Integrated bioinformatics and Toxicogenomics reveal bisphenol A-driven molecular networks and prognostic biomarkers in endometrial cancer

Abstract Bisphenol A (BPA) is a widely used industrial chemical known to exert endocrine-disrupting effects. Recent evidence suggests a potential link between BPA exposure and endometrial cancer. This study aims to investigate the molecular and prognostic implications of BPA-associated gene expression in endometrial cancer through bioinformatics approaches. We performed a comprehensive analysis of differentially expressed genes (DEGs) using the TCGA-UCEC dataset and identified BPA-related targets utilizing the Comparative Toxicogenomics Database (CTD) and SwissTargetPrediction database. Protein–protein interaction (PPI) network and enrichment analyses were conducted on these DEGs. We also developed a BPA-related prognostic risk model using COX and LASSO regression analyses, and evaluated the model’s clinical relevance and immune infiltration impact. Molecular docking analysis was performed to assess binding affinities between BPA and hub proteins. We identified 40 differentially expressed BPA-related toxic targets in endometrial cancer. Enrichment analyses revealed significant roles in tissue development, hormone activity, and cellular signaling pathways. Five prognostic genes (PGR, HTR2B, HTR6, NCAPG, SIX1) were used to construct a risk model demonstrating significant stratification of patient survival outcomes. High-risk scores correlated with advanced histologic grade, older age, and reduced immune infiltration. Molecular docking analysis confirmed strong interactions between BPA and several hub proteins. BPA-related genes show significant differential expression and prognostic value in endometrial cancer, influencing clinical outcomes and immune infiltration. These findings highlight the potential for BPA exposure to affect endometrial carcinogenesis and offer valuable insights for developing therapeutic interventions.