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Molecular Classification and Computational Signatures in ACC

Precision Medicine and Biomarker Development

Molecular classification and computational signatures in adrenocortical carcinoma (ACC) encompass transcriptomic, epigenomic, noncoding RNA, radiomic, pathomic, and integrated multi-omics approaches used to characterize biologic heterogeneity and to estimate diagnosis, prognosis, or treatment sensitivity beyond conventional clinicopathologic assessment.12 Within ACC care, these methods sit in the precision-medicine and biomarker-development domain rather than in the core diagnostic workflow. They are generally intended to complement histopathology, endocrine evaluation, staging, and established postoperative risk systems, not to replace them.342

The field developed from recognition that ACC is clinically and biologically heterogeneous: tumors with similar morphology or stage may differ substantially in recurrence risk, metastatic behavior, and survival.516 Across analytic platforms, the most reproducible finding is the separation of aggressive, proliferation-dominant, genomically disrupted, and often immune-poor tumors from more differentiated or immune-enriched tumors with relatively better outcomes.78910 This broad pattern appears more stable than any individual compact gene panel.

Evidence remains limited by the rarity of ACC and by heavy dependence on retrospective public datasets, especially TCGA-derived cohorts, small institutional series, and repeated reuse of overlapping samples.11122 Many published signatures report favorable discrimination in discovery cohorts but have uncertain cross-platform reproducibility, limited independent validation, and unclear incremental value over ENSAT stage, Ki-67, Weiss score, S-GRAS, and other standard tools.13142 As a result, most molecular and computational classifiers in ACC remain investigational and are better understood as research stratification frameworks than as routine clinical tests.

Diagnostic and biologic context

Early expression and microRNA studies established that adrenocortical tumors can be separated molecularly and that malignant tumors contain prognostically distinct subsets.3515 Subsequent integrated analyses, especially TCGA-based work, reinforced the view that ACC comprises several related molecular states characterized by differences in proliferation, DNA methylation, copy-number disruption, and recurrent pathway alterations such as Wnt/beta-catenin and TP53-RB axis dysregulation.12

What appears most reliable is the existence of broad high-risk versus lower-risk molecular states rather than any single universal marker.16 What is less reliable is transfer of specific cutoffs or compact gene sets across cohorts, laboratories, and platforms. Clinically, molecular classification currently contributes more to biologic understanding and trial design than to routine diagnosis.

Related adrenal cortical disorders provide limited contextual support for the importance of noncoding RNA dysregulation in adrenal biology, but findings from benign or hyperplastic conditions should not be extrapolated directly to ACC without tumor-specific validation.16

Major molecular phenotypes

Proliferation-dominant and poor-outcome states

A recurrent finding across transcriptomic, methylation, and multi-omics studies is a poor-prognosis phenotype enriched for cell-cycle progression, mitotic regulation, replication stress, stemness, and metastatic behavior.717189 Genes such as CDK1, TOP2A, CCNB-family members, AURKA, KPNA2, and FSCN1 repeatedly appear in adverse-risk models and in functional studies of aggressive tumor behavior.192021222324

This theme is supported by multiple independent analytic strategies, which makes the underlying biology more credible than any one signature. The practical implication is that molecular evidence may help explain unexpectedly aggressive behavior within the same clinical stage, but it does not yet replace pathology-based risk assessment.

Immune-enriched and microenvironment-defined states

A second major axis of classification concerns the tumor immune microenvironment. Retrospective expression-based analyses generally suggest that immune-enriched ACCs have better survival, less metastasis, and more favorable checkpoint- and antigen-presentation profiles, whereas immune-poor or immune-excluded tumors are associated with recurrence, stemness, and shorter survival.25262728 Integrated subtype frameworks similarly tend to identify an immune-activated group distinct from a proliferation-dominant group.810

The direction of association is reasonably consistent, but most studies infer immune state computationally and do not prospectively validate treatment response. In practice, these classifications may help prioritize translational immunotherapy hypotheses, but they do not yet provide a validated basis for selecting checkpoint blockade in ACC.

Pathway-centered signatures

Many newer models are organized around specific biologic processes, including ferroptosis, necroptosis, pyroptosis, cuproptosis, hypoxia, senescence, RNA methylation, alternative splicing, and mitochondrial quality control.2930313233343536 Although these categories differ mechanistically, they often converge on the same clinical signal: worse-risk tumors tend to show stronger proliferative programs, reduced immune activity, and inferior survival.

This convergence suggests that many pathway-specific models are capturing overlapping aspects of aggressive ACC biology rather than defining entirely separate disease classes. The practical implication is that claims of novelty should be interpreted cautiously unless a new signature demonstrates reproducibility and added value over existing prognostic frameworks.

Computational modalities beyond bulk transcriptomics

As subtype research expanded, computational biomarker development moved beyond bulk expression profiling into integrated and potentially noninvasive approaches. Multi-omics models combining RNA expression, microRNA, methylation, mutation, copy-number, chromatin, or protein-level information often generate more coherent subtype structures than single-layer analyses and may better reflect ACC heterogeneity.1237910 These are among the strongest tools for biologic discovery, but they are also the least practical for routine use because they require broad profiling and harmonized analytic pipelines.2

Radiomics and digital pathology attempt to extend classification to imaging and whole-slide morphology. Retrospective CT radiomic studies suggest that texture and shape features may correlate with Ki-67 or mitotic grade and may provide prognostic information, while pathomics models in resected ACC have shown associations with overall survival.38394041 These methods are attractive because they may be scalable and less tissue-dependent, but current evidence is based on small retrospective cohorts with substantial methodological heterogeneity. Their clinical role therefore remains investigational.

Evidence for prognosis, diagnosis, and treatment prediction

Across the literature, prognostic stratification is the most consistent use case. Multiple expression-based and integrated models are associated with overall survival, disease-free survival, recurrence, or progression, and some appear to improve discrimination beyond stage alone.13314243 This is the area in which the signal is strongest, although reproducibility and real-world clinical utility remain incompletely established.

Diagnostic discrimination between adenoma and ACC is supported by early gene-expression and microRNA work, but standard histopathology and endocrine evaluation remain the clinical foundation.354 No computational signature has become a routine replacement for expert pathologic assessment. The practical implication is that molecular classifiers may assist difficult cases in research settings, but they are not stand-alone diagnostic tests.

Predictive use for therapy selection is more speculative. Several subtype and pathway studies computationally suggest differential sensitivity to immune checkpoint blockade, antiangiogenic therapy, platinum-based regimens, etoposide, doxorubicin, PARP inhibition, and other targeted strategies.844454623 What remains unreliable is whether these predicted differences translate into improved outcomes in prospective ACC treatment cohorts.

