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Identification of EFNA3 as candidate prognosis marker and potential therapeutic target for adrenocortical carcinoma
YANGHAO TAI1, XINZHE LIU1, YIFAN ZHOU1 and JIWEN SHANG1,2
“Department of Urology, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, Shanxi 030032, P.R. China; 2Department of Urology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, Shanxi 030032, P.R. China
Received June 18, 2025; Accepted September 18, 2025
DOI: 10.3892/ol.2025.15346
Abstract. Adrenocortical carcinoma (ACC) is a rare, but highly aggressive endocrine malignancy with poor prognosis and limited treatment options. Identifying novel biomarkers and therapeutic targets is essential for improving patient outcomes. The present study aimed to systematically characterize ephrin-A3 (EFNA3) expression patterns, its prognostic and diagnostic value, and its functional role in ACC progression through multi-omics bioinformatics and in vitro validation. Transcriptomic, epigenetic and pharmacoge- nomic data were obtained from The Cancer Genome Atlas, Genotype-Tissue Expression, Genomics of Drug Sensitivity in Cancer, Cancer Therapeutics Response Portal and MethSurv databases. Expression, survival, immune infiltration, methyla- tion and drug sensitivity analyses were conducted using the R software and online tools (GEPIA2, CIBERSORT and cBio- Portal). competitive endogenous RNA (ceRNA) networks were constructed based on microRNA (miRNA)/long non-coding RNA (lncRNA) predictions. Functional assays, including CCK-8, flow cytometry, Transwell assays were performed on the ACC cell lines, SW-13 and NCI-H295R, to validate EFNA3 function. EFNA3 was significantly upregulated in numerous types of cancer and associated with poor prognosis. In ACC, upregulated EFNA3 was associated with a poor prognosis [Overall survival (OS), hazard ratio (HR)=3.14, 95% CI, 1.49-7.81; disease-specific survival, HR=4.27, 95% CI, 1.70-10.72; progression-free interval, HR=6.24, 95% CI, 2.94-13.23; P<0.05] and diagnostic efficiency (area under the curve=0.829, 95% CI, 0.760-0.897). EFNA3-mutated cases
Correspondence to: Professor Jiwen Shang, Department of Urology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, 99 Longcheng Street, Xiaodian, Taiyuan, Shanxi 030032, P.R. China E-mail: shangjiwen@sxbqeh.com.cn
Key words: ephrin-A3, adrenocortical carcinoma, pan-cancer analysis, Wnt/ß-catenin signaling, competitive endogenous RNA regulatory network, drug repurposing
had significantly worse OS in ACC specifically (OS, HR=2.97, 95% CI, 1.12-7.90, P=0.029; disease-free survival, HR=8.65, 95% CI, 2.14-34.93, P=0.002). ß-catenin (CTNNB1) was among most frequently co-mutated genes ACC with EFNA3 (P=4.6x10-4). Genetic amplification and DNA methylation alterations were observed in the ACC cohort. EFNA3 expression negatively correlated with immune infiltration and positively correlated with several m6A/m5C regulators. ceRNA network analysis demonstrated key IncRNA-miRNA-EFNA3 axes. Drug sensitivity profiling indicated that EFNA3 expression was associated with statin and proteasome inhibitor responses. The co-expression of positively correlated gene enrichment results suggested that Wnt signaling pathway and ß-catenin/ T-cell factor complex may be involved in the progression of ACC mediated by EFNA3. Functionally, EFNA3 promoted ACC cell proliferation and migration in vitro. The present study demonstrated that EFNA3 acts as an oncogene in ACC and may contribute to tumor aggressiveness via ß-catenin acti- vation and glycolytic reprogramming, and thus may serve as a potential biomarker for prognosis, immunotherapy sensitivity and drug repurposing, particularly involving statins.
Introduction
Adrenocortical carcinoma (ACC) is a rare and highly aggres- sive malignancy arising from steroidogenic cells of the adrenal cortex, characterized by a 5-year overall survival rate of >35% (1). Radical surgical resection remains the only curative approach for localized ACC; however, postoperative recurrence rates are high, ranging between 70-80% (2). For advanced or metastatic ACC, therapeutic options are limited. Mitotane, the only Food and Drug Administration (FDA)-approved agent, demonstrates suboptimal efficacy and substantial toxicity (3). Combination chemotherapy regimens such as mitotane with etoposide, doxorubicin and cisplatin yields modest clinical benefit, with an objective response rate of ~30% and a median progression-free survival of 5.6 months (4). These limita- tions underscore an urgent need to elucidate the molecular mechanisms underlying ACC progression and to identify robust biomarkers for early diagnosis, risk stratification and development of targeted therapeutics.
Reprogramming of cancer cell metabolism is a hallmark of malignancy, with aerobic glycolysis (‘Warburg effect’)
facilitating increased glucose uptake and lactate production, which acidifies the tumor microenvironment (TME) and supports migration and immune evasion (5-7). Dysregulated expression and activity of glycolytic enzymes are central to this phenotype (8). Ephrin-A3 (EFNA3), a transmembrane ligand of the Eph receptor tyrosine kinase family, has been impli- cated in metabolic regulation and tumor progression (9,10). EFNA3 participates in bidirectional cell-cell communication through interactions with Eph receptors, modulating processes such as angiogenesis, cellular motility and tissue remod- eling (11). Notably, EFNA3 functions as a glycolysis-related gene. Previous studies indicate that EFNA3 upregulation promotes glycolytic flux and proliferation in lung adeno- carcinoma, correlating with unfavorable prognosis (12-14), suggesting potential roles as both a metabolic regulator and oncogenic driver.
Members of the EFNA gene family, including EFNA1 and EFNA2, exhibit distinct expression and functional profiles across tumor types. EFNA1 is frequently upregu- lated in various types of cancer, such as gastric cancer and melanomas, and is linked to angiogenesis, immune modula- tion and metastatic potential via interactions with EphA2 and hypoxia-inducible signaling (15-18). Conversely, EFNA2 expression is reduced in certain types of cancer such as gastric carcinoma, with inverse associations to CD8+ T-cell and dendritic cell infiltration, implicating it in immune surveillance escape (19-21). High EFNA3 expression levels are predictive of poorer survival in gastric cancer and correlate negatively with infiltration of B cells, T cells and dendritic cells, as well as with immune checkpoint activity, which indicates a role in immune evasion (22,23). Beyond its prognostic role, EFNA3 expression has been correlated with immune cell infiltration and chemotherapeutic response, indicating potential relevance to tumor immunology and therapeutic resistance (24-26).
The present study aimed to perform an integrative pan-cancer analysis of EFNA3 to evaluate its transcriptional deregulation, genetic and epigenetic alterations, prognostic relevance, associations with tumor immune infiltration and drug sensitivity, and to construct an EFNA3 ceRNA regulatory network. Furthermore, the present study aimed to investigate the effects of EFNA3 on the proliferative, migratory and anti-apoptotic capacities of ACC cells via in vitro experiments.
Materials and methods
Pan-cancer expression profiling of EFNA3. Transcriptome data from 15,776 samples were retrieved via the UCSC Xena Browser (https://xenabrowser.net; The Regents of the University of California), integrating The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) and Genotype-Tissue Expression databases (https://www. gtexportal.org/home/). Raw RNA-Seq data (TPM+1) were log-transformed and normalized using the rms package in R (version 4.2.1; Posit Software, PBC). Batch-corrected data were visualized using ggplot2 (version 3.4.0; Posit Software, PBC) as boxplots to depict EFNA3 expression across tumor and normal tissues. Differential expression was analyzed with DESeq2 (version 1.38.3; Bioconducter), using thresholds of llog2FCI≥1 and FDR ≤0.05 (Benjamini-Hochberg correc- tion). Tumor stage association was analyzed using the ‘Stage
Plot’ module in GEPIA2 (http://gepia2.cancer-pku.cn/). The flowchart of the present study is shown in Fig. S1.
Prognostic and diagnostic evaluation of EFNA3. TCGA clinical and expression datasets were used to assess prognostic and diagnostic relevance of EFNA3. Univariate Cox propor- tional hazards models were constructed using the survival package (version 3.5-5; Posit Software, PBC), with calculated hazard ratios (HR) and 95% confidence intervals (CI). P<0.05 was considered to indicate a statistically significant difference. Samples lacking complete survival data were excluded. Kaplan-Meier survival curves for OS, disease-free survival (DFS) and progression-free interval (PFI) were plotted using survminer (version 0.4.9; Posit Software, PBC) and ggplot2 (version 3.4.0; Posit Software, PBC). Diagnostic value was evaluated using ROC curves generated by the pROC package (version 1.18.0; Posit Software, PBC).
Clinicopathological correlation in ACC. Based on median the expression level of EFNA3, patients with ACC were stratified into high- and low-EFNA3 expression groups (n=40 and n=39, respectively). Clinical parameters were compared using appropriate tests using the stats (version 4.2.1) and car (version 3.1.0) R packages (Posit Software, PBC). Visualization was performed with ggplot2 (version 3.4.0; Posit Software, PBC). The diagnostic performance of EFNA3 in ACC was evaluated via ROC analysis (pROC; version 1.18.0; Posit Software, PBC) using TCGA and UCSC-derived datasets.
Somatic mutation and copy number analysis. Mutation data were obtained from cBioPortal (http://www.cbioportal.org) and TCGA. EFNA3 alterations [mutation type, copy number altera- tions (CNAs) and frequency] were analyzed using 2,683 samples from 2,565 patients. Additional ACC-specific data (n=76) were retrieved from the UCSC Xena (https://xenabrowser.net/) and the International Cancer Genome Consortium (https://dcc.icgc.org/) databases. Kaplan-Meier survival analyses were used to assess survival outcomes based on EFNA3 mutation status. Differential mutation profiles in EFNA3-high vs. - low expression groups were also analyzed.
Epigenetic and mRNA modification analysis. DNA meth- ylation profiles for EFNA3 in ACC were obtained from the MethSurv (https://biit.cs.ut.ee/methsurv/) database. mRNA modification regulator correlations including n1-methyladenosine (m1A), 5-methylcytosine (m5C) and n6-methyladenosine (m6A), were analyzed across various types of cancer from TCGA database using the SangerBox software (version 3.0; http://vip.sangerbox.com). Pearson correlation coefficients and significance levels were reported.
Immune cell infiltration analysis. TME scores (stromal, immune and ESTIMATE scores) were computed using the ESTIMATE R package (version 1.0.13; Posit Software, PBC). Immune infiltration profiling was performed using markers from 22 immune cell types provided by the CIBERSORTx website (https://cibersortx.stanford.edu/) (27). Data were visualized as heatmaps using ggplot2 (version 3.4.0; Posit Software, PBC). Spearman’s correlation coefficients were used to assess statistical associations.
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Competitive endogenous RNA (ceRNA) regulatory network construction. Candidate EFNA3-targeting microRNAs (miRNAs) were predicted using PITA (version 1.0; https:// genie.weizmann.ac.il/pubs/mir07/mir07_data.html), miRanda (version 3.3; http://www.microrna.org/) and TargetScan (version 8.0; http://www.targetscan.org/) software. miRNAs with a negative correlation to EFNA3 were prioritized using the StarBase (version 2.0; http://starbase.sysu.edu. cn/). IncRNA-miRNA interactions were derived from miRNet (version 2.0; https://www.mirnet.ca/) and StarBase (version 2.0; http://starbase.sysu.edu.cn/) under the criteria: Species=human; CLIP-Data=yes; and min stringency=5. Venn diagrams were used for intersecting target prediction using ggplot2 (version 3.4.0; Posit Software, PBC), and VennDiagram (version 1.7.3; Posit Software, PBC). Final IncRNA-miRNA-EFNA3 networks were visualized using mulberry plots using ggplot2 (version 3.4.0; Posit Software, PBC) and ggalluvial (version 0.12.3; Posit Software, PBC).
Drug sensitivity and interaction network analysis. Drug sensitivity data were obtained from the GSCALite (http://bioinfo.life.hust.edu.cn/web/GSCALite/) database, which integrates TCGA, Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Therapeutics Response Portal (CTRP) datasets. EFNA3-associated drug responses were identified based on Pearson correlations analysis with mRNA expression. FDA-approved agents were selected via DrugBank (https://go.drugbank.com/) annotations. Network graphs were generated using graph (version 1.4.1; Posit Software, PBC) and graph (version 2.1.0; Posit Software, PBC) packages.
