Decoding the Most Mutated Genes in Cancer: a Pan-cancer in Silico Analysis of DLBC, CHOL, UCS

Background: Understanding the mutational landscape across different cancer types is crucial for identifying shared and cancer-specific oncogenic drivers. In this study, we perform a pan-cancer analysis of three underexplored malignancies, Diffuse Large B-cell Lymphoma (DLBC), Cholangiocarcinoma (CHOL), and Uterine Carcinosarcoma (UCS), using publicly available data from The Cancer Genome Atlas (TCGA). By analyzing somatic mutation profiles, we identify highly recurrent mutations in TP53, KRAS, and PIK3CA, with TP53 being the most frequently mutated gene across all three cancers. Additionally, we uncover unique mutational signatures, including frequent BCL2 mutations in DLBC and IDH1 mutations in CHOL, reflecting their distinct tumorigenic pathways. Using Fisher’s exact test, we assess the statistical significance of these findings, highlighting key genetic alterations with potential clinical relevance. Our results provide a comparative perspective on mutational drivers, emphasizing shared oncogenic mechanisms and cancer-specific vulnerabilities, which could inform precision oncology strategies. Methods: In this study, we conducted a pan-cancer in silico analysis using whole-exome sequencing (WES) data from The Cancer Genome Atlas (TCGA). We identified frequently mutated genes, analyzed co-occurrence and mutual exclusivity patterns, and performed pathway enrichment analyses to uncover key oncogenic mechanisms. Statistical significance was determined for mutation prevalence, and functional implications were assessed through pathway-based annotation. Results: Our analysis revealed that TP53, KMT2D, and ARID1A are among the most recurrently mutated genes across DLBC, CHOL, and UCS, suggesting their central role in tumorigenesis. KMT2D and ARID1A mutations co-occurred significantly in CHOL and DLBC, implicating chromatin remodeling in these cancers. Conversely, mutations in TP53 and PIK3CA in UCS were mutually exclusive, indicating distinct oncogenic pathways. Pathway analysis highlighted disruptions in TP53 signaling, PI3K-AKT activation, and chromatin modification, reinforcing their role in oncogenesis. Comparative analysis distinguished shared mutations with pan-cancer relevance from cancer-specific alterations such as BAP1 and IDH1 mutations in CHOL, which present unique therapeutic vulnerabilities. Conclusion: This study provides a comprehensive genomic comparison of DLBC, CHOL, and UCS, highlighting both common and cancer-specific mutational drivers. By identifying shared oncogenic pathways and distinct mutational signatures, our findings contribute to precision oncology efforts and suggest potential therapeutic targets. This pan-cancer framework advances our understanding of genetic vulnerabilities in diverse malignancies and provides a foundation for mutation-guided treatment strategies.