Exploring the toxicological impact of bisphenol a exposure on psoriasis through network toxicology, machine learning, and multi-dimensional bioinformatics analysis

Abstract

Psoriasis is a common immune - mediated skin disease, the pathogenesis of which is not completely elucidated. Environmental factors are key to its onset and progression. Bisphenol A (BPA) is a ubiquitous environmental pollutant that endangers human health. Previous research shows that BPA exposure disrupts immunity and causes skin inflammation and autoimmune diseases. However, the role and molecular mechanisms of BPA in psoriasis are unclear. In this study, we used network toxicology, machine learning, and bioinformatics to study BPA - induced psoriasis mechanisms. Public database analyses identified 100 potential targets, with significant enrichment in the PI3K - AKT and Chemokine signaling pathways. Machine learning identified five core targets: PTAFR, MMP9, CXCR2, IDO1, and LCK. These genes are highly expressed in psoriatic lesion tissues than controls and associated to immune cell infiltration. Molecular docking and dynamics simulations confirmed stable interactions between BPA and these targets, which supports their role in disease progression. We also developed a novel Adverse Outcome Pathway (AOP) framework for BPA-induced psoriasis, providing key toxicological insights into the risks of exposure. These findings highlight the impact of BPA on immune regulation, offering a foundation for understanding associated health risks and formulating mitigation strategies. Our study provides an in-depth exploration of the molecular mechanisms underlying BPA-induced psoriasis. The findings underscore the practical application of integrating network toxicology, machine learning, multidimensional bioinformatics approaches, and AOP frameworks in assessing environmental pollutant risks. Furthermore, it lays the foundation for understanding BPA-related health risks and developing strategies to mitigate its impact on psorasis.

Keywords: Bisphenol A; Machine learning algorithm; Molecular docking; Molecular dynamics simulation; Network toxicology; Psoriasis.