Multi-Omics Analysis Reveals the Mechanism of Exosome-Related Genes in Adrenocortical Carcinogenesis

Multi-Omics Analysis Reveals the Mechanism of Exosome-Related Genes in Adrenocortical Carcinogenesis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Multi-Omics Analysis Reveals the Mechanism of Exosome-Related Genes in Adrenocortical Carcinogenesis Zhiping Chen, Tengfei Liu, Weimin Chen, Guoyang Zhao，PhD，Orthopedics This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7597909/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective This study aimed to elucidate the molecular mechanisms underlying adrenocortical carcinoma (ACC) progression from an exosomal perspective and evaluate the clinical potential of exosomes as diagnostic biomarkers and therapeutic targets. Methods We integrated the GeneCards and GEO databases to identify a combined gene set comprising exosome-related genes and differentially expressed genes (DEGs). Subsequent analyses included Gene Ontology (GO) functional annotation, KEGG pathway enrichment, and gene set enrichment analysis (GSEA). Core gene sets were derived through cross-validation using LASSO regression, SVM-RFE algorithm, and random forest analysis, with dimensionality reduction applied to minimize redundancy. ACC diagnosis and prediction were performed based on differential expression levels and ROC curve analysis, while immune cell infiltration characteristics were assessed via the ssGSEA algorithm. Drug enrichment analysis and molecular docking simulations were conducted to screen the most promising candidate drugs. Results Multidimensional analysis and dataset dimensionality reduction identified six hub genes (GPM6A, CXCL12, COBLL1, OMD, S100A16, and BIRC5). Comparative analysis between control and tumor groups revealed significant alterations in immune cell subpopulations. Drug enrichment and molecular docking targeting these six hub genes prioritized three optimal candidate drugs: ML-7, ciglitazone, and N-NITROSODIETHYLAMINE. Conclusion The six exosome-related genes (GPM6A, CXCL12, COBLL1, OMD, S100A16, and BIRC5) may serve as potential ACC biomarkers, while ML-7, ciglitazone, and N-NITROSODIETHYLAMINE represent promising therapeutic candidates for ACC. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Health sciences/Oncology adrenocortical carcinoma exosome immune infiltration machine learning gene regulatory networks Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Adrenocortical carcinoma (ACC) primarily occurs in the adrenal cortex. It is reported that the global annual incidence of ACC is (1 ~ 2) per million [ 1 ] . Although ACC is rare, accounting for only 0.05%~0.2% of all malignant tumors [ 2 ] , it is highly aggressive, progresses rapidly, and has a poor prognosis, with an overall 5-year survival rate of less than 50% [ 3 ] . Most ACC patients already have metastases at diagnosis, particularly those with cortisol-secreting tumors, which have an extremely poor prognosis and are prone to early distant metastasis [ 4 ] . Cortisol-secreting ACC accounts for a relatively high proportion (approximately 80%), possibly due to the hormone-secreting nature of ACC [ 5 ] . Clinical symptoms associated with excessive hormone secretion mainly include Cushing's syndrome, feminization in males, and electrolyte imbalances [ 6 ] . The high heterogeneity of ACC is one of the reasons why early diagnosis is challenging [ 7 ] . Additionally, the lack of tumor markers with sufficient sensitivity and specificity at the molecular diagnostic level poses another difficulty [ 8 ] . Recent research has focused on exploring the molecular mechanisms, genetic background, and novel therapeutic targets of ACC, providing new perspectives and possibilities for its personalized diagnosis and treatment [ 9 ] . Therefore, a comprehensive understanding of the biological characteristics of ACC is crucial for formulating effective diagnostic and therapeutic strategies [ 10 ] . Exosomes, as nano-scale extracellular vesicles with a diameter of 30–150 nm, possess a unique double-layered lipid membrane structure. Their surface not only retains the membrane protein characteristics of their parent cells but also carries specific signal recognition molecules and stability-modifying molecules, collectively endowing exosomes with targeted delivery capabilities [ 11 – 14 ] . The bioactive molecules contained within exosome-including RNA, lipids, and proteins-determine their complex and diverse functional properties [ 15 ] . Among these, microRNAs (miRNAs), a class of endogenous non-coding RNAs approximately 22 ~ 26 nucleotides in length, are widely present in animals, plants, and some viruses [ 16 ] . By mediating translational repression and mRNA degradation, miRNAs regulate gene expression at the post-transcriptional level, making them a key focus in disease mechanism research [ 17 ] . Due to their high stability and disease-specific expression profiles, miRNAs not only serve as promising non-invasive biomarkers for early ACC diagnosis but also provide a new direction for developing miRNA-based targeted therapies to achieve precision medicine [ 18 – 20 ] . This article will analyze the molecular mechanisms by which exosomes influence ACC tumorigenesis and progression from multiple bioinformatics perspectives. Additionally, it will explore the clinical value of exosomes as diagnostic biomarkers and therapeutic targets in ACC. 2. Materials and methods 2.1. Data download Using the GEOquery tool [ 21 ] in R software (version 4.5.1), we downloaded the ACC-related datasets GSE14922 [ 22 ] and GSE33371 [ 23 ] from the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ). Both datasets consist of human samples derived from frozen tumor tissues and normal tissues, with microarray platforms GPL6480 and GPL570, respectively (details in Table 1 ). Specifically, GSE14922 includes 4 ACC samples and 4 control samples, while GSE33371 contains 33 ACC samples and 10 control samples. This study incorporated all available ACC and control samples from both datasets (Table 1 ). Table 1 GEO microarray chip information. Characteristics GSE14922 GSE33371 Platform GPL6480 GPL570 Type Array Array Species Homo sapiens Homo sapiens Tissue Tumor/Noemal tissue Tumor/Noemal tissue Samples in Disease group 4 33 Samples in Control group 4 10 Reference PMID: 19546168 PMID: 22800756 Exosome-related genes (ERGs) were collected from the GeneCards database [ 24 ] ( https://www.genecards.org/ ) and published literature. GeneCards provides comprehensive human gene information; using "Exosome" as the search keyword, we retained protein-coding genes with a relevance score > 2, yielding 852 ERGs. Additionally, we identified 61 ERGs through a PubMed search ( https://pubmed.ncbi.nlm.nih.gov/ ) using the keyword "Exosome". After merging and removing duplicates, a total of 869 ERGs were obtained (see Supplementary Table S1 for details). The R package sva [ 25 ] was employed to remove batch effects between GSE14922 and GSE33371, resulting in a merged GEO dataset comprising 37 ACC samples and 14 control samples. The merged dataset was then normalized using the limma package [ 26 ] in R, including probe annotation and data normalization. To validate the effectiveness of batch effect correction, principal component analysis (PCA) [ 27 ] was performed on the expression matrices before and after adjustment. PCA is a dimensionality reduction method that extracts feature vectors (components) from high-dimensional data, transforming them into lower-dimensional representations for visualization in 2D or 3D plots. 2.2 Differentially expressed genes in ACC-related exosomes The merged GEO dataset samples were divided into ACC and control groups, and inter-group differential gene expression analysis was performed using the R package limma. Differentially expressed genes (DEGs) were screened with the threshold of |logFC| >0.585 and adjusted p 0.585 and adj. p < 0.05 were defined as upregulated genes - Genes with logFC < -0.585 and adj. p < 0.05 were defined as downregulated genes p -values were adjusted using the Benjamini-Hochberg method. The differential analysis results were visualized as a volcano plot using the R package ggplot2. To identify exosome-related differentially expressed genes (ERDEGs) in ACC, we first screened DEGs meeting the criteria (|logFC| >0.585 and adj.p < 0.05), then intersected them with the exosome-related genes (ERGs) to obtain ERDEGs. Visualization was performed using the R packages pheatmap (for heatmaps) and RCircos (for Venn diagrams) [ 28 ] . 2.3 Gene ontology and pathway analysis (KEGG) GO analysisis a widely used method for large-scale studies of functional enrichment, focusing on Biological Processes (BP), Cellular Components (CC), and Molecular Functions (MF) [ 29 ] . Similarly, the KEGG is a popular database that provides information on genomes, biological pathways, diseases, and drugs [ 30 ] . GO and KEGG pathway enrichment analyses were conducted using the R package clusterProfiler [ 31 ] for ERDEGs. The criteria for statistical significance included a p -value of less than 0.05 and a false discovery rate (FDR) of less than 0.25. 2.4. Gene set enrichment analysis Gene Set Enrichment Analysis (GSEA) evaluates the distribution of genes within predefined gene sets to reveal their phenotypic associations [ 32 ] . In this study, genes from the merged dataset were first ranked by their logFC values, followed by GSEA implementation using the R package clusterProfiler. Parameter settings included: - Random seed = 2023 - Minimum/maximum gene set size = 10/500 - Gene sets selected from the MSigDB c2 collection (Cp.All.V2022.1.Hs.Symbols.gmt, containing 3,050 canonical pathways) Significance thresholds were set at adjusted p < 0.05 and FDR < 0.25 (with p-value adjustment via the Benjamini-Hochberg method). 2.5 Identification of hub genes To further identify hub genes, we first performed univariate logistic regression analysis on the obtained ERDEGs to determine potential candidate key genes. Subsequently, we employed three machine learning algorithms—Least Absolute Shrinkage and Selection Operator (Lasso), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Random Forest (RF) —to further screen and validate the significance of these candidate genes. The Lasso regression was implemented using the R package glmnet, while SVM-RFE was performed with the R packages e1071 and glmnet. The Random Forest algorithm, an ensemble learning method, enhances prediction accuracy and stability by constructing multiple decision trees and averaging their results. A key feature of this algorithm is its random selection of feature subsets for splitting during the construction of each tree, which helps reduce model variance and prevent overfitting. Finally, we took the intersection of the results obtained from the three methods, defining the overlapping genes as exosome-related hub genes. These hub genes were then further analyzed to elucidate their core roles in the pathogenesis of ACC. 2.6 Hub gene expression differences and ROC curve analysis The Receiver Operating Characteristic (ROC) curve is primarily used to eliminate suboptimal models, select the optimal model, or determine the best cutoff value within the same model. To further evaluate the screening and diagnostic value of hub genes for ACC, we employed the R package pROC to plot ROC curves for the hub genes and calculate the Area Under the Curve (AUC). The AUC of an ROC curve typically ranges between 0.5 and 1, with values closer to 1 indicating better diagnostic performance. 