The contribution of phenolic endocrine-disrupting chemicals to breast cancer risk: A comprehensive bioinformatics analysis.

Bisphenol A (BPA), nonylphenol (NP), and octylphenol (OP) are common environmental phenolic endocrine disruptors and widely used industrial chemicals that have garnered significant attention due to their potential to disrupt endocrine functions. These compounds are known to interfere with hormonal activities, particularly those related to estrogen, and are linked to the onset and progression of breast cancer. This study aims to systematically investigate the potential relationship between BPA, NP, and OP and breast cancer risk, along with their underlying molecular mechanisms, by synthesizing data from multiple databases. We initially acquired the chemical structures and SMILES representations of BPA, NP, and OP from the PubChem database. Subsequently, we utilized multiple databases, including the Comparative Toxicogenomics Database (CTD), SEA, and Swiss Target Prediction, t0 estimate their probable biological targets. The predicted targets were standardized and consolidated to form a comprehensive target database. Breast cancer-related targets were subsequently identified from the GeneCards and DisGeNET databases, and their overlap with the targets of BPA, NP, and OP was analyzed to pinpoint potential breast cancer risk targets. To elucidate the functional pathways involved, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses using the DAVID database. This analysis offered insights into the molecular pathways influenced by BPA, NP, and OP in the context of breast cancer. Additionally, we utilized machine learning algorithms, specifically Least Absolute Shrinkage and Selection Operator (LASSO) regression and Support Vector Machine (SVM), to identify nuclear targets linked to BPA, NP, and OP-induced breast cancer. These nuclear targets were further validated through differential expression analysis and Receiver Operating Characteristic (ROC) curve analysis using the GEO dataset GSE42568. We also performed a Single Gene Gene Set Enrichment Analysis (GSEA) to investigate the potential regulatory mechanisms of these nuclear genes in breast cancer. The infiltration of immune cells in breast cancer tissues was analyzed using single-sample gene set enrichment analysis (ssGSEA), and the correlation between nuclear targets and immune cell infiltration was examined. Finally, molecular docking and molecular dynamics simulations were conducted to assess the binding affinity and stability of BPA, NP, and OP with their nuclear targets. In this study, we integrated network toxicology, machine learning and multi-omics validation, and identified for the first time that BPA, NP and OP may induce breast cancer through 156 common targets; among them, MAOA, MGLL, ADRA2A, RPN2, GF1R and CTSD were identified as the key causative genes, with a diagnostic efficacy of 0.80–0.94 AUC. Mechanistically, these genes are concentrated in the GPCR/MAPK/JNK, sphingolipid, and prolactin signaling pathways, which regulate the Wnt/TGF-β/chemokine network and dramatically modify the immunological infiltration of nine classes of M0-M2 macrophages and CD4⁺ T cells. Molecular docking and kinetic simulations suggested the strong affinity of BPA for MGLL, and the complex was stabilized with ≥ 3 hydrogen bonds. In conclusion, phenolic endocrine disruptors may cause breast cancer through the “multi-target-immune microenvironment-metabolic reprogramming” axis, and MAOA, MGLL, ADRA2A, and RPN2 may serve as new targets for early detection and management.