Functional enrichment of co-expressed genes. Co-expressed genes were identified using LinkedOmics (http://www.linke- domics.org). Functional enrichment was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms via the clusterProfiler (version 4.6.2; Posit Software, PBC) software. Protein-protein interaction (PPI) networks were generated using the STRING database (https://cn.string-db.org/) and visualized using default Benjamini-Hochberg correction for P-values.
Cell lines and culture conditions. The human ACC cell lines SW-13 (hormonally inactive) and NCI-H295R (hormon- ally active) were obtained from Procell Life Science & Technology Co., Ltd. Cell line authentication was confirmed via short tandem repeat profiling. Cells were maintained in DMEM/F12 medium (Shanghai Zhongqiao Xinzhou Biotechnology Co., Ltd.) supplemented with 10% fetal bovine serum (FBS; cat. no. G24-70500; Genial Biologicals, Inc.) and 1% penicillin-streptomycin (Shanghai Zhongqiao Xinzhou Biotechnology Co., Ltd.). Cultures were incubated at 37℃ in a humidified atmosphere containing 5% CO2.
EFNA3 overexpression and knockdown via lentiviral transfection. The EFNA3 overexpression plasmid was synthesized by Shanghai Sangong Pharmaceutical Co., Ltd. The short hairpin RNA (shRNA) targeting EFNA3 and the non-targeting negative control (NC) were synthesized by Shanghai Gema Gene Biotechnology Co., Ltd. Sequences used were as follows: shNC sense (S), 5’-TTCTCCGAACGT
GTCACGT-3’ and anti-sense (AS), 5’-ACGTGACACGTT CGGAGAA-3’; shEFNA3 S, 5’-GGCATGCGGTGTACT GGAACA-3’ and AS, 5’-TGTTCCAGTACACCGCATGCC-3’. The EFNA3 overexpression plasmid was designed and synthesized by Shanghai Sangong Pharmaceutical Co., Ltd., and constructed by cloning the EFNA3 coding sequence into the pcDNA3.1 plasmid backbone (Thermo Fisher Scientific, Inc.). For transfection, 2.5 µg of plasmid DNA was complexed using Lipofectamine® 2000 (Thermo Fisher Scientific, Inc.) according to the manufacturer’s instructions and then added to the cell culture. Transfection was performed at 37℃ for 48 h. Upon reaching 30-40% confluency in 6-well plates, cells were infected with lentivirus in medium containing Polybrene (Thermo Fisher Scientific, Inc.). Puromycin (4 µg/ml; Thermo Fisher Scientific, Inc.) was added 48 h post-infection for initial screening. Stable-transfected clones were maintained in a 2 µg/ml-puromycin environment.
Real-time quantitative PCR (RT-qPCR). According to the manufacturer’s protocol, total RNA was isolated from NCI-H295R and SW-13 cells using TRIzol reagent (cat. no. R0016; Biocytogen). cDNA was synthesized from mRNA using a cDNA reverse transcription kit (cat. no.4368814; Thermo Fisher Scientific, Inc.) according to the manufacturer’s protocol. qPCR amplification was performed using the SYBR Green fluorescent quantitative PCR kit (cat. no. A46012, Thermo Fisher Scientific, Inc.). The primer sequences are as follows: EFNA3 forward (F), 5’-ATGAAGGTGTTCGTC TGCT-3’ and reverse (R), 5’-CTCAAAGTCTTCCAGCAC G-3’; GAPDH F, 5’-TCAAGATCATCAGCAATGCC-3’ and R, 5’-CGATACCAAAGTTGTCATGGA-3’; GAPDH was used as the internal reference gene. The relative expression level of EFNA3 mRNA was calculated using the 2-44Cq method (28). The thermocycling conditions were as follows: Initial denatur- ation at 95℃ for 10 min; followed by 40 cycles of denaturation at 95℃ for 15 sec and combined annealing/extension at 60℃ for 60 sec.
Transwell migration assay. Cell migration was assessed using 24-well Transwell chambers with 8-um pore inserts (Corning, Inc.). After serum starvation for 24 h, cells were harvested and resuspended in serum-free DMEM at 1x105 cells/ml. Then, 200 ul of cell suspension was seeded into the upper chamber. The lower chamber contained 600 ul of DMEM supplemented with 10% FBS as chemoattractant. After 48 h of incubation at 37℃ with 5% CO2, non-migrated cells were removed. Migrated cells were fixed with 4% paraformaldehyde for 10 min and stained with 0.1% crystal violet for 20 min, both at room temperature. Cell images were captured using an inverted fluorescence microscope (Olympus IX73; Olympus Corporation; magnification, x400). Representative scale bars (200 um) are shown. Each experiment was performed in triplicate and repeated three times independently.
Cell proliferation assay. Cell proliferation was assessed using the Cell Counting Kit-8 (CCK-8; Beyotime Institute of Biotechnology). Cells were seeded in 96-well plates (2,000 cells/well) in 100 ul of complete medium. At 12, 24, 48 and 72 h post-seeding, the medium was replaced with 90 ul of serum-free medium with 10 ul of CCK-8 solution,
followed by 1 h incubation at 37℃. Absorbance was measured at 450 nm using a microplate reader. Each cell experiment was independently repeated three times.
Apoptosis assay. Apoptosis was quantified using Annexin V-FITC/propidium iodide (PI) Apoptosis Detection Kit (BD Biosciences). Transfected cells were seeded into 6-well plates and cultured to ~80% confluence. Cells were harvested, washed twice with PBS and stained in binding buffer with Annexin V-FITC and PI for 10 min at room temperature in the dark. Samples were analyzed within 1 h using flow cytometry (BD FACSCalibur™M; BD Biosciences) and apoptotic populations were quantified. The total apoptosis rate was defined as the sum of early and late apoptotic populations. Data were analyzed using FlowJo software (version 10.8.1; BD Biosciences). Each cell experiment was independently repeated three times.
Cell cycle analysis. Cell cycle analysis was performed using PI staining to quantify DNA content and analyzed via flow cytom- etry. Cells from different groups were collected and washed with ice-cold PBS. After centrifugation at 500 x g for 5 min and aspiration of the supernatant, the cell pellets were fixed in 1 ml of ice-cold 70% ethanol overnight at 4℃. Following PBS washes three times for 5 min each at room temperature with centrifugation at 500 x g for 5 min per wash, cells were incubated in PI/RNase Staining Buffer (BD Biosciences) for 30 min at 37℃ in the dark. Finally, the stained cells were analyzed using a flow cytometer (BD FACSCalibur™M; BD Biosciences). Data were interpreted using the CellQuest Pro software (version 6.0; BD Biosciences). Each cell experiment was independently repeated three times.
Wound healing assay. Cell migration was assessed using a wound healing assay in two human ACC cell lines, SW-13 and NCI-H295R. Cells were harvested during the loga- rithmic growth phase, trypsinized and seeded uniformly into 6-well plates. The cells were cultured until >90% confluence was reached. Prior to wounding, cells were serum-starved in serum-free DMEM for 24 h. A straight wound was introduced in the center area using a sterile pipette tip. The detached cells were removed by washing three times with PBS. Subsequently, serum-free cell culture medium was added. Wound images were captured at 0 and 48 h under an inverted fluorescence microscope (Olympus IX73; Olympus Corporation; magnification, x40). The same magnifica- tion and fields of view were used for each time point. Representative scale bars (500 um) are shown on respective images. The measurement of wound width was performed by annotating the wound edges on the images. Migration rates were quantified using ImageJ (version 1.8.0; National Institutes of Health) and calculated as follows: Cell migra- tion rate=(scratch width at 0 h-scratch width at 48 h)/scratch width at 0 h x100). Each cell experiment was independently repeated three times.
Statistical analysis. All statistical analyses were performed using R (version 4.2.1; Posit Software, PBC). Quantitative data are expressed as mean ± standard deviation. For the compari- sons in Fig. 1, the Wilcoxon rank-sum test (Mann-Whitney U test) was used for unpaired samples, while the Wilcoxon
signed-rank test was applied for paired samples. For in vitro comparisons, unpaired Student’s t-test was used for two-group analyses. For comparisons involving ≥3 groups, data were assessed for normality and homogeneity of vari- ances. The normality of data distribution was verified using the Shapiro-Wilk test, and the homogeneity of variances was assessed using Levene’s test. If these assumptions were met, one-way ANOVA was performed, followed by Tukey’s post hoc test for pairwise comparisons. If the assumptions were violated, the Kruskal-Wallis test was used, followed by Dunn’s post hoc test. The categorical variables were compared using the x2 test. When the applicable conditions of the x2 test were violated (>20% of the expected frequency is <5) the Fisher exact test was used instead. P<0.05 was considered to indicate a statistically significant difference.
Results
EFNA3 expression patterns and prognostic relevance across various types of human cancer. The differential expression of EFNA3 was assessed in a pan-cancer analysis by comparing normal tissues from the GTEx database against tumor tissues from the TCGA dataset. EFNA3 expression levels were signif- icantly downregulated in glioblastoma multiforme (GBM), kidney chromophobe (KICH), acute myeloid leukemia-like (LAML) and skin cutaneous melanoma (SKCM) compared with that of normal tissues (Fig. 1A). Pan-cancer analysis using the TCGA data of paired tumor and paracancerous tissues from the same patients demonstrated that EFNA3 expression levels were significantly upregulated in the majority of tumor types compared with that of paired paracancerous tissues; however, significant downregulation was observed in GBM, KICH, LAML and SKCM (Fig. 1B). The expression levels of EFNA3 in cancer and normal tissues in various types of cancer are shown in Tables SI-III. Forest plots generated from Cox proportional hazard models demonstrated that EFNA3 expression was significantly associated with OS (Fig. 2A), disease-specific survival (DSS; Fig. 2B) and PFI (Fig. 2C) across various cancer types.
