Integrated multi-omics, machine learning, network toxicology, and molecular docking reveal potential mechanisms underlying methyl 4-hydroxybenzoate-associated breast cancer.

Breast cancer (BC) represents a major public-health burden, and epidemiological evidence suggests a potential association with exposure to methyl 4-hydroxybenzoate (MEP), a widely-used cosmetic preservative and estrogen-mimicking endocrine-disrupting chemical. Nevertheless, the potential mechanisms underlying MEP-associated BC oncogenesis and progression remain poorly understood. BC-related targets were curated from CTD, GeneCards, and OMIM, whereas MEP-related targets were interrogated from ChEMBL, PharmMapper, and SEA using stringent filters. The intersecting targets informed subsequent protein-protein interaction network construction and molecular docking studies. Subsequently, consensus molecular subtypes of BC were derived by applying ten clustering algorithms to multi-omics data, which were subsequently employed in three machine learning algorithms to develop a consensus MEP-related signature (CMEPRS) for BC patients. Five core putative toxicological targets (HSP90AA1, CTNNB1, TP53, MYC, and EGFR) with critical regulatory roles in MEP-associated molecular alterations were identified. Based on these findings, we generated MEP-toxicity-related classifiers and the CMEPRS prognostic model, which may facilitate patient stratification and support personalized clinical management for BC patients. The high-CMEPRS patients displayed prominent infiltration of macrophages, myeloid-derived suppressor cells, and cancer-associated fibroblasts. Apart from lapatinib, the high-CMEPRS patients showed higher predicted sensitivity to most conventional chemotherapeutic drugs. This computational study provides preliminary insights into molecular alterations linked to MEP exposure and offers a feasible analytical framework for patient stratification and therapeutic-target exploration in breast cancer.