPortal, functional enrichment (GO, KEGG), PPI network via STRING and Cytoscape, classifier validation via LDA, KNN, and SVM | All 14 genes in the best model were significantly associated with poor overall survival (OS); model 2 (a = 0.6) showed the best prognostic performance with AUC = 0.844 and C-index = 0.851; six genes with highest connectivity were considered hub genes (ASPM, AURKA, CCNB2, CDC20, KIF2C, NUF2) |
| Yin et al. (2023) [25] | CDK1, CCNA2, CCNB1, TOP2A, MAD2L1, BIRC5, BUB1, AURKA | Integrated analysis of GEO datasets (GSE12368, GSE90713, GSE143383), DEGs via GEO2R, enrichment via DAVID (GO, KEGG), PPI via STRING and Cytoscape (MCODE and cytoHubba), survival analysis via cBioPortal, expression and staging via UCSC Xena and GEPIA | High expressions of CDK1, CCNA2, CCNB1, TOP2A, MAD2L1, BIRC5, BUB1, and AURKA was significantly associated with poor overall survival and disease-free survival (P < 0.05); alterations in these genes were also linked to advanced tumor stage in ACC |
| Yuan et al. (2018) [23] | ANLN, ASPM, CDCA5, CENPF, FOXM1, KIAA0101, MELK, NDC80, PRC1, RACGAP1, SPAG5, TPX2 | WGCNA using GSE10927 dataset; DEGs via limma; hub gene selection through co-expression network and PPI (STRING, Cytoscape); validation in GSE19750 and TCGA-ACC using ANOVA, Pearson correlation, ROC curves, survival analysis via GEPIA | All 12 hub genes were significantly associated with advanced tumor stage and poor overall and disease-free survival in TCGA and GEO datasets; ROC curve AUCs ≥ 0.85 confirmed diagnostic potential |
| Yi et al. (2022) [37] | ASPM, BIRC5, CCNB2, CDK1 (MPBs); additional 5 hub genes: DLGAP5, FOXM1, RACGAP1, TOP2A, TPX2 | Integrated multi-omics analysis using six GEO datasets (GSE75415, GSE12368, GSE76021, GSE19750, GSE10927, GSE76019) and TCGA-ACC; methods included DEG analysis, WGCNA, PPI network (STRING, Cytoscape), survival analysis (Kaplan-Meier, Cox), ROC, DCA, LDA, KNN, SVM classifiers, immune infiltration analysis (TIMER), and nomogram construction | All nine hub genes were significantly associated with poor overall and disease-free survival (OS and DFS); four MPBs (ASPM, BIRC5, CCNB2, CDK1) showed the highest predictive accuracy (average classification accuracy ≥ 0.80); ASPM was confirmed as an independent prognostic factor in multivariate Cox analysis (HR = 3.262 for OS, HR = 7.335 for DFS) |
| Tian et al. (2020) [35] | KIF18A, CDCA8, SKA1, CEP55, BUB1, CDK1, SGOL1, SGOL2 | Integrated bioinformatics and real-world analysis using TCGA and GEO datasets (GSE90713, GSE12368), CIBERSORT algorithm, immunohistochemistry (IHC) validation in 39 ACC patients, DEG analysis via limma, GSEA and enrichment analysis via clusterProfiler, PPI network (STRING, Cytoscape), survival analysis using Cox regression | Higher abundance of tumor-infiltrating mast cells (TIMC) was significantly associated with improved overall survival (OS) and progression- free survival (PFS) in both TCGA and FUSCC cohorts; low TIMC infiltration and overexpression of hub genes (KIF18A, CDCA8, SKA1, CEP55, BUB1, CDK1, SGOL1, SGOL2) were associated with poor prognosis |
| Knott and Leidenheimer (2020) [27] | ABAT, ALDH5A1, GAD1 (GABA shunt); GABRB2, GABRD, GABRA3, GABRA5, GABRB3, GABRG1, GABRE (GABAA subunits); GABBR1, GABBR2 (GABAB subunits); SLC6A1, SLC6A11, SLC6A12, SLC6A13, SLC32A1 (GABA transporters) | Targeted bioinformatics analysis of TCGA-ACC dataset focusing on 30 GABA system genes; transcriptomic and methylation data from cBioPortal; validation via GEO datasets and RT-PCR in NCI- H295R cell line; survival analysis via Kaplan-Meier, gene enrichment (GO/KEGG), and correlation with clinical features | Upregulation of ABAT and ALDH5A1 was significantly associated with favorable clinical outcomes, including longer overall and progression-free survival, disease-free status, and absence of metastases; upregulation of GABRB2 was positively prognostic, while upregulation of GABRD was negatively prognostic |
| Sung and Cheong (2021) [17] | TMM-related transcriptional regulators in ACC with ALT included CBX3, NRF1, EP300, and NFYB; enriched biological processes included mitochondrial organization and peptide secretion; no specific hub genes were listed, focus was on TMM pathway signatures | Pan-cancer analysis using TCGA RNA-seq data from 31 cancer types (n = 10,704); classification of TMM types via ssGSVA; survival analysis via Kaplan-Meier and Cox regression; functional analysis via GO and transcription factor network (iRegulon); copy number variation, mutation burden, and stemness analysis included | Value: In ACC, patients with NDTMM had better survival compared to those with ALT, which was associated with poor prognosis; ALT group showed enriched mitochondrial organization, high copy number variation (CNV), and unfavorable transcription factor networks (CBX3, NRF1, EP300, NFYB); TMM classification (ALT, TEL, TEL+ALT, NDTMM) was predictive of survival across multiple cancer types |
| Guo et al. (2020) [24] | CCNB1, CDK1, TOP2A, CCNA2, CDKN3, MAD2L1, RACGAP1, BUB1, CCNB2 | Integrated analysis of GEO datasets (GSE10927, GSE12368, GSE90713); DEGs via limma; functional enrichment via DAVID (GO, KEGG); PPI network via STRING and Cytoscape; validation of hub genes via GEPIA and survival data from TCGA-ACC; univariate | All nine hub genes were significantly upregulated in ACC and associated with poor overall survival in TCGA; among them, BUB1 was identified as an independent prognostic factor (P < 0.01) |
| Model/Study | Genes Included | Method | Prognostic Value |
|---|---|---|---|
| Tian et al. (2021) [33] | Prognostic proteins: FASN, FIBRONECTIN (FN), TFRC, TSC1; DEGs related to model: CENPM, NDC80, DLGAP5, SPC25, CENPF, ZWILCH, AURKB, CENPA, CDC20, CCNA2, KIF4A, BUB1B, CCNB2, UBE2C, AURKA, PLK1, CASC5, RANGAP1, BIRC5, CEP55, NEK2, SGOL2, KIF18A, CCNB1, SKA1, RRM2, ASPM, SGOL1, KIF2C, CDCA8, CENPI, KIF11, BUB1, CDCA5, CDK1, SPC24, SPAG5, NUF2 | and multivariate Cox regression for prognostic evaluation Multiomics analysis using RPPA data from TCPA, gene expression data from TCGA and GEO (GSE10927), and clinical data; candidate proteins selected via Kaplan-Meier and Cox regression; model built using Lasso and multivariate Cox regression; validation via IHC in FUSCC cohort and comparison with Ki-67, TP53, CTNNB1, IGF2, NR5A1; functional analysis via GSEA and DEG analysis | High expression of FASN, FIBRONECTIN, TFRC, and TSC1 was significantly associated with poor overall survival in adult ACC patients (P < 0.05); the IPRPs model (based on these four proteins) showed independent prognostic significance with AUC = 0.933, outperforming Ki-67 and other markers across multiple cohorts (TCGA, GEO, FUSCC) |
| Xiao et al. (2018) [34] Chen et al. (2021) [4] Xing et al. (2019) [2] | TOP2A, NDC80, CEP55, CDKN3, CDK1 | Integrated analysis of six GEO datasets (GSE12368, GSE14922, GSE19750, GSE33371, GSE75415, GSE90713); DEGs identified via robust rank aggregation; PPI network constructed using STRING and Cytoscape; hub genes selected based on connectivity degree > 20; expression validated using TCGA, GTEx, Oncomine, RT-PCR, Western blot, and immunofluorescence RNA-seq data from TCGA and GEO (GSE19750), hypoxia gene list from MSigDB; univariate and multivariate Cox regression, LASSO analysis to select genes; survival analysis (Kaplan-Meier, Cox), ROC curve, nomogram construction, immune infiltration via CIBERSORT, GSEA for pathway analysis DEG analysis using GEO datasets (GSE12368 and GSE19750); functional enrichment via DAVID (GO, KEGG); PPI network via STRING and Cytoscape (MCODE); survival analysis via cBioPortal; validation via Oncomine (Giordano Adrenal datasets) and UCSC Cancer Browser | High expression of all five hub genes was significantly associated with poor overall survival (OS) and disease-free survival (DFS) in TCGA-ACC cohort (P < 0.001); expression of these genes was also positively correlated with advanced pathological stage and T stage |
| CCNA2, EFNA3, COL5A1 AURKA, TYMS, GINS1, RACGAP1, RRM2, EZH2, ZWINT, CDK1, CCNB1, NCAPG, TPX2, MAD2L1, PRC1, SMC4 | High expression of CCNA2, EFNA3, and COL5A1 was significantly associated with poor overall survival in both TCGA and GEO cohorts (P < 0.001); the hypoxia risk score based on these genes was an independent prognostic factor with high predictive accuracy (AUC = 0.949 at 1 year) Upregulation of AURKA, TYMS, GINS1, RACGAP1, RRM2, EZH2, ZWINT, CDK1, CCNB1, NCAPG, and TPX2 was significantly associated with poor overall and disease-free survival in ACC; MAD2L1 and PRC1 were associated with worse disease-free survival; SMC4 showed no significant survival correlation | ||
| Zhou et al. (2021) [3] | Core genes regulated by BCLAF1: CDK1, CCNB1; a 53-gene risk model was also identified (including CDCA8, KIF11, CENPA, AURKB, BUB1, etc.) | RNA-seq and clinical data from TCGA-ACC; differential expression via limma; WGCNA to identify key modules; PPI network via STRING and Cytoscape; hub genes via degree centrality; GSEA, GO, KEGG for pathway analysis; experimental validation in NCI- H295R and SW-13 cell lines (siRNA, shRNA, qPCR, WB, flow cytometry, IHC) | High expression of BCLAF1 was significantly associated with poor overall and disease-free survival (P < 0.01); BCLAF1 regulates cell proliferation and cell cycle in ACC by directly modulating CDK1 and CCNB1 at mRNA and protein levels; high-risk gene signature of 53 BCLAF1-related genes showed AUC = 1 for survival prediction |
| Omidi (2025) [31] | CKS2, ACAT2 | Integrative ceRNA network modeling using TCGA- ACC and GTEx transcriptomes; differential expression; co-expression structure; survival modeling (Kaplan-Meier + CoxPH); ROC diagnostic evaluation | High expression of CKS2 and ACAT2 associated with reduced overall survival; combined signature improves risk stratification and provides strong diagnostic performance (AUC ~ 0.90) |
45-gene signature associated with survival, identified through integra- tion of mRNA profiles, mutational data, and clinical outcomes. This panel included cancer-associated genes such as POLD1, AURKA, and KIF23, which interact with TP53, a frequent germline alteration in ACC. Patients with mutations in these genes showed significantly reduced survival (median 18.1 vs. 80.2 months), highlighting their prognostic potential. Tian et al. [33] constructed the IPRPs model, a proteogenomic signature based on protein-level expression data from TCGA and real-world samples. It included FASN, TFRC, and TSC1, whose high expression was strongly associated with poor survival. Notably, the model outperformed Ki-67 in prognostic discrimination, validating its clinical utility in multiple cohorts.
Alternative splicing (AS) was investigated by Xu et al. [12], who identified 3919 survival-associated AS events and constructed four AS-based prognostic models with outstanding performance (AUC > 0.9). The top events included exon skipping and alternate donor sites, sug- gesting a novel layer of regulation in ACC pathogenesis and prognosis. Xiao et al. [34] reported a five-gene panel, TOP2A, NDC80, CEP55, CDKN3, and CDK1, as a predictive model for progression and prognosis. These genes were significantly upregulated in ACC tissues, validated via RT-PCR, western blot, and immunofluorescence, and correlated with poor survival. Xing et al. [2] identified 14 hub genes from microarray analysis and PPI network construction, including AURKA, RRM2, CDK1, and EZH2, which were functionally enriched in cell division and
p53-related pathways. These genes were significantly associated with poor prognosis and aggressive phenotypes.
Using weighted co-expression network analysis, Xu et al. [26] discovered eight downregulated hub genes such as EFEMP2, CNN1, and MFAP4, some of which (e.g., EFEMP2) correlated with shorter disease-free survival. Their loss may contribute to tumor progression. Tian et al. [35] analyzed immune infiltration and showed that genes like KIF18A, CDK1, CDCA8, and SGOL1 were upregulated in low-mast cell ACC tumors and were associated with worse outcomes. This suggests that gene-based signatures may reflect tumor immune microenviron- ments. Xu et al. [19] focused on m6A RNA methylation regulators and developed a prognostic risk score based on HNRNPC and RBM15. This model predicted survival independently of clinical stage and correlated with immune suppression.
