From Genetic Variants to Immune Dysregulation: Multi-Omics Mendelian Randomization Decodes Molecular Networks in Rosacea

Background: Rosacea is a chronic inflammatory skin disease with incompletely understood pathogenesis.Objective: This study aimed to identify key genes and regulatory mechanisms using multi-omics Mendelian randomization (MR) and machine learning, and to validate the findings in animal experiments.Methods: Bidirectional MR analysis using eQTL, pQTL, and mQTL datasets (GWAS ID: GCST90476178) identified rosacea-associated genes.LASSO regression and random forest were used to screen key genes.Immune infiltration, GSEA/GSVA, non-coding RNA network analysis, and molecular docking were used to explore gene functions and drug targets.Realtime quantitative polymerase chain reaction (RT-qPCR) and Western blot validated findings in a rosacea mouse model.Results: Five key genes (GSTM3, NEO1, UBE2M, CD300A, and EPHB4) were identified.These genes correlated with plasma cells and Tregs and were enriched in Wnt, transforming growth factor (TGF)-β, and interleukin (IL)-17 pathways.Molecular docking predicted the binding of CD300A to bisphenol A and GSTM3 to niacinamide.RT-qPCR confirmed gene expression changes.Western blot supported IL-17 pathway activation.Conclusion: This study reveals five key genes and immune-related mechanisms in rosacea, offering novel targets for diagnosis and therapy.