2.7 Immune infiltration analysis Single-sample Gene Set Enrichment Analysis (ssGSEA) was employed to quantify the relative abundance of each type of immune cell infiltration [ 33 ] . First, different types of infiltrating immune cells were annotated. Subsequently, the R package ggplot2 was used to generate a comparative diagram illustrating the differences in immune cell expression between the control group and the study group. Correlations among immune cells were calculated using the Spearman algorithm, and the results were visualized as a heatmap using the R package pheatmap. The correlations between Hub Genes and immune cells were also assessed using the Spearman algorithm, and finally, a correlation bubble plot was created using the R package ggplot2. 2.8 Construction of regulatory network To further elucidate the regulatory network between RNA-binding proteins (RBPs) and hub genes, we performed interaction analysis using the ENCORI database ( https://rnasysu.com/encori/ ). Additionally, to construct the regulatory network between transcription factors (TFs) and hub genes, interaction analysis was conducted through the TRRUST database ( https://www.grnpedia.org/trrust/ ). The final regulatory networks were visualized using Cytoscape software. 2.9 Predicting potential drugs To identify potential therapeutic agents for ACC, we performed drug enrichment analysis targeting the hub genes. Using gene-drug association data obtained from the DSigDB database ( https://dsigdb.tanlab.org/ ), drug enrichment analysis was conducted via the R package enrichplot, with subsequent visualization of drug interaction networks using Cytoscape software. The three-dimensional structures of drug molecules were retrieved from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ), while protein structures corresponding to hub genes were acquired from the RCSB PDB database ( https://www.rcsb.org/ ). Molecular docking analysis between the most significantly correlated drugs and hub gene-related proteins was performed using the CB-Dock2 platform ( https://cadd.labshare.cn/cb-dock2/index.php ). 2.10 Statistical analysis Data processing and statistical analyses were performed using R software (v4.3.0). For normally distributed quantitative data, group comparisons were conducted using the unpaired Student's t-test. Non-normally distributed data were analyzed with the Mann–Whitney U test for two-group comparisons, while the Kruskal–Wallis test was employed for comparisons across more than two groups. Categorical variables were assessed using Pearson's χ² test or Fisher's exact test, as appropriate based on data characteristics. Correlation analyses were performed using Spearman's rank correlation method. All reported p-values are two-tailed, and statistical significance was set at p < 0.05. 3. Results 3.1. Technology roadmap This study integrated two datasets—GSE14922 (containing 4 ACC patients and 4 controls) and GSE33371 (comprising 33 ACC patients and 10 controls)—to systematically identify exosome-related differentially expressed genes (ERDEGs) in ACC and investigate their biological functions. Through a comprehensive six-tiered analytical approach—including functional enrichment analyses (GO, KEGG, and GSEA), machine learning, ROC curve evaluation, immune infiltration analysis, regulatory network construction, and potential drug prediction—we systematically assessed the diagnostic value of these genes in ACC. This multidimensional investigation not only elucidates the roles of these genes in the immune microenvironment but also provides novel insights into ACC pathogenesis. Furthermore, it reveals potential biomarkers, candidate therapeutic agents, and molecular targets (Fig. 1 ). 3.2. Merging of psoriasis datasets This study employed the R package sva to perform batch effect correction on the GSE14922 and GSE33371 datasets, ultimately obtaining an integrated GEO dataset. The effectiveness of batch correction was systematically validated through two complementary approaches: 1. Expression Distribution Analysis: - Boxplots of expression distributions (Figs. 2 A, 2 B) demonstrated comparable data ranges before and after correction 2. Dimensionality Reduction Visualization: - Principal component analysis (PCA) scatter plots (Figs. 2 C, 2 D) illustrated the low-dimensional feature distributions pre- and post-correction The results conclusively showed that the batch effects in the ACC datasets were effectively eliminated following correction. 3.3. exosome-related differentially expressed genes (ERDEGs) in ACC The integrated GEO dataset was divided into ACC and control groups, and differential gene expression analysis was performed using the R package limma. The results showed that a total of 699 differentially expressed genes (DEGs) were identified in the integrated dataset (screening criteria: |logFC| >1 and adjusted p-value 1 and adj. P < 0.05), while 366 genes were significantly downregulated (logFC < -1 and adj. P < 0.05). By intersecting these DEGs with known exosome-related genes (ERGs) using a Venn diagram (Fig. 3 B), we finally identified 34 exosome-related differentially expressed genes (ERDEGs): OMD, GPM6A, S100A16, CXCL12, SIRT1, THBS1, CCL2, EZH2, CD14, BIRC5, COBLL1, ANGPTL1, ISLR, MYC, CD55, USP36, GIPC2, VASN, CKB, ZFP36, ACTR3C, ZNF114, SMR3B, HPSE, ALDH1A1, MYOC, ABCB1, SLIT2, SERPINF1, FCGBP, C3, ZG16B, RDX, and IL1B. Further analysis of the expression patterns of these ERDEGs in the integrated dataset was performed, and a heatmap was generated using the R package pheatmap (Fig. 3 C). 3.4. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis We conducted GO and KEGG analyses to investigate the biological processes (BP), cellular components (CC), molecular functions (MF), and related signaling pathways associated with the 34 ERDEGs in ACC. The results revealed that: 1. In GO analysis, these 34 ERDEGs were primarily enriched in the following biological processes: regulation of epithelial cell proliferation, epithelial cell proliferation, endopeptidase inhibitor activity, and chromatin silencing complex. 2. KEGG pathway analysis demonstrated significant enrichment of these genes in several important pathways: Carbon metabolism, Biosynthesis of amino acids, Phagosome, Salmonella infection, and Prion disease. The analytical results were visually presented using bar plots and bubble plots (Figs. 4 A-E). Furthermore, based on the GO and KEGG enrichment results, we constructed network diagrams illustrating the relationships among BP, CC, MF, and KEGG pathways (Figs. 4 F, G). In these network diagrams, connecting lines represent the correspondence between molecules and their functional annotations, while node sizes reflect the number of molecules contained in each term - larger nodes indicate a greater number of included molecules. 3.5. Gene set enrichment analysis We performed Gene Set Enrichment Analysis (GSEA) to evaluate the impact of gene expression profiles from the integrated GEO dataset on ACC. This analysis systematically investigated the associations between gene expression and related biological processes, cellular components, and molecular functions (Fig. 5 ). The results demonstrated that: 1. The integrated dataset showed significant enrichment in the following key pathways: Cell cycle (Fig. 5 B), Fanconi anemia pathway (Fig. 5 C), DNA replication (Fig. 5 D), Homologous recombination (Fig. 5 E), and Mismatch repair (Fig. 5 F). 2. GSEA analysis revealed multiple biological mechanisms associated with ACC: Base excision repair, p53 signaling pathway, Oocyte meiosis, Nucleotide excision repair, and Progesterone-mediated oocyte maturation. Detailed analysis results are presented in Table 2 . Table 2 Gene set enrichment analysis for combined datasets Description setSize Enrichment Score NES p value p.adjust qvalue Mismatch repair 23 0.73 2.11 2.61E-05 3.49E-04 2.35E-04 DNA replication 36 0.73 2.33 1.38E-07 6.00E-06 4.05E-06 Fanconi anemia pathway 47 0.72 2.43 2.14E-09 2.48E-07 1.68E-07 Homologous recombination 37 0.67 2.16 2.02E-06 3.91E-05 2.64E-05 Cell cycle 152 0.65 2.68 1.00E-10 1.74E-08 1.17E-08 Base excision repair 42 0.55 1.80 1.90E-03 9.88E-03 6.66E-03 p53 signaling pathway 72 0.50 1.87 1.23E-04 1.12E-03 7.57E-04 Oocyte meiosis 115 0.50 1.98 9.01E-07 2.09E-05 1.41E-05 Nucleotide excision repair 56 0.49 1.72 1.69E-03 8.90E-03 6.00E-03 Progesterone-mediated oocyte maturation 90 0.46 1.80 2.15E-04 1.82E-03 1.23E-03 Nucleocytoplasmic transport 94 0.43 1.68 1.53E-03 8.18E-03 5.52E-03 Motor proteins 180 0.40 1.70 3.67E-05 4.26E-04 2.87E-04 Ubiquitin mediated proteolysis 134 0.39 1.58 1.19E-03 6.87E-03 4.64E-03 Cellular senescence 151 0.37 1.54 2.34E-03 1.18E-02 7.96E-03 3.6 Logistic analysis and machine learning We performed univariate logistic regression analysis to assess the impact of 34 ERDEGs on ACC. The results showed that: 1. Positive correlation with ACC progression: BIRC5, EZH2, ZNF114, and ZG16B. Negative correlation with ACC progression: IL1B, FCGBP, RDX, SLIT2, ALDH1A1, etc.. 2. Machine learning screening: LASSO regression identified 16 ERDEGs associated with ACC development. SVM algorithm selected 28 ERDEGs related to ACC progression. Random Forest analysis screened 10 ERDEGs linked to ACC pathogenesis (Fig. 6 A-F). 3. Hub gene identification showed that intersection analysis revealed 6 hub genes: Glycoprotein M6A (GPM6A), C-X-C Motif Chemokine Ligand 12 (CXCL12), Cordon-Bleu WH2 Repeat Protein Like 1 (COBLL1), Osteomodulin (OMD), S100 Calcium Binding Protein A16 (S100A16) and Baculoviral IAP Repeat Containing 5 (BIRC5) (Fig. 6 G). Upregulated hub gene in ACC: BIRC5. Downregulated hub genes in ACC: GPM6A, CXCL12, COBLL1, OMD, and S100A16. 4. Gene-gene interactions: Positive correlations: CXCL12 with OMD and S100A16. Negative correlations: BIRC5 with CXCL12 and COBLL1 (Fig. 6 H, I). 5. Chromosomal mapping using RCircos package (Fig. 3 D) showed: Chromosome 4: GPM6A, Chromosome 10: CXCL12, Chromosome 2: COBLL1, Chromosome 9: OMD, Chromosome 11: S100A16, Chromosome 17: BIRC5 (Fig. 6 J). 3.7 Hub gene expression differences and ROC curve analysis This study conducted systematic expression analysis and diagnostic efficacy evaluation of hub genes in the integrated GEO dataset. To assess the diagnostic value of these hub genes, we constructed receiver operating characteristic (ROC) curves using the R package pROC. The analysis results (Figs. 7 A) demonstrated that all 6 hub genes exhibited excellent diagnostic performance (AUC > 0.9). The combined diagnostic AUC reaching 1.0 (Figs. 7 B). 3.8 Analysis of immune infiltration Based on the expression matrix of the integrated dataset, this study employed the Single-sample gene set enrichment analysis (ssGSEA) algorithm to quantitatively analyze the infiltration levels of 28 immune cell types. The analysis revealed the following key findings. Inter-group Immune Cell Infiltration Patterns: Comparative analysis across groups (Fig. 8 A) demonstrated distinct immune infiltration profiles. 