In a pioneering multi-omic approach, Sung and Cheong [17] con- ducted a pan-cancer telomere maintenance mechanism (TMM) analysis and showed that TMM classification had prognostic implications in ACC. Although not a conventional gene expression signature, this highlights the potential of telomere biology in ACC stratification. Furthermore, Tömböl et al. [15] conducted one of the earliest studies integrating miRNA and mRNA profiles. They identified distinct miRNA-mRNA regulatory networks in ACC versus adenomas and proposed miR-503 and miR-511 as possible diagnostic and prognostic markers. Their combined ACT expression distinguished ACC from benign tumors with
SGPL1
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FDFT1
PIK3C2B
PIK3CD
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high accuracy.
Yan et al. [36] employed WGCNA and machine learning methods (Ridge, Lasso, and Elastic Net regression) to construct a multi-gene prognostic model based on TCGA-ACC and GEO datasets. From 93 survival-associated genes, two prognostic models were derived, with Model 2 demonstrating superior predictive performance in both internal and external validation cohorts. This model comprised 14 genes, of which six, BIRC5, CCNB1, CDK1, MKI67, NDC80, and TOP2A were further validated as hub genes using PPI network analysis and receiver operating characteristic (ROC) curves (Fig. 4c).
Similarly, Yi et al. [37] integrated seven independent datasets and applied multi-omics and machine learning techniques to identify ASPM, BIRC5, CCNB2, and CDK1 as meaningful prognostic biomarkers (MPBs) for ACC. These genes showed high expression in tumor tissues, were associated with poor survival, and demonstrated strong predictive value in nomogram-based models (Fig. 4d).
Yin et al. [25] confirmed the centrality of cell cycle regulation in ACC
progression. Through bioinformatics analyses of three GEO datasets, they identified CDK1, CCNA2, CCNB1, TOP2A, MAD2L1, BIRC5, BUB1, and AURKA as hub genes. These genes were significantly associated with mitotic cell cycle pathways and overall survival. Yuan et al. [23] used WGCNA to define a gene module significantly associated with clinical features of ACC. From this module, 12 hub genes were validated as prognostic markers, with enrichment in cell cycle processes, further supporting the findings of subsequent studies. Zhou et al. [3] investi- gated the oncogenic role of BCLAF1, showing that it regulates CDK1 and CCNB1, promoting cell cycle progression and ACC proliferation. Knockdown of BCLAF1 significantly inhibited ACC cell growth, sug- gesting its potential as a therapeutic target and a regulator of gene-based prognostic pathways (Fig. 4e).
Omidi [31] identified CKS2 and ACAT2 as consistently upregulated and co-regulated oncogenic drivers in ACC, with strong diagnostic ac- curacy (AUC ~ 0.90) and adverse survival association. Their reinforced co-expression in tumor samples, absent in normal adrenal tissue,
si NC si Bclaf1
EV
FL
145kDa
Bclaf1
ACC (n = 4)
Normal (n = 4)
P=0.0097
p=0.0550
P=0.0138
p= 0.0013
p= 0.0098
TOP2A
200kDa
GAPDH
37kDa
34kDa
CDK1
p = 0.0116
Relative mRNA expression
1.50
3-
58kDa
Cyclin B1
Relative protein expression of TOP2A
1.25
2-
ß-actin
1.
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CDKN3
CEP55
NDC80
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SW-13
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0.8
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0.4
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Anti-TFRC
0.2
Multi-Omic Predictor
Tumor Dimension
0.0
(c)
0.0 0.2 0.4 0.6 0.8 1.0
False Positive Rate (1 - Specificity)
highlights a synergistic oncogenic axis, wherein CKS2 accelerates pro- liferative progression while ACAT2 sustains the metabolic demands of lipid-supported energy reprogramming (Fig. 4f).
Zhang et al. [21] focused on metastatic ACC and identified CDC20, DLGAP5, KIF2C, and MYC among others as metastasis-associated genes with significant prognostic implications. Their survival analyses underscored the prognostic value of these genes in metastatic settings. Ye et al. [22] adopted a ceRNA-based approach, identifying RRM2, CDK1, and MCM4 as key mRNAs involved in prognostically significant ceRNA subnetworks. These genes, through miR-24-3p and IncRNA HOXA11-AS regulation, were implicated in ACC prognosis and therapeutic resistance.
These studies illustrate the rich landscape of gene-based prognostic markers in ACC, covering protein-coding genes, splicing variants, epigenetic modifiers, and non-coding RNAs, each offering complemen- tary insights for future clinical application. These diverse approaches are visually summarized in Fig. 4, including mutation-based risk, gene overexpression, m6A methylation risk signature, alternative splicing- based models, PPI-based hub genes, downregulated survival- associated genes, immune-related gene profiles, telomere maintenance classification, and miRNA-based diagnostic stratification.
3.2. Protein markers and immunohistochemistry
At the protein level, several studies have validated candidate bio- markers for ACC prognosis and diagnosis through immunohistochem- istry (IHC), western blotting, and proteomics. Zhou et al. [3] demonstrated that BCLAF1 is significantly upregulated in ACC tissues and promotes proliferation via transcriptional regulation of CDK1 and CCNB1 (Fig. 5a). Western blot and qPCR confirmed that silencing BCLAF1 leads to downregulation of these two cell cycle genes, high- lighting its role in mitotic control.
Xiao et al. [34] further validated the protein overexpression of TOP2A and CEP55 in ACC via western blot and immunofluorescence, showing their presence in both the cytoplasm and nucleus of tumor cells. High expression levels correlated with poor survival and higher patho- logical stage, making them robust prognostic markers (Fig. 5b). Simi- larly, Fernandez-Ranvier et al. [8] used RT-PCR to validate 37 differentially expressed genes between benign and malignant adrenal tumors. Among them, CCNB2, IL13RA2, and HTR2B showed high diagnostic accuracy (AUC > 0.85), strongly distinguishing ACC from benign lesions (Fig. 5c). Tian et al. [33] constructed an Integrated Prognosis-Related Proteins (IPRPs) model based on protein-level data. Proteins like FASN, fibronectin, TFRC, and TSC1 showed significant prognostic power in both TCGA and clinical cohorts. Notably, IHC validation showed that this model outperformed Ki-67 in prognostic accuracy (Fig. 5d).
Another layer of proteomic confirmation was presented by Yi et al. [37], who identified CDK1, CCNB2, ASPM, and BIRC5 as multi-platform validated prognostic biomarkers. These proteins were enriched in the cell cycle and validated via expression correlation with copy number alterations, immune infiltration, and clinical outcomes. Finally, the infiltration of tumor-associated mast cells (TIMCs), evaluated via IHC in the Fudan University cohort, revealed a positive correlation between TIMC abundance and improved survival. However, lower mast cell infiltration coincided with higher expression of poor-prognosis proteins such as CDK1 and CEP55 (Tian et al. [33]). Together, these findings support the clinical relevance of protein-level biomarkers and their integration with transcriptomic profiles for ACC characterization.
4. Tumor microenvironment and immune landscape
The tumor microenvironment (TME) of ACC represents a dynamic interplay between malignant cells, immune infiltrates, stromal compo- nents, and molecular signaling networks. Disruption in immune sur- veillance and immune suppression within the TME are key factors
contributing to ACC’s aggressiveness and poor prognosis.
One of the most comprehensive analyses of tumor-infiltrating im- mune cells in ACC was performed by Tian et al., who utilized the CIBERSORT algorithm on TCGA and GEO datasets to quantify immune cell subsets and validated their findings in real-world tissue samples. Notably, the presence of tumor-infiltrating mast cells (TIMCs) was positively correlated with favorable prognosis, while low mast cell infiltration accompanied by overexpression of genes such as CDK1 and BUB1 predicted poor outcomes (Fig. 6a) [35]. Chen et al. [4] also characterized the tumor microenvironment (TME) in ACC by applying the CIBERSORT algorithm to assess immune cell composition. Their analysis demonstrated that the high-risk cohort, defined by a hypoxia-related signature, exhibited increased infiltration of regulatory T cells (Tregs) and M2 macrophages along with reduced CD8+ T-cell levels, reflecting a distinctly immunosuppressive microenvironment.
Zhou et al. [3] provided further mechanistic insight by showing that BCLAF1, a transcription factor implicated in cell cycle progression, also correlates with immune cell infiltration. High BCLAF1 expression was associated with reduced CD8 + T cell infiltration and immune-silent phenotypes, suggesting an immunosuppressive role in the ACC micro- environment [3] (Fig. 6b). In a multi-omics study, Guan et al. classified ACC into three molecular subtypes (ACC1-ACC3). ACC1 was enriched for immune-related pathways and showed sensitivity to PD-1 checkpoint blockade, while ACC2 demonstrated high infiltration of immunosup- pressive cells and worse prognosis [12] (Fig. 6c). Li et al. [32] identified a group of 18 hub genes related to immune and stromal scores in the TME. Patients with lower immune scores, often seen in metastatic ACC, had significantly worse survival outcomes. Among the hub genes, several (e.g., KIF11, KIF4A) showed marginal positive correlation with immune infiltration [32].
Further bioinformatics analyses by Yi et al. [37] confirmed that several key prognostic genes (CDK1, ASPM, BIRC5, CCNB2) were significantly enriched in cell-cycle and immune evasion pathways. Expression of these markers was also linked to immune cell infiltration and survival prediction models, as demonstrated by their correlation with immune cell fractions and nomogram-based survival curves [37] (Fig. 6d).
Another pan-immune profiling effort by Li et al. [30] incorporated genomic, transcriptomic, and proteomic data to identify distinct im- mune subtypes in ACC, marked by variations in antigen presentation, immune checkpoint expression, and immune cell density. These classi- fications offer promising frameworks for personalized immunotherapy [30]. Zhang et al. [21] observed that hub genes involved in ACC metastasis, such as VEGFA, CCL2, and MMP9, also play roles in immune regulation and leukocyte migration, suggesting a bridge between metastasis and immune modulation. Their interaction network revealed overlapping roles in inflammatory response and immune recruitment.
Sung and Cheong [17] highlighted that telomere maintenance mechanisms (TMM), particularly the alternative lengthening of telo- meres (ALT), were associated with specific immune features in ACC. Patients with ALT activity showed distinct immune activation patterns, which may have prognostic and therapeutic implications. Arastonejad et al. [38] showed overexpression of NCAPG and NCAPH in ACC, both of which were associated with immune pathway enrichment and poor survival, pointing again to a link between chromosomal structure regulation and immune contexture. Finally, Li et al. [11] noted that certain kinesin family genes (KIFs), such as KIF20A and KIF22, were overexpressed in ACC and marginally correlated with immune infiltra- tion. These findings support the hypothesis that cell cycle regulators also impact immune surveillance and escape.
These studies underscore the heterogeneity and prognostic relevance of immune features in ACC. They highlight the therapeutic potential of integrating immune profiling with molecular classification to guide personalized treatment strategies. These findings are collectively sum- marized in Table 3, which outlines the immune landscape and check- point expression in ACC.
KEGG
Biological process
Cell cycle
(a)
(b)
Spliceosome
mitotic cell cycle phase transition
Steroid biosynthesis
chromosome segregation
Terpenoid backbone
Gene count
cell cycle G1/S phase transition
biosynthesis
Small cell lung cancer
50
regulation of cel cycle
-phase transition
MicroRNAs in cancer
100
organelle Sosion
Gene count
DNA replication-
150
spindle organization
200
Pancreatic cancer-
DNA conformation change
200
300
TIMChigh
12-
*** , ANOVA P=0.003
12-
cell cycle checkpoint
Oocyte meiosis-
400
Pathogenic Escherichia
250
regulation of chromosome organization
couples
TIMCs purity
9
TIMCs purity
protein-DNA complex
9
scoli infection
Chronic myeloid leukemia-
Pyrimidine metabolism-
negative regulation of cell cycle process .
Gene Ratio
Gene Ratio
organization involved in mitosis
2.4
6
..
E
…
-
Colorectal cancer
4
negative regulation of mitotic cell cycle
2.2
IRNA surveillance pathway-
processing in Protein Prox Celle In
3
protensomal protein catabolic process
2.0
3-
3
Adherens junction-
steroid metabolic process -
1.8
TIMClow
Endocytosis
2
response to radiation
0
0
IRNA processing
Stage I
Stage II
Stage Ill
Stage IV
T1-T2
T3-T4
10
47
40
M1
Necrosis™
Necrosis
Cellular senescence Human T-cell leukemia
positive regulation of cell cycle - establishment ce
-virus I infection
RNA transport
-maintenance of cell polarity”
DNA replication
0.00
0.01
0.02
FDR
0.03
0.04
De+00
5e-08
FDR
1e-05
TCGA
GSE10927
GSE10927
GSE90713
HALLMARK_MITOTIC_SPINDLE
Enrichment plet: HALLMARK_MITOTIC_SPINDLE
Enrichment plot: HALLMARK_G2M_CHECKPOINT
Enrichment plot: HALLMARK_Q3M_CHECKPOINT
Overall Survival
Overall Survival
E
22 22 2 22
TIMCslow
TIMCslow
Percent survival (%)
100
n=22
TIMCshigh
Percent survival (%)
100
+ :
n=10
TIMCshigh
…
*
50
50
-
-
n=17
n=4
p=0.019, HR(high)=3.258
p=0.018.
