Deciphering the molecular networks of 3-methylcholanthrene-induced clear cell renal cell carcinoma through multi-omics integration

Abstract

Abstract Exposure to 3-Methylcholanthrene (3-MC) may be associated with the development and progression of clear cell renal cell carcinoma (ccRCC); however, its underlying molecular mechanisms remain unclear. We conducted differential expression analyses across multiple datasets to identify target genes linked to ccRCC. By integrating SHapley Additive exPlanations (SHAP) with machine learning algorithms, network toxicology and molecular docking, we investigated the binding interactions between 3-MC and these target proteins. A total of 99 genes were identified as potential targets involved in 3-MC-induced ccRCC pathogenesis. Four core genes (GPC3, PIK3C2G, PPARA, and TRPA1) were selected through machine learning approaches. SHAP analysis demonstrated the combined contribution of these genes to the model’s predictive performance. GPC3, PIK3C2G, and PPARA were significantly downregulated, whereas TRPA1 was upregulated ( P  < 0.05). Survival analysis using data from The Cancer Genome Atlas (TCGA) revealed significant differences in patient survival based on the expression levels of these genes. Molecular dynamics simulations validated the structural stability of the interaction between 3-MC and PPARA. These findings suggest that 3-MC promotes ccRCC pathogenesis by targeting specific genes.