21 immune cell types showed statistically significant differences between groups (p < 0.05), including: Immature B cell, Plasmacytoid dendritic cell, MDSC, Macrophage, Immature dendritic cell, Type 2 T helper cell, Effector memeory CD8 T cell, Monocyte, Mast cell, Natural killer cell, Activated B cell, Eosinophil, Regulatory T cell, Central memory CD8 T cell, Activated CD8 T cell, Type 1 T helper cell, Type 17 T helper cell, Gamma delta T cell, Memory B cell, Activated dendritic cell, Activated CD4 T cell. Further correlation heatmap analysis (Fig. 8 B) uncovered a comprehensive regulatory network between hub genes and immune infiltration. CXCL12 and BIRC5 exhibited the strongest correlations with immune cell infiltration levels. 3.9 Construction of regulatory networks In this study, we performed interaction analysis between the 6 hub genes and RNA-binding proteins (RBPs) using the ENCORI database, successfully constructing an RBP regulatory network. Visualization with Cytoscape software demonstrated that a total of 45 RBPs were associated with CXCL12, COBLL1, OMD, S100A16, and BIRC5 (Fig. 9 A). Furthermore, we conducted interaction analysis between the 6 hub genes and transcription factors (TFs) through the TRRUST database, successfully establishing a TF regulatory network. The results revealed that 21 TFs were associated with CXCL12 and BIRC5 (Fig. 9 B). 3.10 Drug enrichment analysis To further explore potential therapeutic drugs for ACC, we conducted drug enrichment analysis targeting the hub genes. Using drug-gene relationship data obtained from the DSigDB database, drug enrichment analysis was performed via the R package enrichplot. The results identified a total of 86 drugs associated with hub gene expression, with the top 30 most significant drugs presented in Table 3 . Subsequently, we acquired three-dimensional structures of both drugs (from PubChem) and hub gene-related proteins (from RCSB PDB), followed by molecular docking analysis of the top 3 most relevant drugs (ML-7, ciglitazone, N-NITROSODIETHYLAMINE) with corresponding proteins using the CB-Dock2 platform. The docking results demonstrated that all 3 drugs could form stable binding conformations with their target proteins encoded by the hub genes (Fig. 10 ). Table 3 Drug enrichment analysis targeting the hub genes on adrenocortical carcinoma ID p value p .adjust qvalue Count geneID ML-7 4.34E-05 9.90E-03 4.25E-03 2 CXCL12/BIRC5 ciglitazone 1.36E-03 4.04E-02 1.73E-02 2 COBLL1/BIRC5 N-NITROSODIETHYLAMINE 1.60E-03 4.04E-02 1.73E-02 2 S100A16/BIRC5 melphalan 3.01E-03 4.04E-02 1.73E-02 2 CXCL12/BIRC5 epiandrosterone 3.05E-03 4.04E-02 1.73E-02 1 CXCL12 rebamipide 3.05E-03 4.04E-02 1.73E-02 1 BIRC5 3,7,11-trimethyldodeca-2,6,10-trien-1-yl trihydrogen diphosphate 3.36E-03 4.04E-02 1.73E-02 1 BIRC5 Angelicin 3.36E-03 4.04E-02 1.73E-02 1 BIRC5 CICLOPIROX 3.36E-03 4.04E-02 1.73E-02 1 BIRC5 2',4'-Dihydroxychalcone 3.66E-03 4.04E-02 1.73E-02 1 BIRC5 IB-MECA 3.66E-03 4.04E-02 1.73E-02 1 BIRC5 NSC113090 3.66E-03 4.04E-02 1.73E-02 1 BIRC5 Fulvestrant 3.80E-03 4.04E-02 1.73E-02 2 CXCL12/BIRC5 1-BROMOPROPANE 3.96E-03 4.04E-02 1.73E-02 1 CXCL12 benzo[g][1]benzofuran-8,9-dione 3.96E-03 4.04E-02 1.73E-02 1 BIRC5 Dehydroxymethylepoxyquinomicin 3.96E-03 4.04E-02 1.73E-02 1 BIRC5 Ginsenoside Rg3 3.96E-03 4.04E-02 1.73E-02 1 BIRC5 Maraviroc 3.96E-03 4.04E-02 1.73E-02 1 CXCL12 Palmatine 3.96E-03 4.04E-02 1.73E-02 1 BIRC5 R-atenolol 4.23E-03 4.04E-02 1.73E-02 2 CXCL12/OMD Cdk2 Inhibitor IV, NU6140 4.27E-03 4.04E-02 1.73E-02 1 BIRC5 5114445 4.57E-03 4.04E-02 1.73E-02 1 CXCL12 Cinobufagin 4.57E-03 4.04E-02 1.73E-02 1 BIRC5 corticosterone 5.18E-03 4.04E-02 1.73E-02 1 CXCL12 thioguanosine 5.29E-03 4.04E-02 1.73E-02 2 CXCL12/COBLL1 Seocalcitol 5.49E-03 4.04E-02 1.73E-02 1 BIRC5 rapamycin 5.70E-03 4.04E-02 1.73E-02 2 CXCL12/BIRC5 Hydroxychlor 5.79E-03 4.04E-02 1.73E-02 1 CXCL12 mifepristone 5.90E-03 4.04E-02 1.73E-02 2 CXCL12/BIRC5 dilazep 6.09E-03 4.04E-02 1.73E-02 1 CXCL12 4. Discussion This study employed stringent bioinformatic screening criteria to systematically reveal the dysregulation characteristics of immune cell networks in ACC and their interactions with hub genes through integration of multi-source databases. We constructed a dual-layered transcriptional-posttranscriptional regulatory network of ACC-associated hub genes, which not only identified potential drug molecules and therapeutic targets for ACC treatment, but also visually demonstrated the central role of hub genes in regulatory hierarchies through network visualization analysis. Our findings provide a systematic perspective for deciphering the molecular mechanisms underlying disease pathogenesis, while offering theoretical support for developing clinical diagnostic and therapeutic strategies. ACC is a rare and highly aggressive endocrine malignancy with limited treatment options and poor prognosis [ 34 ] . Surgical intervention remains the most effective therapeutic approach, with the primary objective of achieving clear surgical margins (R0 resection) [ 35 ] . However, surgical treatment alone fails to effectively prevent ACC recurrence or distant metastasis. For patients with advanced metastatic disease or those unsuitable for surgery, adjuvant therapies including chemotherapy, radiotherapy, and molecular immunotherapy are typically employed [ 36 ] . Among existing pharmacological agents, Mitotane has served as the cornerstone of both adjuvant and metastatic treatment, and remains the only drug approved by the U.S. Food and Drug Administration (FDA) specifically for ACC [ 37 ] . Nevertheless, its clinical efficacy remains controversial due to significant toxicity, adverse effects, and drug resistance issues, with no studies conclusively demonstrating improved long-term survival rates [ 38 ] . Other adjuvant therapies such as chemoradiotherapy and immunotherapy demonstrate even more limited efficacy. Consequently, the development of safer and more effective adjuvant therapeutic agents represents a critical unmet clinical need in ACC management. When multivesicular bodies containing multiple intraluminal vesicles fuse with the plasma membrane, their internal vesicles are released extracellularly to form bioactive exosomes [ 39 ] . This distinctive biogenesis process enables exosomes to not only inherit the functional characteristics of their parent cells but also acquire unique biological advantages [ 40 ] . Recent studies have revealed the crucial role of exosomes in tumor pathogenesis. As nanometer-scale vesicles secreted by cells, exosomes can transport diverse bioactive cargo including cytokines, proteins, and nucleic acids, thereby regulating recipient cell functions through intercellular communication [ 41 , 42 ] . Our study demonstrates that bioactive molecules within exosomes can modulate DNA repair, immune response, and cellular proliferation processes in ACC. By directly activating the immune system, exosomes enhance immune cell recognition and cytotoxic activity against ACC cells. Notably, the CXCL12 and BIRC5 genes showed the strongest correlation with immune cell infiltration levels, providing novel molecular insights into the regulatory mechanisms of ACC immune microenvironment. Through differential gene expression analysis, we identified 6 ERDEGs associated with ACC, all demonstrating excellent diagnostic performance (AUC > 0.9). The combined diagnostic AUC reached 1.00. These 6 hub genes participate in ACC pathogenesis through the following key pathways, with their respective regulatory networks and functions detailed below: BIRC5 (Apoptosis inhibitor): Cell cycle regulation (apoptosis inhibition via Caspase-9 blockade), PI3K/AKT/mTOR pathway (promoting cell survival/proliferation), Wnt/β-catenin pathway (synergizing with CTNNB1 mutations to drive tumor growth). High expression enables ACC cells to evade programmed death, showing significant correlation with poor ACC prognosis. CXCL12 (Chemokine): CXCR4/CXCL12 axis (immune cell recruitment shaping immunosuppressive microenvironment). Downregulation leads to impaired immune surveillance, facilitating ACC immune escape and distant metastasis. GPM6A (Neural glycoprotein): Ras/MAPK signaling (regulating cell differentiation), Integrin-ECM interactions (affecting cell adhesion/metastasis). Low expression disrupts cell-matrix interactions, potentially promoting aggressive ACC phenotypes. COBLL1 (Cytoskeletal regulatory protein): TGF-β/Smad signaling (regulating EMT), Rho GTPase pathway (affecting cell motility/metastasis). Downregulation may activate EMT processes, enhancing ACC cell migration capacity. OMD (Osteomodulin): NF-κB pathway (mediating inflammatory responses). Low expression in ACC may disrupt tissue microenvironment homeostasis, indirectly promoting tumor progression. S100A16 (Calcium-binding protein): p53-dependent apoptosis (genomic stability regulation), JAK/STAT signaling (affecting cell proliferation). Downregulation may cause DNA damage repair defects, accelerating ACC malignant transformation. It is particularly noteworthy that this study represents the first identification of the regulatory roles played by GPM6A, CXCL12, COBLL1, OMD, S100A16, and BIRC5 in ACC pathogenesis. Through these genes, we have further integrated and delineated a synergistic regulatory network formed by intersecting pathways: 1. Apoptosis resistance (BIRC5↑+ S100A16↓), 2. Immune evasion (CXCL12↓+ immune checkpoint activation), 3. Metastatic propensity (COBLL1↓+ GPM6A↓→enhanced EMT). The synergistic interactions among these hub genes collectively exacerbate ACC progression and amplify the complexity of ACC treatment. Accordingly, in our drug enrichment analysis, we identified 86 potential therapeutic agents targeting ERDEG binding sites-marking the first such discovery, and highlighted the 30 most strongly correlated compounds. Most significantly, the 3 top candidate (ML7, Ciglitazone, and N-NITROSODIETHYLAMINE) demonstrated dual-targeting capability against two hub gene sites each. Molecular docking analyses confirmed their ability to form stable binding conformations with hub gene-related proteins. These findings provide crucial references and a solid foundation for selecting ACC therapeutic agents. There are limitations of the study. 1. Focus on Exosome-Related Genes: The analysis was restricted to exosome-associated genes and did not account for genetic alterations originating from other cellular sources. 2. Limited Sample Size: The cohort size was relatively small, necessitating validation in larger populations to confirm the robustness of the findings. 