Prognostic and pathological correlations of EFNA3 in pan-cancer analysis. Survival data from TCGA were used to assess the prognostic role of EFNA3. The association of EFNA3 expression levels with survival events and time in various cancer types are shown in Table SIV-VI. Due to incom- plete survival annotations, certain end points, specifically DSS and PFI, were unavailable for LAML. Univariate Cox regres- sion showed that increased EFNA3 expression was associated with poor OS, DSS and PFI in bladder cancer (BLCA), kidney clear cell carcinoma (KIRP), ACC and mesothelioma (HR>1; P<0.05). By contrast, increased EFNA3 expression conferred a protective role in lower-grade glioma (LGG; HR<1; P<0.05; Fig. 2). EFNA3 was an adverse marker for PFI in breast cancer (BC), esophageal carcinoma (ESCA), prostate adenocarci- noma (PRAD), rectum adenocarcinoma (READ) and cervical endocervical squamous carcinoma (CESC), and for DSS in liver hepatocellular carcinoma (LIHC), SKCM and CESC (Figs. 3 and S2). Specifically, the Kaplan-Meier survival anal- ysis and log-rank testing demonstrated that, compared to those in the EFNA3-low expression group, high EFNA3 expression
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Expression of EFNA3 Log2 (TPM+1)
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Normal
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M
Tumor
2
0
0
ACC
BLCA
BC
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
B
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ns
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Expression of EFNA3 Log2 (TPM+1)
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ns
ns
ns
ns
ns
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Normal
Tumor
ns
2
0
BLCA
BC
CESC
CHOL
COAD
ESCA
HNSC
KICH
KIRC
KIRP
LIHC
LUAD
LUSC
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
THCA
THYM
UCEC
A
EFNA3 - OS
B
| Group | Total (N) | HR (95% CI) | P-value | |
|---|---|---|---|---|
| ACC | 79 | 3.412 (1.491 - 7.806) | 0.0037 | |
| BLCA | 411 | 1.445 (1.077 - 1.939) | U 0.0140 | |
| BC | 1086 | 1.139 (0.828 - 1.569) | 0.4237 | |
| CESC | 306 | 1.629 (1.017 - 2.610) | 1 0.0424 | |
| CHOL | 35 | 2.071 (0.773 - 5.553) | 0.1479 | |
| COAD | 477 | 1.247 (0.845 - 1.839) | 0.2663 | |
| DLBC | 48 | 0.939 (0.234 - 3.768) | 0.9290 | |
| ESCA | 163 | 0.689 (0.420 - 1.130) | 0.1399 | |
| GBM | 168 | 1.009 (0.718 - 1.416) | 0.9609 | |
| HNSC | 503 | 1.098 (0.841 - 1.433) | 0.4924 | |
| KICH | 64 | 1.164 (0.312 - 4.336) | 0.8211 | |
| KIRC | 541 | 1.431 (1.061 - 1.931) | 0.0189 | |
| KIRP | 290 | 1.156 (0.637 - 2.097) | 0.6345 | |
| LAML | 139 | 0.985 (0.645 - 1.505) | 0.9457 | |
| LGG | 530 | 0.674 (0.480 - 0.945) | 0.0224 | |
| LIHC | 373 | 1.700 (1.198 - 2.411) | 0.0029 | |
| LUAD | 530 | 1.273 (0.955 - 1.698) | 0.1000 | |
| LUSC | 496 | 0.779 (0.594 - 1.022) | 0.0713 | |
| MESO | 86 | 2.156 (1.317 - 3.528) | 0.0022 | |
| OV | 379 | 0.895 (0.692 - 1.158) | 0.3993 | |
| PAAD | 179 | 1.286 (0.854 - 1.936) | 0.2289 | |
| PCPG | 184 | 0.960 (0.239 - 3.854) | 0.9543 | |
| PRAD | 501 | 0.622 (0.159 - 2.431) | 0.4949 | |
| READ | 166 | 1.803 (0.816 - 3.982) | 0.1450 | |
| SARC | 263 | 1.528 (1.025 - 2.278) | 0.0375 | |
| SKCM | 457 | 1.407 (1.075 - 1.842) | 0.0128 | |
| STAD | 370 | 0.755 (0.544 - 1.048) | 0.0929 | |
| TGCT | 139 | 0.349 (0.036 - 3.357) | 0.3621 | |
| THCA | 512 | 0.883 (0.331 - 2.354) | 0.8040 | |
| THYM | 119 | 0.605 (0.149 - 2.458) | 0.4819 | |
| UCEC | 553 | 1.469 (0.972 - 2.220) | 0.0678 | |
| UCS | 57 | 0.834 (0.421 - 1.654) | 0.6037 | |
| UVM | 80 | 5.756 (2.107 - 15.724) | I 0.0006 |
EFNA3 - DSS
| Group | Total (N) | HR (95% CI) | P-value |
|---|---|---|---|
| ACC | 77 | 4.272 (1.703 - 10.718) | 0.0020 |
| BLCA | 397 | 1.524 (1.068 - 2.174) | 0.0203 |
| BC | 1066 | 1.707 (1.097 - 2.655) | 0.0178 |
| CESC | 302 | 1.781 (1.034 - 3.069) | 0.0376 |
| CHOL | 34 | 1.768 (0.629 - 4.968) | 0.2795 |
| COAD | 461 | 1.319 (0.804 - 2.162) | N 0.2727 |
| DLBC | 48 | 0.315 (0.033 - 3.030) | 0.3173 |
| ESCA | 162 | 0.616 (0.341 - 1.113) | 0.1084 |
| GBM | 155 | 0.965 (0.673 - 1.383) | 0.8447 |
| HNSC | 478 | 1.116 (0.790 - 1.578) | 0.5326 |
| KICH | 64 | 1.229 (0.275 - 5.493) | 0.7876 |
| KIRC | 530 | 1.786 (1.212 - 2.632) | - 0.0034 |
| KIRP | 286 | 1.594 (0.746 - 3.404) | 0.2288 |
| LGG | 522 | 0.685 (0.480 - 0.978) | 0.0375 |
| LIHC | 365 | 1.433 (0.920 - 2.231) | 0.1111 |
| LUAD | 495 | 1.128 (0.785 - 1.621) | 0.5149 |
| LUSC | 444 | 0.852 (0.559 - 1.299) | 0.4561 |
| MESO | 66 | 2.792 (1.460 - 5.341) | 0.0019 |
| OV | 353 | 0.877 (0.664 - 1.158) | 0.3538 |
| PAAD | 173 | 1.243 (0.784 - 1.968) | H 0.3550 |
| PCPG | 184 | 0.935 (0.188 - 4.660) | 0.9351 |
| PRAD | 499 | 3.047 (0.326 - 28.457) | 0.3283 |
| READ | 160 | 2.283 (0.760 - 6.853) | 0.1411 |
| SARC | 257 | 1.606 (1.032 - 2.499) | 0.0357 |
| SKCM | 451 | 1.437 (1.078 - 1.915) | 0.0134 |
| STAD | 349 | 0.766 (0.505 - 1.162) | 0.2095 |
| TGCT | 139 | 0.532 (0.048 - 5.868) | 0.6063 |
| THCA | 506 | 0.676 (0.151 - 3.024) | 0.6089 |
| THYM | 119 | 1.263 (0.173 - 9.214) | 0.8179 |
| UCEC | 551 | 1.301 (0.790 - 2.142) | 0.3019 |
| UCS | 55 | 0.890 (0.435 - 1.822) | 0.7498 |
| UVM | 80 | 6.491 (2.161 - 19.495) | 0.0009 |
C EFNA3 - PFI
| Group | Total (N) | HR (95% CI) | P-value |
|---|---|---|---|
| ACC | 79 | 6.237 (2.940 - 13.231) | 1.84c-06 |
| BLCA | 412 | 1.481 (1.102 - 1.990} | 0.0093 |
| BC | 1086 | 1.262 (0.911 - 1.748} | 0.1617 |
| CESC | 306 | 1.435 (0.901 - 2.285} | 0.1279 |
| CHOL | 35 | 1.119 (0.464 - 2.699] | 0.8021 |
| COAD | 477 | 1.287 (0.909 - 1.823] | N 0.1554 |
| DLBC | 48 | 0.742 (0.224 - 2.455} | 0.6246 |
| ESCA | 163 | 0.625 (0.400 - 0.976) | 0.0387 |
| GBM | 168 | 0.817 (0.581 - 1.148} | 0.2442 |
| HNSC | 503 | 1.248 (0.939 - 1.658] | 0.1272 |
| KICH | 64 | 1.136 (0.347 - 3.722} | 0.8336 |
| KIRC | 539 | 1.725 (1.255 - 2.372} | 0.0008 |
| KIRP | 289 | 1.444 (0.851 - 2.448] | 0.1728 |
| LGG | 530 | 0.762 (0.581 - 1.000} | 0.0501 |
| LIHC | 373 | 1.300 (0.972 - 1.739] | 0.0766 |
| LUAD | 530 | 1.155 (0.888 - 1.502) | 0.2834 |
| LUSC | 497 | 0.945 (0.683 - 1.307) | 0.7318 |
| MESO | 84 | 2.090 (1.214 - 3.598) | 0.0078 |
| OV | 379 | 0.935 (0.738 - 1.184) | 0.5757 |
| PAAD | 179 | 1.068 (0.728 - 1.567) | 0.7353 |
| PCPG | 184 | 2.057 (0.837 - 5.056) | 0.1160 |
| PRAD | 501 | 1.540 (1.018 - 2.329) | 0.0411 |
| READ | 166 | 1.972 (1.010 - 3.852) | 0.0467 |
| SARC | 263 | 1.069 (0.769 - 1.486) | 0.6913 |
| SKCM | 458 | 1.118 (0.894 - 1.398) | 0.3298 |
| STAD | 372 | 0.736 (0.517 - 1.048) | 0.0887 |
| TGCT | 139 | 1.010 (0.542 - 1.883) | 0.9747 |
| THCA | 512 | 1.010 (0.592 - 1.723) | 0.9698 |
| THYM | 119 | 0.794 (0.331 - 1.905) | 0.6047 |
| UCEC | 553 | 1.381 (0.974 - 1.957) | 0.0701 |
| UCS | 57 | 0.857 (0.445 - 1.651) | 0.6447 |
| UVM | 79 | 3.298 (1.434 - 7.586) | 0.0050 |
0
3
10
8
10
1’5
8
$
10
Figure 2. Forest plots demonstrate the prognostic value of EFNA3 in 33 types of cancer. (A) Forest plot of EFNA3 expression levels versus OS in patients with cancer. (B) Forest plot of EFNA3 expression levels versus DSS in patients with cancer. (C) Forest plot of EFNA3 expression level versus PFI in patients with cancer. Red text represents high expression levels associated with poor prognosis, while green text represents high expression levels associated with good prognosis. Conditional assumptions applied: Observations were independent and the risk ratio does not change over time (proportional risk assumption). The univariate Cox regression test was employed to calculate the HR with 95% CI and to determine the P-value. EFNA3, ephrin-A3; ACC, adrenocortical carcinoma; OS, overall survival; DSS, disease-specific survival; PFI, progression-free interval; HR, hazard ratio.
1.0
EFNA3
1.0
EFNA3
1.00
EFNA3
Low
Low
Low
Survival probability
High
Survival probability
High
Survival probability
High
0.8
0.8
0.75
ACC
#
0.6
0.6
0.50
os
DSS
PFI
0.4
HR=3.41(1.49-7.84)
0.4
HR=4.27(1.70-10.72)
0.25
HR=6.24(2.94-13.23)
P=0.004
+
H
P=0.002
#
P<0.001
#
0
1000
2000
3000
4000
0
1000
2000
3000
4000
0
1000
2000
3000
4000
No. at risk
Time (days)
No. at risk
Time (days)
No. at risk
Time (days)
Low
39
25
15
5
0
Low
37
24
15
5
0
Low
39
21
12
4
0
High
40
21
7
3
2
High
40
21
7
3
2
High
40
7
4
2
2
1.0
EFNA3
1.0
EFNA3
1.00
EFNA3
Low
Low
High
0.9
Low
Survival probability
0.8
Survival probability
High
Survival probability
0.75
High
0.8
BLCA
0.6
0.7
0.50
+
0.6
0.4
OS
DSS
0.25
0.5
+T
PFI
HR=1.45(1.08-1,94)
HR=1.52(1.07-17)
HR=1.48(1.10-1.99)
P=0.014
P=0.020
+
0.4
0.00
P=0.009
0
1000
2000
3000
4000
5000
0
1000
2000
3000
4000
5000
0
1000
2000
3000
4000
5000
No. at risk
Time (days)
No. at risk
Time (days)
No. at risk
Time (days)
Low
206
55
21
4
3
1
Low
202
55
21
4
3
1
Low
206
47
19
3
2
High
205
45
17
8
1
1
High
195
44
17
8
1
1
High
206
32
13
7
1
1.0
EFNA3
1.0
EFNA3
1.0
EFNA3
Low
Low
Low
Survival probability
0.9
High
Survival probability
High
Survival probability
High
0.9
0.8
0.8.
KIRC
0.7
0.8
0.6.
0.6
0.5
os
0.7
DSS
HR=1.43(1.06-1.93)
HR=1.79(1.21-2.63)
0.4.
PFI
HR=1.73(1.25-2.37)
P=0.019
P=0.003
P<0.001
0
1000
2000
3000
4000
0
1000
2000
3000
4000
0
1000
2000
3000
4000
No. at risk
Time (days)
No. at risk
Time (days)
No. at risk
Time (days)
Low
270
161
66
23
2
Low
262
159
64
23
2
Low
268
136
53
16
1
High
271
149
56
17
1
High
268
146
56
17
1
High
271
132
43
12
0
1.00
EFNA3
1.00
EFNA3
1.00
EFNA3
Low
Low
Low
Survival probability
High
Survival probability
High
High
0.75
0.75
Survival probability
0.75
LGG
0.50
0.50
0.50
0.25
os
0.25
DSS
0.25
PFI
HR=0.51(0.40-0.65)
HR=0.50(0.39-0.65)
HR=0.57(0.46-0.71)
+
P<0.001
1
P<0.001
P<0.001
+
+
0.00
0
2000
4000
6000
0
2000
4000
6000
0
1000
2000
3000
4000
5000
No. at risk
Time (days)
No. at risk
Time (days)
No. at risk
Time (days)
Low
348
24
5
0
Low
336
24
5
0
Low
348
48
15
6
3
0
High
350
39
10
1
High
341
37
10
1
High
350
82
19
6
1
1
1.00
EFNA3
1.00
EFNA3
1.00
EFNA3
Low
Low
Low
Survival probability
High
High
High
0.75
Survival probability
Survival probability
0.75
0.75
0.50
0.50
0.50
MESO
0.25
os
0.25
DSS
0.25
PFI
HR=2.16(1.32-3.53)
HR=2.79(1.46-5.34)
HR=2.09(1.21-3.60)
+
0.00
P=0.002
0.00
P=0.002
P=0.008
+
0
1000
2000
0
1000
2000
0
500
1000
1500
2000
No. at risk
Time (days)
No. at risk
Time (days)
No. at risk
Time (days)
Low
42
12
4
Low
32
10
3
Low-
41
19
7
3
1
High
44
2
0
High
34
1
0
High
43
8
0
0
0
SPANDIDOS PUBLICATIONS
.8.
was significantly associated with poorer PFI in PRAD, READ, BC, ESCA and CESC, and poorer DSS in LIHC, SKCM and CESC (Fig. 3). Significant correlations between EFNA3 and pathological stage were present in certain tumors, including ACC, cholangiocarcinoma (CHOL), ESCA, KIRP, LIHC, testicular germ cell tumors, thyroid carcinoma and SKCM (Fig. S3).
