Linking bisphenol a exposure to pancreatic cancer: Evidence from population epidemiology, multi-omics screening, and cellular validation.

Metabolomics insights into the association between organochlorine pesticides, synthetic pyrethroids exposure and thyroid cancer risk.

BACKGROUND The association between organochlorine pesticides (OCPs)/synthetic pyrethroids (SPs) and thyroid cancer (TC) remains poorly understood, with metabolic mechanisms unexplored. METHODS We conducted a 1:1 age- and sex-matched case-control study (n = 668). Serum levels of 27 target analytes (19 OCPs and 8 SPs) were quantified; subsequent analyses were restricted to 13 compounds (10 OCPs and 3 SPs) with detection frequencies ≥85%. Eight machine learning (ML) algorithms with Shapley Additive Explanations (SHAP) were used to identify key pollutants in the 334 case-control pairs. Untargeted metabolomics was performed in a subset of 50 age- and sex-matched case-control pairs. Mixture effects were assessed by Bayesian kernel machine regression (BKMR) and weighted quantile sum (WQS) regression. Furthermore, the Latent Unknown Clustering Integrating Multi-Omics Data (LUCID) model was employed to integrate exposure and metabolic data, enabling the identification of TC patient subgroups and the exploration of underlying metabolic mechanisms. RESULTS Participants (mean age 45.2 years, 82.3% female) had serum OCPs at 0.007-0.333 ng/mL and SPs at 0.046-0.095 ng/mL. ML algorithms identified fenpropathrin, β-BHC, cyhalothrin, α-BHC, and p,p'-DDD as the top five contributors to TC. Elevated OCPs/SPs exposure was significantly associated with increased TC risk (WQS: adjusted OR = 1.45, 95%CI = 1.34-2.24, P = 0.019; LUCID: OR = 9.33). Fenpropathrin was the primary contributor (BKMR posterior inclusion probability = 1.00; WQS weight = 67.6%). A total of 45 significant differential metabolites (DMs) were identified (VIP ≥1, P < 0.05, and qualitative level 1). LUCID revealed a distinct TC cluster characterized by upregulated S-sulfo-L-cysteine/adenosine and downregulated 2-hydroxycaprylic acid. CONCLUSION OCPs/SPs mixtures, driven by fenpropathrin, disrupt amino acid/nucleotide metabolism while suppressing organic acid metabolism, representing a potential TC-associated metabolic signature.

BDE47 exposure in lung adenocarcinoma: prognostic biomarkers, immune dysregulation, and mechanistic insights from toxicogenomic and docking studies

A network toxicology and machine learning approach to uncover the molecular machinery of bisphenol A-induced colorectal cancer

The carcinogenic relevance of environmental contaminant bisphenol A (BPA) to colorectal cancer (CRC) has gained growing attention, yet the molecular networks potentially linking BPA exposure to CRC remain incompletely characterized. This study integrated network toxicology and machine learning to predict candidate molecular targets and putative signaling networks associated with BPA-correlated colorectal carcinogenesis. The study’s methodology involved an initial differential expression screening across several CRC transcriptomic datasets to establish a disease-specific gene signature. Subsequently, a multi-tiered computational strategy was employed: network toxicology was used to map potential BPA-protein interactions, machine learning algorithms were applied to distill a minimal set of high-impact targets, and molecular docking simulations provided atomic-level validation of the proposed binding events. We identified 53 overlapping genes between predicted BPA-interacting proteins and CRC-related transcripts. Machine learning screening further filtered a 12-gene panel with favorable predictive performance for CRC status, including MET, SORD, DPEP1, KIT, RIPK2, SET, HSP90AB1, DBF4, MMP1, MMP12, ANPEP, and GLA. Molecular docking simulations predicted stable binding interactions between BPA and the protein products encoded by these 12 genes. This study delineates a specific gene network potentially targeted by BPA to promote CRC pathogenesis. The machine learning-derived 12-gene signature, interpreted as CRC-associated genes overlapping with predicted BPA targets and supported by in silico molecular docking, offers valuable insights into the molecular basis of BPA-associated colorectal carcinogenesis and presents candidate targets for subsequent experimental validation.

Bisphenol A promotes colorectal cancer malignancy through CXCL8 upregulation associated with p38 MAPK activation: A potential link to type 2 diabetes mellitus comorbidity.

Integrative Multi-Omics Analysis Identifies Key Molecular Targets Linking Bisphenol A Exposure to Gestational Diabetes Mellitus.

Bisphenol A and ovarian cancer: emerging evidence and potential biological mechanisms

This review summarizes current evidence concerning the relationship between bisphenol A (BPA) exposure and ovarian cancer (OC). Human epidemiological studies have mainly examined possible associations between BPA concentrations and OC occurrence, but their cross-sectional or case–control designs do not establish that BPA exposure increases OC risk. Experimental studies in OC cell lines and tumor-bearing animals suggest that BPA may alter tumor-related phenotypes, including proliferation, migration, invasion, angiogenesis, and stemness. These findings concern potential tumor progression rather than patient prognosis. Separate studies in normal ovarian tissue and granulosa cells indicate possible ovarian toxicity, including oxidative stress, apoptosis, and steroidogenic disturbances, but these findings should not be interpreted as direct evidence of OC risk. Mechanistically, BPA exposure has been reported to be associated with changes in endocrine signaling, oxidative processes, metabolism, and epigenetic regulation in selected experimental models. Overall, evidence linking BPA exposure to ovarian-cancer-related outcomes remains limited, and current findings do not establish that BPA causes OC or worsens patient prognosis in humans.