(high)=6.569
0
si Bclaf1
NCI-H295R
0
EV
FL
150-
GO/G1
S
All patients
0
20
40
60
80
Necrosisneg patients
100
0
20
40
60
80
I-
si NC
Time (months)
..
1.
% of total cells
- G2/M
Time (months)
4
R-
1.
100
8 -
1-
Progression-free Survival
Progression-free Survival
1
10
1.
J
-
50-
TIMCslow
TIMCslow
E-
1
9-
A
2.
«+
**
Percent survival (%)
100
n=22
TIMCshigh
Percent survival (%)
100
n=10
0
TIMCshigh
:
-
20
U
SINC
siBclaf1
6
-
.-
-
0
€
€
0
4
SW-13
50
50
第一
si NC
!-
si Bclaf1
EV
FL
150-
GO/G1
5
7
1 G2/M
n=17
n=4
R-
R-
I-
1.
% of total cells
100-
p=0.034, HR(high)=2.714
.030,
HR(high)=5.420
-
#
A
.
0
0
1
1
1
1
E
50
0
20
40
60
80
0
20
40
60
80
1
T
Time (months)
Time (months)
8-
R
3
0
7
SINC.
siBclaf1
#
1ª
T
E
€
-
(c)
(d)
Overall survival (ESTIMATE score)
Disease-free survival (ESTIMATE score)
Subtype # ACCI ACC2 ] ACC3
Subtype # ACCI # ACC2 ] ACC3
Subtype ] ACC1 # ACC2 # ACC3
ESTIMATEScore
High
Low
ESTIMATESONO
High
Low
ESTIMATE NOTE
ESTIMATE NOOTE
0.81
Anova, p = 1.9x 10-6
Anova, p = 1.2 x 10$
Anova, p = 6.5 x 10៛
1.00
1.00
Estimate Immune Score
0.4
Immune Cell Subsets
Immune Signaling Molecules
Survival probability
Survival probability
0.5
0.4
0.75
0.75
-2000
2900
0.50
0,50
0.0
0.0
.
0.0
BIRCS espression
0.25
p = 0.0039
0.25
p = 0.00015
2000
2000
:
0.00
0.00
1000
-0.4
-0.5
-0.4
0
1000
2000
3000
4000
6000
0
1000
2000
3000
4000
5000
:
ESTIMATEscore
ESTIMATEscore
ESTIMATE NON
ESTIMATE NONE
.
OS (days)
DFS (days)
·
Number at risk
Number at risk
ACC1
ACC2
ACC3
ACC1
ACC2
ACC3
-0.8
ACC1
ACC2
ACC3
sa
1
19
4
9
0
High
Low-
2
24
17
6
2
0
-2000
-2000
Subtype
Subtype
Subtype
0
1000
2000
3000
4000
5000
0
3000
OS (days)
1000
2000
DFS (days)
4000
5000
CcNB2 mpression
Low
Subtype Gender sender
1 05
Subtype
p
15
0
Laterality Gender Age
0
Subtype
-05
Overall survival (immune score)
Disease-free survival (immune score)
Stage
0.5
Stage
Central memory CD4 T cel
pra moed Plasmacytold dendritic
Cytotoxic cells ENK. meta
ADC3
Immune score
High
LOW
Immune score
High
Low
500
Mast cel
Laterality
Activated dendritic cel Macrophage
Immune noone
CYT
1.00
1.00
·
Treg cells
Right
MOSC
Survival probability
0.75
Survival probability
Immuno noone
500
500
Regulatory T cell
T cells
Gender
0.75
Activated CD& T cell
Monocyte
13 T-cell signature
FEMALE MALE
-1000
-1000
TLS
0.50
0,50
Effector memory CD4 T cel
Natural killer cell
WNTTGFB signature
Age
Type 1 T helped Cod T cel
B cell cluster
80
0.25
6 gene IFN signature
p = 0.071
0.25
Acvated BY
p = 0.049
··
:
Immature B cel Neutrochal
Macrophages
20
0.00
0.00
Type 17 T helper cel
MDSC
0
1000
2000
3000
4000
5000
0
1000
2000
3000
4000
5000
chobbnight natural kiler cell CD56dim killer
B.P. meta
Stage
Immature dendritic cell
TGFB1 activated
Immune score
05 (days)
DFS (days)
unknown stage Stage I
Number at risk
Immune score
Number at risk
-900-
Central memory CD8 T cel Memory B cell
TITR
CECM
Sa
20
32
14
8
9
0
18
12
1
9
0
Activated CD4 T cell
Stage IV
-1900
Chermna della T cell
CDB T cells
0
1000
2000
3000
4000
6000
0
OS (days)
1000
2000
3000
Natural kiler T cell
Th17 cells
DFS (days)
4000
5000
CONB2 expression
samples in TCGA-ACC cohort
samples in TCGA-ACC cohort
Overall survival (stromal score)
Disease-free survival (stromal score)
type
ACC1
ACC2
ACC3
ACC1
1
Stromal score
High
Low
Stromal score
High
Low
500
500
Stromal soure
1.00
1.00
Stromal noore
5
.
ACC2
0.8
000
0.75
0.75
-9000
-1000
Immunocyte Infiltration
Survival probability
Survival probability
4
ACC3
0.6
0.50
0.50
-1900-
-1906
3
ACC1
0.4
Low
ASIPM expression
0.25
0.25
1000
ACC2
p = 0.048
p < 0.0001
1000
2
0.2
0.00
0.00
ACC3
0
1000
2000
3000
4000
5000
0
1000
2000
3000
4000
5000
Stromal soore
Stromal NuONG
1
OS (days)
DFS (days)
pvalue
CTAL4-noR
CTLA4-R
PD1-noR
PD1-R
pvalue
Stromal score
Number at risk
Stromal score
Number at risk
500
0
Nominal p value
35
17
7
2
0
45
25
17
8
3
8
1000
PD-L1
PD-1
PD-L2
CTLA4
Bonferroni corrected
0
1000
2000
3000
4000
6000
0
1000
2000
3000
4000
5000
OS (days)
DFS (days)
-4500
Low
-15004
CCNB2 expression
Low
| Study | Main Findings | Immune Factors | Checkpoint Expression |
|---|---|---|---|
| Chen et al. | High-risk group shows t | Tregs, M2, | Not analyzed |
| (2021) | Tregs, 1 M2 macrophages, Į | CD8 + | |
| [4] | CD8 + T cells | ||
| Thampi et al. | PD-L1 and CTLA-4 | General TME | PD-L1, CTLA- |
| (2020) [1] | overexpressed in ACC | overview | 4 |
| Zhou et al. | BCLAF1 linked to | Į CD8 +, 1 Treg- associated | Indirect |
| (2021) | immunosuppressive cell | ||
| [3] | infiltration | ||
| Li et al. | ACC subtypes defined by | APCs, NK, T cells | PD-L1, LAG3, |
| (2022) [30] | immune gene signatures and checkpoint variation | TIM3 | |
| Tian et al. | TIMCs positively associated with survival; Į mast cells =1 CDK1, BUB1 | Mast cells, CD8 +, CDK1- linked genes | Not analyzed |
| (2020) [35] | |||
| Zhou et al. | BCLAF1 linked to | CD8 + T cells 4, | Indirect |
| (2021) [3] | immunosuppressive TME; Į CD8 + T cells, t cell cycle genes | Treg-associated ↑ | |
| Guan et al. | ACC1: Immune-active; | NK cells, Tregs, | PD-1, PD-L1 |
| (2022) [12] | ACC2: Immune- suppressive; ACC1 sensitive to PD-1 blockade | macrophages | |
| Li et al. | Hub genes (e.g., KIF11, KIF4A) linked to immune/ stromal scores; low immune score -> poor prognosis | Stromal cells, APCs, general immune | Not analyzed |
| (2020) [32] | |||
| Yi et al. | CDK1, CCNB2, ASPM | CD8 +, general immune infiltration | Not analyzed |
| (2022) [37] | enriched in cell-cycle and immune evasion; linked to infiltration & prognosis | ||
| Zhang et al. | VEGFA, CCL2, MMP9 | M2 | Indirect |
| (2023) [21] | linked to immune regulation and metastasis | macrophages, chemokine- expressing | |
| Sung & Cheong | ALT subtype shows distinct immune activation; TMM types linked to immune pathways | General immune activity by TMM | Not analyzed |
| (2021) [17] | |||
| Arastonejad | NCAPG, NCAPH | Immune | Not analyzed |
| et al. (2024) | overexpressed; associated | pathway- | |
| [38] | with poor survival and immune pathway enrichment | enriched genes | |
| Li et al. (2023) [11] | KIF20A, KIF22 marginally correlated with immune infiltration | Kinesin family correlated | Not analyzed |
5. Pediatric vs. adult ACC: comparative genomics
ACC presents as two biologically distinct diseases across age groups, with notable differences in genetic predisposition, transcriptional pro- grams, immune microenvironment, and clinical outcomes. Pediatric ACC frequently arises in the context of hereditary cancer syndromes, most prominently germline TP53 mutations, as exemplified by the well- recognized R337H founder variant in Southern Brazil, which drives early-onset tumorigenesis and underlies the marked geographic clus- tering and bimodal age distribution of disease [1]. In contrast, adult ACC is more commonly sporadic and genomically heterogeneous, involving alterations in TP53, CTNNB1, ZNRF3, MEN1, and aberrant activation of Wnt/ß-catenin and IGF2 signaling, which contribute to tumor progres- sion and therapeutic resistance (Fig. 7a) [5].
Comparative transcriptome profiling also reveals divergent regula- tory architectures between pediatric and adult tumors. Network-based analyses in pediatric ACC identified cell-cycle-associated hubs, including CDK1, CCNB1, BUB1B, and CDC20, as central effectors of proliferative signaling, emphasizing a developmental and mitotic reprogramming phenotype in childhood disease (Fig. 7a) [6]. In contrast, adult ACC tumors exhibit transcriptional patterns linked to
chromosomal instability, mitotic checkpoint deregulation, and DNA repair stress, with recent bioinformatics analysis highlighting the NCAP gene family (NCAPG, NCAPG2, NCAPH) as key mediators of chromo- somal condensation and as predictors of poor survival outcomes in adult patients (Fig. 7b) [38].
Differences also extend to the tumor immune microenvironment. Pediatric ACC is generally characterized by low immune infiltration and a less inflamed stromal context, whereas adult ACC displays stronger hypoxia-associated transcriptional signatures, immune checkpoint expression, and tumor-associated immunosuppression, reflecting more advanced immune escape and tumor-host adaptation dynamics (Fig. 7c) [4]. These contrasting immune phenotypes have direct implications for response to immunotherapies, which have shown limited benefit in unselected ACC populations. These findings indicate that pediatric and adult ACC represent distinct molecular and immunobiological entities, despite partially overlapping oncogenic drivers. Consequently, patient stratification strategies and therapeutic decision-making should not rely on a uniform framework, but instead incorporate age-specific genomic features, immune signatures, and progression-associated biomarkers. A comparative summary of these key distinctions is provided in Table 4.
6. Therapeutic targets and drug repositioning
Despite advances in molecular profiling, targeted therapy in ACC remains a formidable challenge due to the tumor’s inherent heteroge- neity and aggressive phenotype. Nevertheless, bioinformatic and multi- omics investigations have identified a growing list of potential thera- peutic targets and repositioned drugs with translational relevance.
Among the most frequently implicated targets are cell cycle regula- tors such as CDK1, TOP2A, AURKA, and CCNB1. These genes are not only consistently overexpressed in ACC but also strongly associated with poor overall survival [3,25,37]. High-throughput network analyses have shown that these proteins function as central hubs in mitotic regulatory pathways and PPI networks [5,30]. Notably, Zhou et al. [3] demon- strated that silencing BCLAF1, a transcription factor that modulates CDK1 and CCNB1, reduced proliferation in ACC cell lines and enhanced mitotane sensitivity, indicating its dual role in tumor growth and che- moresistance (Fig. 8a).
Another promising avenue is the identification of lipid metabolism- related enzymes such as SGPL1, FDFT1, and SQLE, which are signifi- cantly upregulated in ACC and correlate with worse prognosis. These targets are embedded in sphingolipid and steroid biosynthesis pathways, and their expression profiles, along with associated Kaplan-Meier sur- vival curves, are visualized in [18].