Limitations, pitfalls, and role in management

The principal limitation of this field is not absence of biologic signal but limited validation. Small sample sizes, high-dimensional modeling, reuse of nonindependent datasets, variable preprocessing, and inconsistent endpoint definitions all increase the risk of overfitting and inflated performance estimates.112 Pan-cancer analyses may generate useful hypotheses, but their ACC-specific generalizability is often uncertain when only a small adrenal subset contributes to the result.47

Accordingly, molecular classification currently has greater value in ACC research than in routine care. It may help define biologically coherent subgroups, identify candidate therapeutic targets, and support biomarker-enriched trials or future integrated postoperative risk models.20222 In current practice, any molecular or computational signature is best interpreted alongside surgery, pathology, stage, hormone secretion status, and validated clinical prognostic systems rather than in isolation.13142

Included Articles

  • PMID 19465894: This review highlights early transcriptomic biomarker development in ACC, describing gene-expression signatures that distinguished benign from malignant adrenocortical tumors and stratified outcomes within malignant cases. DLG7 plus PINK1 predicted disease-free survival, while BUB1B plus PINK1 predicted overall survival, supporting molecular tools as adjuncts to conventional pathology and staging.3
  • PMID 21472710: MicroRNA profiling identified a malignant adrenocortical tumor signature with miR-483-5p upregulation and miR-100, miR-125b, and miR-195 downregulation. In this cohort, miR-483-5p alone showed high accuracy for distinguishing benign from malignant adrenocortical tumors and remained elevated in most recurrent and metastatic ACC samples.5
  • PMID 21859927: This study identifies a distinct microRNA signature in ACC versus adenoma and normal cortex, with overexpression of miR-483-3p/5p, miR-210, and miR-21 and underexpression of miR-195 and miR-497. Functional experiments and survival associations suggest selected microRNAs may serve as diagnostic and prognostic biomarkers while reflecting tumor biology.15
  • PMID 27385106: This review summarizes proteomic, immunohistochemical, and molecular studies aimed at distinguishing adrenal cortical adenoma from ACC and identifying therapeutic targets. It highlights candidate biomarkers such as Ki-67 and proteomic signatures, while emphasizing the lack of standardized diagnostic cutoffs and the need for further validation.4
  • PMID 28968749: This article describes a web-based TCGA omics analysis platform that can identify genes associated with tumor stage and predict survival outcomes across multiple cancers, including adrenocortical carcinoma. It highlights machine-learning-enabled linkage of omics profiles with clinical phenotypes as a precision oncology approach.48
  • PMID 31156552: Bioinformatic analysis of TCGA ACC transcriptomic data constructed a ceRNA network involving 10 ceRNAs, 35 miRNAs, and 34 mRNAs, with enrichment in immune and extracellular matrix related pathways. Two lncRNA-like ceRNAs, CTB-63M22.1 and RP1-241P17.4, were associated with disease-free and overall survival as investigational prognostic biomarker candidates.49
  • PMID 31611976: A bioinformatics analysis of two ACC microarray datasets identified 228 differentially expressed genes and 14 hub genes enriched in cell division, mitotic cell cycle, and p53-related pathways. Higher expression of several hub genes, including AURKA, TYMS, GINS1, RRM2, EZH2, CDK1, CCNB1, NCAPG, and TPX2, was associated with worse survival and higher Weiss grade, supporting their candidacy as investigational diagnostic and prognostic biomarkers.7
  • PMID 31856409: Bioinformatic analysis of TCGA ACC samples linked lower immune scores with distant metastasis, locoregional recurrence, and worse overall survival, and derived a tumor-microenvironment gene risk score with strong prognostic discrimination. The study also identified immune-associated hub genes, including HLA-DOA, as candidate biomarkers connected to immune cell infiltration.25
  • PMID 31941752: This pan-cancer analysis notes that SHMT2 expression increases during disease progression in adrenocortical carcinoma, particularly in late-stage tumors, alongside similar findings in other cancers. The excerpt frames SHMT2 as a potential progression-associated biomarker and investigational therapeutic target rather than an ACC-validated clinical marker.50
  • PMID 32089260: This retrospective study suggests that preoperative contrast-enhanced CT radiomic features may noninvasively predict Ki-67 expression in ACC. Shape flatness and elongation showed the strongest performance for identifying high Ki-67 status, supporting imaging-based biomarker development for recurrence risk assessment.38
  • PMID 32147961: Integrated analysis of three GEO expression datasets in ACC identified 200 shared differentially expressed genes, with enrichment of cell-cycle and p53-related pathways. Nine hub genes were overexpressed in ACC and linked to worse overall survival, with BUB1 emerging as an independent prognostic factor in TCGA-based analysis.17
  • PMID 32217810: A TCGA-based retrospective analysis in 92 ACC cases identified thousands of alternative splicing events associated with overall survival and derived multi-event prognostic signatures, with several splicing-event classes showing high discriminatory performance. The study also mapped splicing factor networks, positioning alternative splicing as an investigational prognostic biomarker domain in ACC.51
  • PMID 32765768: A bioinformatic analysis of three ACC microarray datasets identified eight downregulated hub genes, with EFEMP2 linked to worse disease-free survival and lower CSRP1 and MFAP4 expression associated with adverse capsular invasion, grade, and vascular invasion. The study proposes these genes as exploratory biomarker candidates for ACC diagnosis, prognosis, and future therapeutic investigation.52
  • PMID 32915499: A TCGA-based pan-cancer analysis reported that in adrenocortical carcinoma, higher expression of most heterogeneous nuclear ribonucleoprotein family genes was associated with worse survival. The study frames hnRNP-related alternative splicing biology as a potential prognostic biomarker area for future ACC investigation.53
  • PMID 33101358: A TCGA-based analysis of 79 ACC cases identified numerous survival-related alternative splicing events and developed prognostic models with reported discriminatory performance, highlighting THNSL2 mutually exclusive exon splicing and six hub splicing factors as candidate prognostic biomarkers. The study supports aberrant splicing patterns as an investigational molecular layer for ACC risk stratification.54
  • PMID 33187258: Targeted TCGA bioinformatics identified a distinct ACC GABA-system transcriptomic subset in which ABAT upregulation was common and associated with longer overall and progression-free survival, fewer metastases, and lower cortisol-excess frequency. The study proposes GABA-pathway gene expression, including ABAT and selected receptor subunits, as investigational prognostic biomarkers and highlights NCI-H295R as a relevant translational model.55
  • PMID 33209656: Using TCGA and GTEx transcriptomic data, this study developed an immune-related gene risk signature for ACC in which higher immune scores were associated with earlier clinical stage and better survival. The derived risk score correlated with stage and metastasis and was reported as an independent prognostic factor for overall survival.26
  • PMID 33289788: A pan-cancer meta-analysis and database review identified elevated miR-196a as an adverse prognostic biomarker, with database analysis showing particularly poor survival association in adrenocortical carcinoma. The ACC signal is presented as biomarker-level evidence rather than disease-specific diagnostic or therapeutic guidance.56
