Potential Molecular Links Between Triclosan Exposure and Prostate Cancer: An Integrative Computational Analysis

Triclosan (TCS) is a widely used antimicrobial and endocrine-disrupting chemical whose potential relevance to prostate cancer (PCa) biology remains insufficiently understood. We integrated multi-omics and computational analyses to identify candidate targets that overlap between predicted TCS-related molecular targets and PCa-related transcriptomic alterations. Cross-database target mapping, differential expression, and network analyses were combined with machine learning, molecular docking, molecular dynamics simulations, Mendelian randomization, and single-cell transcriptomic profiling. Pathway analyses highlighted oxidative stress and metabolic dysregulation as key processes shared between predicted TCS-related targets and PCa-associated transcriptomic alterations. Machine learning prioritized five candidate genes—FERMT2, F5, PRKCA, ABCC4, and ACOX2—with discriminative value for PCa classification in the analyzed cohorts. Docking and molecular dynamics simulations supported plausible binding compatibility of TCS to ABCC4 and ACOX2. Mendelian randomization suggested that genetically predicted ABCC4 expression may be associated with PCa risk. Single-cell analysis further showed cell-type-specific expression of candidate genes in tumor-enriched epithelial populations. These findings provide an integrative framework for generating mechanistic hypotheses on how TCS-related molecular targets may be associated with PCa biology.