Genetic alterations of EFNA3 and their prognostic signifi- cance. Analysis 2,683 samples from a pan-cancer database of 2,565 patients demonstrated that 15% of the cohort harbored EFNA3 alterations (Fig. 4A), with the highest frequency observed in BC (>40%; Fig. 4B). In ACC, the mutation frequency was 8%. CNAs of the ‘mRNA high’ and ‘amplifi- cation’ subtypes occurred most frequently in ACC. EFNA3 CNAs positively correlated with mRNA expression (Fig. 4℃). Differential expression of genes between EFNA3 altered and unaltered groups in ACC are shown in Table SVII. Survival analyses demonstrated that EFNA3-mutated cases had significantly lower OS in the pan-cancer analysis (HR=2.52, 95% CI, 1.45-4.37; P<0.001) and in ACC specifically (OS, HR=2.97, 95% CI, 1.12-7.90, P=0.029; DFS HR=8.65, 95% CI, 2.14-34.93, P=0.002; Fig. 4D-F). In ACC, ß-catenin (CTNNB1) was among the most frequently co-mutated genes with EFNA3 (P=4.6x10-4; Fig. 4G).
DNA methylation and RNA modifications in the epigenetic regulation of EFNA3. The MethSurv software was used to identify 29 CpG methylation sites for EFNA3 in ACC (Fig. 5A and Table SVIII). The expression levels of EFNA3 positively correlated with multiple mRNA methylation regulators including m1A-, m5C- and m6A-related enzymes (Fig. 5B). The correlation and P-values between the expression level of EFNA3 and mRNA methylation regulatory factors are shown in Table SIX and SX. Top regulators included HNRNPC, ALKBH5, NSUN6, HNRNPA2B1, ELAVL1, METTL3, YTHDF2, LRPPRC and ALYREF.
Correlation between EFNA3 expression levels and the immune microenvironment. In ACC, EFNA3 expression was inversely correlated with stromal, immune and ESTIMATE scores (Fig. 6A). Using the CIBERSORT algorithm, EFNA3 expression was significantly associated with the infiltration levels of multiple immune cell subtypes (Fig. 6B), which indicated potential immunomodulatory roles. For instance, in ACC, EFNA3 expression showed the strongest positive corre- lation with the infiltration levels of activated dendritic cells and the strongest negative correlation with M1 macrophages, both of which were statistically significant (P<0.05).
EFNA3 expression and drug sensitivity prediction. were screened CTRP analysis demonstrated a positive correla- tion between EFNA3 expression and sensitivity to lovastatin and fluvastatin, and a negative correlation with austocystin D, ibrutinib and lapatinib (Fig. 7A). In the GDSC dataset, EFNA3 expression levels correlated positively with bort- ezomib, dimethyloxalylglycine and dasatinib sensitivity but negatively with CP-724714, WZ3105 and KIN001-102. Additionally, using a cut-off of FDR<0.05, 24 (CTRP) and 14 (GDSC) FDA-approved antitumor drugs were significantly
associated with EFNA3 expression levels (Fig. 7B and C; Tables SXI and SXII).
Association of EFNA3 with clinicopathological features in ACC. EFNA3 expression levels were significantly associated with primary treatment outcome (P=0.015), Weiss necrosis (P=0.039), tumor status (P<0.001), diffuse architecture (P=0.026), pathological stage (P=0.034), N-stage (P=0.037) and sex (P=0.030; Table I and Fig. 8A-F). Diagnostic ROC analysis demonstrated a high discriminatory power of EFNA3 [area under the curve (AUC)=0.829; Fig. 8G]. The ROC curve analysis demonstrated that EFNA3 exhibited diagnostic accuracy for ACC across multiple time points. The AUC was 0.764 for 1-year survival, 0.756 for 3-year survival and 0.812 for 5-year survival. Furthermore, on the 1-year ROC curve, a cut-off value of 5.526 was identified for EFNA3 expression (Fig. 8H). The time-dependent ROC analysis assessed the trend of EFNA3 diagnostic accuracy over time. The AUC remained at a high level (≥0.7) across all time points (Fig. 8I).
EFNA3 related ceRNA network construction in ACC. The PITA, miRanda and TargetScan databases were used to analyze and predict 85, 30 and 20 EFNA3 target miRNAs, respectively. A total of 12 target miRNAs were found to be in common from the three database predictions, including hsa-miR-30d-5p, hsa-miR-224-5p, hsa-miR-30c-5p, hsa-miR-30a-5p, hsa-miR-30b-5p, hsa-miR-30e-5p, hsa- miR-330-5p, hsa-miR-326, hsa-miR-145-5p, hsa-miR-491-5p, hsa-miR-153-3p and hsa-miR-210-3p (Fig. 9A). In addition, correlation analysis between target miRNAs and EFNA3 expression was performed to identify candidates for further investigation of ceRNA interactions. Correlation analysis demonstrated the expression levels of 6 target miRNAs negatively correlated with EFNA3 expression levels, namely hsa-miR-145-5p (r =- 0.365, P=9.26x10x10-4), hsa-miR-30b-5p (r =- 0.327, P=3.23x10-3), hsa-miR-30a-3p (r =- 0.44, P=5.06x10-5), hsa-miR-30c-5p (r =- 0.343, P=1.95x10-3), hsa-miR-224-5p (r =- 0.281, P=1.20x10-2) and hsa-miR-30d-5p (r =- 0.456, P=2.45x10-5; Fig. 9B). TargetScan was used to predict the potential binding sites of EFNA3 to the target miRNAs identified (Fig. 9C).
The miRNet and starBase online databases were used to further predict lncRNAs that may bind to the six target miRNAs (hsa-miR-145-5p, hsa-miR-30b-5p, hsa-miR-30a-5p, hsa-miR-30c-5p, hsa-miR-224-5p and hsa-miR-30d-5p; Fig. 10A). A negative correlation between specific lncRNAs and miRNA was observed; the ceRNA network hypothesis suggests that the lncRNA may act as a molecular sponge, sequestering the miRNA and reducing its regulatory activity, consistent with miRNA-mediated ceRNA crosstalk (29). Therefore, the starBase database was used to analyze the correlation between target lncRNA expression and miRNA in ACC. Correlation analysis proved that there are 5 target lncRNAs expression levels that are negatively correlated with hsa-miR-30d-5p, namely AC239868.1, EPB41L4A-AS1, AL049840.4, OIP5-AS1 and SNHG16 (Fig. 10B). However, only the expression level of SNHG16 was negatively correlated with hsa-miR-30c-5p (Fig. 10C). Furthermore, the expression of OIP5-AS1 and SNHG16 were negatively correlated with hsa-miR-30b-5p (Fig. 10D). The expression of MAGI2-AS3 and
A
Pan-cancer 15%
Amplification
ACC 8%
Deep deletion
Inframe mutation (unknown significance)
Missense mutation (unknown significance)
mRNA high
No deletion
B
Deep deletion
8%
40%
Multiple alterations
Amplification
6%
Alteration frequency
30%
mRNA high
Mutation
4%
20%
10%
2%
Mutation data
+
+
×
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
*
+
+
X
+
+
+
+
+
+
CNA data +
X
X
+
+
+
+
+
+
+
+
+
+
+ -
+
+
+
X
+
+
+
+
X
X
+
+
+
+
mRNA data
+
+
+
+
HCC +
LUCA +
BLCA +
UCEC +
+
EC
CRC
CESC
NSCLC
SKCM +
ESCA +
Ov +
HNSC
BC
PAAD
STS -
1
BONE CA
PRAD +
MBCL +
EMBR
KIRC +
-
AML
MB
THCA +
MBCN +
LGG +
+
ET
MDS/MPN
ACC
C
Missense (VUS)
4
Not mutated
13
mRNA expression z-scores relative to all samples (log FPKM capture)
Amplification
O
3
Diploid
12
Shallow deletion
mRNA expression (RNA Seq V2 RSEM)
11
2
O
0
(log2(value+1))
10
9
1
8
0
7
6
-1
5
-2
CESC
MBCN
PTLD
-4
BLCA
BC
CRC
EC
ESCA
LGG
HNSC
HCC
LUCA
MBCL
MEL
NSCLC
OV
PRAD
KIRC
STS
THCA
UCEC
ACC
D
Pan-cancer OS
E
ACC OS
F
ACC DFS
1.00
Unaltered group
1.0
Unaltered group Altered group
1.00
Altered group
5
Unaltered group Altered group
Survival probability
0.75
Survival probability
0.8
Survival probability
0.75
0.50
0.6
0.50
0.25
0.4
0.25
HR=2.52(1.45-4.37)
0.2
HR=2.97(1.12-
.90)
HR=8.65(2.14-34.93)
0.00
P<0.001
P=0.029
0.00
P=0.002
G
0
25
50
75
0
50
100
Time
Time
150
0
50
100
Time
150
2.0
1.5
MutCount
MutCount
1.0
Nonsensev mutation
0.5
Frame shift Del
0.0
Sample group
0
5
10
Missense mutation
Splice site
In frame Del
TP53 (0.06)
56.5%
Frame shift ins
Sample group:
High expression
CTNNB1 (4.6e-4)
52.2%
Low expression
SÍ
SPANDIDOS PUBLICATIONS
V
8.