  • PMID 33680972: A pan-cancer bioinformatics analysis found CD96 expression increased in ACC versus GTEx normal tissue, but in ACC it was not associated with prognosis and showed no robust correlation with recognized immune checkpoints or Th1 markers. These findings suggest CD96 is not yet a useful ACC-specific immune biomarker despite broader pan-cancer interest.57
  • PMID 33725886: A TCGA/GTEx-based bioinformatic study found altered expression of multiple m6A RNA methylation regulators in ACC and derived a four-gene signature involving HNRNPC, RBM15, METTL14, and FTO that was associated with advanced stage and independently predicted overall survival.29
  • PMID 33746977: Integrative transcriptomic analyses of TCGA and GEO cohorts identified three ACC m6A modification patterns linked to distinct survival and immune microenvironment features, and derived an eight-gene m6A prognostic signature. High HNRNPA2B1 expression was associated with worse overall and event-free survival, supporting m6A-related biomarkers as investigational prognostic tools.58
  • PMID 33776902: Bioinformatic analysis of TCGA ACC data identified 1,005 survival-associated alternative splicing events and derived a 12-event risk model with internal validation, plus a nomogram combining risk score with clinicopathologic factors for 1- and 3-year survival prediction. The findings suggest alternative splicing may serve as a prognostic biomarker source, with enrichment implicating Wnt signaling.59
  • PMID 33889305: A pan-cancer TCGA analysis reported that deaminase-associated mutational metrics combined in a machine-learning panel could stratify adrenocortical carcinoma patients by progression-free survival. In the ACC cohort, lower progression-free survival was associated with higher SNV burden, and the model achieved high validation accuracy using a 24-month threshold.60
  • PMID 33952721: A bioinformatic and experimental study identified an m6A-related prognostic signature based on RBM15 and HNRNPC in ACC, with validation in an external cohort and integration into a nomogram with T stage. Higher m6A risk was also associated with suppressed immune-related processes and lower immune-checkpoint marker expression, supporting investigation of epigenetic biomarkers for prognosis and therapy selection.61
  • PMID 34094911: A pan-cancer bioinformatics study reported that high NAT10 mRNA expression was associated with worse survival outcomes in adrenocortical carcinoma across several survival analyses. The paper frames NAT10 as a potential prognostic biomarker candidate, though the mechanistic immune findings were developed mainly in other tumor types rather than ACC specifically.62
  • PMID 34164395: This bioinformatics study developed and externally validated a 14-gene prognostic model for ACC using public transcriptomic datasets, with nomogram, ROC, and Cox analyses suggesting independent survival prediction. It also identified six hub genes and reported that copy number variation and expression changes in model genes were associated with survival.63
  • PMID 34220331: This TCGA-based bioinformatics study identified ACC survival-associated hub genes and miRNAs that differentiated alive versus dead patient groups, and proposed candidate prognostic biomarkers alongside exploratory small-molecule leads from connectivity mapping. The findings are hypothesis-generating and position transcriptomic and miRNA signals as potential tools for future biomarker development.64
  • PMID 34312987: A pan-cancer bioinformatics study reported that YTHDC2 is downregulated in ACC, that ACC had the highest YTHDC2 amplification frequency among surveyed tumors, and that YTHDC2 hypermethylation in ACC was associated with worse overall survival. These findings suggest a possible prognostic epigenetic biomarker role in ACC but remain exploratory.65
  • PMID 34335745: A retrospective TCGA and GEO analysis proposed a six-gene ferroptosis-related expression signature that stratified ACC overall survival, appeared independent of standard clinicopathologic factors, and was associated with lower immune-cell and immune-pathway scores in the high-risk group. The study frames ferroptosis-linked transcriptomic profiling as a potential prognostic biomarker approach rather than a practice-ready tool.30
  • PMID 34367952: A TCGA-based bioinformatics study found that higher transcriptomic stemness index in ACC was associated with shorter overall survival, greater metastatic tendency, lower immune and stromal scores, and reduced CD8-positive T-cell infiltration. The authors propose stemness index and related cell-cycle genes as investigational biomarkers for predicting immune checkpoint inhibitor efficacy.18
  • PMID 34490347: A TCGA-based pan-cancer analysis found that higher C1ORF112 expression in adrenocortical carcinoma was associated with worse overall, disease-free, and progression-free survival, and with more advanced pathological stage. In ACC, C1ORF112 expression also correlated positively with cancer-associated fibroblast infiltration, supporting its candidacy as an investigational prognostic biomarker.66
  • PMID 34531868: In a pan-cancer TCGA-based analysis, higher PARP1 expression in adrenocortical carcinoma was positively correlated with tumor mutation burden. The study frames PARP1-related alterations and expression as investigational immunotherapy-associated biomarkers rather than ACC-specific validated clinical predictors.67
  • PMID 34572898: Machine-learning analysis of ACC mRNA expression data re-identified two transcriptomic subgroups that largely match known prognostic clusters and differ in overall survival. Random-forest modeling also nominated candidate prognostic biomarker genes, including SOAT1 and EIF2A1, that warrant further validation.6
  • PMID 34603443: Bioinformatics analysis of TCGA and GEO cohorts identified a three-gene hypoxia signature comprising CCNA2, COL5A1, and EFNA3 that was associated with advanced stage, independently predicted overall survival, and correlated with immune microenvironment features including lower PDL1 and CTLA4 expression in high-risk tumors.68
  • PMID 34681758: A TCGA-based pan-cancer analysis found that telomere maintenance mechanism subtype had prognostic relevance in ACC, with ALT associated with worse survival, higher copy number variation, and distinct transcriptional and biologic pathway signatures, while NDTMM was associated with better survival. These findings suggest telomere maintenance classification as a potential investigational prognostic biomarker in ACC.69
  • PMID 34700129: A pan-cancer bioinformatics analysis identified GSDMD as a potential prognostic biomarker in adrenocortical carcinoma, with higher expression associated with worse overall survival and correlated with pyroptosis-related genes and broad immune features in ACC.70
  • PMID 34722530: A pan-cancer bioinformatic analysis identified ferroptosis-related genes as candidate prognostic biomarkers in ACC: higher SLC7A11, GPX4, and AIFM2 expression was associated with shorter disease-free survival, and higher SLC7A11 and AIFM2 with shorter overall survival. The findings are exploratory and database-derived rather than practice-defining.71
  • PMID 34729929: A pan-cancer bioinformatics study reported that MYL9 expression was associated with prognosis in adrenocortical carcinoma, with higher expression linked to longer overall survival in ACC within GEPIA2 analysis. The paper frames MYL9 as an investigational prognostic and immune-related biomarker derived from public transcriptomic datasets rather than a validated ACC-specific clinical tool.72
  • PMID 34731149: A TCGA pan-cancer analysis reported that higher EMX2OS enhancer RNA expression was associated with poor survival in adrenocortical carcinoma, based on exploratory Kaplan–Meier analysis. The study also linked EMX2OS with its target gene EMX2, but emphasized small cohorts and lack of external validation.73
