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

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.