Exploring the Toxicological Impact of 6PPDQ Exposure on Psoriasis through Network Toxicology, Machine Learning, and Multidimensional Bioinformatics Analysis

N-(1,3-Dimethylbutyl)-N′-phenyl-p-phenylenediamine quinone (6PPDQ), a derivative of the tire antioxidant 6-PPD, has recently emerged as an environmental contaminant of concern. This study employed a network toxicology framework to explore its potential role in psoriasis. Through in silico target prediction and disease database integration, 290 overlapping 6PPDQ–psoriasis targets were identified. Gene Ontology and KEGG pathway analyses implicated MAPK and TNF signaling, along with the IL-23/Th17 inflammatory axis—key pathways in psoriasis pathogenesis. Machine learning (LASSO and SVM-RFE) prioritized four core genes (CD3D, IRF7, NR3C1, HDAC1). These genes displayed markedly altered expression in psoriatic transcriptomes (GSE13355) and demonstrated strong diagnostic performance (AUC > 0.99). Immune infiltration analysis (ssGSEA) and single-cell RNA-seq data linked these genes to pro-inflammatory T-cells, dendritic cells (DCs), and keratinocytes in psoriatic lesions. Molecular docking revealed high-affinity binding of 6PPDQ to all four proteins, with NR3C1 exhibiting the strongest interaction (−8.1 kcal/mol). Molecular dynamics simulations confirmed the stability of this complex. An adverse outcome pathway model was established, linking 6PPDQ exposure to immune dysregulation in psoriasis via these core genes. Collectively, the findings suggest that 6PPDQ may exacerbate psoriasis by disrupting key immune-inflammatory regulators—particularly NR3C1—thereby offering mechanistic insights into how environmental pollutants contribute to disease progression.