5. Conclusions Exosomes regulate critical pathological processes in ACC, including DNA repair, immune response, and cellular proliferation, through the delivery of bioactive molecules. This study identified GPM6A, CXCL12, COBLL1, OMD, S100A16, and BIRC5 as being closely associated with ACC pathogenesis, all demonstrating excellent diagnostic performance (AUC > 0.9), with a combined diagnostic AUC reaching 1.0. These 6 hub genes likely contribute to ACC initiation and progression through multiple pathways, including the PI3K/AKT/mTOR pathway, CXCR4/CXCL12 axis, and Ras/MAPK signaling. Furthermore, they form a synergistic regulatory network through intersecting pathways that exacerbates disease progression and increases therapeutic challenges. Notably, ML7, Ciglitazone, and N-NITROSODIETHYLAMINE were found to simultaneously target two hub gene sites each and form stable docking conformations with hub gene-related proteins. These discoveries provide: (1) a systematic perspective for deciphering the molecular mechanisms underlying ACC development, (2) new insights into the tumor immune microenvironment, and (3) crucial theoretical support for developing clinical diagnostic and therapeutic strategies. Declarations Funding This study was funded by Young and middle-aged doctors training project of excellent talent for osteoporosis and bone mineral disease (G-X-2019-1107); Scientific Research Project of Jiangsu Provincial Health Committee (M2022119). Availability of data and materials The data achieved and analyzed in the current study are available in the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ) and the GeneCards database ( https://www.genecards.org/ ). Further reasonable inquiries can be directed to the corresponding author. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author Contribution ZPC, TFL, WMC, and GYZ contributed to the study design and conceptual framework. ZPC, TFL conducted data acquisition, performed statistical analysis, and interpreted the results. Technical support was provided by ZPC, GYZ. Manuscript composition and critical revision were completed by ZPC, TFL, WMC, and GYZ. All authors have reviewed and approved the final version of the manuscript. Acknowledgements Not applicable. 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1","display":"","copyAsset":false,"role":"figure","size":194179,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7597909/v1/53648dcbafa254869a976da6.jpg"},{"id":94041761,"identity":"f57325c9-9221-46d8-9235-9536660b64f3","added_by":"auto","created_at":"2025-10-21 18:49:18","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":284780,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7597909/v1/59b15332f3eaf6059c83dcbc.jpg"},{"id":94042332,"identity":"c5f1af75-455d-4976-8a4e-319becbb15a2","added_by":"auto","created_at":"2025-10-21 18:57:18","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":486426,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7597909/v1/eb059a32734bbaed93063f15.jpg"},{"id":94041444,"identity":"d8368552-10f9-496c-b384-311a17f79c7a","added_by":"auto","created_at":"2025-10-21 18:41:18","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":743381,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7597909/v1/45f3ae4665cf0a2cc9b6a91a.jpg"},{"id":94041443,"identity":"783e7186-865d-4c9f-bc45-fc33866c4501","added_by":"auto","created_at":"2025-10-21 18:41:18","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":450127,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7597909/v1/560839db229cbe6520b69c05.jpg"},{"id":94042578,"identity":"e7716e03-3549-4605-b1a1-fce1339eab11","added_by":"auto","created_at":"2025-10-21 19:05:18","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":659713,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7597909/v1/73871c16daa4ef10266dd352.jpg"},{"id":94041765,"identity":"524013b2-2fda-4485-821b-c928c753abe1","added_by":"auto","created_at":"2025-10-21 18:49:18","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":139278,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7597909/v1/6d48220036a176e35ca8944e.jpg"},{"id":94041452,"identity":"79d50873-ce4e-49fd-a039-2dc7c114d755","added_by":"auto","created_at":"2025-10-21 18:41:18","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":464016,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7597909/v1/3fd7073b41647ae92dde0bcf.jpg"},{"id":94041762,"identity":"b9223613-d927-4414-8da6-3c6f5ed01702","added_by":"auto","created_at":"2025-10-21 18:49:18","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":254316,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Picture10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7597909/v1/8586bb4910b67a61588eb8dd.jpg"},{"id":94042336,"identity":"9a002e83-abbf-42da-b411-95cf53a8a546","added_by":"auto","created_at":"2025-10-21 18:57:18","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":391932,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Picture11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7597909/v1/011d2499f864c0cd71368917.jpg"},{"id":95221569,"identity":"0b51d64f-93d1-4293-92c4-3608d0b2abb7","added_by":"auto","created_at":"2025-11-05 16:19:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5292443,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7597909/v1/125fdf81-8c97-4593-8578-75dfb92a360e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-Omics Analysis Reveals the Mechanism of Exosome-Related Genes in Adrenocortical Carcinogenesis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAdrenocortical carcinoma (ACC) primarily occurs in the adrenal cortex. It is reported that the global annual incidence of ACC is (1\u0026thinsp;~\u0026thinsp;2) per million\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Although ACC is rare, accounting for only 0.05%~0.2% of all malignant tumors\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, it is highly aggressive, progresses rapidly, and has a poor prognosis, with an overall 5-year survival rate of less than 50%\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Most ACC patients already have metastases at diagnosis, particularly those with cortisol-secreting tumors, which have an extremely poor prognosis and are prone to early distant metastasis\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Cortisol-secreting ACC accounts for a relatively high proportion (approximately 80%), possibly due to the hormone-secreting nature of ACC\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Clinical symptoms associated with excessive hormone secretion mainly include Cushing's syndrome, feminization in males, and electrolyte imbalances\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. The high heterogeneity of ACC is one of the reasons why early diagnosis is challenging\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Additionally, the lack of tumor markers with sufficient sensitivity and specificity at the molecular diagnostic level poses another difficulty\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Recent research has focused on exploring the molecular mechanisms, genetic background, and novel therapeutic targets of ACC, providing new perspectives and possibilities for its personalized diagnosis and treatment\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Therefore, a comprehensive understanding of the biological characteristics of ACC is crucial for formulating effective diagnostic and therapeutic strategies\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eExosomes, as nano-scale extracellular vesicles with a diameter of 30\u0026ndash;150 nm, possess a unique double-layered lipid membrane structure. Their surface not only retains the membrane protein characteristics of their parent cells but also carries specific signal recognition molecules and stability-modifying molecules, collectively endowing exosomes with targeted delivery capabilities\u003csup\u003e[\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The bioactive molecules contained within exosome-including RNA, lipids, and proteins-determine their complex and diverse functional properties\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Among these, microRNAs (miRNAs), a class of endogenous non-coding RNAs approximately 22\u0026thinsp;~\u0026thinsp;26 nucleotides in length, are widely present in animals, plants, and some viruses\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. By mediating translational repression and mRNA degradation, miRNAs regulate gene expression at the post-transcriptional level, making them a key focus in disease mechanism research\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Due to their high stability and disease-specific expression profiles, miRNAs not only serve as promising non-invasive biomarkers for early ACC diagnosis but also provide a new direction for developing miRNA-based targeted therapies to achieve precision medicine\u003csup\u003e[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. This article will analyze the molecular mechanisms by which exosomes influence ACC tumorigenesis and progression from multiple bioinformatics perspectives. Additionally, it will explore the clinical value of exosomes as diagnostic biomarkers and therapeutic targets in ACC.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Data download\u003c/h2\u003e\u003cp\u003eUsing the GEOquery tool\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e in R software (version 4.5.1), we downloaded the ACC-related datasets GSE14922\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e and GSE33371\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e from the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Both datasets consist of human samples derived from frozen tumor tissues and normal tissues, with microarray platforms GPL6480 and GPL570, respectively (details in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Specifically, GSE14922 includes 4 ACC samples and 4 control samples, while GSE33371 contains 33 ACC samples and 10 control samples. This study incorporated all available ACC and control samples from both datasets (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGEO microarray chip information.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGSE14922\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGSE33371\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatform\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPL6480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGPL570\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArray\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eArray\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHomo sapiens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHomo sapiens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTissue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTumor/Noemal tissue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTumor/Noemal tissue\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSamples in Disease group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSamples in Control group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePMID: 19546168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePMID: 22800756\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eExosome-related genes (ERGs) were collected from the GeneCards database\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and published literature. GeneCards provides comprehensive human gene information; using \"Exosome\" as the search keyword, we retained protein-coding genes with a relevance score\u0026thinsp;\u0026gt;\u0026thinsp;2, yielding 852 ERGs. Additionally, we identified 61 ERGs through a PubMed search (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using the keyword \"Exosome\". After merging and removing duplicates, a total of 869 ERGs were obtained (see Supplementary Table S1 for details).