Identification and validation of susceptibility modules and hub genes of adrenocortical carcinoma through WGCNA and machine learning.

PURPOSE

Adrenocortical carcinoma (ACC) is a rare and aggressive endocrine malignancy characterized by rapid progression, significantly impacting patients' quality of life. Analyzing gene co-expression modules offers valuable insights into the molecular mechanisms driving ACC progression. In this study, we applied Weighted Gene Co-Expression Network Analysis (WGCNA) to identify gene co-expression modules associated with ACC progression.

METHODS

Before conducting WGCNA, differential gene expression and immune infiltration analyses were performed on the GSE90713 dataset (available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi ). Dynamic tree cutting was utilized to identify co-expression modules, which were subsequently analyzed to determine their correlations and associations with traits. A total of 21 co-expression modules were identified, with the yellow module demonstrating a strong correlation with the progression of ACC. Enrichment analysis was carried out on differentially expressed genes, the yellow module, cross-module interactions, and the final hub genes to identify the associated Biological Processes (BPs) and pathways relevant to ACC. Additionally, the CIBERSORT algorithm was employed to predict immune cell infiltration in ACC.

RESULTS

The enrichment analysis revealed that pathways associated with cell division, protein synthesis, and metabolism play significant roles in the progression of ACC. Additionally, CDK1, AURKA, CCNB2, BIRC5, CCNB1, TYMS, and TOP2A were identified as key regulatory hub genes. Survival analysis further demonstrated that elevated expression levels of these genes in ACC tissues are significantly correlated with lower overall survival rates in patients, underscoring their critical involvement in ACC development and progression.

CONCLUSION

This study sheds light on the mechanisms underlying ACC progression and highlights potential therapeutic targets. By identifying specific immune cell subtypes associated with ACC, the findings may aid in developing immune modulation therapies aimed at preventing or treating ACC.