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Multi-omics data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were integrated, and Weighted Gene Co-expression Network Analysis (WGCNA), a toxicology database, and lactylation-related gene sets were combined for intersection screening. Machine learning algorithms, including LASSO, SVM, and random forest, were then applied to identify key genes. Four prioritized BPA\u2013lactylation-associated candidate genes\u2014ENO1, WBP11, GTF2F1, and SPR\u2014were ultimately identified and showed consistent associations with metabolic, immune, and transcription-related features. Multi-level validation, including immune infiltration analysis, single-cell transcriptome localization, proteomic validation, and molecular docking and kinetic simulation, supported the structural plausibility of BPA\u2013protein interactions at the molecular level. This study proposes a toxicogenomics-informed, hypothesis-generating framework that prioritizes candidate genes and pathways potentially linking BPA-related signatures with lactylation-associated processes in bladder cancer.<\/jats:p>","DOI":"10.1371\/journal.pone.0347134","type":"journal-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T17:25:15Z","timestamp":1778001915000},"page":"e0347134","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrating multi-omics and machine learning to decipher the molecular pathways of bisphenol a-associated lactylation-related genes driving bladder 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