Integrated Transcriptomic Analysis Unveils Hub Genes and Immune Dysregulation in Chronic Spontaneous Urticaria Pathogenesis

整合转录组分析揭示慢性自发性荨麻疹发病机制中的关键基因和免疫失调

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Abstract

BACKGROUND: Chronic spontaneous urticaria (CSU) is a debilitating inflammatory skin disorder with a significant impact on quality of life. Current treatment algorithms are ineffective for a substantial portion of patients, highlighting an urgent need to elucidate its underlying pathological mechanisms for the development of novel therapies. METHODS: Microarray datasets (GSE72540 and GSE57178) from CSU lesional skin (LS), non-lesional skin (NLS), and healthy controls (HC) were analyzed using weighted gene co-expression network analysis (WGCNA) and differential expression to identify characteristic genes. Functional enrichment, transcription factor networks, protein-protein interactions (PPI), and immune cell deconvolution via CIBERSORT were performed. Hub genes were correlated with immune infiltrates, and expression was validated in IgE-stimulated RBL-2H3 basophil cells via RT-qPCR. RESULTS: WGCNA identified six modules (3,114 genes) correlated with LS status. Differential analysis revealed 101 characteristic DEGs enriched in inflammatory, cytokine, and metabolic pathways. Fifteen transcription factors, including STAT1 and JUN, regulated these DEGs. PPI networks identified 10 hub genes (CXCL2, MYC, THBS1, EGR1, SOCS3, CXCL1, ICAM1, IL6, PTGS2, CCL2). LS showed increased activated mast cells, eosinophils, neutrophils, and monocytes, with depleted regulatory T cells and plasma cells. Hub genes positively correlated with activated mast cells and eosinophils. Validation confirmed biphasic hub gene expression: early downregulation (2 hours) and late upregulation (8 hours) post-stimulation. CONCLUSION: This study reveals a multifaceted inflammatory and metabolic landscape in CSU, identifying hub genes as candidates warranting further investigation for the development of targeted therapeutic strategies. These findings require validation in larger, clinically characterized cohorts.

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