A
Ethnicity
cg08185345
cg10143807
cg17582777
cg08242010
cg10317026
cg16257681
cg00145979
cg18828883
cg05788417
cg07196758
cg11750116
cg12741345
cg06058618
cg14848832
cg17222196
cg06787675
cg11688696
cg10954985
cg05813084
cg17749735
cg13096820
cg10516701
cg27045062
cg00267713
cg00449821
cg21199495
cg11869514
cg25745642
0.8
Race
Age
Event
0.4
0.4
0.2
Ethnicity
Not evaluted
Unknown
Hispanic or latino
Not hispanic or latino
Race
Not evaluted
Unknown
Asian
Black and afrain american
White
Age
14-34
34-48
48-59
59-77
Event
Alive Dead
Ralation_to_UCSC_CpG
Island
N_Shore
S_Shore
UCSC_RefGene_Group
3’UTR
5’UTR; 1stExon
Body
TSS1500
TSS200
UCSC_RefGene_Group
Ralation_to_UCSC_CpG
B
TRMT61A
TRMT10C
TRMT61B
TRMT6
YTHDF1
YTHDF3
YTHDF2
YTHDC1
ALKBH1
ALKBH3
NSUN7
NSUN6
Correlation coefficient
NSUN3
TRDMT1
NSUN5
-0.5
0.0
0.5
1.0
DNMT1
P-value
NOP2
NSUN2
0.0
0.5
NSUN4
1.0
DNMT3A
DNMT3B
Modification:
TET2
m1A
ALYREF
m5C
KIAA1429
RBM15
m6A
WTAP
RBM15B
Type:
METTL3
Writer
CBLL1
Reader
METTL14
ZC3H13
Eraser
ALKBH5
FTO
YTHDC1_1
YTHDF2_1
HNRNPA2B1
HNRNPC
YTHDF1_1
ELAVL1
FMR1
YTHDC2
YTHDF3_1
IGF2BP1
LBPPRC
TGCT (n=148) SKCM (n=102)
READ (n=92)
GBMLGG (n=662)
LGG (n=509)
HNSC (n=518)
CESC (n=304)
ESCA (n=181)
OV (n=419)
SARC (n=258)
UCS (n=57)
PAAD (n=178)
LUAD (n=513)
LUSC (n=498)
LIHC (n=369)
STAD (n=414)
STES (n=595)
BLCA (n=407)
PRAD (n=495)
CHOL (n=36)
THYM (n=119)
LAML (n=173)
MESO (n=87)
COAD (n=288)
COADREAD (n=380)
BRCA (n=1092)
PCPG (n=177)
KIPAN (n=884)
KIRC (n=530)
NB (n=153)
THCA (n=504)
UCEC (n=180)
ACC (n=77)
WT (n=120)
DLBC (n=47)
GBM (n=153)
ALL (n=132)
KIRP (n=288)
KICH (n=66)
UVM (n=79)
A
Cor
Stromal score
1.0
-0.55
-0.21
-0.13
-0.11
-0.02
-0.09
-0.28
-0.18
-0.40
-0.17
0.18
0.07
-0.11
-0.32
-0.19
-0.26
-0.26
-0.34
-0.31
-0.20
-0.35
0.03
0.01
-0.11
-0.32
-0.24
-0.38
-0.31
-0.06
-0.04
-0.29
-0.34
0.40
0.5
Immune score
-0.64
-0.26
-0.10
-0.09
-0.25
-0.15
-0.55
-0.23
-0.42
-0.36
0.03
0.00
-0.03
-0.20
-0.18
0.01
-0.32
-0.49
-0.47
-0.35
-0.39
0.04
0.00
-0.20
-0.36
-0.28
-0.37
0.12
-0.15
-0.16
-0.35
-0.33
0.49
0.0
ESTIMATE score
-0.64
-0.25
-0.14
-0.13
-0.19
-0.13
-0.51
-0.23
-0.43
-0.31
0.10
0.02
-0.05
0.27
-0.19
0.13
-0.32
-0.45
-0.47
-0.30
-0.40
0.05
0.01
-0.16
-0.37
-0.29
-0.42
-0.07
-0.12
0.18
0.36
-0.37
0.49
-0.1
ACC
BLCA
0 B
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
-1.0
B
B cells naive
*
**
* ☒
**
**
* ☒
*
*
**
*
☒
*
B cells memory
*
*
*
*
**
*
*
* ☒
*
Plasma cells
* ☒
*
*
*
* ☒
**
*
*
* ☒
T cells CD8
**
**
*
**
*
**
**
*
T cells CD4 naive
**
T cells CD4 memory resting
**
**
** ☒
*
**
**
**
*
T cells CD4 memory activated
** ☒
**
**
**
**
* ☒
T cells follicular helper
*
*P<0.05
T cells regulatory Tregs
**
** ☒
*
**
**
**
** ☒
*
*
** ☒
**
*
γo T cells
Cor
**
** ☒
**
*
1.0
NK cells resting
*
NK cells activated
0.5
☒
* ☒
* ☒
Monocytes
**
**
☒
**
*
**
**
* ☒
** ☒
0.0
☒
Macrophages MO
*
*
*
*
*
*
*
*
*
**
**
*
*
**
*
*
**
-0.5
Macrophages M1
*
*
*
*
*
*
*
*
*
*
*
*
*
Macrophages M2
-1.0
*
*
*
**
**
*
**
**
**
**
*
Dendritic cells resting
*
*
**
**
*
**
*
*
*
*
*
Dendritic cells activated
*
**
*
*
*
*
*
*
*
Mast cells resting
*
**
**
**
**
**
**
**
*
**
*
Mast cells activated
**
**
**
*
**
**
Eosinophils
*
**
*
*
*
**
**
*
*
*
Neutrophils
**
**
*
**
*
*
*
*
*
*
ACC
BLCA
BC
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
LINC00205 were negatively correlated with hsa-miR-224-5p (Fig. 10E). There were 4 target lncRNAs expression levels that are negatively correlated with hsa-miR-30a-5p, respectively EPB41L4A-AS1, PVT1, HELLPAR and DLEU2 (Fig. 10F). Based on the ceRNA hypothesis, regarding the inverse relationship between miRNA and lncRNA or mRNA (29), 14 pairs of ceRNA networks (EPB41L4A-AS1-hsa- miR-30a-5p-EFNA3, PVT1-hsa-miR-30a-5p-EFNA3, HELLPAR-hsa-miR-30a-5p-EFNA3, DLEU2-hsa- miR-30a-5p-EFNA3, OIP5-AS1-hsa-miR-30b-5p-EFNA3, SNHG16-hsa-miR-30b-5p-EFNA3, SNHG16-hsa- miR-30c-5p-EFNA3, AC239868.1-hsa-miR-30d-5p- EFNA3, EPB41L4A-AS1-hsa-miR-30d-5p-EFNA3, AL049840.4-hsa-miR-30d-5p-EFNA3, OIP5-AS1-hsa- miR-30d-5p-EFNA3, SNHG16-hsa-miR-30d-5p-EFNA3, MAGI2-AS3-hsa-miR-224-5p-EFNA3 and LINC00205-hsa- miR-224-5p-EFNA3) were constructed based on the correlation analysis results (Fig. 10G).
Analysis of genes and functions co-expressed with EFNA3 in ACC. The LinkedOmics database was used to analyze EFNA3 co-expression in ACC; under the condition of FDR<0.05, 2,000 genes were significantly positively correlated with EFNA3 expression levels (Fig. 11A), while 2,967 genes were signifi- cantly negatively correlated with EFNA3 expression levels. The genes that were positively and negatively correlated with EFNA3 expression levels in ACC are provided in Tables SXIII.
The top 50 genes most significantly positively (Fig. 11B) and negatively (Fig. 11C) correlated with EFNA3 expression levels are displayed in the heat map. Tables SXIV-XV summarizes the GO and KEGG enrichment analyses of genes positively and negatively correlated with EFNA3 expression. As shown in the functional enrichment analysis, genes positively corre- lated with EFNA3 expression were significantly enriched in pathways and biological terms including ‘Cushing syndrome’, ‘Wnt signaling pathway’, ‘C2H2 zinc finger domain binding’, ‘forebrain development’, and the ‘B-catenin-TCF complex’ (Fig. 11D). Genes negatively correlated with EFNA3 expres- sion were significantly associated with immune-related processes and molecular functions, such as ‘T-cell-mediated immunity’, ‘N-glycan processing’, ‘immunological synapse formation’ and ‘cytokine receptor activity’ (Fig. 11E).
The STRING database was used to investigate the PPI network of EFNA3; EFNA3 was associated with ephrin type-A receptor 4 (EPHA4), ephrin type-A receptor 2 (EPHA2), ephrin type-A receptor 3 (EPHA3), ephrin type-A receptor 7 (EPHA7), ephrin type-A receptor 5 (EPHA5), 1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase y-1, ephrin type-A receptor, ephrin type-B receptor 1 and ephrin type-B receptor 3 (0.999, 0.948, 0.936, 0.929, 0.924, 0.914, 0.904, 0.895, 0.88 and 0.869 respectively; Fig. 11F). The confidence scores represent the calculated probability that these associated proteins have a functional interaction with EFNA3. EPHA4, EPHA2 and EPHA3 had the highest
Sİ
TIL 8.
SPANDIDOS PUBLICATIONS
A
Correlation between drug sensitivity and mRNA expression
B
Ciclopirox
Belinostat
Valdecoxib
CTRP
GDSC
Dasatinib
Afatinib
Fluvastatin ☒
(5Z)-7-Oxozeaenol
Bosutinib
Lovastatin
☒
A-770041
Cytarabine hydrochloride
Tivozanib
Afatinib ☒
Panobinostat
Correlation
Austocystin D
☒
AP-24534
Tirbanibulin
AZD7762
BRD-K41597374 ☒
Paclitaxel
Alpelisib
0.25
Bleomycin (50 µM)
Erlotinib
EFNA3
Canertinib
☒
☐
0.00
☒
Bortezomib
cerulenin
☒
Neratinib
☒
Itraconazole
-0.25
Correlation
COL-3
Bx-795
Fluorouracil
-0.3
Erlotinib
☒
CEP701
Vincristine
☒
0.0
Fluorouracil
CHIR-99021
Fluvastatin
Cabozantinib
Lovastatin
FQI-2
Dabrafenib
0.3
Lapatinib
Gefitinib ☒
Dasatinib
Gefitinib
Ibrutinib
Vorinostat
Ibrutinib ☒
DMOG
ISOX
Foretinib
C
Bortezomib
Selumetinib
Ko-143
☒
HG-6-64-1
Ponatinib
Lapatinib
MG-132
Dabrafenib
Linsitinib
☒
PFI-1
Marinopyrrole A
☒
Cabozantinib
RDEA119
Narciclasine
Dasatinib
Selumetinib
Correlation
Neratinib
Bleomycin (50 µM)
0.2
Neuronal differentiation inducer III
☒
Sunitinib
EFNA3
0.1
Panobinostat
☒
Trametinib
Midostaurin
0.0
☒
PD 153035
☒
Vinblastine
-0.1
-0.2
☒
Vinblastine
Pifithrin-mu
☒
Afatinib
☒
SB-743921
CP724714
Idelalisib
Skepinone-L
GSK690693
Valdecoxib
☒
KIN001-102
Sunitinib
Afatinib
Vorinostat
☒
☒
WZ3105
Trametinib
Temsirolimus
correlation with EFNA3, suggesting that these genes may serve a promoting role in certain types of tumors. These results suggested that EFNA3 may be closely related to the occurrence and development of ACC.
Validation of ACC cell transfection efficiency. ACC cell transfection efficiency was demonstrated using RT-qPCR. In NCI-H295R and SW-13 cell lines, the mRNA expression level in EFNA3-OE group was significantly increased compared with that of the vector group, and the EFNA3 mRNA expres- sion in sh-EFNA3 group was significantly decreased compared with that of the shNC group (P<0.05; Fig. 12).
EFNA3 enhances the viability and invasiveness of ACC cells in vitro. CCK-8 assays demonstrated that EFNA3 overex- pression significantly promoted cell viability compared with that of the control group, whereas EFNA3 knockdown had the opposite effect. Flow cytometric analysis demonstrated that EFNA3 knockdown induced apoptosis and G1/S phase cell cycle arrest. Conversely, EFNA3 overexpression signifi- cantly reduced apoptosis and facilitated S-phase progression (Fig. 13A-F). In the Transwell and wound healing assays, EFNA3 overexpression significantly enhanced cell migration
capabilities, respectively. By contrast, EFNA3 knockdown significantly impaired these abilities (Fig. 14A-D).
Discussion
EFNA3, a glycolysis-associated gene, has been implicated in the progression of several malignancies, including BC, hepa- tocellular carcinoma (HCC), oral squamous cell carcinoma, pancreatic adenocarcinoma, lung adenocarcinoma (LUAD), pheochromocytoma and stomach adenocarcinoma, and has been proposed as a diagnostic and prognostic biomarker in these contexts (14,30-33). A recent multi-omics study demonstrated epigenetic regulation of EFNA3 in metastatic pheochromocytomas and paragangliomas, identifying a differentially methylated probe (cg12741345) located within the gene body of EFNA3 (34). The present study also found the cg12741345 probe among the 28 CpG sites identified in ACC, which suggested potential epigenetic dysregulation of EFNA3 in ACC pathogenesis.
To the best of our knowledge, no comprehensive pan-cancer analysis of EFNA3 incorporating multi-dimensional data has been reported. The present results demonstrated that EFNA3 is significantly upregulated in tumor tissues when compared
| Characteristic | Low expression of EFNA3, n (%) | High expression of EFNA3, n (%) | P-value |
|---|---|---|---|
| Total patients | 39 (49.4) | 40 (50.6) | |
| Pathological N stage | 0.037 | ||
| 0 | 37 (46.8) | 31 (39.2) | |
| 1 | 1 (1.3) | 8 (10.1) | |
| Unknown | 1 (1.3) | 1 (1.3) | |
| Pathological stage | 0.034 | ||
| I | 6 (7.6) | 3 (3.8) | |
| II | 23 (29.1) | 14 (17.7) | |
| III | 5 (6.3) | 11 (13.9) | |
| IV | 4 (5.1) | 11 (13.9) | |
| Unknown | 1 (1.3) | 1 (1.3) | |
| Tumor status | <0.001 | ||
| Tumor free | 29 (36.7) | 10 (12.7) | |
| With tumor | 8 (10.1) | 30 (38.0) | |
| Unknown | 2 (2.5) | 0 (0.0) | |
| Primary therapy outcome | 0.015 | ||
| Progressive disease | 4 (5.1) | 14 (17.7) | |
| Stable disease | 1 (1.3) | 1 (1.3) | |
| Partial response | 1 (1.3) | 0 (0.0) | |
| Complete response | 30 (38) | 16 (20.3) | |
| Unknown | 3 (3.8) | 9 (11.4) | |
| Sex | 0.030 | ||
| Female | 19 (24.1) | 29 (36.7) | |
| Male | 20 (25.3) | 11 (13.9) | |
| Weiss-Necrosis | 0.039 | ||
| Absent | 12 (15.2) | 5 (6.3) | |
| Present | 24 (30.4) | 33 (41.8) | |
| Unknown | 3 (3.8) | 2 (2.5) | |
| Weiss-Diffuse architecture | 0.026 | ||
| Absent | 5 (6.3) | 14 (17.7) | |
| Present | 24 (30.4) | 18 (22.8) | |
| Unknown | 10 (12.7) | 8 (10.1) |
The percentages presented are derived from the proportion of each variable category relative to the total sample size (n=79). Cases were categorized as unknown when source of the sample was not defined by clinical characteristics. EFNA3, ephrin-A3; N, node.
with that of adjacent normal tissues across multiple cancer types, including BLCA, CHOL and colon adenocarcinoma. However, in GBM, KICH, LAML and SKCM, EFNA3 expres- sion levels were significantly reduced. These cancer types have not been well explored regarding the biological role of EFNA3, and the clinical significance of this downregulation remains currently unclear.