\u003c/p\u003e\u003cp\u003eThe R package sva\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e was employed to remove batch effects between GSE14922 and GSE33371, resulting in a merged GEO dataset comprising 37 ACC samples and 14 control samples. The merged dataset was then normalized using the limma package\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e in R, including probe annotation and data normalization.\u003c/p\u003e\u003cp\u003eTo validate the effectiveness of batch effect correction, principal component analysis (PCA)\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e was performed on the expression matrices before and after adjustment. PCA is a dimensionality reduction method that extracts feature vectors (components) from high-dimensional data, transforming them into lower-dimensional representations for visualization in 2D or 3D plots.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Differentially expressed genes in ACC-related exosomes\u003c/h2\u003e\u003cp\u003eThe merged GEO dataset samples were divided into ACC and control groups, and inter-group differential gene expression analysis was performed using the R package limma. Differentially expressed genes (DEGs) were screened with the threshold of |logFC| \u0026gt;0.585 and adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e- Genes with logFC\u0026thinsp;\u0026gt;\u0026thinsp;0.585 and adj.\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were defined as upregulated genes\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e- Genes with logFC \u0026lt; -0.585 and adj.\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were defined as downregulated genes\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-values were adjusted using the Benjamini-Hochberg method. The differential analysis results were visualized as a volcano plot using the R package ggplot2.\u003c/p\u003e\u003cp\u003eTo identify exosome-related differentially expressed genes (ERDEGs) in ACC, we first screened DEGs meeting the criteria (|logFC| \u0026gt;0.585 and adj.p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), then intersected them with the exosome-related genes (ERGs) to obtain ERDEGs. Visualization was performed using the R packages pheatmap (for heatmaps) and RCircos (for Venn diagrams)\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Gene ontology and pathway analysis (KEGG)\u003c/h2\u003e\u003cp\u003eGO analysisis a widely used method for large-scale studies of functional enrichment, focusing on Biological Processes (BP), Cellular Components (CC), and Molecular Functions (MF)\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Similarly, the KEGG is a popular database that provides information on genomes, biological pathways, diseases, and drugs\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. GO and KEGG pathway enrichment analyses were conducted using the R package clusterProfiler\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e for ERDEGs. The criteria for statistical significance included a \u003cem\u003ep\u003c/em\u003e-value of less than 0.05 and a false discovery rate (FDR) of less than 0.25.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Gene set enrichment analysis\u003c/h2\u003e\u003cp\u003eGene Set Enrichment Analysis (GSEA) evaluates the distribution of genes within predefined gene sets to reveal their phenotypic associations\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. In this study, genes from the merged dataset were first ranked by their logFC values, followed by GSEA implementation using the R package clusterProfiler. Parameter settings included:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e- Random seed\u0026thinsp;=\u0026thinsp;2023\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e- Minimum/maximum gene set size\u0026thinsp;=\u0026thinsp;10/500\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e- Gene sets selected from the MSigDB c2 collection (Cp.All.V2022.1.Hs.Symbols.gmt, containing 3,050 canonical pathways)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eSignificance thresholds were set at adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.25 (with p-value adjustment via the Benjamini-Hochberg method).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Identification of hub genes\u003c/h2\u003e\u003cp\u003eTo further identify hub genes, we first performed univariate logistic regression analysis on the obtained ERDEGs to determine potential candidate key genes. Subsequently, we employed three machine learning algorithms\u0026mdash;Least Absolute Shrinkage and Selection Operator (Lasso), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Random Forest (RF) \u0026mdash;to further screen and validate the significance of these candidate genes. The Lasso regression was implemented using the R package glmnet, while SVM-RFE was performed with the R packages e1071 and glmnet. The Random Forest algorithm, an ensemble learning method, enhances prediction accuracy and stability by constructing multiple decision trees and averaging their results. A key feature of this algorithm is its random selection of feature subsets for splitting during the construction of each tree, which helps reduce model variance and prevent overfitting. Finally, we took the intersection of the results obtained from the three methods, defining the overlapping genes as exosome-related hub genes. These hub genes were then further analyzed to elucidate their core roles in the pathogenesis of ACC.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Hub gene expression differences and ROC curve analysis\u003c/h2\u003e\u003cp\u003eThe Receiver Operating Characteristic (ROC) curve is primarily used to eliminate suboptimal models, select the optimal model, or determine the best cutoff value within the same model. To further evaluate the screening and diagnostic value of hub genes for ACC, we employed the R package pROC to plot ROC curves for the hub genes and calculate the Area Under the Curve (AUC). The AUC of an ROC curve typically ranges between 0.5 and 1, with values closer to 1 indicating better diagnostic performance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Immune infiltration analysis\u003c/h2\u003e\u003cp\u003eSingle-sample Gene Set Enrichment Analysis (ssGSEA) was employed to quantify the relative abundance of each type of immune cell infiltration\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. First, different types of infiltrating immune cells were annotated. Subsequently, the R package ggplot2 was used to generate a comparative diagram illustrating the differences in immune cell expression between the control group and the study group. Correlations among immune cells were calculated using the Spearman algorithm, and the results were visualized as a heatmap using the R package pheatmap. The correlations between Hub Genes and immune cells were also assessed using the Spearman algorithm, and finally, a correlation bubble plot was created using the R package ggplot2.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Construction of regulatory network\u003c/h2\u003e\u003cp\u003eTo further elucidate the regulatory network between RNA-binding proteins (RBPs) and hub genes, we performed interaction analysis using the ENCORI database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rnasysu.com/encori/\u003c/span\u003e\u003cspan address=\"https://rnasysu.com/encori/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Additionally, to construct the regulatory network between transcription factors (TFs) and hub genes, interaction analysis was conducted through the TRRUST database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.grnpedia.org/trrust/\u003c/span\u003e\u003cspan address=\"https://www.grnpedia.org/trrust/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The final regulatory networks were visualized using Cytoscape software.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Predicting potential drugs\u003c/h2\u003e\u003cp\u003eTo identify potential therapeutic agents for ACC, we performed drug enrichment analysis targeting the hub genes. Using gene-drug association data obtained from the DSigDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dsigdb.tanlab.org/\u003c/span\u003e\u003cspan address=\"https://dsigdb.tanlab.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), drug enrichment analysis was conducted via the R package enrichplot, with subsequent visualization of drug interaction networks using Cytoscape software. The three-dimensional structures of drug molecules were retrieved from the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), while protein structures corresponding to hub genes were acquired from the RCSB PDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Molecular docking analysis between the most significantly correlated drugs and hub gene-related proteins was performed using the CB-Dock2 platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cadd.labshare.cn/cb-dock2/index.php\u003c/span\u003e\u003cspan address=\"https://cadd.labshare.cn/cb-dock2/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Statistical analysis\u003c/h2\u003e\u003cp\u003eData processing and statistical analyses were performed using R software (v4.3.0). For normally distributed quantitative data, group comparisons were conducted using the unpaired Student's t-test. Non-normally distributed data were analyzed with the Mann\u0026ndash;Whitney U test for two-group comparisons, while the Kruskal\u0026ndash;Wallis test was employed for comparisons across more than two groups. Categorical variables were assessed using Pearson's χ\u0026sup2; test or Fisher's exact test, as appropriate based on data characteristics. Correlation analyses were performed using Spearman's rank correlation method. All reported p-values are two-tailed, and statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Technology roadmap\u003c/h2\u003e\u003cp\u003eThis study integrated two datasets\u0026mdash;GSE14922 (containing 4 ACC patients and 4 controls) and GSE33371 (comprising 33 ACC patients and 10 controls)\u0026mdash;to systematically identify exosome-related differentially expressed genes (ERDEGs) in ACC and investigate their biological functions. Through a comprehensive six-tiered analytical approach\u0026mdash;including functional enrichment analyses (GO, KEGG, and GSEA), machine learning, ROC curve evaluation, immune infiltration analysis, regulatory network construction, and potential drug prediction\u0026mdash;we systematically assessed the diagnostic value of these genes in ACC.