  • PMID 34858497: A bioinformatics study developed and externally validated a three-gene expression signature using MKI67, TIGD1, and SGK1 that independently predicted overall survival in ACC. Integrating this score with clinicopathologic variables improved risk stratification and individualized postoperative survival estimation, with potential relevance to adjuvant-treatment decisions.13
  • PMID 34918636: A bioinformatic study developed and externally validated a five-gene metabolic expression signature consisting of CYP11B1, GSTM2, IRF9, RPL31, and UBE2C that stratified ACC patients into prognostic risk groups and supported a nomogram for individualized overall survival prediction.74
  • PMID 34966575: Using TCGA and external GEO cohorts, this study derived and validated a 13-transcription-factor gene-expression risk score that separated ACC patients into high- and low-risk groups for overall survival and remained an independent prognostic factor after adjustment for age, sex, and stage.75
  • PMID 35070171: A pan-cancer transcriptomic analysis including TCGA ACC found that violations of the proportional hazards assumption are common in gene-level Cox survival models, and in ACC hub genes were more likely than non-hub genes to show non-proportional hazards. Adding time-interaction terms improved model fit and altered significance for many gene-survival associations.11
  • PMID 35116967: A bioinformatic study built an ACC lncRNA-miRNA-mRNA ceRNA network from public datasets, identifying 87 lncRNAs, 31 miRNAs, 78 mRNAs, and five lncRNAs associated with overall survival. The findings suggest candidate transcriptomic biomarkers and pathways for future validation rather than practice-ready markers.76
  • PMID 35216602: A pan-cancer bioinformatic analysis reported that higher PDCD2L expression in adrenocortical carcinoma was associated with clinical stage, molecular subtype, worse overall survival, and worse disease-free survival. These findings suggest PDCD2L as a potential investigational prognostic biomarker in ACC.77
  • PMID 35312867: A pan-cancer transcriptomic study identified adrenocortical carcinoma as one of the tumor types in which necroptosis-related gene expression patterns were prognostic, and a LASSO-derived risk model showed prognostic prediction for ACC using public datasets. The work positions necroptosis-associated signatures as exploratory biomarker candidates rather than practice-ready tools.78
  • PMID 35359851: A pan-cancer bioinformatics study reported that high PTBP3 expression in ACC was associated with worse overall and disease-free survival, varied by pathologic stage, and correlated positively with tumor mutational burden. In ACC, PTBP3 alterations were uncommon and were mainly amplifications at about 3%.79
  • PMID 35500219: A TCGA-based study developed and externally validated a 17-gene ferroptosis-related prognostic model for ACC, with a nomogram that outperformed conventional TNM staging for 1-, 3-, and 5-year overall survival prediction. The authors frame these genes as candidate biomarkers and potential targets, while noting the need for larger prospective validation.31
  • PMID 35529270: A pan-cancer TCGA-based analysis identified HNRNPA2B1 expression as associated with ACC clinicopathologic stage and with worse overall and disease-free survival in ACC. The study also linked HNRNPA2B1 expression in ACC to cancer-associated fibroblast infiltration, suggesting a potential investigational biomarker rather than a practice-ready marker.80
  • PMID 35571400: A pan-cancer bioinformatic analysis found WNT5A expression elevated in ACC and associated with worse overall survival, disease-specific survival, and progression-free interval. The study also links WNT5A to immune infiltration and checkpoint-related features, suggesting a possible investigational prognostic and immunologic biomarker in ACC.81
  • PMID 35647181: A retrospective transcriptomic study developed and externally validated a seven-gene necroptosis-associated signature that stratified overall survival in ACC and, when combined with stage, improved nomogram-based risk prediction. The signature was also associated with mutation patterns, immune features, and predicted sensitivity to several cytotoxic agents.32
  • PMID 35652069: Bioinformatic analysis of TCGA and GEO cohorts identified hypoxia-related ACC molecular subtypes with differing survival and developed a five-gene hypoxia-ferroptosis signature that independently stratified overall survival. The model was incorporated into a nomogram with age, sex, and stage and linked to tumor mutation burden and cell-cycle related pathways.82
  • PMID 35656324: A TCGA-based pan-cancer bioinformatic study reported that PPP1R14A mRNA is downregulated in ACC compared with normal tissue, while higher PPP1R14A expression within ACC was associated with worse overall survival, disease-specific survival, and progression-free interval, supporting its candidacy as a prognostic biomarker.83
  • PMID 35681785: This study proposes four novel multigene expression panels that, in a TCGA ACC cohort, showed strong discrimination of relapse and death risk beyond age and tumor stage. The signatures were linked to TP53-mutant disease, immune-cell exclusion, immune checkpoint expression, and mesenchymal stem cell enrichment, supporting investigational biomarker development for prognostic stratification.42
  • PMID 35693556: A multi-omics bioinformatics study identified four candidate prognostic biomarkers in ACC—ASPM, BIRC5, CCNB2, and CDK1—linked to cell-cycle programs, survival, copy-number or mutation status, and immune infiltration. The authors also built overall-survival and disease-free-survival nomograms intended to support individualized prognostic prediction.19
  • PMID 35730296: A TCGA-based ACC study linked higher tumor mutation burden with older age, more advanced stage, and worse survival, and developed a six-gene tumor mutation burden prognostic signature validated in external GEO cohorts. It also identified differences in immune cell infiltration between low- and high-TMB groups.84
  • PMID 35751045: A pan-cancer analysis reported CDKN2C as having independent prognostic significance in adrenocortical carcinoma, while also linking CDKN2C expression to the immune microenvironment. The ACC-relevant signal is limited to biomarker association rather than disease-specific validation or therapeutic guidance.85
  • PMID 35782736: A pan-cancer bioinformatics study reported that SHCBP1 is upregulated in ACC, showed moderate diagnostic discrimination for ACC, and that higher expression was associated with worse overall, disease-specific, and progression-free survival. The work frames SHCBP1 as an investigational biomarker linked to tumor-immune features rather than a practice-ready ACC marker.86
  • PMID 35782741: A pan-cancer bioinformatic analysis identified AXL expression as strongly associated with immune infiltration across tumors, with ACC among the most significantly correlated entities. The study suggests AXL may have biomarker relevance for tumor microenvironment characterization, but provides no ACC-specific validation or therapeutic data.87
  • PMID 35848940: A pan-cancer bioinformatics analysis identified cell-death related hub genes with potential prognostic relevance in ACC, highlighting PARP1 and CASP3 as part of a nomogram that predicted survival in TCGA-based ACC cohorts. The study frames these genes as investigational biomarkers linked to expression, methylation, and immune interactions rather than practice-ready markers.88
  • PMID 35910232: This pan-cancer bioinformatics study reported that BCL7B expression is elevated in ACC relative to normal tissue and correlates with immune-cell infiltration in ACC. The findings position BCL7B as a preliminary investigational biomarker candidate rather than an ACC-validated prognostic or therapeutic marker.89
