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. Real-time 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.