Drug repositioning strategies have also gained traction. For instance, Shahreza et al. [10] applied the Heter-LP algorithm to predict reposi- tioned drugs targeting ACC-specific molecules such as IGF1R, TOP2A, and MAP3K3, identifying cosyntropin and sorafenib as potential can- didates. Supporting this, Yi et al. [37] found that ASPM, BIRC5, CCNB2, and CDK1 were enriched across multiple repositioning analyses, showing strong associations with drugs like vorinostat, chlorpromazine, and trifluoperazine. Guan et al. [12] further classified ACC into molec- ular subtypes using multi-omics integration. Notably, the ACC2 subtype, characterized by high tumor mutation burden and poor prognosis showed greater sensitivity to chemotherapeutic agents such as doxoru- bicin, gemcitabine, and etoposide, while ACC1 patients responded bet- ter to PD-1 blockade therapy, highlighting the potential of stratified therapeutic targeting.
Recent insights into non-coding RNA networks, such as the BIRC5-miR-335-5p-PAX8-AS1 axis, have unveiled novel vulnerabilities in ACC (Subramanian et al. [14]). A significant proportion of research has converged on cell cycle regulators as critical therapeutic targets. Multiple studies have independently identified overexpression of genes such as CDK1, CCNB1, AURKA, TOP2A, and CEP55 in ACC tumors [2, 24,34]. These genes not only function as central hubs in PPI networks but also strongly correlate with poor prognosis. For instance, Xiao et al.
TCGA
ACC versus NAT
(b)
type
3
2
EFNA3
1
0
-1
-2
119
CCNA2
-3
62
331
215
COL5A1
2942
1551
high
684
GEO
low
type
2
EFNA3
1
0
ACC versus ACA
ACC versus NAT
-1
CCNA2
-2
(a)
DNAJB1
BARD1
COL5A1
TUBB3
HSPA4
SKP2
CCK1
SIRT1
CCNB1
DICE
SLE15A4
SMC2
TOP2A
DNMTI
GADDISA
BRCA2
HECTD3
c
CDC20
CCNB2
ZWINT
TACC3
CONA2
NEK6
MLEIP
PRIM2
CENPF
NCAPG
CONE1
SMARCA4
CEPSS
MKI67
NCAPG2
NCAPH
BUB1
IARS
SMC4
DOX39A
BUB1B
TOP2A
NELK
TARS
MARS
NCAPD3
NCAPG
LMNB1
TCP1
TXN
EPRS
ENO1
HSQ
C14 orf1
PLEC
NDC80
Physical Interaction
Co-expression
CAT
Co-localization
HSPA5
LOS
AURKB
NCAPH2
Pathway
PRIMI
HSF2
Predicted
Shared protein domains
NCAPD2
SR
F1
OUR
[34] demonstrated in their PPI network analysis that CDK1 had the strongest association with low overall survival (HR = 11), followed by CEP55, CDKN3, and TOP2A. Similarly, Guo et al. [24] and Xing et al. [2] used STRING-based analyses to confirm that these genes are central to mitotic regulation and tumor proliferation. Additionally, Kaplan-Meier survival curves for these genes demonstrate their association with poor clinical outcomes.
Beyond cell cycle genes, emerging targets have been discovered through integrative multi-omics approaches. Tian et al. [33] constructed a proteomic-based prognostic model and identified TFRC, TSC1, and FASN as key protein markers with strong predictive power for survival
outcomes in ACC. Kaplan-Meier plots from their study clearly support the prognostic significance of these proteins, and additional expression heatmaps and pathway enrichment results further highlight their bio- logical relevance. These proteins may serve as both prognostic in- dicators and potential therapeutic targets.
Novel metabolic and neurotransmitter-related targets have also been highlighted. Knott and Leidenheimer [27] conducted a focused tran- scriptomic analysis of the GABAergic system in ACC, revealing that upregulation of ABAT, a key enzyme in the GABA shunt was signifi- cantly associated with favorable prognosis and reduced metastatic po- tential (Fig. 8b). They presented detailed Kaplan-Meier survival
| Feature | Pediatric ACC | Adult ACC | References |
|---|---|---|---|
| Common | TP53 (R337H, | TP53, CTNNB1, | Thampi et al. (2020) [1] |
| Mutations | germline) | ZNRF3, MEN1 (somatic) | Sun-Zhang et al. (2024) [5] |
| Hub Genes | CDK1, | NCAPG, TOP2A, | Kulshrestha et al. (2016) |
| CCNB1, | CEP55 | [6] Arastonejad et al. (2024) [38] | |
| BUB1B | |||
| Immune Features | Low immune | High PD-L1, CTLA-4, hypoxia-related expression | Chen et al. (2021) [4] |
| infiltration | |||
| Prognosis | Favorable if | Poor with recurrence/ metastasis | Thampi et al. (2020) [1] |
| localized |
analyses and transcript expression comparisons, indicating that ABAT may serve as a marker of better outcome and a potential entry point for repurposing neurological drugs. In a complementary systems biology approach, Shahreza et al. [10] applied the Heter-LP computational framework for drug repositioning and constructed a drug-target inter- action network specific to ACC. Their results predicted novel in- teractions between Cosyntropin and targets such as DHCR7, IGF1R, MAP3K3, and TOP2A, genes known to be overexpressed in ACC. The network graph provides a visual foundation for validating drug reposi- tioning hypotheses based on integrated biological knowledge graphs. Finally, telomere maintenance mechanisms (TMMs) may influence drug responsiveness in ACC. Sung and Cheong [17] demonstrated that pa- tients with non-defined TMM (NDTMM) had comparatively better sur- vival outcomes, suggesting a potential biomarker for stratifying patients in therapeutic trials (Fig. 8c). Their pan-cancer survival plots highlight how different TMM categories, including ALT, telomerase, and NDTMM, correlate with outcomes in ACC.
Yan et al. [36] developed a robust multi-gene prognostic model using machine learning approaches such as Lasso and Elastic Net, ultimately identifying six hub genes with strong prognostic potential (Fig. 8d). These genes, highlighted through WGCNA and survival analysis were linked to crucial oncogenic pathways such as cell cycle regulation and p53 signaling and were further validated by CNV and mutation analyses. Complementing this, Zhang et al. [21] focused on metastatic ACC and identified ten hub genes via PPI network analysis, among which CCL2, CDC20, DLGAP5, KIF2C, MMP9, and MYC demonstrated significant correlations with patient survival. These metastasis-related targets are involved in immune evasion, cell adhesion, and microtubule dynamics, reinforcing their relevance for drug development. The convergence of findings from both studies underscores a set of potential molecular targets, laying the groundwork for precision medicine strategies and the rational repurposing of drugs for advanced or metastatic ACC.
MiRNA-centered network analyses have also begun to highlight therapeutically actionable regulatory nodes in ACC. Omidi [39] identi- fied miR-507 and miR-665 as tumor-specific hub miRNAs within the ACC ceRNA network, with miR-507 regulating cell-cycle checkpoint machinery and Rho-GTPase signaling, suggesting its potential relevance to mitosis-targeted therapeutic strategies. Notably, miR-665 exhibited radiation-responsive expression patterns, indicating a potential role in modulating treatment sensitivity. These findings underscore the possi- bility that miRNA-centered network perturbation may represent a complementary therapeutic avenue alongside conventional IGF2, Wnt/ß-catenin, and steroidogenesis-directed approaches (Fig. 8e).
Given their functional involvement in cell-cycle control and lipid metabolic flux, Omidi [31] proposed CKS2 and ACAT2 as potential therapeutic intervention points, aligning with ongoing drug develop- ment efforts targeting CDK regulatory complexes and fatty-acid syn- thesis/metabolic enzymes in endocrine malignancies. Importantly, expression levels of both CKS2 and ACAT2 remained consistently elevated across patients receiving pharmaceutical therapy alone or in
combination with radiation, suggesting that their oncogenic activity is therapy-independent and may represent fundamental biological drivers rather than treatment-specific signatures (Fig. 8f).
In summary, the integration of gene expression, survival analysis, PPI networks, and drug repositioning frameworks has yielded a set of promising targets, some of which may be actionable with existing or repurposed compounds. Fig. 8 summarizes these emerging strategies across five thematic axes: gene silencing effects, metabolic vulnerabil- ities, computationally inferred drug-target relationships, regulatory non-coding RNA networks, and subtype-specific therapeutic responses. These findings are comprehensively summarized in Table 5, which outlines the key therapeutic targets and drug repositioning opportu- nities identified in ACC.
7. Multi-omics integration and systems biology in ACC
The integration of multi-omics data, such as genomics, tran- scriptomics, epigenomics, and proteomics has significantly advanced our systems-level understanding of ACC. These approaches help identify novel molecular signatures and reveal underlying mechanisms of tumorigenesis and progression that are not evident in single-omics analyses.
Sun-Zhang et al. [5] conducted a comprehensive genomic study combining TCGA and GEO data to identify 45 survival-associated genes in ACC, many of which showed protein-protein interactions previously unexplored in this context. Notably, POLD1, AURKA, and KIF23 formed central nodes in the PPI network, highlighting critical mitotic regulation pathways. Subramanian et al. [14] integrated multiple genomic datasets and constructed a miRNA-IncRNA-mRNA network, identifying BIRC5-hsa-miR-335-5p-PAX8-AS1 as a central axis strongly associated with poor survival in ACC. Their findings underline the clinical rele- vance of non-coding RNA in prognosis. Ye et al. [22] built a ceRNA network combining lncRNA, miRNA, and mRNA layers, revealing HOXA11-AS as a key lncRNA that regulates RRM2 and CDK1 through miR-24-3p/let-7a-5p. Their multi-layered network uncovered action- able ceRNA motifs with prognostic relevance.
Xu et al. [20] analyzed alternative splicing (AS) events in 92 ACC patients from TCGA, identifying 3919 AS events significantly associated with survival. Their AS-based risk score outperformed traditional tran- scriptomic models, emphasizing the potential of splicing as a systems biomarker layer. Arastonejad et al. [38] applied network-level modeling to evaluate the role of the NCAP family genes. NCAPG, NCAPG2, and NCAPH were identified as central regulators in mitotic chromosomal organization and correlated with worse survival outcomes, reinforcing their role in aggressive ACC biology (Fig. 9a). Li et al. [11] investigated the prognostic value of kinesin superfamily (KIF) genes using a bioin- formatics approach. KIF4A, KIF11, and KIF20A were overexpressed in ACC and associated with immune cell infiltration and worse prognosis, linking mitotic machinery to immune modulation.
Zhang et al. [21] conducted a meta-analysis of metastatic ACC and identified a hub gene network including MYC, CDC20, and VEGFA. Integration of DEGs, miRNAs, and functional enrichment revealed pathways involved in metastasis, such as phagosome formation and cell adhesion. Li et al. [32] used immune and stromal scoring algorithms to reveal 18 hub genes associated with the tumor microenvironment. Their risk model based on TME-derived gene expression predicted survival with high accuracy (AUC = 0.887), linking immune contexture to omics-based prognosis. Crona et al. [28] applied pan-cancer tran- scriptomic analysis to compare ACC with 35 tumor types. ACC was found to form a unique cluster but also shared molecular proximity with neural crest-derived tumors, suggesting lineage-based systems biology relevance.