EFNA3 is involved in multiple cellular functions, including tumor malignancy, angiogenesis, energy metabo- lism and intratumoral hypoxia. In HCC, EFNA3 upregulation correlates with more aggressive tumor behavior, promotion of self-renewal, proliferation, migration and tumor stemness (35). Mechanistically, under hypoxic conditions, hypoxia-inducible factor la (HIF-1a) increases EFNA3 expression in HCC by increasing copy number (12). Deng et al (14) demonstrated that
knocking down EFNA3 significantly inhibits the proliferation and glycolytic capacity of LUAD cells. Yiminniyaze et al (36) further demonstrated that EFNA3 induces epithelial-mesen- chymal transition by enhancing ERK and AKT phosphorylation levels, while upregulating MMP2 and MMP9 expression. In choroidal melanoma, EFNA3 promotes cell prolifera- tion and migration by activating the STAT3/AKT signaling pathway (37). In prostate cancer, EFNA3 knockout suppresses disease progression by reducing Ras/Braf/MEK/Erk1/2 phosphorylation levels (38). In pancreatic ductal adenocar- cinoma cells, EFNA3 enhances tumor angiogenesis and cell permeability through the Wnt/ß-catenin pathway (39). This divergent expression pattern may reflect cancer-type-specific regulatory mechanisms or TME influences. Further experi- mental studies are warranted to elucidate the role of EFNA3
SH 22
TILI ®
SPANDIDOS PUBLICATIONS
A
B
C
8
8
**
8
Expression of EFNA3 Log2 (TPM+1)
Expression of EFNA3 Log2 (TPM+1)
Expression of EFNA3 Log2 (TPM+1)
6
6
6
4
4
4
2
2
2
Absent
Present
Tumor free
With tumor
Stage I & II Pathological stage
Stage III & IV
D
Weiss-Necrosis
E
Tumor status
F
8
8
8
Expression of EFNA3 Log2 (TPM+1)
Expression of EFNA3 Log2 (TPM+1)
Expression of EFNA3 Log2 (TPM+1)
6
6
6
4
4
4
2
2
2
Stage | Stage II Stage III Stage IV Pathological stage
NO
N1
T1 & T2
T3 & T4
Pathological N stage
Pathological T stage
G
1.0
H
1.0
1.0
0.8
0.8
5.5256
0.8
Sensitivity (TPR)
Sensitivity (TPR)
0.6
0.6
AUC
0.6
0.4
0.4
0.4
0.2
EFNA3
0.2
EFNA3
AUC: 0.829
1-year (AUC = 0.764)
0.2
0.0
CI: 0.760-0.897
3-year (AUC = 0.756)
0.0
5-year (AUC = 0.812)
0.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
1
2
3
4
5
1-Specificity (FPR)
1-Specificity (FPR)
Time (years)
in these malignancies. In the future, in vivo models can be used to elucidate whether reduced EFNA3 expression confers tumor-suppressive effects or reflects compensatory biological processes, in order to expand the current understanding of EFNA3 as a context-dependent modulator in tumor biology.
Genetic alterations such as single nucleotide and CNAs are key drivers of oncogenesis and tumor progression (40). In the present pan-cancer analysis, EFNA3 exhibited a notably high mutation frequency, >40% in BC, suggesting a potential
tumor-promoting role in this context. Within the ACC cohort, the most prevalent genomic events associated with EFNA3 were elevated mRNA expression and gene amplification, both indicative of CNA-driven dysregulation. Survival analyses across pan-cancer datasets demonstrated that EFNA3 muta- tions were associated with poorer overall survival, supporting its relevance as a clinically significant genomic alteration. Specifically, patients harboring EFNA3 mutations in ACC showed significantly reduced OS and DFS compared with
A
PITA
miRanda
52
17
1
12
4
0
4
TargetScan
hsa-miR-30d-5p hsa-miR-224-5p
hsa-miR-30c-5p hsa-miR-30a-5p
hsa-miR-30b-5p hsa-miR-30e-5p
hsa-miR-330-5p hsa-miR-326
hsa-miR-145-5p hsa-miR-491-5p
hsa-miR-153-3p hsa-miR-210-3p
B
hsa-miR-145-5p vs. EFNA3, 79 samples (ACC)
hsa-miR-30b-5p vs. EFNA3, 79 samples (ACC)
hsa-miR-30a-5p vs. EFNA3, 79 samples (ACC)
Data Source: starBase v3.0 project
Data Source: starBase v3.0 project
Data Source: starBase v3.0 project
8
Regression (y = - 0.7953x + 10.3727)
8
Regression (y = - 0.8572x + 9.8073)
8
Regression (y = - 0.9537x + 15.1604)
r =- 0.365, P-value=9.26x104
r =- 0.327, P-value=3.23x10-3
r =- 0.440, P-value=5.06x10$
EFNA3, Expression level: log2[FPKM+0.01)]
6
EFNA3, Expression level: log2[FPKM+0.01)]
6
EFNA3, Expression level: log2[FPKM+0.01)]
6
4
4
4
2
2
2
0
0
0
-2
-2
-2
7
8
9
10
11
12
13
6
7
8
9
10
11
12
10
11
12
13
14
15
16
hsa-miR-145-5p, Expression level: log2(RPM+0.01)
hsa-miR-30b-5p, Expression level: log2(RPM+0.01)
hsa-miR-30a-5p, Expression level: log2(RPM+0.01)
hsa-miR-30c-5p vs. EFNA3, 79 samples (ACC)
hsa-miR-224-5p vs. EFNA3, 79 samples (ACC)
hsa-miR-30d-5p vs. EFNA3, 79 samples (ACC)
Data Source: starBase v3.0 project
Data Source: starBase v3.0 project
Data Source: starBase v3.0 project
8
Regression (y = - 1.2887x + 15.0565)
8
Regression (y = - 0.2703x + 3.5753)
8
Regression (y = - 1.2008x + 17.9154)
r =- 0.343, P-value=1.95x10-3
r =- 0.281, P-value=1.20x10-2
r =- 0.456, P-value=2.45x10-5
EFNA3, Expression level: log2[FPKM+0.01)]
6
EFNA3, Expression level: log2[FPKM+0.01)]
6
EFNA3, Expression level: log2[FPKM+0.01)]
6
4
4
4
2
2
2
0
0
0
-2
-2
-2
8.5
9
9.5
10
10.5
11
11.5
0
2.5
5
7.5
10
12.5
11
12
13
14
15
hsa-miR-30c-5p, Expression level: log2(RPM+0.01)
hsa-miR-224-5p, Expression level: log2(RPM+0.01)
hsa-miR-30d-5p, Expression level: log2(RPM+0.01)
| C Target site: chr1 : 155059997-155060003 | Target site: chr1 : 155059556-155059562 | Target site: chr1 : 155059556-155059562 |
|---|---|---|
| EFNA3-WT: 5' agucuaaaaaaaauAAACUGGAg 3' | EFNA3-WT: 5' uuggauugaaaccaaGUUUACa 3' | EFNA3-WT: 5' uuggauugaaaccaaGUUUACa 3' |
| miR-145-5P : 3' ucccuaaggacccuUUUGACCUg 5' | miR-30b-5P : 3' ucgacucacauccuaCAAAUGu 5' | miR-30a-5P : 3' gaaggucagcuccuaCAAAUGu 5' |
| Target site: chr1: 155059556-155059562 | Target site: chr1: 155059960-155059966 | Target site: chr1 : 155059556-155059562 |
| EFNA3-WT: 5' uuuggauugaaaccaaGUUUACa 3' | EFNA3-WT: 5' agugcuuugGCU-GUGACUUu 3' : | | | || | |11 | EFNA3-WT: 5' uuggauugaaaccaaGUUUACa 3' |
| miR-30c-5P : 3' cgacucucacauccuaCAAAUGu 5' | miR-224-5P : 3' uugccuuggUGAUCACUGAAC 5' | miR-30d-5P : 3' gaaggucagccccuaCAAAUGu 5' |
Figure 9. Prediction of miRNAs targeting EFNA3 in ACC. (A) Venn diagram showing the prediction results of EFNA3 targets using the PITA, miRanda and TargetScan software. (B) Scatter plots demonstrate the miRNA-mRNA associations with significant correlation. The starBase software was used to analyze the correlation between EFNA3 and the target miRNA. (C) The TargetScan software was used to predict the potential binding site of EFNA3 to the target miRNA. EFNA3, ephrin-A3; ACC, adrenocortical carcinoma; miRNA, microRNA; WT, wild-type; chr, chromosome.
those with wild-type EFNA3, underscoring its potential role as a negative prognostic marker.
To further elucidate the genetic landscape associated with EFNA3 expression, mutation profiles between high- and low-EFNA3 expression groups were compared. CTNNB1
emerged as a differentially mutated gene, suggesting possible co-regulatory or downstream interactions. By contrast, EFNA3 expression did not significantly differ between groups stratified by TP53 or PRKAR1A mutation status, two well-established drivers in ACC, which suggested that EFNA3 regulation may
SPANDIDOS PUBLICATIONS
A
hsa-miR-145-5p
hsa-miR-30b-5p
hsa-miR-30a-5p
B
miRNet
miRNet
miRNet
asa-mik-bod-5p vs. AC239868.1. 79 samples (ACC)
hsa-mik-30d-5p vs. AL049840.4. 79 samples (ACC)
hsa-mik-30d-5p vs. EPB41444-AS1. 79 samples (ACC)
Data Source: startlane v3.@ project
Data Source: Mariase v5.0 project
Data Source: startimnie w3.@ project
Regression dy # -0.47058 + 63410
Regression fy = - 4.3867x + 8.15221
· 1-8.400, P-value=2.54=10*
Regression ly = - 0.22060 + 5.5584)
· -0.209, P-value:2.67x3b!
AL043840.A. Expression level: log.plP.M.+0.0EN
32
10
16
37
9
10
36
10
12
StarBase
StarBase
StarBase
JPX
LINC00852
1
TUGI MEG3 SNHGI MALATI
LINC01089 KCNQIOT1
PVT1 XIST
OIP5-ASI
PVTI
OIPS-ASI
DLEUZ
2
3
MUC20-OTI OTUDSB-ASI
NORAD SNHG16
HELLPAR NOP14-ASI
NEATI DLEU2
HELLPAR NOP14-ASI EPB41L4A-ASI
TBCID3PI-DHX40PI
NORAD SNHG16
TBCID3P1-DHX40P1
-3
31
1
11
14
15
.
11
12
14
11
0
u
13
N
15
ha-mail-30d-1p. Expression level: log./port/t=0.011
bsa-mit-100-5p, Expressions level: log200PM8+0.01)
hsa-miR-30c-5p
hsa-miR-224-5p
hsa-miR-30d-5p
miRNet
miRNet
hsa-miRt-30d-Sp vs. OIPS-AS1, 79 samples (ACC)
hsa-mift-30d-Sp vs. SNHG16, 79 samples (ACC)
miRNet
Data Source: starBase v3.@ project
Data Source: MarBase v3 0 projet
$
- Regression ly = - 6.3722x + 6.50711
..
