Annals of dermatology · 2026
From Genetic Variants to Immune Dysregulation: Multi-Omics Mendelian Randomization Decodes Molecular Networks in Rosacea.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Chen Meng, Tan Bowen, He Jia, Zhang Lei, Shuai Wenlong, Hu Xinru, Wu Tao, Duan Xi
Аннотация
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.
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.