Marquardt et al. [29] applied machine learning on transcriptomic data to classify ACC into two distinct clusters overlapping with known molecular subtypes (C1A and C1B). Their model identified novel prog- nostic markers like SOAT1 and EIF2A1, enhancing predictive
si NC
si Bclaf1
EV
FL
Relative protein levels
1.5
Relative protein levels
2.0
20
(b)
20
p = 3.0 x 10-21
1.0
1.5
1.0
15
p = 3.1 x 10-13
15
0.5
0.5
Cyclin B1
Cyclin B1
RSEM (log2)
10
RSEM (log2)
0.0
Bclaf1
CDK1
0.0
Bclaf1
CDK1
10
5
3
si NC
si Bclaf1
sh NC
sh Bclaf1
Relative protein levels
0
5
1.5
**
Relative protein levels
1.5
**
3
1.0
1.0
-5
0
T
T
GAD1
GAD2
ABAT
ALDH5A1
upregulated
unaltered
upregulated
unaltered
0.5
T
0.5
T
¥0.0
CDK1
Cyclin B1
0.0
Bclaf1
Bclaf1
CDK1
Cyclin B1
a
Overall survival on risk score calculated by model 2 (training set)
GAD1
ABAT
ACC
GBM
HNSC
Risk score
High risk Low risk
1.00
1.00
1.00
1.00
Survival probability
0.75
Survival probability
0.75
Survival probability
0.75
0.50
.0.50
2.0.50
0.75
0.25
p = 0.04
0.25
p = 0.043
0.25
Survival probability
p = 0.046
0.00
0.00
0.00
0.50
0
50
100
Time (Day)
150
0
20
40
Time (Day)
60
80
0
50
100
Time (Day)
150
200
(c)
PAAD
SARC
0.25
1.00
1.00
p < 0.0001
Survival probability
Survival probability
(d)
0.75
0.75
TEL+ALT
ALT
0.00
0.50
0.50
0
30
60
90
120
TEL
OS (months)
Risk score
Number at risk
0.25
p = 0.04
0.25
p = 0.0074
NDTMM
High risk Low
26
26
11
16
BLON
0
5
0 2
0
30
60
90
120
(e)
0.00
0.00
OS (months)
0
20
40
Time (Day)
60
80
0
50
100
Time (Day)
150
200
1.0
0.4
A Normalized Expression (Pharma - Radiation)
2.5
Pharmaceutical
0.8
2.0
Radiation
0.2
1.5
miR-507
log10(RPM)
Sensitivity
0.6
0.0
1.0
0.5
0.4
-0.2
miR-665
0.0
-0.5
0.2
-0.4
AUC at 1 year: 0.92
miR-507
miR-665
0.0
AUC at 3 years: 0.91
AUC at 5 years: 0.95
Relative Expression Level per Therapy Group
0.0
0.2
0.4
0.6
0.8
1.0
Mean Expression (TPM)
1-Specificity
600
Pharma+Radiation
Pharma only
400
All Cases
(f)
200
IIT
0
ERP44
DEPDC1
CENPI
LSM5
SUB1
BRIX1
STC2
ACAT2
CKS2
IDO1
PLK4
LYAR
CHEK1
HOMER1
GOLGA7B
MELK
CDYL2
RAB3B
ZNF562
GJC1
NHLRC1
MRPL42
C4orf46
HSBP1
| Target Gene | Role in ACC | Repurposed Drug | Mechanism/Interaction | Evidence Source |
|---|---|---|---|---|
| SGPL1 | Sphingolipid metabolism, cell survival, steroidogenesis | Fingolimod | Inhibits sphingosine-1-phosphate lyase (SGPL1), suppressing downstream sphingolipid signaling | Subramanian et al. (2022) [18] |
| BIRC5 | Inhibits apoptosis; part of dysregulated | YM155 | Direct inhibitor of BIRC5 (survivin), | Subramanian |
| mRNA-miRNA-IncRNA network associated with poor survival | induces apoptosis in cancer cells | et al. (2023) [14] | ||
| hsa-miR-335-5p | Downregulated miRNA; modulates expression of key hub genes (e.g., BIRC5), associated with metastasis and poor survival | Not directly listed; therapeutic strategy involves miRNA mimic or restoration | miR-335-5p suppresses BIRC5 and other oncogenic targets; therapeutic potential in restoring tumor suppression | Subramanian et al. (2023) [14] |
| PAX8-AS1 | Dysregulated lncRNA; interacts with miR- | Not specified; potential future | Modulates miR-335-5p levels and | Subramanian |
| 335-5p in regulatory network affecting gene expression | target | downstream oncogenic genes | et al. (2023) [14] | |
| AURKA | Overexpressed; associated with poor survival; strong interaction with TP53; key regulator of mitosis and cell cycle | AURKA inhibitors (e.g., alisertib) | Inhibition of AURKA leads to cell cycle arrest and increased radiosensitivity, particularly in TP53-mutant tumors | Sun-Zhang et al. (2024) [5] |
| POLD1 CCL2 | Overexpressed; independently associated with poor survival; involved in DNA replication and repair | Potential immunotherapy target (clinical trial: NCT03810339) | POLD1 mutation/dysregulation associated with improved immunotherapy response across solid tumors | Sun-Zhang et al. (2024) [5] |
| Overexpression associated with better survival; functions in immune cell recruitment and modulation of tumor microenvironment | Bindarit | Selective CCL2 synthesis inhibitor; reduces monocyte/macrophage infiltration, potentially affecting tumor progression | Zhang et al. (2023) [21] | |
| CDC20, DLGAP5, KIF2C, MMP9, MYC | Overexpression associated with poor survival; involved in cell cycle regulation, proliferation, and metastasis | Indirectly targetable; potential inhibitors include CDK inhibitors, MYC-targeted therapies, MMP inhibitors | Modulate tumor cell proliferation, mitotic progression, extracellular matrix remodeling | Zhang et al. (2023) [21] |
| miR-21-3p, miR-21-5p, miR-451a, let-7a-5p, let- 7e-5p | Dysregulated miRNAs modulating expression of hub genes (e.g., MYC, MMP9, VEGFA, CCL2); associated with metastasis and prognosis | miRNA-based therapeutics (e.g., anti-miR-21, miRNA mimics) | Regulation of gene expression through post-transcriptional repression; impact angiogenesis, immune evasion, metastasis | Zhang et al. (2023) [21] |
| PDCD1 (PD-1), CD274 (PD-L1) | Highly expressed in ACC1 subtype with high immune infiltration; potential immune checkpoint markers; associated with better response to immunotherapy | Anti-PD-1 agents (e.g., nivolumab, pembrolizumab) | Blocking PD-1/PD-L1 pathway restores T- cell function and enhances anti-tumor immunity | Guan et al. (2022) [12] |
| ACC2-subtype-related proliferation genes (not individually listed) | Associated with high tumor-mutation burden, DNA repair activation, cell cycle dysregulation; subtype ACC2 shows poorest prognosis | Cisplatin, doxorubicin, gemcitabine, etoposide | Higher sensitivity of ACC2 subtype to these chemotherapy agents due to underlying molecular vulnerabilities | Guan et al. (2022) [12] |
| ACC3-subtype markers (not individually listed) | Enriched in steroid and cholesterol biosynthesis pathways; moderate prognosis | Anti-CTLA-4 agents (e.g., ipilimumab) | ACC3 subtype shows relative sensitivity to anti-CTLA-4 therapy via immunophenotyping (SubMap analysis) | Guan et al. (2022) [12] |
| BCLAF1 | Overexpressed in ACC; promotes proliferation and cell cycle progression via regulation of CDK1 and Cyclin B1; associated with poor prognosis | CDK1 inhibitors (e.g., RO-3306, dinaciclib) - indirect targeting via BCLAF1 regulation of CDK1 | BCLAF1 directly regulates transcription and protein levels of CDK1 and CCNB1; downregulation of BCLAF1 reduces proliferation and induces G2/M cell cycle arrest | Zhou et al. (2021) [3] |
| CCNB1, NDC80 | Highly overexpressed in ACC; associated with poor prognosis; central regulators of mitosis and cell cycle progression | CDK inhibitors (e.g., flavopiridol, dinaciclib) - targeting downstream cell cycle pathways | Disruption of CCNB1-CDK1 complex or NDC80-mediated mitotic spindle assembly halts cell cycle and tumor proliferation | Li et al. (2022) [30] |
| ASPM, AURKA, CCNB2, CDC20, CENPA, EXO1, FBXO5, HJURP, KIF2C, MKI67, NUF2, PARPBP, TACC3, TROAP | Overexpressed; all 14 genes were part of the best prognostic model and significantly associated with poor survival; involved in cell cycle regulation, mitotic progression, DNA replication, and chromosomal stability | CDK inhibitors (e.g., dinaciclib, flavopiridol), AURKA inhibitors (e. g., alisertib), PARP inhibitors (for PARPBP interaction) | Disruption of cell cycle and mitotic regulators leads to growth arrest and apoptosis; high copy number variations (CNVs) and overexpression of these genes enhance proliferation and worsen prognosis | Yan et al. (2021) [36] |
| CDK1 | Key regulator of cell cycle; promotes mitosis and ACC proliferation; associated with poor prognosis | CDK1 inhibitors (e.g., RO-3306, flavopiridol) | Inhibition of CDK1 leads to G2/M arrest and apoptosis; proposed therapeutic strategy in combination with MELK inhibitors | Yin et al. (2023) [25] |
| CCNB1, CCNA2 | Overexpressed in ACC; regulate cell cycle progression and mitosis; associated with advanced tumor stage and poor prognosis | CDK inhibitors (e.g., dinaciclib, flavopiridol) | Targeting cyclin-CDK complexes can suppress proliferation and improve survival in ACC | Yin et al. (2023) [25] |
| AURKA | Overexpressed; regulates mitotic progression and is associated with TP53 mutation and poor outcome | AURKA inhibitors (e.g., alisertib, MLN8054) | Inhibition of AURKA induces mitotic failure, apoptosis, and reduces tumor growth; dual targeting with Wnt/ ß-catenin suggested | Yin et al. (2023) [25] |
| TOP2A | Overexpressed; promotes tumor progression; associated with poor survival and drug resistance | Etoposide, doxorubicin (TOP2A- targeting agents) | Inhibition of TOP2A disrupts DNA topology and replication, inducing apoptosis | Yin et al. (2023) [25] |
| BIRC5 | Inhibits apoptosis; associated with poor prognosis in ACC and other cancers | YM155 (survivin inhibitor) | Suppresses BIRC5 expression and restores apoptotic signaling in tumor cells | Yin et al. (2023) [25] |
(continued on next page)
| Target Gene | Role in ACC | Repurposed Drug | Mechanism/Interaction | Evidence Source |
|---|---|---|---|---|
| ASPM | Overexpressed; associated with poor OS and DFS; regulates mitosis and tumor progression | Lucanthone, Monobenzone, 8-Aza- guanine, Thioguanosine, Resveratrol | Enriched through CMap and DSigDB; drugs predicted to reverse ASPM-driven oncogenic signature | Yi et al. (2022) [37] |
| BIRC5 | Inhibits apoptosis; high expression linked to poor OS and DFS; affects immune infiltration | Dasatinib, Lucanthone, Monobenzone, 8-Azaguanine, Thioguanosine, Resveratrol | Targeted through multiple compounds from DSigDB and CMap; involved in cell cycle control | Yi et al. (2022) [37] |
| CCNB2 | Promotes cell cycle progression; overexpression linked to poor survival and advanced stage | Dasatinib, Lucanthone, Monobenzone, 8-Azaguanine, Thioguanosine, Resveratrol | Cell cycle-associated gene enriched for inhibitors disrupting mitotic checkpoints | Yi et al. (2022) |
| [37] | ||||
| CDK1 ABAT | Drives mitotic entry; highly expressed in ACC; poor prognostic indicator Upregulated in ~40 % of ACC tumors; strongly associated with improved overall and progression-free survival; linked to reduced metastasis, lower cortisol levels, and favorable metabolic phenotype | Dasatinib, Lucanthone, Monobenzone, 8-Azaguanine, Thioguanosine, Resveratrol Vigabatrin (Sabril®) | Cell cycle arrest induced by CDK1 inhibition; multiple compounds suggested via enrichment analysis | Yi et al. (2022) [37] |
| Irreversible inhibitor of ABAT; suppresses brain metastasis in preclinical models; modulates GABA shunt activity, mitochondrial metabolism, and immune- related pathways | Knott & Leidenheimer (2020) [27] | |||
| GABRB2 | Upregulated in ~36 % of tumors; positively correlated with ABAT expression and favorable survival outcomes; may form functional GABA A receptors in ACC cells | Brexanolone®, Klonipin®, Solfoton® (positive allosteric modulators of GABA A receptors) | Enhances GABA A receptor activity; potential to modulate proliferation and immune signaling | Knott & Leidenheimer (2020) [27] |
| GABRD | Upregulated in ~15 % of tumors; negatively correlated with ABAT; associated with poor survival and increased recurrence | None specified; potential candidate for modulation via GABA A receptor-targeting agents | Enhances inhibitory signaling; high expression may reflect suppressed ABAT activity and aggressive tumor phenotype | Knott & Leidenheimer (2020) [27] |