SNHG36, Expression level: log2):PKOM+0.DIN
36
10
10
24
7
11
23
22
0
5
A
StarBase
StarBase
StarBase
PVT1 XIST NEATI DLEU2 NORAD
AL049840.4
SNHG16
AC008124.1
PVTI NEATI
AC239868.1
SNHG16 DLEU2
OIPS-ASI
NEATI
AC005034.3
0
HELLPAR NOP14-AS1 EPB41LAA-ASI
MALATI LINC00205
MAGI2-AS3 MCM3AP-AS1
AL137129.1
AC021078.1
MALATI
LINC00665
AL035425.3 AC012236.1 AC023632.6
AL161756.1
OIPS-ASI
STAG3L5P-PVRIG2P-PILRB
NOP14-ASI
HELLPAR
CTBPI-AS2
TBCID3PI-DHX40PI
9
11
12
13
14
15
13
12
AC018648.1
EPB41L4A-ASI
ha-mi-306-5g. Expression Irul: Ing 200PM +4.01)
C
D
E
hsa-mil-30c-5p vs. SNIG16, 79 samples (ACC)
hsa-miR-30b-5p vs. OIPS-A51, 79 samples (ACC)
hsa-miR-30b-Sp vs. SNHG 16, 79 samples (ACC)
hsa-miR-224-Sp vs. LINC00205, 79 samples (ACC)
hsa-miR-224-5p vs. MAGI2-A53, 79 samples (ACC)
Data Source: MarBase v3.0 project
Data Source: startiuse w5.@ project
Data Source: MarBase v3.0 project
Data Source: starBase v3.0 project
Data Source: starBase w5.D project
$
Regression ly - - 44648x + 6.25031
$
Regression ty = - 0.2548x + 3.42341
Regression fy . - 4.1976x + 5.1290
Regression (y = - 0.21048 + 2.74990
Regression fy = - 0.1466x + 2.03610
SNHG16. Expression level: log.29PM+: 0.0100
SNHG16, Expression Irvel: lagh://t.M.+- 0.0 2N
LINC00295, Expression level: log2;FPKUM+0.011|
MAGI2-ASK. Expression level: log.i’ll PCM +-9.0100
E
1
A
-
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.
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10
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12
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.
.
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11
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ha-8-300-1p. Expression level: log//go/tt-0.02)
Psa-tik-100-5p. Expression level: log/00/1+0.0.2)
Bsa-mi8-226:3p. Expression level: log//pt/t=0.021
F
hsa-miR-100-Sp vs. DLEUZ, 79 samples (ACC)
hsa-miR-30a-Sp vs. EPB4IL4A-AS1, 79 samples (ACC)
G
Data Source: tharBase v3.0 project
Data Source: vtarBase v3.0 project
AC239868.1
Regression fy = - 4.3494x 4 3.72784
Regression (y = = 6.2058% + 53382)
[PHIL4A-ASI, Expression level: log.i .P.M.+@.DEN
AL049840.4
DLEU2
hsa-miR-30d-5p
EPB41L4A-AS1
1
4
HELLPAR
4
12
14
n
0%
11
#1
16
LINC00205
hsa-md-304-1p. Expression level: log.hoursd .o.01)
hsa-ma-104-10. Expression level: loghurt+o.ot)
hsa-miR-30a-5p
EFNA3
bsa-miR-304-Sp vs. HELLPAR, 79 samples (ACC)
hsa-mik-30a-Sp vs. PVT1, 79 samples (ACC)
Data Source: startase vi o prajelt
Data Source: starBane vi.ở project
MAGI2-AS3
-Regresslan (y = - 0,6122 . 6.5562)
HELLPAR, Expression level: log29PKM+0.010
PVT L. Expression levet: log2:3pxMe+0.016
1
OIP5-AS1
5.5
PVT1
hsa-miR-224-5p
4
hsa-miR-30b-5p
41
SNHG16
10
11
12
13
1
-9
10
11
1
13
15
16
hsa-miR-30c-5p
hsa-mill-304-5g. Expression level: Ing 201840.01)
hoe-mill-10a-5p. Expression level: log 20UM+-4.01)
IncRNA
miRNA
mRNA
operate through mechanisms independent of TP53/PRKAR1A signaling.
Previous studies have shown that CTNNB1 mutations in ACC are primarily missense mutations localized to exon 3, which impair ß-catenin degradation and promote Wnt pathway activation (41,42). Future research should elucidate whether specific CTNNB1 mutation types, such as exon 3 hotspots versus null alleles, modulate EFNA3 expression or function. A mechanistic dissection of EFNA3-CTNNB1 cross-talk may yield novel insights into the oncogenic circuitry of ACC.
Unlike genomic mutations, epigenetic modifications, such as DNA methylation and RNA methylation, alter gene expres- sion without changing the DNA sequence itself (43). These modifications are increasingly recognized as pivotal regulators
of oncogenesis and tumor behavior (44). The present study identified 28 CpG methylation sites associated with EFNA3 in ACC; a CpG island methylator phenotype (CIMP) has been previously reported in ACC, with the CIMP-high subgroup associated with poorer clinical outcomes, compared with that of the CIMP-low subgroup (45,46). Aberrant DNA meth- ylation in promoter or gene body regions can silence tumor suppressor genes or activate oncogenes, thereby influencing tumor aggressiveness and therapeutic response (47). Although no methylation-targeted therapy has been clinically approved for ACC, DNA methylation inhibitors such as 5-azacytidine and decitabine have demonstrated efficacy in other types of cancer, such as BC and ACC, and are under investigation in preclinical ACC models (48-53). Decitabine demonstrates
A
EFNA3 association result
B
EFNA3
CENAA
EFNA4
6N6411
KDM4B
EMID2
W
C9orf84
KAZ
TYRO3
15
SEMA6B
SIM
RHBDL3
Q4S234E
-Log10 (pvalue)
NOV
VPREB3
EPO
10
CHRNA4
Z-Score
Group
LMX1B
RTN4R
PITX1
>3
3
C17orf96
1
2
LOC572558
NKD1
0
1
2
-1
0
ASGBI
5
TMEM120B
← 3
-1
2WE
-2
SYTL2
NRXN2
VGF
LEF1
MC2R
SPSB3
KCNH3
0
NLK AVD
NB4A3
CYP27B1
-1
0
1
2
PPE19
Locka LOC642852
Pearson correlation coefficient (Pearson test)
CNOT3
ATP8B3
BESK1
HSD3B2
CBX4
KIAA1644
C
MGAT4A
FMNL3
FLI1 CYSLTR1
D
Axon guidance
FILIP1L
GIMAP6
GVIN1
CSBORB
Wnt signaling pathway
FAM124B
MALL
ETS1
HLA-E
Cushing syndrome
P.adjust
PAG1
TEK
Transmembrane receptor protein
0.04
APBB2
ACSL5
RIENS
Z-Score Group
tyrosine kinase activity
0.03
VNN2
KIAA0748
>3
3
0.02
1
Transmembrane-ephrin protein receptor activity
PTPRB
2
CISH
GPRIN3
0
1
SNRK
-1
Counts
DERA
0
C2H2 zinc finger domain binding
TNFSF8
GNPTAB
← 3
-1
2
TLR4
-2
B-catenin-TCF complex -
4
CAPS
ARAP3
7
Sy
GIMAP8
Forebrain development
IPCEF1
SBIC
PRF1
HCG11
Androgen biosynthetic process-
ATP6V1B2
SCML4
CLIC2
DOCK2
SYNPO2
Negative regulation of hormone biosynthetic process
IL1ORA
IL2BB
5385
CO20081 CD200R1
0.05 0.10 0.15 0.20
SLC40A1
Gene Ratio
E
F
EPHB1
Amide binding
EPHA5
Peptide binding
P.adjust
Cytokine receptor activity
EPHA10
EPHB3
0.06
EPHA7
EPHA3
Rac guanyl-nucleotide exchange factor activity
0.04
Negative regulation of defense response
0.02
EFNA3
EPHA1
Counts
Positive regulation of cell killing
2
EPHA4
4
T cell mediated immunity
6
N-glycan processing -
EPHA2
PLCG1
Known interactions
Others
From curated databases
Textmining
Immunological synapse formation -
Experimentally determined
Co-expression
Predicted interactions
Protein homology
0.04 0.06 0.08 0.12 0.14 0.16
Gene neighborhood
Gene ratio
Gene fusions
Gene co-occurrence
anti-tumor effects in ACC cells at clinically relevant concen- trations by reactivating silenced genes in the 11q13 region, suggesting a role for epigenetic mechanisms in adrenocortical carcinogenesis and indicating its potential as an adjuvant treat- ment for advanced cases (49). The present data supported the
hypothesis that EFNA3 methylation patterns may contribute to ACC pathogenesis and could be leveraged as a predictive biomarker or therapeutic target.
Furthermore, significant positive correlations between EFNA3 expression and mRNA modification regulators across
SỐ TILL
SPANDIDOS PUBLICATIONS
5 3
A
NCI-H295R
B
SW-13
Relative mRNA expression
2.5
ns
**
ns
**
ns
Relative mRNA expression
1.5
2.5
1.5
Relative mRNA expression
Relative mRNA expression
ns
*
2.0
2.0
1.0
1.0
1.5
1.5
1.0
0.5
1.0
0.5
0.5
0.5
0.0
0.0
0.0
0.0
Control
Vector
EFNA3-OE
Control
shNC
sh-EFNA3
Control
Vector
EFNA3-OE
Control
shNC
sh-EFNA3
A
B
1.4 -
NCI-H295R
0
sh-NC
0
sh-EFNA3
6
Vector
0
OE-EFNA3
Vector
OE-EFNA3
Q1
Q2
Q1
Q2
Q2
Q2
1.26
13.55
Q1
Q1
20
1.2
sh-NC
”
3.39
“0
2.20
0
0.33
2.66
”
3.23
3.05
Proliferation rate (%)
sh-EFNA3
Apoptosis rate (%)
1.0
15
NCI-H295R
5
102
0
8
8
0.8
Q4
Q3
Q4
Q3
Q4
Q3
Q3
93.06
2.29
80.9
4.16
93.20
3.81
Q4
91.8
1.85
10
0.6
0
0
9
0
5
0.4
0
%
0
8
0.2
10°
10
102
103
104
10º
10
102
103
104
10º
10
102
103
10ª
10°
10
102
103
104
0
12 h
24 h
48 h
72 h
sh-NC
sh-
EF
NA3
A3 Vector OE-EFNA3
Annexin V-FITC
C
D
2.0-
Vector
SW-13
0
sh-NC
0
sh-EFNA3
8
Vector
6
OE-EFNA3
30
OE-EFNA3
sh-NC
Q1
0.86
Q2
Q1
Q2
Q1
25
Proliferation rate (%)
1.5
sh-EFNA3
”
0.91
0
2.67
14.73
Q1
Q2
8
1.16
9
3.38
Q2
2.06
1.95
Apoptosis rate (%)
20
1.0
a
SW-13
102
6
0
8
15
Q4
Q3
Q4
Q3
91.77
72.24
Q3
Q4
91.12
Q4
Q3
6.46
10.36
5.66
91.57
3.10
10
0
9
9
0
0.5
5
%
0
8
8
12 h
24 h
48 h
72 h
10°
101
102
103
104
10°
10
104
10°
10
104
10
104
0
102
103
102
10ª
10°
102
103
sh-NC
sh-
EF
FNA3
Vector
OE-EFNA3
Annexin V-FITC
E
G1
S
G2
100
80
G1
10
NCI-H295R Percentage of cells (%)
S
40
80
Percentage of cells (%)
60
Percentage of cells (%)
Percentage of cells (%)
G2
8
60
30
40
6
40
20
4
20
20
10
2
0
OE-EFNA3
0
OE-EFNA3
0
0
sh-NC
sh-EFNA3
Vector
sh-NC
sh-EFNA3
Vector
sh-NC
sh-EFNA3
Vector
OE-EFNA3
sh-NC
sh-EFNA3
Vector
OE-EFNA3
F
G1
S
G2
G1
40
100
10
ns
Percentage of cells (%)
S
Percentage of cells (%)
80
Percentage of cells (%)
80
G2
30
Percentage of cells (%)
8
SW-13
60
60
6
40
40
20
4
20
20
10
2
0
sh-NC
sh-EFNA3
Vector
OE-EFNA3
0
0
0
sh-NC
sh-EFNA3
Vector
OE-EFNA3
sh-NC
sh-EFNA3
Vector
OE-EFNA3
sh-NC
sh-EFNA3
Vector
OE-EFNA3
pan-cancer datasets were demonstrated. In ACC specifically, several m6A modulators demonstrated expression altera- tions with prognostic implications. For instance, HNRNPC,
a known splicing regulator, was downregulated in ACC and associated with a worse prognosis (54). Conversely, ALKBH5 and YTHDF2 were upregulated and linked to
A
OE-EFNA3
sh-EFNA3
Relative cell count
2.0
Relative cell count
1.0
1.5
0.8
1.0
0.6
0.4
0.5
0.2
Vector
sh-NC
0.0
0.0
Vector
OE-EFNA3
sh-NC
sh-EFNA3
NCI-H295R
B
2.0
1.0
OE-EFNA3
sh-EFNA3
Relative cell count
1.5
Relative cell count
0.8
1.0
0.6
0.4
0.5
0.2
Vector
sh-NC
0.0
Vector
OE-EFNA3
0.0
sh-NC
sh-EFNA3
200 0m
SW13
C
D
sh-NC
sh-EFNA3
sh-NC
sh-EFNA3
40
0 h
Migration rate (%)
0 h
30
Migration rate (%)
30
500um
500um
20
20
48 h
10
48 h
10
0
500um
500um
0
sh-NC
sh-EFNA3
sh-NC
sh-EFNA3
NCI-H295R
SW-13
tumor progression (54-58). METTL3, a methyltransferase, was downregulated and associated with a favorable prognosis. These findings suggested that EFNA3 may be integrated into broader epigenetic regulatory networks, including m6A RNA methylation pathways, that modulate tumor biology in ACC. Collectively, the present results suggested a complex regula- tory landscape in which EFNA3 is subject to both DNA- and RNA-level epigenetic control, offering novel insights into its diagnostic and therapeutic relevance in ACC. Future studies may explore combinations with DNA methyltrans- ferase inhibitors (such as decitabine) or m6A modulators to reverse EFNA3-driven oncogenicity, leveraging epigenetic vulnerabilities common in types of endocrine cancer.
