Obesity-associated gene mutations across cancer types: a pan-cancer analysis of TCGA data

BackgroundObesity is a recognized risk factor for numerous cancers. Although several biological mechanisms have been proposed to explain obesity-associated carcinogenesis, the extent to which excess adiposity influences tumor genomic profiles remains incompletely understood. In particular, whether obesity-related selective pressures shape cancer-specific mutational landscapes is still underexplored.MethodsA pan-cancer analysis of non-synonymous somatic mutations across 14 tumor types using data from The Cancer Genome Atlas (TCGA) has been conducted. Body mass index (BMI) at diagnosis was analyzed as a continuous variable. Associations between gene mutations and BMI were assessed using logistic regression models adjusted for age, sex, and tumor mutational burden, with false discovery rate correction. Genes were prioritized using a two-step ranking strategy based on mutation frequency and regression strength. Functional inactivation, exon-level mutation distribution, and Gene Ontology enrichment analyses were performed for significantly BMI-associated genes.ResultsIn particular, bladder urothelial cancer (BLCA) resulted as the most frequently mutated neoplasia in association with higher body mass index. Among Eighty-six genes significantly associated with BMI in BLCA, a prioritized set of ten genes (BRCA2, DNAH9, GRIA4, PLXNA4, UNC13C, FCGBP, SF3B1, ELP1, NES, TRERF1) has been selected for further analyses. Overweight and obese patients exhibited distinct BMI-specific exon-level mutational patterns and concurrent deleterious mutations across multiple candidate genes. Functional inactivation analysis suggested loss-of-function mechanisms in most top-ranked genes, while Gene Ontology (GO) analysis highlighted deregulation of extracellular matrix-related pathways.DiscussionThese findings support a role for obesity in shaping the genomic landscape of tumors, highlighting the importance of integrating clinical parameters such as BMI into genomic studies to determine the potential impact of obesity on tumor evolution, heterogeneity, and treatment response.