  • PMID 35926818: A transcriptomic signature derived from transformed cells arising after senescence was associated with worse survival in adrenocortical carcinoma within TCGA-based analyses. The study frames this senescence-related expression program as a potential prognostic biomarker, though it is not ACC-specific and comes from preclinical modeling.90
  • PMID 35983531: A multiomics bioinformatics analysis integrating GEO, TCGA, GEPIA, STRING, and cBioPortal identified 490 differentially expressed genes in ACC and prioritized CCNB1 and NDC80 as candidate diagnostic and prognostic biomarkers. Their higher expression was associated with ACC progression and unfavorable outcomes, highlighting investigational molecular markers rather than practice-ready tests.91
  • PMID 36039643: A TCGA-, GTEx-, and GEO-based bioinformatics study identified a three-gene immune-related signature composed of INHBA, HELLS, and HDAC4 that stratified ACC overall survival and remained an independent prognostic indicator in multivariable analysis. Risk groups also showed differing inferred immune-cell infiltration, supporting these genes as investigational prognostic biomarkers and possible future immunotherapy targets.92
  • PMID 36060966: A pan-cancer bioinformatics study identified NBPF1 in ACC as strongly inversely correlated with multiple tumor microenvironment components, and an NBPF1-derived immune gene risk model associated high-risk status with poorer prognosis and immune hyporesponsiveness. The findings position NBPF1 as an investigational prognostic and immunotherapy-related biomarker in ACC.93
  • PMID 36255654: A pan-cancer bioinformatics study identified COMP as a potential investigational biomarker in ACC, with moderate diagnostic discrimination and associations between higher expression and worse overall survival, disease-specific survival, and progression-free interval. The article frames COMP within tumor microenvironment and immune-evasion analyses rather than as a validated ACC-specific clinical marker.94
  • PMID 36291587: In a TCGA-based pan-cancer analysis, high HSP90B1 expression in adrenocortical carcinoma was associated with worse overall survival and disease-free survival. The study positions HSP90B1 as a potential prognostic biomarker candidate in ACC, but evidence is exploratory and not ACC-specific mechanistic validation.95
  • PMID 36457749: A TCGA-based study proposed a 24-lncRNA ferroptosis-related signature in ACC that stratified overall survival and outperformed conventional clinicopathologic factors in ROC analyses. The model was linked to immune-related pathways and differential checkpoint-associated gene expression, suggesting investigational biomarker potential rather than practice-ready clinical use.96
  • PMID 36468013: A pan-cancer bioinformatics study reported that ACC was among the tumor types in which higher RAD51AP1 expression was associated with worse overall and progression-free outcomes. The analysis also linked RAD51AP1 broadly to immune microenvironment features, stemness, and p53 or DNA repair-related pathways, while emphasizing that cancer-specific validation is still needed.97
  • PMID 36497046: Integrated multi-omics clustering identified three ACC subtypes with distinct survival, pathway activation, immune contexture, and predicted therapy sensitivity. The immune-enriched ACC1 subtype was predicted to be more responsive to PD-1 therapy, whereas the poor-prognosis ACC2 subtype showed higher mutation burden and greater predicted sensitivity to several cytotoxic agents.8
  • PMID 36497135: A pan-cancer multi-omics analysis identified adrenocortical carcinoma among 21 tumor types with BRCAness features, suggesting homologous recombination deficiency-related biology that could support investigation of PARP inhibitor sensitivity. The finding is hypothesis-generating and positions BRCAness as a potential biomarker framework rather than established ACC treatment selection.44
  • PMID 36528469: Bioinformatic analysis of ACC datasets identified a ferroptosis-based score using ACSL4, FANCD2, and SLC7A1 that was independently associated with prognosis and linked to tumor mutation burden, immune-checkpoint expression, and immune-cell infiltration. FANCD2 emerged as a candidate biomarker related to prognosis and potential immunotherapy efficacy.98
  • PMID 36555598: A pan-cancer TCGA analysis reported that lower ABCG2 expression was associated with worse overall survival in adrenocortical carcinoma. The study also linked ABCG2 broadly to stemness, tumor microenvironment, TMB/MSI, and immune checkpoint patterns, suggesting possible but still exploratory biomarker relevance.99
  • PMID 36572958: A bioinformatics study using TCGA, GTEx, and GEPIA reported that KIF4A, KIF11, KIF20A, and KIF22 are overexpressed in ACC, correlate with more advanced stage, and are associated with worse overall, disease-specific, and progression-free outcomes. The work also found only marginal positive correlations between KIF4A or KIF11 expression and immune infiltration, positioning these genes as investigational prognostic biomarkers.100
  • PMID 36684185: A SEER-based retrospective study developed machine-learning prognostic models for ACC using clinical variables such as age, T and N stage, surgery, tumor size, and liver, lung, and bone metastases. Across internal validation and repeated cross-validation, a backpropagation artificial neural network showed the best discrimination for 1-, 3-, and 5-year survival prediction.14
  • PMID 36685952: This bioinformatics study identified ferroptosis-related differentially expressed genes in ACC and found AURKA overexpression to be associated with advanced pathologic stage and worse survival. AURKA showed prognostic performance similar to a three-gene risk model and was linked to immune microenvironment features, supporting its candidacy as an investigational prognostic biomarker.101
  • PMID 36899891: This bioinformatic study identified two ACC tumor-microenvironment subtypes with different survival and predicted immunotherapy sensitivity, including an immune-enriched subtype showing higher checkpoint and MHC expression, lower TIDE scores, and no CTNNB1 mutations. It also proposed a 7-gene TME-related prognostic signature validated across public datasets.27
  • PMID 36911522: A bioinformatics study in ACC derived a five-gene pyroptosis-related prognostic signature based on CASP3, CASP9, GSDMB, GSDMD, and NLRC4, with moderate to high overall survival prediction accuracy. CASP9 emerged as an independent prognostic factor and pyroptosis-related genes showed associations with immune infiltration, tumor mutation burden, microsatellite instability, and immune checkpoint expression.33
  • PMID 36994687: A pan-cancer bioinformatics study reported that GIT1 expression in ACC correlated with cancer-associated fibroblast infiltration. The finding is exploratory and frames GIT1 as a potential microenvironment-related biomarker signal in ACC rather than a validated diagnostic, prognostic, or therapeutic marker.102
  • PMID 37027952: A pan-cancer bioinformatics study identified MTF2 overexpression as associated with poorer prognosis in several tumor types including adrenocortical carcinoma, while also examining MTF2 mutation, methylation, and immune microenvironment correlations. For ACC, this supports MTF2 as a possible investigational biomarker rather than a validated clinical marker.103
  • PMID 37248547: A pan-cancer bioinformatics study identified elevated WAC-AS1 lncRNA expression in ACC relative to normal tissue and associated higher expression with worse overall survival. The article frames WAC-AS1 as a potential prognostic biomarker candidate, with broader links to immune infiltration and tumor microenvironment features across cancers.104
  • PMID 37465344: Bioinformatic and cell-line analyses identified CENPI as an overexpressed ACC-associated candidate biomarker linked to higher stage, primary therapy outcome, and worse overall, disease-specific, and progression-free survival. Experimental knockdown reduced ACC cell growth and invasion, and showed synergy with the AURKB inhibitor barasertib, supporting exploratory therapeutic relevance.105