| DNMT1 | Upregulation associated with hypermethylation and suppression of ABAT expression; poor prognostic factor | Demethylating agents (e.g., decitabine, azacytidine) | Reverses methylation-driven silencing of ABAT; potentially restores favorable metabolic and immune phenotype | Knott & Leidenheimer (2020) [27] |
| CBX3, NRF1, EP300, NFYB | Master transcriptional regulators enriched in ACC with ALT subtype; associated with poor prognosis and elevated mitochondrial biogenesis, CNV, and interferon-gamma production | No specific drug reported; suggested potential for TMM- targeted therapy in ALT-ACC | Regulate expression of peptide secretion and mitochondrial pathways; influence prognosis through modulation of telomere maintenance mechanism (TMM) | Sung & Cheong (2021) [17] |
| CCNB1, CDK1, TOP2A, CCNA2, CDKN3, MAD2L1, RACGAP1, BUB1, CCNB2 | All genes significantly upregulated in ACC; involved in mitotic spindle checkpoint, chromosome segregation, and cell cycle regulation; high expression correlated with worse overall survival; BUB1 was identified as an independent prognostic factor | CDK inhibitors (e.g., dinaciclib, flavopiridol), TOP2A inhibitors (e. g., etoposide, doxorubicin), AURKA/B inhibitors (for BUB1- related mitotic checkpoint modulation) | Disruption of mitotic checkpoint and cell cycle progression through inhibition of CDKs, topoisomerase IIx, and checkpoint regulators; potential for targeted suppression of highly proliferative ACC cells | Guo et al. (2020) [24] |
| FASN, FN (Fibronectin), TFRC, TSC1 | Overexpressed in high-risk ACC group; independently associated with poor prognosis; part of the IPRPs model (Integrated Prognosis-Related Proteins); higher expression linked to shorter overall survival | a) FASN -> TVB-2640, C75, Orli- stat (FASN inhibitors) b) TFRC -> Anti-TFRC antibodies (e.g., ch128.1Av), ferritin-based nanoparticles c) TSC1 -> Targeted indirectly via mTOR inhibitors (e.g., rapamycin, everolimus) | a) FASN promotes lipid synthesis and tumor growth | Tian et al. (2021) [33] |
| b) TFRC involved in iron uptake, supporting tumor proliferation c) TSC1 suppresses mTORC1 pathway; its overexpression dysregulates growth signals | ||||
| TOP2A | Significantly overexpressed in ACC; associated with poor overall and disease-free survival; promotes proliferation and invasiveness | Aclarubicin | Inhibits topoisomerase II alpha activity, disrupting DNA topology and replication; induces apoptosis in tumor cells | Xiao et al. (2018) [34] |
| NDC80, CEP55, CDKN3, CDK1 | All significantly overexpressed and associated with advanced pathology stages and poor prognosis; involved in mitotic progression, chromosome segregation, and cell cycle regulation | a) CDK1 - CDK inhibitors (e.g., RO-3306, flavopiridol) b) CEP55 - Potentially targetable via PI3K/AKT pathway modulation | a) CDK1: Inhibition induces G2/M arrest b) CEP55: Regulates PI3K pathway and metastasis-related signals (e.g., FOXM1, MMP-2) | Xiao et al. (2018) [34] |
| AURKA, TYMS, GINS1, | Overexpressed in ACC; significantly associated with poor overall and disease-free survival; involved in cell division, DNA replication, mitotic checkpoint, chromosomal stability, and angiogenesis | a) AURKA -> Alisertib b) TYMS - 5-FU, Pemetrexed c) RRM2 - Triapine, Didox d) EZH2 -> Tazemetostat e) CDK1, CCNB1 - Flavopiridol, RO-3306 f) TOP2A-related genes (e.g., TPX2) -> Etoposide | a) AURKA: Inhibition blocks mitotic spindle formation b) TYMS: Inhibition disrupts DNA synthesis c) EZH2: Epigenetic silencing via histone methylation; inhibition reactivates tumor suppressors d) CDK1/CCNB1: Cell cycle arrest at G2/ M | Xing et al. |
| RACGAP1, RRM2, EZH2, ZWINT, CDK1, CCNB1, NCAPG, TPX2 | (2019) [2] | |||
| DHCR7, IGF1R, MC1R, MAP3K3, TOP2A | Predicted disease-related targets by Heter- LP; IGF1R and TOP2A previously linked to ACC; others (DHCR7, MC1R, MAP3K3) show plausible association with adrenal signaling and tumor phenotype | Cosyntropin | Cosyntropin stimulates adrenal cortex function; proposed as adjunct to mitotane for adrenal support; identified as novel ACC drug via Heter-LP method | Lotfi Shahreza et al. (2020) [10] |
| hsa:1584 (mitotane target) | Existing mitotane target; validates network prediction accuracy | Spironolactone | Identified as additional drug interacting with mitotane's known target (hsa:1584); potential ACC therapy candidate | Lotfi Shahreza et al. (2020) [10] |
| hsa:2230 | Predicted novel target related to flavin adenine dinucleotide; no prior literature | Flavin adenine dinucleotide | Interaction suggested by Heter-LP; role in mitochondrial metabolism and energy | Lotfi Shahreza et al. (2020) |
| links; proposed for experimental follow-up | pathways | [10] |
(continued on next page)
| Target Gene | Role in ACC | Repurposed Drug | Mechanism/Interaction | Evidence Source |
|---|---|---|---|---|
| miR-507 | Tumor-suppressive regulatory hub; maintains mitotic checkpoint integrity; higher expression correlates with improved survival | Restoration-based strategies (miRNA mimic delivery; epigenetic re-activation; nanoparticle miRNA replacement) | Suppresses oncogenic targets involved in cell cycle progression, mitotic spindle assembly, Rho-GTPase signaling, and genomic stability regulation | Omidi (2025) [39] |
| miR-665 | Context-dependent regulatory miRNA; downregulated in ACC; displays radiation- responsive dynamics | Treatment-sensitization approach (miR-665 expression monitoring to stratify radiotherapy response) | Involved in immune-signaling modulation, endothelial remodeling, and treatment-associated stress response signaling | Omidi (2025) [39] |
| CKS2 | Promotes cell-cycle acceleration and mitotic progression; associated with poor survival | CDK inhibitors (e.g., Palbociclib, Ribociclib) | CKS2 enhances CDK complex activity, facilitating G1/S and G2/M transitions; CDK inhibitors can reduce proliferative drive in CKS2-high tumors | Omidi (2025) [31] |
| ACAT2 | Drives lipid metabolic reprogramming and supports tumor energy production | Fatty-acid synthesis inhibitors (e.g., TVB-2640 - FASN inhibitor; Etomoxir - CPT1 inhibitor) | ACAT2 fuels lipid-derived acetyl-CoA and B-oxidation, supporting tumor growth; metabolic inhibitors may starve high- ACAT2 tumors | Omidi (2025) [31] |
capabilities of omics-driven classifiers. Guan et al. [12] applied the MOVICS algorithm to multi-omics datasets (mRNA, lncRNA, miRNA, somatic mutations, methylation) from TCGA and GEO, categorizing ACC into three distinct molecular subtypes. These subtypes exhibited sig- nificant differences in prognosis, immune infiltration, and drug sensi- tivity, thereby guiding personalized treatment decisions (Fig. 9b). Li et al. [30] employed a comprehensive multi-omics machine learning framework integrating gene expression, mutation, and immune land- scape data. Their approach identified CEP55, CENPF, and BIRC5 as robust biomarkers across multiple platforms, capable of distinguishing immune-high and immune-low ACC subtypes.
Yan et al. [36] constructed and validated multi-gene prognostic models using WGCNA and machine learning on transcriptomic data. Six hub genes including MKI67 were identified, which were linked to copy number variations and survival outcomes (Fig. 9c). Yi et al. [37] expanded this concept by integrating WGCNA with immune infiltration and drug prediction, pinpointing ASPM, BIRC5, CCNB2, and CDK1 as multi-omics prognostic markers enriched in the cell cycle and responsive to small-molecule drugs. Chen et al. [4] introduced a hypoxia-related transcriptomic signature that independently predicted prognosis and correlated with immune cell infiltration patterns. High-risk patients had distinct immune suppressive profiles, showcasing the value of inte- grating tumor microenvironmental features. Di Dalmazi et al. [16] performed RNA-Seq on ACC and adenoma samples, demonstrating how transcriptomic clustering aligns with mutation profiles. Their results suggest the existence of transitional states between adenoma and car- cinoma, supporting a continuum model of tumorigenesis.
Tömböl et al. [15] performed the first miRNA-mRNA co-profiling in ACC. Using tissue-specific miRNA-target predictions, they identified regulatory networks linked to G2/M checkpoint control, revealing novel insights into miRNA-mediated pathogenesis. Guo et al. [24] combined multiple gene expression datasets to identify 9 hub genes enriched in cell cycle and senescence pathways, showing strong correlation with poor survival in ACC patients. Tian et al. [33] proposed a protein-based prognostic model (IPRPs) incorporating data from RPPA and gene expression profiles. Their model outperformed traditional biomarkers like Ki-67 and was validated across three independent cohorts.
Yuan et al. [23] conducted co-expression and PPI network analysis on GEO and TCGA data, revealing 12 hub genes involved in mitotic progression and strongly associated with ACC prognosis. These findings support the central role of mitotic dysregulation in ACC pathophysi- ology (Fig. 9d). Recent systems-level ceRNA network analyses have highlighted tumor-specific regulatory rewiring in ACC. Omidi [39] identified miR-507 and miR-665 as central hub miRNAs within the ACC transcriptomic network, demonstrating strong differential network centrality and prognostic relevance, particularly for miR-507. This study underscores the value of multi-omics-integrated network modeling for
discovering non-coding RNA biomarkers beyond standard differential expression (Fig. 9e).
These studies illustrate that multi-omics integration and systems biology approaches in ACC are unveiling complex regulatory networks and convergent biomarkers, paving the way for precision oncology in this rare malignancy. A comparative overview of the included multi- omics studies is provided in Table 6.
8. Discussion
8.1. Challenges and future directions
Although ACC is well-characterized as a rare and clinically aggres- sive malignancy with a bimodal age distribution and poor prognosis [40-42], it continues to present major therapeutic and molecular chal- lenges. Surgical resection, currently the only curative option offers limited efficacy, with high recurrence rates and 5-year survival ranging from 16 % to 47 % [42]. In parallel, significant advances in the molec- ular understanding of ACC have not yet translated into meaningful clinical benefit. While the omics revolution has enabled deep profiling of ACC at genetic, epigenetic, transcriptomic, and immunologic levels, several structural and methodological barriers continue to hinder diagnostic refinement, therapeutic innovation, and biomarker valida- tion [43,44].
One of the most pressing challenges is the rarity of ACC, which severely limits access to large, well-annotated patient cohorts. Most current studies rely on TCGA and ENSAT datasets that, while valuable, comprise fewer than 100 tumor samples. This restricts statistical power, limits the ability to capture population heterogeneity, and complicates the identification of robust prognostic subgroups [5,30]. Moreover, pediatric ACC remains drastically underrepresented, rendering cross-age comparisons dependent on indirect inference [1,6]. Moving forward, the establishment of international biobanks and cross-institutional data harmonization will be essential to overcome these limitations.