\u003c/p\u003e\u003cp\u003eThis multidimensional investigation not only elucidates the roles of these genes in the immune microenvironment but also provides novel insights into ACC pathogenesis. Furthermore, it reveals potential biomarkers, candidate therapeutic agents, and molecular targets (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Merging of psoriasis datasets\u003c/h2\u003e\u003cp\u003eThis study employed the R package sva to perform batch effect correction on the GSE14922 and GSE33371 datasets, ultimately obtaining an integrated GEO dataset. The effectiveness of batch correction was systematically validated through two complementary approaches:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e1. Expression Distribution Analysis:\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e- Boxplots of expression distributions (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) demonstrated comparable data ranges before and after correction\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e2. Dimensionality Reduction Visualization:\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e- Principal component analysis (PCA) scatter plots (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) illustrated the low-dimensional feature distributions pre- and post-correction\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe results conclusively showed that the batch effects in the ACC datasets were effectively eliminated following correction.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.3. exosome-related differentially expressed genes (ERDEGs) in ACC\u003c/h2\u003e\u003cp\u003eThe integrated GEO dataset was divided into ACC and control groups, and differential gene expression analysis was performed using the R package limma. The results showed that a total of 699 differentially expressed genes (DEGs) were identified in the integrated dataset (screening criteria: |logFC| \u0026gt;1 and adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). According to the volcano plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), 555 genes were significantly upregulated (logFC\u0026thinsp;\u0026gt;\u0026thinsp;1 and adj. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while 366 genes were significantly downregulated (logFC \u0026lt; -1 and adj. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eBy intersecting these DEGs with known exosome-related genes (ERGs) using a Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), we finally identified 34 exosome-related differentially expressed genes (ERDEGs): OMD, GPM6A, S100A16, CXCL12, SIRT1, THBS1, CCL2, EZH2, CD14, BIRC5, COBLL1, ANGPTL1, ISLR, MYC, CD55, USP36, GIPC2, VASN, CKB, ZFP36, ACTR3C, ZNF114, SMR3B, HPSE, ALDH1A1, MYOC, ABCB1, SLIT2, SERPINF1, FCGBP, C3, ZG16B, RDX, and IL1B. Further analysis of the expression patterns of these ERDEGs in the integrated dataset was performed, and a heatmap was generated using the R package pheatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis\u003c/h2\u003e\u003cp\u003eWe conducted GO and KEGG analyses to investigate the biological processes (BP), cellular components (CC), molecular functions (MF), and related signaling pathways associated with the 34 ERDEGs in ACC. The results revealed that: 1. In GO analysis, these 34 ERDEGs were primarily enriched in the following biological processes: regulation of epithelial cell proliferation, epithelial cell proliferation, endopeptidase inhibitor activity, and chromatin silencing complex. 2. KEGG pathway analysis demonstrated significant enrichment of these genes in several important pathways: Carbon metabolism, Biosynthesis of amino acids, Phagosome, Salmonella infection, and Prion disease. The analytical results were visually presented using bar plots and bubble plots (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-E). Furthermore, based on the GO and KEGG enrichment results, we constructed network diagrams illustrating the relationships among BP, CC, MF, and KEGG pathways (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF, G). In these network diagrams, connecting lines represent the correspondence between molecules and their functional annotations, while node sizes reflect the number of molecules contained in each term - larger nodes indicate a greater number of included molecules.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Gene set enrichment analysis\u003c/h2\u003e\u003cp\u003eWe performed Gene Set Enrichment Analysis (GSEA) to evaluate the impact of gene expression profiles from the integrated GEO dataset on ACC. This analysis systematically investigated the associations between gene expression and related biological processes, cellular components, and molecular functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The results demonstrated that: 1. The integrated dataset showed significant enrichment in the following key pathways: Cell cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), Fanconi anemia pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), DNA replication (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), Homologous recombination (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE), and Mismatch repair (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). 2. GSEA analysis revealed multiple biological mechanisms associated with ACC: Base excision repair, p53 signaling pathway, Oocyte meiosis, Nucleotide excision repair, and Progesterone-mediated oocyte maturation. Detailed analysis results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGene set enrichment analysis for combined datasets\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003esetSize\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEnrichment\u003c/p\u003e\u003cp\u003eScore\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003evalue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep.adjust\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eqvalue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMismatch repair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.61E-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.49E-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.35E-04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDNA replication\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.38E-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.00E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.05E-06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFanconi anemia pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.14E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.48E-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.68E-07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHomologous recombination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.02E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.91E-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.64E-05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCell cycle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00E-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.74E-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.17E-08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBase excision repair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.90E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.88E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.66E-03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep53 signaling pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.23E-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.12E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.57E-04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOocyte meiosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.01E-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.09E-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.41E-05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNucleotide excision repair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.69E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.90E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.00E-03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProgesterone-mediated oocyte maturation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.15E-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.82E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.23E-03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNucleocytoplasmic transport\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.53E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.18E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.52E-03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMotor proteins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.67E-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.26E-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.87E-04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUbiquitin mediated proteolysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.19E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.87E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.64E-03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCellular senescence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.34E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.18E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.96E-03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Logistic analysis and machine learning\u003c/h2\u003e\u003cp\u003eWe performed univariate logistic regression analysis to assess the impact of 34 ERDEGs on ACC. The results showed that: 1. Positive correlation with ACC progression: BIRC5, EZH2, ZNF114, and ZG16B. Negative correlation with ACC progression: IL1B, FCGBP, RDX, SLIT2, ALDH1A1, etc.. 2. Machine learning screening: LASSO regression identified 16 ERDEGs associated with ACC development. SVM algorithm selected 28 ERDEGs related to ACC progression. Random Forest analysis screened 10 ERDEGs linked to ACC pathogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-F). 3. Hub gene identification showed that intersection analysis revealed 6 hub genes: Glycoprotein M6A (GPM6A), C-X-C Motif Chemokine Ligand 12 (CXCL12), Cordon-Bleu WH2 Repeat Protein Like 1 (COBLL1), Osteomodulin (OMD), S100 Calcium Binding Protein A16 (S100A16) and Baculoviral IAP Repeat Containing 5 (BIRC5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). Upregulated hub gene in ACC: BIRC5. Downregulated hub genes in ACC: GPM6A, CXCL12, COBLL1, OMD, and S100A16. 4. Gene-gene interactions: Positive correlations: CXCL12 with OMD and S100A16. Negative correlations: BIRC5 with CXCL12 and COBLL1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH, I). 5. Chromosomal mapping using RCircos package (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) showed: Chromosome 4: GPM6A, Chromosome 10: CXCL12, Chromosome 2: COBLL1, Chromosome 9: OMD, Chromosome 11: S100A16, Chromosome 17: BIRC5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Hub gene expression differences and ROC curve analysis\u003c/h2\u003e\u003cp\u003eThis study conducted systematic expression analysis and diagnostic efficacy evaluation of hub genes in the integrated GEO dataset. To assess the diagnostic value of these hub genes, we constructed receiver operating characteristic (ROC) curves using the R package pROC. The analysis results (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA) demonstrated that all 6 hub genes exhibited excellent diagnostic performance (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.9). The combined diagnostic AUC reaching 1.0 (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Analysis of immune infiltration\u003c/h2\u003e\u003cp\u003eBased on the expression matrix of the integrated dataset, this study employed the Single-sample gene set enrichment analysis (ssGSEA) algorithm to quantitatively analyze the infiltration levels of 28 immune cell types. The analysis revealed the following key findings. Inter-group Immune Cell Infiltration Patterns: Comparative analysis across groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA) demonstrated distinct immune infiltration profiles. 