  • PMID 37501187: A pan-cancer multi-omics study identified AAMP high expression as associated with worse overall, disease-specific, and progression-free outcomes in ACC, and reported a positive correlation between AAMP expression and tumor mutational burden in ACC. The findings position AAMP as a candidate prognostic biomarker rather than a validated ACC-specific clinical marker.106
  • PMID 37559587: A pan-cancer bioinformatics study reported that SLC17A9 expression is decreased in ACC tumor tissue versus normal tissue, while higher SLC17A9 expression was associated with overall and disease-specific survival risk in ACC. The study also linked SLC17A9 with stemness and immune-related features across cancers, suggesting an investigational prognostic biomarker role rather than a validated ACC-specific clinical marker.107
  • PMID 37583897: A pan-cancer bioinformatic study identified high NQO1 expression as associated with worse overall survival in adrenocortical carcinoma. The analysis also linked NQO1 to immune-related features across cancers, suggesting it as a possible investigational prognostic and therapeutic biomarker rather than a practice-ready ACC marker.108
  • PMID 37842529: Integrated transcriptomic analysis identified a 3-gene ferroptosis regulator signature with strong overall survival discrimination across TCGA and external datasets in ACC, and highlighted HELLS as an overexpressed ferroptosis-suppressor gene linked to prognosis, immune infiltration, and cell-cycle related pathways.109
  • PMID 37895143: Using TCGA and GEO datasets, this study identified two immune-related molecular subtypes of ACC and derived a 3-gene immune-related signature based on PRKCA, LTBP1, and BIRC5 that independently stratified prognosis. The model was also linked to immune infiltration patterns, tumor mutation burden, and predicted immunotherapy responsiveness, supporting its potential as a biomarker framework rather than established clinical practice.28
  • PMID 37968537: In a retrospective single-center cohort of 39 surgically treated ACC patients, a contrast-enhanced CT radiomic index based on four features was associated with progression-free and overall survival and improved nomogram performance beyond ENSAT stage or S-GRAS. The study supports radiomics as an investigational prognostic biomarker approach rather than established standard practice.39
  • PMID 37997497: This bioinformatic study developed a six-gene cuproptosis-related risk score from TCGA and GEO datasets for ACC prognosis, with CDKN2A and FDX1 emerging as independent overall survival predictors. The work also linked cuproptosis-related genes to immune infiltration, highlighting investigational biomarker development rather than practice-ready clinical use.34
  • PMID 38028548: This study used integrated RNA expression, microRNA, and DNA methylation data with network algorithms and machine learning to derive ACC prognostic biomarkers. In TCGA and an external validation dataset, a 9-feature multi-omics signature stratified high- versus low-risk patients and outperformed previously identified prognostic markers for stage discrimination.12
  • PMID 38098097: A TCGA-based pan-cancer bioinformatic study identified ACBD3 as a potential diagnostic and prognostic biomarker, with ACC among the tumor types whose survival correlated with ACBD3 expression. The report frames ACBD3 as an investigational molecular marker rather than a validated ACC-specific clinical biomarker.110
  • PMID 38109896: Integrated bioinformatics analysis of two ACC microarray datasets identified 114 differentially expressed genes and 10 overexpressed hub genes, all associated with worse overall survival. Drug-matching analysis linked three hub genes, TOP2A, TYMS, and CDK1, to existing compounds as potential biomarker-guided therapeutic targets.20
  • PMID 38187219: A bioinformatic study using TCGA, GTEx, and GEO datasets developed a cuproptosis-related gene risk score and nomogram in ACC, linking differential expression of genes such as CDKN2A and CYP2D6 with worse overall survival, progression-free interval, and immune infiltration patterns.111
  • PMID 38224097: Integrative multi-omics analysis identified LMNB1 as a candidate prognostic biomarker in adult and pediatric ACC, with high expression linked to high tumor mutational burden, lower chromatin accessibility, TP53 mutations, reduced CD8+ T-cell infiltration, and poorer outcomes. Experimental data also suggested LMNB1 promotes ACC cell proliferation and invasion.37
  • PMID 38239349: A TCGA-based pan-cancer analysis reported that abnormal TEDC2 expression was associated with survival outcomes in adrenocortical carcinoma and linked broadly to immune infiltration, cell-cycle, extracellular matrix regulator, and immune checkpoint-related pathways. The study presents TEDC2 as a possible prognostic biomarker candidate rather than a validated ACC-specific clinical marker.112
  • PMID 38322570: A pan-cancer analysis identified CACYBP expression as prognostically relevant in adrenocortical carcinoma, with higher expression associated with worse survival and more advanced AJCC stage in the ACC cohort. The finding suggests CACYBP as a potential investigational prognostic biomarker rather than a validated ACC-specific clinical marker.113
  • PMID 38410859: In a retrospective pan-cancer analysis, higher MMP12 expression was associated with worse overall and disease-specific survival in adrenocortical carcinoma. The study suggests MMP12 as a candidate prognostic biomarker in ACC, but the evidence is exploratory and derived from broad pan-cancer datasets rather than ACC-specific validation.114
  • PMID 38421176: A 2024 study evaluated intratumor bacterial features in ACC using 16S rRNA sequencing with rigorous decontamination and external comparison to two TCGA-derived cohorts. Although overall bacterial diversity and whole-signature subtyping were not prognostic, a five-genera intratumor bacteria risk score was validated as an independent predictor of overall survival in external cohorts.115
  • PMID 38501000: A pan-cancer bioinformatics study reported that higher ADGRE5 expression in ACC was associated with worse overall and disease-free survival, and ADGRE5 alterations in ACC were also linked to unfavorable prognosis. The study frames ADGRE5 as a potential investigational prognostic and immunotherapy-related biomarker rather than a practice-ready ACC marker.116
  • PMID 38505747: This bioinformatic study proposes a mitochondrial quality-related gene score for ACC that stratified patients by overall survival and was reported as an independent prognostic factor. The score was also linked to immune cell infiltration patterns and explored as a possible predictor of immunotherapy response.35
  • PMID 38617524: A pan-cancer bioinformatics study identified TPGS2 as a potential prognostic biomarker in adrenocortical carcinoma, with reported associations between TPGS2 expression, immune infiltration, molecular and immune subtypes, and broader immunotherapy-related features such as tumor mutational burden and microsatellite instability.117
  • PMID 38819212: A pan-cancer bioinformatics study identified high PUS7 expression as associated with poorer survival in ACC and linked it with more advanced TNM features in ACC. The report frames PUS7 as a putative prognostic biomarker, but the evidence is exploratory and derived from public-database analyses rather than ACC-specific clinical validation.118
  • PMID 38935991: A multi-omics analysis across 162 ACC cases identified a 45-gene signature associated with significantly worse overall survival independent of stage and age, highlighting candidate prognostic biomarkers and potential therapeutic targets. The study also linked cortisol-producing tumors to poorer survival and described novel protein interaction networks involving TP53-associated genes.9
