ArticleCurrent issues in molecular biology2026
Network Toxicology and Machine Learning Uncover BPA-Driven Molecular Mechanisms in Atopic Dermatitis.
Article in Current issues in molecular biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Bisphenol A (BPA) is a common industrial chemical primarily used in the manufacture of plastics, and it has been found in more than 90% of people worldwide. As an endocrine disruptor, BPA can impair reproduction, development, immunity, metabolism, and cognition; it also disturbs immune balance and thus fosters chronic inflammation. A number of population-based studies have indicated a link between environmental BPA exposure and atopic dermatitis (AD). Nevertheless, the detailed molecular pathways connecting BPA to AD remain poorly understood. AD is the leading chronic recurrent inflammatory skin disorder, characterized by severe itching and repeated eczema-like lesions. Its prevalence is roughly 13% among children and 5% among adults, and its global incidence continues to rise, imposing heavy health and economic burdens on societies. To clarify whether and how BPA may promote or worsen AD, we carried out a comprehensive computational study that integrated network toxicology, transcriptomic data, machine learning, molecular docking, and molecular dynamics simulations. From the CTD, ChEMBL, and SwissTargetPrediction databases, we collected 5701 potential BPA targets; from GeneCards and OMIM, we obtained 3270 genes linked to AD. The overlap between these two gene sets gave a group of common candidate genes. Enrichment analyses using GO and KEGG showed that these common genes were significantly overrepresented in the PI3K-Akt signaling pathway, Th17 cell differentiation, and the JAK-STAT signaling pathway-all central to immune and inflammatory regulation. We then built a protein-protein interaction (PPI) network by submitting the common genes to the STRING database and employed Cytoscape to extract hub genes from that network. By integrating human AD transcriptomic profiles with the hub genes and applying two machine learning techniques (LASSO and SVM), we identified six core toxic targets of BPA in AD:
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