21 immune cell types showed statistically significant differences between groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including: Immature B cell, Plasmacytoid dendritic cell, MDSC, Macrophage, Immature dendritic cell, Type 2 T helper cell, Effector memeory CD8 T cell, Monocyte, Mast cell, Natural killer cell, Activated B cell, Eosinophil, Regulatory T cell, Central memory CD8 T cell, Activated CD8 T cell, Type 1 T helper cell, Type 17 T helper cell, Gamma delta T cell, Memory B cell, Activated dendritic cell, Activated CD4 T cell.\u003c/p\u003e\u003cp\u003eFurther correlation heatmap analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB) uncovered a comprehensive regulatory network between hub genes and immune infiltration. CXCL12 and BIRC5 exhibited the strongest correlations with immune cell infiltration levels.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.9 Construction of regulatory networks\u003c/h2\u003e\u003cp\u003eIn this study, we performed interaction analysis between the 6 hub genes and RNA-binding proteins (RBPs) using the ENCORI database, successfully constructing an RBP regulatory network. Visualization with Cytoscape software demonstrated that a total of 45 RBPs were associated with CXCL12, COBLL1, OMD, S100A16, and BIRC5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). Furthermore, we conducted interaction analysis between the 6 hub genes and transcription factors (TFs) through the TRRUST database, successfully establishing a TF regulatory network. The results revealed that 21 TFs were associated with CXCL12 and BIRC5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.10 Drug enrichment analysis\u003c/h2\u003e\u003cp\u003eTo further explore potential therapeutic drugs for ACC, we conducted drug enrichment analysis targeting the hub genes. Using drug-gene relationship data obtained from the DSigDB database, drug enrichment analysis was performed via the R package enrichplot. The results identified a total of 86 drugs associated with hub gene expression, with the top 30 most significant drugs presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Subsequently, we acquired three-dimensional structures of both drugs (from PubChem) and hub gene-related proteins (from RCSB PDB), followed by molecular docking analysis of the top 3 most relevant drugs (ML-7, ciglitazone, N-NITROSODIETHYLAMINE) with corresponding proteins using the CB-Dock2 platform. The docking results demonstrated that all 3 drugs could form stable binding conformations with their target proteins encoded by the hub genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDrug enrichment analysis targeting the hub genes on adrenocortical carcinoma\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e.adjust\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eqvalue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003egeneID\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eML-7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.34E-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.90E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.25E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCXCL12/BIRC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eciglitazone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.36E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCOBLL1/BIRC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN-NITROSODIETHYLAMINE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.60E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eS100A16/BIRC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emelphalan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.01E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCXCL12/BIRC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eepiandrosterone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.05E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCXCL12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erebamipide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.05E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBIRC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3,7,11-trimethyldodeca-2,6,10-trien-1-yl trihydrogen diphosphate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.36E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBIRC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAngelicin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.36E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBIRC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCICLOPIROX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.36E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBIRC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2',4'-Dihydroxychalcone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.66E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBIRC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIB-MECA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.66E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBIRC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNSC113090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.66E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBIRC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFulvestrant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.80E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCXCL12/BIRC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1-BROMOPROPANE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.96E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCXCL12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ebenzo[g][1]benzofuran-8,9-dione\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.96E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBIRC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDehydroxymethylepoxyquinomicin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.96E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBIRC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGinsenoside Rg3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.96E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBIRC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaraviroc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.96E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCXCL12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePalmatine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.96E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBIRC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-atenolol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.23E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCXCL12/OMD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCdk2 Inhibitor IV, NU6140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.27E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBIRC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5114445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.57E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCXCL12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCinobufagin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.57E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBIRC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecorticosterone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.18E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCXCL12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ethioguanosine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.29E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCXCL12/COBLL1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeocalcitol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.49E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBIRC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erapamycin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.70E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCXCL12/BIRC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHydroxychlor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.79E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCXCL12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emifepristone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.90E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCXCL12/BIRC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edilazep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.09E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCXCL12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study employed stringent bioinformatic screening criteria to systematically reveal the dysregulation characteristics of immune cell networks in ACC and their interactions with hub genes through integration of multi-source databases. We constructed a dual-layered transcriptional-posttranscriptional regulatory network of ACC-associated hub genes, which not only identified potential drug molecules and therapeutic targets for ACC treatment, but also visually demonstrated the central role of hub genes in regulatory hierarchies through network visualization analysis. Our findings provide a systematic perspective for deciphering the molecular mechanisms underlying disease pathogenesis, while offering theoretical support for developing clinical diagnostic and therapeutic strategies.\u003c/p\u003e\u003cp\u003eACC is a rare and highly aggressive endocrine malignancy with limited treatment options and poor prognosis\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Surgical intervention remains the most effective therapeutic approach, with the primary objective of achieving clear surgical margins (R0 resection)\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. However, surgical treatment alone fails to effectively prevent ACC recurrence or distant metastasis. For patients with advanced metastatic disease or those unsuitable for surgery, adjuvant therapies including chemotherapy, radiotherapy, and molecular immunotherapy are typically employed\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Among existing pharmacological agents, Mitotane has served as the cornerstone of both adjuvant and metastatic treatment, and remains the only drug approved by the U.S. Food and Drug Administration (FDA) specifically for ACC\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, its clinical efficacy remains controversial due to significant toxicity, adverse effects, and drug resistance issues, with no studies conclusively demonstrating improved long-term survival rates\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Other adjuvant therapies such as chemoradiotherapy and immunotherapy demonstrate even more limited efficacy. Consequently, the development of safer and more effective adjuvant therapeutic agents represents a critical unmet clinical need in ACC management.