  • PMID 38976216: A TCGA/GTEx pan-cancer bioinformatics study reported that in adrenocortical carcinoma, higher NR4A2 and NR4A3 expression was associated with worse overall survival. The paper frames NR4A family genes as potential prognostic and therapeutic-response biomarkers, but evidence is exploratory and not ACC-specificly validated.119
  • PMID 39266915: A pan-cancer bioinformatic study identified RRP8 expression as associated with ACC prognosis across overall, disease-specific, disease-free, and progression-free outcomes. In ACC, RRP8-high versus low expression groups also showed differing mutation patterns, with TP53 the most frequently mutated gene, suggesting an investigational prognostic biomarker rather than a practice-ready marker.120
  • PMID 39377045: A TCGA/GEO-based transcriptomic study defined three cuproptosis-related ACC subtypes with distinct survival and immune features, and derived a CRG_score that stratified prognosis. Higher-risk scores were associated with worse survival, possible immunotherapy resistance, and differential predicted sensitivity to doxorubicin and etoposide.45
  • PMID 39394548: A pan-cancer bioinformatics study reported that lower EBNA1BP2 expression in adrenocortical carcinoma was associated with longer overall and disease-free survival, suggesting EBNA1BP2 as a possible prognostic biomarker candidate in ACC. The evidence is exploratory and based on retrospective public-database analyses without ACC-specific mechanistic validation.121
  • PMID 39414693: A pan-cancer multi-omics study proposed an ACC prognostic signature based on FUT family genes, reporting that a five-gene FUT-related model served as an independent prognostic indicator and was associated with immune cell infiltration. The work frames FUTs, particularly POFUT2, as investigational biomarkers linked to tumor immune context and potential immunotherapy relevance.122
  • PMID 39545598: Bioinformatic analysis in ACC linked low CDH2 and low CDH13 expression with more favorable survival metrics, identified distinct coexpression and immune-infiltration associations, and nominated foretinib and elesclomol as candidate drugs based on predicted and cell-line sensitivity signals. The study frames CDH2 and CDH13 as investigational prognostic biomarkers and potential precision-therapy targets.46
  • PMID 39624961: Multi-omics analyses in pediatric and adult ACC identified KPNA2 as an independent adverse prognostic biomarker, with high expression associated with increased proliferation and invasion, reduced chromatin accessibility in poor-outcome cases, and lower CD8-positive T-cell infiltration. The study also suggests KPNA2 may help predict immunotherapy responsiveness.21
  • PMID 39678601: A pan-cancer analysis reported that high MMP9 expression in ACC was associated with poor survival and tumor progression and was positively correlated with the tumor microenvironment. The study also computationally nominated two potential MMP9 inhibitor compounds, but these findings remain preclinical and exploratory.123
  • PMID 39678610: A pan-cancer bioinformatics study reported CD19 upregulation in adrenocortical carcinoma relative to some other tumor types, while emphasizing broader links between CD19 expression, immune-related genes, and immune cell infiltration. The ACC finding is exploratory and presented as part of biomarker discovery rather than validated ACC-specific clinical application.124
  • PMID 39697730: A pan-cancer TCGA-based analysis reported that higher KTN1 expression was associated with worse disease-free survival in ACC, suggesting KTN1 as a possible prognostic biomarker candidate. The ACC signal was observational and derived from cross-cancer bioinformatic analysis rather than ACC-specific functional validation.125
  • PMID 39871591: This retrospective study developed a whole-slide image pathomics signature for resected ACC that was associated with overall survival and improved prognostic discrimination when combined with M stage in a nomogram. The work supports digital pathology as an investigational biomarker approach for more individualized postoperative risk assessment.41
  • PMID 40003919: A pan-cancer bioinformatics review identifies LCAT as a potential biomarker in adrenocortical carcinoma, where higher expression is associated with worse overall, disease-specific, and progression-free outcomes. The excerpt also notes ACC has frequent LCAT copy-number gain and positive correlations between LCAT expression and mismatch-repair genes and DNA methyltransferases.126
  • PMID 40088713: A pan-cancer multi-omics analysis reported that resting and effector Treg gene signatures showed positive correlations with immune cell infiltration in adrenocortical carcinoma. The study frames these immune signatures as exploratory biomarkers within broader transcriptomic, genomic, methylation, and drug-sensitivity analyses rather than ACC-specific validated clinical tools.127
  • PMID 40098960: Pan-cancer multi-omics analysis identified high NUP62 expression as associated with poor prognosis in ACC and with differences across nodal and metastatic stage categories in ACC within TCGA-derived datasets. The study frames NUP62 as a potential biomarker linked to tumour immunity and treatment-response prediction, but not as an ACC-validated clinical marker.128
  • PMID 40109215: A bioinformatics study identified APOC1 and APOE as candidate ACC biomarkers, showing overall downregulation in tumors, sex-associated expression differences, and associations between higher expression and worse survival outcomes. The work also linked these genes to immune infiltration and proposed exploratory therapeutic targets and drug-sensitivity leads.129
  • PMID 40156647: A pan-cancer bioinformatic study identified high WTAP expression as associated with worse overall survival in ACC and linked WTAP broadly to immune-related features and cell-cycle pathways. In ACC, the finding positions WTAP as a possible prognostic biomarker candidate rather than a validated clinical marker.130
  • PMID 40230849: A pan-cancer bioinformatics study reported that abnormal ESCRT family gene expression was associated with poor prognosis in ACC and linked ESCRT expression to immune-related features across tumors. The ACC-relevant evidence was exploratory and derived from retrospective public transcriptomic datasets rather than ACC-specific validation.131
  • PMID 40253660: A pan-cancer analysis identified TRIP13 as a potential biomarker relevant to ACC, noting that TRIP13 mutations were associated with poor prognosis and that TRIP13 expression varied by pathological stage in ACC. The study frames TRIP13 within broader immunotherapy-response and biomarker analyses rather than ACC-specific clinical validation.132
  • PMID 40266460: A pan-cancer TCGA analysis reported that higher ADCYAP1 expression in ACC was associated with longer overall survival, in contrast to adverse associations seen in several other tumor types. The finding suggests ADCYAP1 as a potential prognostic biomarker candidate in ACC, but the evidence is exploratory and not ACC-specific.133
  • PMID 40299539: This bioinformatics study developed and externally validated a four-gene senescence-associated signature composed of HJURP, CDK1, FOXM1, and CHEK1 that independently predicted overall survival in ACC. Higher risk scores were linked to advanced stage, higher mutation burden, immune-related pathway suppression, predicted lower immunotherapy sensitivity, and candidate drug-response differences.36
  • PMID 40317315: Transcriptomic network analysis with WGCNA and machine learning identified a progression-associated ACC module enriched for cell division, protein synthesis, and metabolic pathways, and highlighted seven hub genes whose high expression correlated with worse overall survival. The study proposes these genes as candidate prognostic and diagnostic biomarkers, with associated immune infiltration findings suggesting future immune-modulation relevance.134
