The widespread use of plastic has led to exposure to chemicals like bisphenols (BPs), particularly bisphenol A (BPA), which are crucial in the production of polycarbonate plastics and epoxy resins. Recent studies have raised concerns about the effect of bisphenols as endocrine disruptors as the risk factor for hormone-dependent gynecological diseases. Taking into attention the mechanism of BPs' action, particularly their ability to bind to estrogen receptors and subsequently activate further estrogen pathways, it seems essential to understand their impact on different diseases. Moreover, it is not only the estrogen pathway, which is activated, but also signaling pathways, contributing to cell proliferation and potentially leading to carcinogenesis, particularly in tissues with hormonal dependencies. This review examines the mechanisms by which BPs interact with hormonal pathways and highlights their potential role in various hormone-related conditions, including benign gynecological diseases: endometriosis and polyendocrine metabolic ovarian syndrome (PMOS)/polycystic ovary syndrome (PCOS) and female gynecological cancers concerning breast, uterus and ovaries. The review also highlights the need to re-evaluate pre-analytical, analytical, and post-analytical standard operating procedures to reduce confounding factors in measuring BPs in clinical samples and to better model their potential roles as risk factors in disease etiology.
Klaudia Żak, M. Bobiński, G. Moreno-Bueno et al.· Chemosphere· 0 citations
Uterine Corpus Endometrial Carcinoma (UCEC) is the most common gynecologic malignancy, with molecular heterogeneity influencing prognosis and treatment response. Although TCGA-defined molecular subtypes and multi-omics datasets have improved biological understanding of UCEC, externally evaluated computational frameworks for molecular stratification remain limited. To address this, we developed EMMA-STRAT, a supervised multi-omics machine learning framework integrating mRNA expression, miRNA expression, and DNA methylation data to classify UCEC genomic subtypes and microsatellite instability (MSI) status. Using the TCGA cohort (N = 433) for model development and internal validation, we benchmarked six classifiers and evaluated final model performance on two independent Clinical Proteomic Tumor Analysis Consortium (CPTAC) cohorts (N = 95 and N = 108). Multi-omics integration consistently outperformed single-omics models, with RNA expression as the strongest standalone modality. For MSI-H versus MSS classification, a LightGBM model trained on 20 SVM-selected features per omics layer achieved an internal balanced accuracy of 98.1% and external balanced accuracies of 93.1–94.9%. For four-class genomic subtyping, a Multi-Layer Perceptron trained on 50 LASSO-selected features per omics layer achieved an internal balanced accuracy of 89.1% and external balanced accuracies of 84.7–86.2%. Both models showed favorable discrimination and probability calibration relative to reference baselines, although calibration estimates for low-prevalence classes including POLE should be interpreted cautiously. SHapley Additive exPlanations (SHAP)-based interpretability analysis identified model-selected features including MLH1, CDKN2A, PPP4R4, and hsa-miR-378a, with downstream analyses supporting their biological plausibility. All results are openly accessible via an interactive browser at https://naisarg14.github.io/EMMA-STRAT-web-viewer/index.html. EMMA-STRAT provides an externally evaluated, research-grade computational framework for multi-omics molecular stratification of endometrial carcinoma. Integration of mRNA, miRNA, and DNA methylation data supported prediction of MSI-H versus MSS status and TCGA-defined genomic subtypes across independent cohorts. However, since EMMA-STRAT requires multi-omics data and was not directly compared with established clinical classifiers, it should currently be interpreted as a research-oriented molecular stratification framework rather than a clinically deployable decision-making model. The developed framework provides a basis for future prospective validation, incorporation of clinicopathological variables, and direct comparison with ProMisE-based or integrated clinical risk models.
Naisarg Patel, A. Salumets, V. Modhukur· BioData Mining· 1 citation