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Peripheral blood mononuclear cell- transcriptome signatures of atopic dermatitis and prediction for the efficacy of dupilumab

杜皮鲁玛 转录组 外周血单个核细胞 特应性皮炎 免疫学 医学 免疫系统 湿疹面积及严重程度指数 基因表达 基因 生物 内科学 遗传学 体外
作者
Yu Wang,Yuemeng Wu,Chaoying Gu,Shangshang Wang,Huibin Yin,Ronghui Zhu,Ce Wang,Zheng Li,Xu Yao,Wei Li
出处
期刊:Journal of Dermatological Science [Elsevier BV]
卷期号:111 (3): 83-92 被引量:4
标识
DOI:10.1016/j.jdermsci.2023.06.002
摘要

Few studies have explored transcriptome of the peripheral blood mononuclear cells (PBMCs) of atopic dermatitis (AD). Parameters for prediction of the efficacy of dupilumab in AD remain obscure.To explore transcriptome signature of the PBMCs from Chinese AD patients and the usage in predication for the efficacy of dupilumab.A total of 56 moderate-to-severe adult AD patients were enrolled and followed up for 16 week-dupilumab treatment. PBMCs samples were collected at baseline and 16 weeks after dupilumab treatment. Thirty-five patients were subjected to RNA-sequencing. Weighted gene co-expression network analysis (WGCNA) was used to find genes for prediction of dupilumab efficacy, which was validated in the rest 21 AD patients. Another 30 healthy individuals were enrolled and subjected to RNA-sequencing as healthy controls.Upregulation of the T helper (Th) 2/Th22 pathway, Th17 antimicrobial genes, and natural T-regulatory cell abundance in the PBMCs of AD cases was observed, whereas TGF-β signaling and NK-cell signaling were decreased. Dupilumab treatment reversed the increase in the expression of Th2 cytokine receptors. WGCNA identified two immune-related modules that were correlated significantly with the efficacy of dupilumab. Hub gene MAP2K3 and UBE2L3 of these two modules demonstrated potential predictive ability for efficacy in the RNA-sequencing group by Spearman correlation, ROC analysis, and regression analysis, which was further validated in additional 21 AD cases.We firstly revealed the molecular phenotype of PBMCs in Chinese patients with AD, and uncovered two molecules that might be useful for prediction of the efficacy of dupilumab.
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