  • PMID 40384267: This study used integrated transcriptomic, ATAC-seq, mutational, immunohistochemical, and functional experimental data to identify FSCN1 as an independent adverse prognostic biomarker in ACC. Higher FSCN1 was linked to progressive disease, increased invasion and proliferation, and reduced CD8-positive T-cell infiltration, supporting its potential as a biomarker and therapeutic target.22
  • PMID 40386627: A pan-cancer bioinformatics study identified high AUNIP expression as associated with worse overall and disease-free survival in ACC, with additional correlations to higher T stage, nodal involvement, pathologic stage, and tumor mutational burden. The findings position AUNIP as a potential investigational prognostic and immune-related biomarker in ACC.135
  • PMID 40417238: A pan-cancer multi-omics analysis reported that elevated FANCI expression was associated with unfavorable prognosis in adrenocortical carcinoma, suggesting FANCI as a potential prognostic biomarker candidate. The study frames this signal as hypothesis-generating and supportive of further mechanistic and biomarker-validation work rather than immediate clinical application.136
  • PMID 40462856: A pan-cancer bioinformatics study identified MTHFD1 as a candidate immune-related biomarker in ACC, where its expression was negatively correlated with immune cell infiltration and significantly associated with tumor mutational burden. The ACC finding is exploratory and derived from cross-tumor TCGA-style analyses rather than ACC-specific validation.137
  • PMID 40696947: A multicohort cohort study developed and externally validated a seven-gene ATF/CREB transcriptomic signature that independently stratified overall survival in ACC, with ATF4 protein validation and in vitro evidence supporting biologic relevance. The signature was also associated with immune-cell composition, checkpoint and MHC expression, suggesting potential but still unconfirmed use for immune stratification.43
  • PMID 40851816: This retrospective CT radiomics study in 32 histologically confirmed ACCs found that venous-phase texture features, particularly first-order skewness, showed strong performance for preoperative discrimination of low- versus high-mitotic-grade tumors, while radiomic differentiation of conventional, oncocytic, and myxoid variants was not validated after multiple-comparison correction.40
  • PMID 40873400: Multi-omics analyses across TCGA and GEO cohorts identified TOP2A overexpression in ACC as an adverse prognostic biomarker, with experimental data supporting oncogenic effects in ACC cells. The study also linked higher TOP2A expression to reduced CD8+ T-cell infiltration and explored resminostat and etoposide as candidate TOP2A-targeted therapies in vivo.23
  • PMID 40927315: A pan-cancer transcriptomic analysis found that SLC16A3 was not upregulated in adrenocortical carcinoma and, unlike many other malignancies, was not associated with poor outcomes in ACC. These findings suggest limited current support for SLC16A3 as an ACC-specific prognostic or immunotherapy-related biomarker.138
  • PMID 41037214: A multi-omics analysis defined two reproducible ACC subtypes, with MACCS1 showing a proliferation-driven, poorer-prognosis profile and MACCS2 showing an immune-activated phenotype. The classification was linked to differential predicted sensitivity to immune checkpoint blockade versus antiangiogenic tyrosine kinase inhibition, and identified HOXC11 as a potential prognostic and therapeutic biomarker.10
  • PMID 41178767: Bioinformatic analysis of TCGA ACC identified tumor mutation burden as associated with worse survival, advanced stage, lower PD-L1 expression, and a less immune-enriched microenvironment. Eight TMB-related prognostic genes were nominated, with ASPM and KIF11 showing preliminary functional evidence of promoting ACC cell proliferation and tumor growth.139
  • PMID 41244050: Bioinformatic analysis of adult ACC transcriptomic data identified seven metastasis-associated hub genes, all upregulated in metastatic cases and linked to worse overall survival. These candidate biomarkers were enriched in cell cycle and DNA repair pathways, and database-based drug matching suggested investigational targetable opportunities.140
  • PMID 41274066: This review summarizes integrative molecular biomarker development in ACC, including recurrent genomic and epigenomic alterations, transcriptomic and non-coding RNA signatures, immune microenvironment features, and multi-omics prognostic models. It emphasizes that candidate biomarkers and computationally nominated targets require rigorous multilayer validation and standardized clinical integration before routine use.2
  • PMID 41307806: In a TCGA-based pan-cancer analysis, high GLO1 expression was associated with poor prognosis in adrenocortical carcinoma. The study frames GLO1 as a preliminary biomarker candidate, but ACC-specific evidence is limited to bioinformatic association without direct validation in ACC cohorts.141
  • PMID 41339227: This study identifies PAK4 as an overexpressed ACC biomarker associated with advanced TNM features, venous invasion, and worse overall, disease-specific, and progression-free outcomes. Bioinformatic and immunohistochemical findings also link PAK4 to Hedgehog signaling, proliferation, and immune-cell infiltration, supporting investigation as a prognostic and therapeutic biomarker.142
  • PMID 41491065: A pan-cancer multi-omics analysis reports that PCMT1 is upregulated in ACC among other epithelial tumors, but ACC-specific survival or therapeutic response results are not detailed. The article mainly suggests PCMT1 as an investigational biomarker linked to immune microenvironment features and potential immunotherapy relevance across cancers.143
  • PMID 41496120: A TCGA pan-cancer analysis found that the relationship between ENPP1 expression and homologous recombination deficiency is heterogeneous and can be grouped into three molecular clusters with distinct survival patterns. ACC was among the tumor types showing significant cluster-associated survival differences, suggesting a possible biomarker-stratification signal that remains exploratory and tissue-context dependent.144
  • PMID 41617445: A pan-cancer transcriptomic analysis identified CDK1 overexpression in ACC as strongly associated with worse overall survival and increasing stage, supporting CDK1 as a potential prognostic biomarker. The study also used structure-based virtual screening to nominate candidate natural-compound CDK1 inhibitors for future therapeutic development in ACC.24
  • PMID 41790623: A pan-cancer transcriptomic analysis reported that GPR37 and GPR37L1 had divergent prognostic associations in adrenocortical carcinoma, suggesting possible context-specific biomarker value. The ACC finding is exploratory and derived from retrospective public-dataset analysis rather than ACC-focused validation.145
  • PMID 35189794: A pan-cancer analysis of the histone methyltransferase KMT2D reported broad associations with prognosis and immunotherapy-related features across tumor types, but its relevance to ACC is indirect and mainly highlights the limits of transferring pan-cancer biomarker claims into ACC without dedicated validation.47
  • PMID 27505681: TCGA pan-genomic profiling provided a foundational multi-omics framework for ACC, showing that integrated molecular subtypes track major prognostic differences and recurrent pathway alterations. It supports the note’s emphasis on ACC as a biologically heterogeneous disease better captured by subtype models than by single markers alone.1
  • PMID 34986816: A 2022 PMAH microRNA study identified separate familial and sporadic expression signatures and suggested apoptosis-related regulatory effects. Its relevance to ACC is indirect, but it adds context for adrenal cortical microRNA dysregulation while reinforcing the need for tumor-specific validation before extrapolation to ACC classifiers.16

References

Footnotes

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