Integrating machine learning and molecular docking to elucidate the mechanism of atrial fibrillation induced by di(2-ethylhexyl) phthalate.

Environmental exposure is closely associated with the development of cardiovascular diseases. This study aims to explore the molecular mechanism by which Di (2-ethylhexyl) phthalate (DEHP) induces atrial fibrillation (AF). AF-related target genes were identified through differential expression analysis of multiple datasets. Machine learning algorithms, Weighted Gene Co-expression Network Analysis (WGCNA), Machine learning (ML) and molecular docking technology were integrated to investigate the binding interaction between DEHP and target proteins. A total of 8 potential key targets (ITGB2, ARPC1B, RYR2, FPR2, MPEG1, PRKCD, LCP1, RAC2) involved in DEHP-induced AF were identified. ML analysis confirmed these genes as core regulatory genes, among which ITGB2, ARPC1B, and RYR2 exhibited high diagnostic potential (Area Under the Receiver Operating Characteristic Curve, AUC ≥ 0.85). Molecular docking simulations showed stable binding specificity between DEHP and these core targets, with binding energies all below -3 kcal/mol. DEHP may promote AF pathogenesis by targeting specific genes and signaling pathways. DEHP has high binding affinity with ITGB2, ARPC1B, and RYR2, which may serve as targets for future interventions. These findings provide important insights into the in-depth exploration of the mechanism underlying DEHP-induced AF.