\u003c/p\u003e\u003cp\u003eWhen multivesicular bodies containing multiple intraluminal vesicles fuse with the plasma membrane, their internal vesicles are released extracellularly to form bioactive exosomes\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. This distinctive biogenesis process enables exosomes to not only inherit the functional characteristics of their parent cells but also acquire unique biological advantages\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Recent studies have revealed the crucial role of exosomes in tumor pathogenesis. As nanometer-scale vesicles secreted by cells, exosomes can transport diverse bioactive cargo including cytokines, proteins, and nucleic acids, thereby regulating recipient cell functions through intercellular communication\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Our study demonstrates that bioactive molecules within exosomes can modulate DNA repair, immune response, and cellular proliferation processes in ACC. By directly activating the immune system, exosomes enhance immune cell recognition and cytotoxic activity against ACC cells. Notably, the CXCL12 and BIRC5 genes showed the strongest correlation with immune cell infiltration levels, providing novel molecular insights into the regulatory mechanisms of ACC immune microenvironment.\u003c/p\u003e\u003cp\u003eThrough differential gene expression analysis, we identified 6 ERDEGs associated with ACC, all demonstrating excellent diagnostic performance (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.9). The combined diagnostic AUC reached 1.00. These 6 hub genes participate in ACC pathogenesis through the following key pathways, with their respective regulatory networks and functions detailed below: BIRC5 (Apoptosis inhibitor): Cell cycle regulation (apoptosis inhibition via Caspase-9 blockade), PI3K/AKT/mTOR pathway (promoting cell survival/proliferation), Wnt/β-catenin pathway (synergizing with CTNNB1 mutations to drive tumor growth). High expression enables ACC cells to evade programmed death, showing significant correlation with poor ACC prognosis. CXCL12 (Chemokine): CXCR4/CXCL12 axis (immune cell recruitment shaping immunosuppressive microenvironment). Downregulation leads to impaired immune surveillance, facilitating ACC immune escape and distant metastasis. GPM6A (Neural glycoprotein): Ras/MAPK signaling (regulating cell differentiation), Integrin-ECM interactions (affecting cell adhesion/metastasis). Low expression disrupts cell-matrix interactions, potentially promoting aggressive ACC phenotypes. COBLL1 (Cytoskeletal regulatory protein): TGF-β/Smad signaling (regulating EMT), Rho GTPase pathway (affecting cell motility/metastasis). Downregulation may activate EMT processes, enhancing ACC cell migration capacity. OMD (Osteomodulin): NF-κB pathway (mediating inflammatory responses). Low expression in ACC may disrupt tissue microenvironment homeostasis, indirectly promoting tumor progression. S100A16 (Calcium-binding protein): p53-dependent apoptosis (genomic stability regulation), JAK/STAT signaling (affecting cell proliferation). Downregulation may cause DNA damage repair defects, accelerating ACC malignant transformation.\u003c/p\u003e\u003cp\u003eIt is particularly noteworthy that this study represents the first identification of the regulatory roles played by GPM6A, CXCL12, COBLL1, OMD, S100A16, and BIRC5 in ACC pathogenesis. Through these genes, we have further integrated and delineated a synergistic regulatory network formed by intersecting pathways: 1. Apoptosis resistance (BIRC5\u0026uarr;+ S100A16\u0026darr;), 2. Immune evasion (CXCL12\u0026darr;+ immune checkpoint activation), 3. Metastatic propensity (COBLL1\u0026darr;+ GPM6A\u0026darr;\u0026rarr;enhanced EMT). The synergistic interactions among these hub genes collectively exacerbate ACC progression and amplify the complexity of ACC treatment. Accordingly, in our drug enrichment analysis, we identified 86 potential therapeutic agents targeting ERDEG binding sites-marking the first such discovery, and highlighted the 30 most strongly correlated compounds. Most significantly, the 3 top candidate (ML7, Ciglitazone, and N-NITROSODIETHYLAMINE) demonstrated dual-targeting capability against two hub gene sites each. Molecular docking analyses confirmed their ability to form stable binding conformations with hub gene-related proteins. These findings provide crucial references and a solid foundation for selecting ACC therapeutic agents.\u003c/p\u003e\u003cp\u003eThere are limitations of the study. 1. Focus on Exosome-Related Genes: The analysis was restricted to exosome-associated genes and did not account for genetic alterations originating from other cellular sources. 2. Limited Sample Size: The cohort size was relatively small, necessitating validation in larger populations to confirm the robustness of the findings.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eExosomes regulate critical pathological processes in ACC, including DNA repair, immune response, and cellular proliferation, through the delivery of bioactive molecules. This study identified GPM6A, CXCL12, COBLL1, OMD, S100A16, and BIRC5 as being closely associated with ACC pathogenesis, all demonstrating excellent diagnostic performance (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.9), with a combined diagnostic AUC reaching 1.0. These 6 hub genes likely contribute to ACC initiation and progression through multiple pathways, including the PI3K/AKT/mTOR pathway, CXCR4/CXCL12 axis, and Ras/MAPK signaling. Furthermore, they form a synergistic regulatory network through intersecting pathways that exacerbates disease progression and increases therapeutic challenges. Notably, ML7, Ciglitazone, and N-NITROSODIETHYLAMINE were found to simultaneously target two hub gene sites each and form stable docking conformations with hub gene-related proteins. These discoveries provide: (1) a systematic perspective for deciphering the molecular mechanisms underlying ACC development, (2) new insights into the tumor immune microenvironment, and (3) crucial theoretical support for developing clinical diagnostic and therapeutic strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was funded by Young and middle-aged doctors training project of excellent talent for osteoporosis and bone mineral disease (G-X-2019-1107); Scientific Research Project of Jiangsu Provincial Health Committee (M2022119).\u003c/p\u003e\u003cp\u003eAvailability of data and materials\u003c/p\u003e\u003cp\u003eThe data achieved and analyzed in the current study are available in the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the GeneCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Further reasonable inquiries can be directed to the corresponding author.\u003c/p\u003e\u003cp\u003eDeclarations\u003c/p\u003e\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003cp\u003eConsent for publication\u003c/p\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003cp\u003eCompeting interests\u003c/p\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZPC, TFL, WMC, and GYZ contributed to the study design and conceptual framework. ZPC, TFL conducted data acquisition, performed statistical analysis, and interpreted the results. Technical support was provided by ZPC, GYZ. Manuscript composition and critical revision were completed by ZPC, TFL, WMC, and GYZ. All authors have reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data achieved and analyzed in the current study are available in the GEO database (https://www.ncbi.nlm.nih.gov/geo/) and the GeneCards database (https://www.genecards.org/). Further reasonable inquiries can be directed to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eXu, F. et al. Bioinformatic screening and identification of downregulated hub genes in adrenocortical carcinoma. \u003cem\u003eExp. Ther. Med.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (3), 2730\u0026ndash;2742 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDaga, G., Sharma, S. \u0026amp; Mittal, V. 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Glioma-Stem-Cell-Derived Exosomes Remodeled Glioma-Associated Macrophage via NEAT1/miR-125a/STAT3 Pathway.Cancers,2024,\u003cb\u003e16\u003c/b\u003e(14):2500\u0026ndash;2500 .\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"adrenocortical carcinoma, exosome, immune infiltration, machine learning, gene regulatory networks","lastPublishedDoi":"10.21203/rs.3.rs-7597909/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7597909/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis study aimed to elucidate the molecular mechanisms underlying adrenocortical carcinoma (ACC) progression from an exosomal perspective and evaluate the clinical potential of exosomes as diagnostic biomarkers and therapeutic targets.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe integrated the GeneCards and GEO databases to identify a combined gene set comprising exosome-related genes and differentially expressed genes (DEGs). Subsequent analyses included Gene Ontology (GO) functional annotation, KEGG pathway enrichment, and gene set enrichment analysis (GSEA). Core gene sets were derived through cross-validation using LASSO regression, SVM-RFE algorithm, and random forest analysis, with dimensionality reduction applied to minimize redundancy. ACC diagnosis and prediction were performed based on differential expression levels and ROC curve analysis, while immune cell infiltration characteristics were assessed via the ssGSEA algorithm. Drug enrichment analysis and molecular docking simulations were conducted to screen the most promising candidate drugs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eMultidimensional analysis and dataset dimensionality reduction identified six hub genes (GPM6A, CXCL12, COBLL1, OMD, S100A16, and BIRC5). Comparative analysis between control and tumor groups revealed significant alterations in immune cell subpopulations. Drug enrichment and molecular docking targeting these six hub genes prioritized three optimal candidate drugs: ML-7, ciglitazone, and N-NITROSODIETHYLAMINE.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe six exosome-related genes (GPM6A, CXCL12, COBLL1, OMD, S100A16, and BIRC5) may serve as potential ACC biomarkers, while ML-7, ciglitazone, and N-NITROSODIETHYLAMINE represent promising therapeutic candidates for ACC.\u003c/p\u003e","manuscriptTitle":"Multi-Omics Analysis Reveals the Mechanism of Exosome-Related Genes in Adrenocortical Carcinogenesis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-21 18:41:13","doi":"10.21203/rs.3.rs-7597909/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3ec9ab98-c7c1-4742-9b57-fff178598821","owner":[],"postedDate":"October 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56589413,"name":"Health sciences/Biomarkers"},{"id":56589414,"name":"Biological sciences/Cancer"},{"id":56589415,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":56589416,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-11-03T12:38:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-21 18:41:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7597909","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7597909","identity":"rs-7597909","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}