Immunotherapy has revolutionized the treatment paradigm for various types of cancer, offering durable responses in a subset of patients, such as patients with relapsed ovarian cancer and relapsed gastric cancer (59). However, a significant proportion of individuals fail to achieve sustained benefits, which is often attributed to the complexity and heterogeneity of the TME (60,61). Immunotherapy is primarily used in patients with advanced ACC after the failure of traditional chemotherapy (62). Among these treatments, pembroli- zumab is the most studied and recommended in guidelines, although its monotherapy objective response rate remains
limited (63-65). The present study identified a significant correlation between EFNA3 expression levels and immune cell infiltration across multiple cancer types, including ACC. This suggested that EFNA3 may serve a role in modulating the immune landscape, potentially impacting tumor immune evasion and responsiveness to immunotherapeutic agents. In ACC specifically, where immune-based treatment options currently remain limited, EFNA3 may serve as a potential immunological biomarker or therapeutic target. Given its asso- ciation with immune infiltration, EFNA3 might be involved in shaping the immunosuppressive or immunoactive features of the TME. Further studies are warranted to delineate its mechanistic role in regulating immune cell recruitment, antigen presentation or checkpoint molecule expression. The correlation between EFNA3 and TME features demonstrated in the present study provide a rationale for evaluating EFNA3 not only as a diagnostic or prognostic marker but also as a modulator of tumor-immune interactions, opening avenues for combination strategies involving EFNA3-targeted agents and immune checkpoint inhibitors (such as anti-programmed cell death protein 1 and programmed cell death ligand 1) in ACC.
The ceRNA hypothesis proposes that lncRNAs can regulate gene expression by sequestering miRNAs, thereby preventing them from binding to target mRNAs (66). Increasing evidence
SPANDIDOS PUBLICATIONS
.8.
has implicated the EFNA3-centered ceRNA network in various tumor types. For example, miR-210-3p has been shown to target EFNA3, thereby modulating the PI3K/AKT pathway and influencing tumor progression in oral squamous cell carcinoma (67). Similarly, miR-210-mediated suppression of EFNA3 has been reported to affect cell proliferation and invasiveness in peripheral nerve sheath tumors (68). Under hypoxic conditions, EFNA3 can also be regulated through HIF-induced IncRNA activation, promoting metastatic spread in BC (69).
The present study constructed a IncRNA-miRNA-EFNA3 regulatory axis in ACC, highlighting novel non-coding RNA molecules such as OIP5-AS1 and hsa-miR-30d-5p. Previous studies have shown that OIP5-AS1 can act as a ceRNA to modulate oncogenic signaling in endocrine tumors, while miR-486-3p is downregulated in adrenocortical neoplasms and may serve as a tumor suppressor (70,71). The ceRNA network involving EFNA3 identified in the present analysis demonstrated a potential mechanism of post-transcriptional regulation that could contribute to tumor progression, immune modulation and drug resistance in ACC. Furthermore, targeting the IncRNA-miRNA-EFNA3 regulatory axis may have thera- peutic potential. For example, salazosulfapyridine has been proposed to exert anticancer effects in ACC by interacting with the OIP5-AS1-miR-92a-3p-SLC7A11 pathway (72). The present findings underscored the potential of EFNA3-centered ceRNA regulatory networks as diagnostic tools and therapeutic targets in ACC, warranting further functional validation.
Drug repurposing offers a cost-effective and time-efficient strategy to identify new therapeutic options for rare and refrac- tory malignancies such as ACC. In the present study, drug sensitivity correlation analysis was performed based on EFNA3 expression was performed, identifying 24 EFNA3-associated compounds in the CTRP database and 14 in the GDSC data- base. Notably, EFNA3 expression was significantly correlated with sensitivity to HMG-COA reductase inhibitors (statins), which have gained interest for their potential antitumor effects (73,74). Statins, primarily used to treat hypercho- lesterolemia, have demonstrated tumor-suppressive effects in multiple cancer types, including HCC, breast, lung and colorectal cancer (75-80). Mechanistically, statins exert anti- tumor effects through inhibition of the mevalonate pathway, leading to suppression of AKT/NF-KB signaling, induction of apoptosis via caspase cascade activation and impairment of metastatic potential through modulation of MAPK and mTOR pathways (81-88). Simvastatin, for example, has been shown to activate AMPK, upregulate p21 and induce apoptosis in HCC cells (86). HMG-COA reductase inhibitors reduce isoprenoid synthesis by inhibiting the mevalonate pathway, thereby affecting the tumor procession (89).
Despite this promising pharmacological profile, limited studies have examined the application of statins in endocrine malignancies, particularly in ACC. Given the dependence of ACC cells on cholesterol biosynthesis and isoprenoid metabo- lism (90), the mevalonate pathway represents a potential target. Furthermore, given the key role of EFNA3 in glycolysis, combining EFNA3 inhibition with glycolytic inhibitors or statins represents a metabolic ‘double-hit’ strategy against the Warburg-dependency of ACC. The present results highlight EFNA3 as a potential biomarker for predicting statin sensitivity
in ACC. The therapeutic implications of this finding warrant further validation through in vitro mechanistic assays and in vivo efficacy studies. Furthermore, integrating statins with EFNA3-targeted strategies may provide a synergistic approach to disrupt tumor metabolism and reduce ACC aggressiveness.
The Wnt/B-catenin signaling cascade serves a pivotal role in ACC by regulating tumor cell proliferation, migration and metabolic reprogramming. Dysregulation of Wnt/ß-catenin pathway, commonly through activating mutations in the CTNNB1 gene, is a hallmark of ACC pathogenesis (91,92). In the present transcriptome-based co-expression analysis, EFNA3 was closely associated with genes involved in Wnt signaling, suggesting a potential regulatory interaction. Specifically, EFNA3 expression was significantly increased in CTNNB1-mutated samples, demonstrating an association between EFNA3 activity and Wnt/ß-catenin pathway dysregu- lation. Previous research has demonstrated that ß-catenin and Transcription Factor 4 modulate the expression of EphB receptors and their ligands, including ephrin-B1, thereby orchestrating spatial organization along epithelial axes such as the crypt-villus axis in colorectal cancer (93). The present findings suggested a similar interaction may exist between EFNA3 and ß-catenin in ACC, which potentially contributes to malignant transformation. Furthermore, EFNA3 is a glycol- ysis-related gene, and the Wnt/ß-catenin pathway is a known driver of metabolic reprogramming in cancer cells (94). This pathway enhances aerobic glycolysis by upregulating glyco- lytic enzymes, promoting a tumor-favorable microenvironment characterized by increased lactate production and glucose uptake (95-101). In colon and breast cancer, activation of Wnt signaling induces pyruvate dehydrogenase kinase 1 expression and modulates adipogenic enzymes, respectively, reinforcing glycolytic flux and tumor proliferation (96-98).
The present in vitro experiments demonstrated that EFNA3 promotes invasive behavior in ACC cells. These data suggested that EFNA3 may act as a downstream effector or modulator of Wnt/ß-catenin signaling to coordinate both metabolic and invasive phenotypes in ACC. Future mechanistic studies are warranted to dissect the precise molecular interactions between EFNA3, ß-catenin and glycolysis-related signaling pathways in ACC progression. These findings present EFNA3 not only as a key mediator of Wnt-driven ACC pathogenesis but also as a potential node for combinatorial therapeutic intervention. Given the established challenges in targeting Wnt/B-catenin signaling directly in endocrine tumors, primarily the disrup- tion of normal somatic stem cell function critical for cellular repair and tissue homeostasis (102), EFNA3 inhibition offers a tractable approach to disrupt downstream oncogenic outputs (such as metabolic reprogramming and invasion) while potentially synergizing with Wnt pathway modulators or endocrine-disrupting agents.
Despite the comprehensive nature of the present study, several limitations should be acknowledged. First, the bioin- formatics analyses were primarily based on the relatively small TCGA-ACC cohort. Future studies incorporating larger, multi-center datasets are needed to strengthen the robustness and generalizability of these findings. Although the integrative bioinformatics and in vitro findings suggested that EFNA3 is associated with enhanced sensitivity to HMG-COA reductase inhibitors, clinical evidence remains lacking. Large-scale,
randomized controlled trials are needed to validate the thera- peutic efficacy of these agents and safety in patients with ACC. Second, the ceRNA regulatory network involving EFNA3 was constructed through computational predictions and partially supported by molecular data. however, extensive experimental validation, particularly through gain and loss-of-function; assays in vivo, is required to confirm biological relevance and establish causal relationships between EFNA3 and these pathways. Third, although significant epigenetic alterations associated with EFNA3 expression in ACC were observed, the potential of DNA methylation inhibitors as a therapeutic strategy remains unexplored in clinical settings. Additional preclinical studies are necessary to determine whether demethylating agents can reverse EFNA3-mediated oncogenic effects. While the present in vitro experiments demonstrated EFNA3 enhances ACC cell invasiveness, the absence of in vivo validation remains a key constraint. The precise molecular mechanism governing the interplay between EFNA3, Wnt/ß-catenin signaling and metabolic reprogram- ming requires further elucidation through mechanistic studies in animal models complemented by patient-derived organoids.
Future research should also explore the feasibility of targeting EFNA3 as a multi-modal biomarker for ACC prognosis, immu- nomodulation and drug responsiveness. Future research should focus on verifying the synergistic potential of EFNA3-targeted therapy with four major categories of drugs: Immune checkpoint inhibitors (to reverse immunosuppression), epigenetic modula- tors (to regulate the methylation/m6A network), metabolic disruptors (to block glycolysis and mevalonate pathways) and endocrine-specific drugs (adrenal diuretics and steroid synthesis inhibitors). This is essential to overcome ACC drug resistance, achieve synergistic effects, and are key to translating the present findings into clinically actionable strategies.
The present study identified EFNA3 as a potential onco- genic driver and prognostic biomarker in adrenocortical carcinoma. Through integrative bioinformatics analyses and in vitro validation, it was demonstrated that EFNA3 may modulate tumor invasiveness, potentially through its interac- tion with the Wnt/B-catenin signaling pathway and glycolytic reprogramming. EFNA3 expression was also associated with immune cell infiltration, epigenetic alterations and drug sensitivity, particularly to HMG-COA reductase inhibitors, highlighting its potential as a multifaceted therapeutic target. Furthermore, construction of a ceRNA regulatory network provided novel insights into the post-transcriptional control of EFNA3 in ACC. These findings collectively support the trans- lational relevance of EFNA3 and warrant further mechanistic and clinical investigation.
Acknowledgements
Not applicable.
Funding
The present study was supported by the Natural Science Research in Shanxi Province (grant no. 202203021211072), Postgraduate Practice and Innovation Project (grant no. 2024SJ173) and the Task Book of High-Level Research Results Continuation Funding Project from Shanxi
Bethune Hospital (Shanxi Medical Science Academy; grant no. 2024GSPYJ04).
Availability of data and materials
The data generated in the present study may be requested from the corresponding author.
Authors’ contributions
YT, XL and JS designed and implemented the study. YT and YZ performed acquisition, analysis or interpretation of data for the work. YT, XL, YZ and JS contributed to drafting the work or revising it critically for important intellectual content. JS supervised the project, acquired funding and provided final approval of the published version. YT and XL confirm the authenticity of all the raw data. All authors agreed to be accountable for all aspects of the work in ensuring that ques- tions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved All authors have read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable
Patient consent for publication
Not applicable
Competing interests
The authors declare that they have no competing interests.
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