A second challenge is the persistent disconnect between computa- tional prediction and experimental validation. Numerous studies have proposed gene signatures using algorithms such as LASSO regression [2], PPI-based hub analysis [24,37], or ceRNA network construction [22], yet relatively few of these findings have been validated by RT-qPCR, immunohistochemistry, or functional assays. In some cases, proposed biomarkers (e.g., BIRC5, CCNB1) recur across studies, but are rarely tested in independent patient cohorts. This bottleneck highlights the urgent need for integrated pipelines that combine in silico discovery with in vitro and in vivo validation. This need for integrated computa- tional and experimental workflows is supported by recent studies that combined in silico co-expression analyses with RT-qPCR validation to
Gene Ontology
regulation of chromosome
Overall survival
Disease specific survival
segregation
regulation of chromosome
separation
100
100
chromosome separation
positive regulation of
8
Survival probability (%)
Survival probability (%)
chromosome segregation
75
75
positive regulation of
chromosome separation
regulation of chromosome
condensation
p.adjust
50
50
condensed chromosome
0.01
0.02
nuclear chromosome
0.00
25
Overall p < 0.001
25
Overall p < 0.001
condensed nuclear chromosome
0.04
8
ACC1
ACC2
-ACC1
ACC1
ACC2
ACC2
+ACC1
chromosomal region
ACC2
<0.001
ACC3
ACC2
00.001
ACC2
Count
0
ACC3
0.008
<0.001
0
ACC3
0.014
<0.001
ACC3
DNA packaging complex
5
0
12
24
36
48
60
72
84
96
108
120
0
12
24
36
48
60
2
84
96
108
120
chromosome, centromeric region
10
Time (Months)
Time (Months)
Number at risk
Number at risk
single-stranded DNA binding
33
33
30
21
17
15
11
8
6
4
2
33
33
30
21 17
15
11
8
6
4
2
21
18
8
6
1
1
0
0
0
0
0
20
17
8
6
1
1
0
0
0
0
Q
magnesium ion binding
24
22
20
16
12
8
5
3
2
2
2
23
21
19
15
11
8
5
3
2
2
2
DNA-directed 5’-3’ RNA
S
0
12
24
36
48
60
72
84
96
108
120
0
12
24
36
48
80
72
84
96
108
120
polymerase activity
Time (Months)
Time (Months)
5’-3’ RNA polymerase activity
0
a
RNA polymerase activity
Disease free interval
Progression free interval
·
100
100
0.2
GeneRatio
0.4
0.6
Survival probability (%)
Survival probability (%)
TOP2A
75
75
NCAPH2
gulation of chromosome segregation
ACC1
ACC2
egulation of chrom
condensation
ACC2 <0.001
Overall p < 0.001
NEK6
50
ACC3 0.007
0.089
regulation of chvo
me separation
50
ACC1
ACC2
MKI67
chromosome separation
ACC3
category
25
Overall p < 0.001
25
chromosome separation
+ACC1
ACC1
positive regulation of chromosome segregation
ACC2
ACC2
ACC2 <0.001
positive population of chromosome segregation
positive regulation of chromosome separation
ACC3
NCAPD3
q
ACC3
<0.001
0.002
regulation of chromosome condensation
- regulation of chromasame organization
0
12
24
36
48
60
72
84
96
108
120
0
12
24
36
48
60
72
84
96
120
regulation of chromosome segregation
Time
108
(Months)
Time (Months)
CENPF
regulation of chromosome separation
Number at risk
Number at risk
sister chromatid segregation
28 27 24
17
13
13
9
7
5
4
2
33
31
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e)
| Summary of multi-omics findings and their implications in ACC. | Study | Omics Layers Used | Key Findings | Contribution to ACC Research | |||
|---|---|---|---|---|---|---|---|
| Study | Omics Layers | Key Findings | Contribution to ACC | ||||
| Used | Research | Xu et al. (2020) [20] Arastonejad et al. (2024) [38] | Alternative splicing (AS) transcriptome data + clinical | Identified 3919 | First comprehensive | ||
| Subramanian | Gene expression (mRNA) | Identified a novel regulatory network | Systems-level insight into | survival-associated AS events across seven AS types (e.g., ES, RI, AA, | multi-omics study | ||
| et al. | focusing on splicing | ||||||
| (2023) | + miRNA | (BIRC5-miR- | ncRNA-mRNA | regulation in ACC; | |||
| [14] | + lncRNA | 335-5p-PAX8-AS1) strongly associated with poor overall survival; uncovered | dysregulation; prioritization of novel multi-omic biomarkers and | data + splicing factors (SFs) expression profiles | etc.); built eight prognostic signatures using Lasso and Cox regression with AUC | highlighted the prognostic significance of AS patterns and SFs; | |
| 12 hub genes, 5 miRNAs, and 4 | therapeutic targets | > 0.9; established AS-SF regulatory | introduced novel candidate | ||||
| lncRNAs through integrative network analysis | network involving hub AS events and SFs such as EIF3A and | therapeutic targets based on splicing dysregulation | |||||
| Sun-Zhang | mRNA | Identified 45 genes | Provided a robust | HNRNPR | |||
| et al. (2024) [5] | expression (RNA-Seq), DNA somatic mutations (WES), clinical survival data | whose expression and mutation status significantly correlate with survival; constructed a multi- parameter gene | multi-omics framework for stratifying ACC patients by survival risk; highlighted new prognostic | Transcriptome (mRNA expression microarray), survival data, pathological staging, PPI network, GO enrichment | Identified 3 NCAP family genes (NCAPG, NCAPG2, NCAPH) as significantly overexpressed in ACC; high expression linked | Introduced NCAP family genes as novel multi-omics biomarkers and diagnostic/ prognostic | |
| signature integrating expression, mutation, and survival associations; revealed novel protein-protein interactions (e.g., AURKA-POLD1, KIF23-TP53) | markers and therapeutic candidates via systems-level interaction analysis | to poor survival and advanced stages; constructed a 23-node PPI network; GO enrichment revealed roles in chromosomal segregation and condensation; ROC | indicators in ACC; provided a systems- level view of chromosomal regulation in ACC pathogenesis and precision diagnostics | ||||
| Ye et al. (2020) | Transcriptome (mRNA) | Identified 9 key hub genes (e.g., CDK1, | Provided a multi- layer ceRNA | Li et al. (2020) [32] Crona et al. (2018) [28] Marquardt et al. (2021) [29] | analysis showed strong discriminatory | ||
| [22] | + miRNA + lncRNA | RRM2), 4 prognostic miRNAs (miR- 212-3p, miR-24-3p, | regulatory network highlighting ncRNA-mediated | Transcriptome (mRNA expression from TCGA), immune and stromal cell estimation (ESTIMATE algorithm), PPI network, survival data | power (AUC > 0.85) | ||
| Identified 1122 intersect DEGs based on immune and stromal scores; selected 18 hub genes via PPI network (e.g., CD4, HLA-DRA, HCK, PTPRB); lower immune scores correlated with worse survival and | Integrated TME profiling with transcriptomic data; constructed a prognostic gene signature with high predictive accuracy (AUC =0.887); highlighted immunological | ||||||
| let-7a-5p, miR-196a- 5p), and 1 lncRNA (HOXA11-AS) via integrated analysis; constructed ceRNA sub-networks like RRM2-miR-24-3p/ let-7a-5p-HOXA11- AS associated with | dysregulation in ACC; proposed novel prognostic biomarkers and therapeutic targets based on multi- omics crosstalk | ||||||
| dysregulation in | |||||||
| poor prognosis | recurrence; immune- | ACC and proposed | |||||
| Li et al. (2023) | Transcriptome (mRNA | Identified 4 key KIF genes (KIF4A, KIF11, | Provided integrative systems | related pathways such as MHC class II, T-cell | novel immune- related biomarkers | ||
| [11] | expression), clinical data, PPI network, immune cell infiltration analysis | KIF20A, KIF22) significantly upregulated in ACC and associated with advanced stage and | biology perspective on kinesin superfamily in ACC; proposed KIFs as novel biomarkers and potential therapeutic targets by combining transcriptomic, immunologic, and network-level evidence | Transcriptome (RNA-seq) from TCGA and TARGET; unsupervised clustering; principal component analysis (PCA); consensus clustering | receptor signaling, and cytokine signaling enriched ACC forms a homogenous transcriptomic cluster across 3319 pan- cancer samples; unexpectedly clusters near neural crest tumors (GBM, LGG, NBL, PNET, PPGL); identified 78 discriminative transcripts, although | Provided transcriptomic evidence that ACC has closer molecular proximity to neural crest tumors than previously understood; opened new perspectives for classifying ACC beyond traditional | |
| poor survival; constructed gene-gene and protein-protein networks; linked KIFs to immune infiltration and functional pathways like mitosis and antigen presentation | |||||||
| Zhang et al. (2023) | Transcriptome (mRNA) + miRNA + PPI networks + clinical | Identified 10 hub genes (e.g., CDC20, | Provided a systems- level map of metastatic mechanisms in ACC; integrated | no strong shared GO signatures between ACC and neural crest tumors | clinicopathological criteria; suggested | ||
| [21] | DLGAP5, KIF2C, MYC) and 5 key miRNAs (e.g., miR- | potential for extrapolating therapeutic | |||||
| survival data | 21-3p, let-7a-5p) linked to ACC metastasis; built a DEmiR-hub gene | transcriptomic, regulatory, and survival data to prioritize multi- | strategies from gliomas and neuroblastomas to ACC | ||||
| network; showed prognostic impact of several genes and miRNAs on patient survival | omic biomarkers for metastasis and prognosis | Transcriptome (RNA-seq from TCGA and ENSAT), mutational data, supervised | Identified two robust transcriptomic clusters (ACC-UMAP1 and ACC-UMAP2) matching known C1A/C1B subtypes; | Offered an unbiased machine-learning- based stratification of ACC; validated clusters across | |||
| datasets; proposed | |||||||
| (continued on next page) | |||||||
| Table 6 (continued) | Table 6 (continued) | ||||||
|---|---|---|---|---|---|---|---|
| Study | Omics Layers Used | Key Findings | Contribution to ACC Research | Study | Omics Layers Used | Key Findings | Contribution to ACC Research |
| Guan et al. (2022) [12] | machine learning (Random Forest), UMAP clustering | 100 differentially expressed genes identified via RF (e.g., SLC2A1, SOAT1, EIF2S1, MYC, | novel biomarker candidates for prognosis and therapy based on transcriptomic signatures and mutational correlation Provided one of the most | miR-mRNA target prediction + pathway analysis (IPA, GSEA) | introduced a novel tissue-specific miR target prediction strategy integrating inverse expression | functional miR target discovery; revealed post- transcriptional regulatory mechanisms driving ACC pathogenesis; proposed miRs as both diagnostic biomarkers and potential | |
| FSCN1); ACC-UMAP2 linked to poorer survival and higher mutation rates in TP53 and CTNNB1 | patterns and filtering; found G2/M DNA damage checkpoint as the top dysregulated pathway; validated | ||||||
| mRNA | Identified three robust | miR-503 and miR-511 | |||||
| + lncRNA | ACC molecular | as potential | modulators of the | ||||
| + miRNA | subtypes (ACC1-3) using 10 multi-omics clustering algorithms; ACC2 had worst survival and highest mutation burden; ACC1 showed strongest immune activation and response to anti-PD-1 therapy; distinct metabolic and DNA repair pathways enriched in subtypes | comprehensive multi-omics classifications in ACC; enabled precise therapeutic stratification and prediction of immunotherapy and chemotherapy response; established a novel and clinically actionable molecular taxonomy | Yan et al. (2021) [36] | biomarkers distinguishing ACC | cell cycle machinery | ||
| + DNA methylation + somatic mutations + clinical data | |||||||
| Transcriptome (from TCGA and GEO), survival data, CNV and mutation data, PPI network, machine learning models (Ridge, Elastic Net, Lasso), | from benign tumors (AUC~100 % sens/ spec) Constructed and validated 11 multi- gene prognostic models; selected a 14- gene signature (e.g., ASPM, AURKA, CCNB2, CDC20, MKI67) with strong predictive power (AUC > 0.84, C-index | Provided a robust, externally validated, multi- omics-based prognostic model; demonstrated the clinical utility of nomogram and machine-learning- based prediction; | |||||
| Li et al. (2022) [30] Di Dalmazi et al. (2020) [16] Tömböl et al. (2009) [15] | Transcriptome | Identified 490 DEGs | Delivered an integrated multi- | GSEA | = 0.851); Model 2 | offered a panel of novel ACC | |
| (from GEO and | (28 upregulated, 462 | chosen as best | |||||
| TCGA), PPI network, | downregulated); 17 hub genes (e.g., | omics pipeline combining transcriptomics, interactome, pathway biology, and survival/ mutation analysis; provided two robust diagnostic/ prognostic biomarkers for potential clinical translation Delivered a multi- layer integrative analysis revealing the interplay between transcriptome, mutation status, and steroidogenic | Yuan et al. (2018) [23] | Transcriptome (microarray) + clinical data (tumor grade, survival) + WGCNA | performing; CNVs and mutations in these genes significantly influenced mRNA expression and patient survival; six hub genes identified via PPI network Identified 12 real hub genes (ANLN, ASPM, CDCA5, CENPF, FOXM1, KIAA0101, MELK, NDC80, PRC1, RACGAP1, SPAG5, TPX2) through a stepwise validation pipeline; all | biomarkers strongly associated with survival and actionable for precision medicine First study applying WGCNA to ACC; provided a comprehensive systems-level identification and validation of robust | |
| pathway enrichment (KEGG, Reactome, PANTHER, BioCyc), survival data (GEPIA), mutational | CCNB1, NDC80, TPX2, FOXM1, KIF11, RRM2) were strongly associated with poor prognosis; CCNB1 and NDC80 selected as top core genes through multi-step prioritization (Degree, MCC, MNC); enriched in cell cycle and p53 signaling | ||||||
| analysis | |||||||
| (cBioPortal) | |||||||
| pathways | + PPI network + validation in | ||||||
| Transcriptome | Identified distinct | prognostic biomarkers; suggested hub genes as candidate drivers of proliferation and disease progression in ACC | |||||
| (RNA-seq) | transcriptomic | TCGA, GEO, | |||||
| + lncRNAs + gene fusion detection + somatic mutation | clusters among ACAs and ACCs, correlating with hormonal activity and driver mutations (e.g., PRKACA, GNAS, | GEPIA | associated with tumor grade and poor prognosis; enriched in cell cycle, DNA replication, and mitotic pathways | ||||
| analysis (WES & | |||||||
| RNA-seq) | CTNNB1, TP53); ACC samples showed | phenotype; provided new pathogenetic insights including gene fusions and lncRNA profiles | Yi et al. (2022) [37] | Transcriptome (RNA-seq + microarray), WGCNA, DEG analysis, PPI network, | Identified 9 hub genes (e.g., ASPM, BIRC5, CCNB2, CDK1, TOP2A, FOXM1); 4 MPBs (ASPM, BIRC5, CCNB2, CDK1) with | Provided a robust integrative pipeline combining | |
| distinct expression profiles, enriched gene fusions, and lncRNA | |||||||
| transcriptomics, machine learning, and immune | |||||||
| miRNA (miR) expression | downregulation; genes like TOP2A and CCNB1 were selectively overexpressed in ACCs; fusion events (e.g., AKAP13-PDE8A) and altered steroidogenesis pathways observed Identified six miRs with significant | distinguishing benign from malignant tumors Delivered the first integrative miR-mRNA analysis in ACC; introduced a new method for | survival data, mutation & CNV data, drug signature enrichment, immune infiltration analysis | strong predictive power for OS (AUCs > 0.8 for 1-, 3-, 5-year survival); MPBs correlated with CNVs, immune infiltration, tumor grade, and stage; drug signatures like vorinostat, trifluoperazine, and alpha-estradiol predicted to target MPBs | analysis; proposed clinically applicable nomograms for survival prediction; revealed multi- omics-based MPBs as promising diagnostic, prognostic, and therapeutic targets in ACC | ||
| profiling + mRNA | expression differences in ACC (e.g., miR- | Guo et al. (2020) [24] | Transcriptome (three GEO datasets), GO/ KEGG functional | Identified 200 consensus DEGs across GSE10927, GSE12368, and | Delivered an integrative cross- cohort | ||
| transcriptomics | 5031, miR-1841, miR- | ||||||
| + tissue-specific | 5114, miR-2144); | transcriptomic (continued on next page) | |||||
| Study | Omics Layers Used | Key Findings | Contribution to ACC Research |
|---|---|---|---|
| annotation, PPI network, TCGA survival data | GSE90713; nine hub genes (CCNB1, CDK1, TOP2A, CCNA2, CDKN3, MAD2L1, RACGAP1, BUB1, CCNB2) enriched in cell cycle and p53 pathways; all hub genes significantly upregulated and associated with poor overall survival; BUB1 identified as independent prognostic factor | analysis for biomarker discovery in ACC; validated multi- gene panel associated with mitotic dysregulation and adverse prognosis; highlighted the utility of combining public datasets for robust gene signature identification | |
| Tian et al. (2021) | Proteomics (RPPA from TCPA), transcriptome (TCGA, GEO), | Developed a prognostic model | First large-scale proteogenomic analysis in ACC; introduced a robust multi-omics-based |
| [33] | (IPRPs) based on 4 key proteins: FASN, Fibronectin (FN), | ||
| real-world IHC data, clinical data | TFRC, TSC1; validated in TCGA, GEO (GSE10927), and FUSCC cohorts; model outperformed Ki-67 and ß-catenin in prognostic accuracy (AUC = 0.933); hub genes (e.g., CCNB1, CDK1, AURKA, PLK1) linked to cell cycle pathways; GSEA showed enrichment in chromosome separation and metaphase-anaphase transition | prognostic model validated across cohorts; provided protein-level biomarkers (not just mRNA), reinforcing translational potential for clinical application and precision medicine | |
| Chen et al. (2021) [4] | Transcriptome (RNA-seq), hypoxia-related gene sets, immune infiltration (CIBERSORT), survival data | Developed a 3-gene hypoxia-associated signature (CCNA2, EFNA3, COL5A1) predictive of overall survival; high-risk score correlated with worse prognosis, immune suppression (e.g., lower PDL1 and CTLA4), and increased resting NK cells | Provided a clinically actionable hypoxia-based prognostic model; linked hypoxia signaling to immune microenvironment remodeling; validated performance across TCGA and GEO cohorts with high AUC values (0.949-0.871) |
| Omidi (2025) [39] | Gene expression (mRNA) + miRNA expression (multi-dataset) + network topology analysis | Identified miR-507 and miR-665 as tumor-specific regulatory hub miRNAs in ACC; revealed regulatory network rewiring between normal and tumor states; demonstrated miR- 507 association with improved survival and miR-665 as radiation- responsive | Provides systems- level multi-omics insight into miRNA- centered regulatory architecture; prioritizes miR-507 and miR-665 as clinically relevant network biomarkers with diagnostic and therapeutic relevance in ACC |
| Omidi (2025) [31] | mRNA expression (TCGA-ACC + GTEx) | Identified CKS2 and ACAT2 as co- upregulated, transcriptionally co- | Provides systems- level evidence for a synergistic proliferative- |
| integrated with regulatory network topology and clinical | regulated oncogenic drivers; demonstrated reinforced tumor- specific regulatory coupling and strong | metabolic oncogenic axis, highlighting clinically actionable biomarker |
| Study | Omics Layers Used | Key Findings | Contribution to ACC Research |
|---|---|---|---|
| survival/ | prognostic and diagnostic performance (AUC~ 0.90) | candidates and network-defined therapeutic vulnerabilities in ACC | |
| diagnostic | |||
| modeling |
confirm IncRNA-oncogene regulatory interactions in colorectal cancer [53].
