Elucidating the mechanisms of aristolochic acid-induced upper tract urothelial carcinoma: A multi-omics approach combining bioinformatics and computational modeling.

Aristolochic acids (AAs) are established human carcinogens strongly associated with upper tract urothelial carcinoma (UTUC). However, the multi-target oncogenic network beyond their genotoxic mechanism remains incompletely elucidated. This study employed an integrated computational approach combining network toxicology, machine learning, molecular docking, and molecular dynamics (MD) simulations to systematically explore the potential molecular mechanisms of AA-induced UTUC. We identified 97 shared potential targets of AAs and UTUC. Enrichment analyses revealed their significant involvement in lipid metabolism, xenobiotic detoxification, and cancer-related pathways such as PI3K-Akt signaling. Topological analysis of the protein-protein interaction network and a nested cross-validation machine learning model highlighted five core genes: CASP3, EGFR, PARP1, PTGS2, and HSP90AA1. Molecular docking predicted high binding affinities of AA with these core targets, particularly for PTGS2 (-9.3 kcal/mol) and EGFR (-8.2 kcal/mol). Subsequent 100-ns MD simulations and Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) calculations confirmed the structural stability and spontaneous binding (ΔG_bind = -55.68 kcal/mol) of the AA-EGFR complex. Our multi-omics analysis suggests that AAs may promote UTUC not only via canonical DNA adduct formation but also potentially through direct interactions with key signaling proteins, implicating a synergistic mechanism involving both genotoxic and non-genotoxic pathways. These findings provide a theoretical foundation for novel preventive and therapeutic strategies against AA-associated UTUC.