Unraveling the toxicological impact of Bisphenol A exposure on dermatomyositis: An integration of network toxicology and machine learning approaches

Lili Cheng, Zhongfu Tang, Ming Li and Chuanbing Huang

PLOS ONE, 2026, vol. 21, issue 3, 1-17

Abstract: Background: Dermatomyositis is a common immune-mediated skin disorder whose pathogenesis has not been fully elucidated. Environmental factors play a key role in its onset and progression. Bisphenol A (BPA) is a widespread environmental pollutant known to pose risks to human health. Previous studies have indicated that BPA exposure can disrupt immune function and trigger skin inflammation and autoimmune diseases. However, the role and molecular mechanisms of BPA in dermatomyositis remain unclear. This study aims to systematically elucidate whether and how bisphenol A (BPA) may contribute to the development of dermatomyositis by identifying key toxicological targets and underlying molecular mechanisms through an integrated computational framework. Methods: The toxicity and pharmacokinetic properties of BPA were predicted using the ProTox 3.0 and ADMElab 2.0 platforms. Network toxicology approaches were employed to explore the pathogenic pathways and mechanisms of BPA in dermatomyositis. Seven machine learning algorithms were applied for cross-validation and identification of core genes. Molecular docking and molecular dynamics (MD) simulations were conducted to evaluate the binding efficiency and stability between BPA and the identified targets. Results: Integrated results from both prediction platforms revealed that BPA exhibits significant neurotoxicity, nephrotoxicity, hepatotoxicity, skin sensitization, and immunotoxicity. Network toxicology analysis suggested that BPA may influence the progression of dermatomyositis by regulating key factors such as AKT1, BCL2, MMP9, ESR1, and INS, thereby affecting apoptosis, immune-inflammatory responses, pathways in cancer, and the PI3K-Akt signaling pathway. Using LASSO regression, SVM, random forest (RF), GBM, GLM, KNN, and NNET machine learning algorithms, four core genes were identified: SAA1, NACAD, SLC14A1, and MYBPH, all of which were highly expressed in dermatomyositis lesion tissues. Molecular docking studies demonstrated strong binding affinities between BPA and these targets, with the highest binding energy observed for SAA1 at –8.4 kcal/mol. Molecular dynamics simulations further confirmed the high binding stability of the BPA–SAA1 protein–ligand complex. Collectively, these findings suggest that BPA may increase the risk of dermatomyositis by modulating SAA1 protein. Conclusion: This study identifies SAA1 as a potential target in BPA-induced dermatomyositis, highlighting the impact of BPA on immune regulation and providing a foundation for understanding associated health risks and developing mitigation strategies. Given the limited research on dermatomyositis, further experimental validation is essential to elucidate the pathogenic mechanisms of BPA.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0344169

DOI: 10.1371/journal.pone.0344169

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