Therapeutic translation faces additional barriers. Mitotane remains the only FDA-approved drug for ACC, despite limited efficacy and sub- stantial toxicity. Recent computational repositioning studies have nominated agents such as Cosyntropin, Linsitinib, and CDK1 inhibitors [3,10,25], but no repositioned drug has yet entered advanced clinical trials for ACC. Moreover, few preclinical models exist to test drug effi- cacy in relevant tumor systems, and systems pharmacology validation remains largely unexplored. Bridging this translational gap will require preclinical screening of prioritized candidates in xenograft or organoid models, as well as biomarker-informed patient stratification in trial designs.
Another significant obstacle is the fragmentation of omics data. While transcriptomics is widely used in ACC research, integration with proteomics, epigenomics, copy number variation, and immune land- scapes is rare and often lacks standardized workflows. Promising studies have demonstrated the power of multi-omics integration in stratifying ACC subtypes and identifying convergent markers [4,30], yet such ap- proaches are not yet the norm. Multi-cohort, multi-layered frameworks will be critical for defining clinically meaningful subgroups and pre- dicting therapy response with higher accuracy.
Additionally, systems-level interpretation of gene-gene interactions remains in its infancy. Most existing analyses rely on linear pathways or static PPI networks, which fail to capture temporal dynamics or feed- back mechanisms. Emerging evidence on NCAP family genes [38], TPX2/FOXM1 circuits [37], and ceRNA-mRNA-IncRNA crosstalk [22] suggests that combinatorial and non-linear regulatory logic underlies aggressive ACC phenotypes. To model this complexity, future studies should incorporate dynamic systems biology approaches using time-series, perturbation-based, or single-cell datasets.
The immune landscape of ACC also remains incompletely charac- terized. While immune subtyping [12] and TME-associated gene panels [21,32] have advanced our understanding, most computational models lack integration of checkpoint markers, immune suppression signatures, or therapy response predictors. Given the limited success of immune checkpoint inhibitors in ACC so far, personalized immunogenomic classifiers are urgently needed to optimize patient selection and com- bination therapies.
Finally, open science and data-sharing practices must be strength- ened to accelerate collective progress. Although initiatives such as ENSAT and A5 have provided foundational resources, widespread public access to raw sequencing data, clinical metadata, and reproducible pipelines remains limited. Harmonized data formats, metadata stan- dards, and open-access repositories would greatly improve collabora- tion, benchmarking, and cross-cohort validation.
In conclusion, ACC research stands at the threshold of a new era driven by bioinformatics, systems biology, and translational innovation. To fully realize this potential, the field must bridge existing methodo- logical gaps, validate predictions through experimental pipelines, and expand collaborative infrastructure. These coordinated efforts will be essential to move from descriptive omics to actionable insights that improve diagnosis, prognosis, and therapy in ACC. A schematic sum- mary of these key challenges and proposed solutions is presented in Fig. 10, illustrating the translational bottlenecks across biomarker dis- covery, therapeutic development, and systems-level integration.
Challenges
Strategic Directions
Rare Datasets
Multi-Institutional Biobanks
Prediction Validation Gap
Integrated In Silico-Wet-Lab Pipelines
Limited Therapy Transition
ACC-Specific Preclinical Models
Fragmented Omics
Multi-Omics Integration
Static Modeling
Dynamic Systems Biology Frameworks
8.2. Multi-omics integration in clinical decision-making
Despite extensive molecular characterization efforts, the trans- lational impact of multi-omics discoveries in ACC remains limited. However, evidence from recent studies underscores the potential of in- tegrated transcriptomic, epigenetic, and immunogenomic approaches to refine patient stratification and guide individualized therapeutic stra- tegies. For example, lncRNA signatures have demonstrated robust ca- pacity to delineate tumor subtypes and correlate with clinically meaningful phenotypes, suggesting that non-coding RNA networks may function as stable biomarkers in precision oncology frameworks [54]. Similarly, miRNA-based regulatory axes such as miR-489-mediated suppression of oncogenic signaling highlight the feasibility of leveraging post-transcriptional modulators to enhance therapeutic responsiveness and overcome malignant phenotypes [55]. These find- ings reinforce the premise that non-coding RNA-centered multi-omics integration can yield clinically actionable biomarker panels for risk prediction and treatment tailoring in ACC. Similar findings have been reported for other oncogenic lncRNAs, where coordinated dysregulation across transcriptomic datasets and validation in clinical tissues has confirmed their role in tumor progression and metastatic behavior [56].
In parallel, growing evidence indicates that the tumor microenvi- ronment, particularly macrophage polarization dynamics plays a deci- sive role in modulating therapeutic resistance and shaping clinical outcomes. Non-coding RNAs have been shown to regulate macrophage phenotype transitions and immune evasion circuits, suggesting that incorporation of immune-omics layers into ACC profiling may improve prediction of immunotherapy responsiveness [57]. Furthermore, trans- lational pipelines involving siRNA-based gene targeting and nanoparticle-enabled delivery systems illustrate practical strategies to
overcome biological instability, systemic degradation, and off-target constraints that currently limit the therapeutic implementation of RNA-directed interventions [58]. However, successful clinical trans- lation will require standardized delivery platforms, validated pharma- cokinetic profiles, and controlled evaluation in preclinical ACC-specific disease models.
Finally, insights from hematologic malignancies demonstrate that lncRNAs can function as diagnostic and prognostic determinants and can predict therapy resistance, supporting their inclusion in multi-layered biomarker frameworks designed for real-world clinical decision- making [59]. Integrating these lessons into ACC research may facili- tate the transition from descriptive genomic catalogs to clinically deployable precision medicine tools, particularly if coupled with coor- dinated prospective validation efforts, inclusion of pediatric ACC co- horts, and harmonized biorepository infrastructure.
8.3. Methodological limitations and future validation needs
Current findings should be interpreted considering several method- ological constraints. The rarity of ACC limits the availability and di- versity of well-annotated patient cohorts, which may introduce sampling bias and restrict the statistical power needed to resolve clini- cally meaningful subgroups or molecular phenotypes. This challenge has been consistently emphasized in recent genomic and multi-omics char- acterizations of ACC, where small cohort sizes constrained the gener- alizability of inferred regulatory programs and subtype-specific alterations [39,60]. Moreover, although integrated transcriptomic and regulatory analyses offer valuable hypotheses regarding oncogenic drivers and miRNA-mediated network remodeling, retrospective in sil- ico inference cannot fully recapitulate dynamic regulatory states, tumor-microenvironment interactions, or treatment-induced adaptation [61,62]. To ensure translational rigor, the candidate biomarkers and regulatory modules proposed here will require multi-layered experi- mental validation, including RT-qPCR and immunohistochemical confirmation in independent ACC cohorts, followed by functional perturbation assays (e.g., siRNA or CRISPR-mediated silencing) to establish mechanistic relevance [63]. In addition, evaluation in patient-derived organoid and xenograft systems will be necessary to determine phenotypic consequences and therapeutic exploitability [64]. Ultimately, prospective, biomarker-stratified clinical studies are essen- tial to determine whether these molecular signatures can reliably inform risk classification and guide targeted or immunomodulatory treatment decisions in real-world clinical settings.
9. Conclusion
This review integrates emerging molecular, transcriptomic, and immunogenomic insights to provide a comprehensive overview of the current state of ACC research. Studies have repeatedly identified key oncogenic drivers, such as CDK1, CCNB1, FOXM1, and BIRC5 as being central to tumor progression and poor prognosis [2,3,37]. The increasing recognition of non-coding RNAs, including HOXA11-AS and LINC00271, has further expanded the landscape of potential biomarkers and regulatory axes in ACC [13,22]. Recent ceRNA- and network-based studies have also highlighted synergistic dual oncogenic pathways involving CKS2 and ACAT2, as well as the emergence of miR-507 and miR-665 as central regulatory nodes in ACC, providing refined molec- ular stratification and prognostic insight [31,39].
Moreover, the integration of bioinformatics and systems biology has enabled deeper insights into the complex interaction networks within ACC tumors. Multi-omics clustering approaches have successfully stratified patients into molecular subtypes with distinct survival out- comes and therapeutic vulnerabilities [12,29]. These integrative models, when combined with immune profiling and protein-based risk signatures, provide new opportunities for patient-specific management strategies [32,33].
Drug repositioning has emerged as a promising avenue for expanding therapeutic options in ACC, with computational studies identifying agents such as Linsitinib, Roscovitine, and Cosyntropin that may target ACC-specific vulnerabilities [4,10]. However, translation into clinical application remains limited, underscoring the need for functional vali- dation and ACC-specific preclinical models.
By synthesizing recent discoveries from molecular, transcriptomic, and immunogenomic studies, this review highlights both the progress and the gaps in ACC research. Continued development of integrative pipelines, validation strategies, and cross-institutional collaborations will be critical in transforming molecular discoveries into clinically actionable tools. In this regard, systems-level and multi-omics frame- works hold particular promise for shaping the next generation of di- agnostics and therapeutics in ACC. However, realizing this potential will require coordinated multi-center biobanking efforts, ACC-specific organoid and xenograft model development, and prospective biomarker-stratified clinical trial designs, to bridge the remaining translational gap between computational discovery and real-world pa- tient care.
CRediT authorship contribution statement
Javad Omidi: Writing - review & editing, Writing - original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
Declaration of Generative AI and AI-assisted technologies in the writing process
During the preparation of this work the author used ChatGPT (OpenAI) in order to improve English language and readability. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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