采用综合生物信息学分析鉴定特应性皮炎的新型枢纽基因和免疫浸润。
Identification of novel hub genes and immune infiltration in atopic dermatitis using integrated bioinformatics analysis.
机构信息
Department of Dermatology, First Medical Center of Chinese PLA General Hospital, Beijing, 100853, China.
Medical School of Chinese PLA, Beijing, 100853, China.
出版信息
Sci Rep. 2024 Oct 4;14(1):23054. doi: 10.1038/s41598-024-73244-8.
The aim of this study was to identify key genes and investigate the immunological mechanisms of atopic dermatitis (AD) at the molecular level via bioinformatics analysis. Gene expression profiles (GSE32924, GSE107361, GSE121212, and GSE230200) were obtained for screening common differentially expressed genes (co-DEGs) from the gene expression omnibus database. Functional enrichment analysis, protein-protein interaction network and module construction, and identification of common hub genes were performed. Hub genes were validated using receiver operating characteristic curve analysis based on GSE130588 and GSE16161. NetworkAnalyst was used to detect microRNAs (miRNAs) and transcription factors (TFs) associated with the hub genes. The immune cell infiltration was analyzed using the CIBERSORT algorithm to further analyze the correlation between hub genes and immune cells. A total of 146 co-DEGs were obtained, showing significant enrichment in cytokine-cytokine receptor interaction and JAK-STAT signaling pathway. Seven hub genes were identified by Cytoscape and validated with external datasets. Subsequent prediction of miRNAs and TFs targeting these hub genes revealed their regulatory roles. Analysis of immune cell infiltration and correlation revealed a significant positive correlation between CCL22 expression and the number of dendritic cells activated. The identified hub genes represent potential diagnostic and therapeutic targets in the immunological pathogenesis of AD.
本研究旨在通过生物信息学分析,从分子水平上鉴定特应性皮炎(AD)的关键基因,并探讨其免疫学机制。我们从基因表达综合数据库中获取了基因表达谱(GSE32924、GSE107361、GSE121212 和 GSE230200),以筛选常见差异表达基因(co-DEGs)。我们进行了功能富集分析、蛋白质-蛋白质相互作用网络和模块构建,以及共同枢纽基因的鉴定。利用 GSE130588 和 GSE16161 进行基于接收器操作特征曲线分析,对枢纽基因进行验证。使用 NetworkAnalyst 检测与枢纽基因相关的 microRNAs(miRNAs)和转录因子(TFs)。利用 CIBERSORT 算法分析免疫细胞浸润,进一步分析枢纽基因与免疫细胞之间的相关性。共获得 146 个 co-DEGs,在细胞因子-细胞因子受体相互作用和 JAK-STAT 信号通路中表现出显著富集。通过 Cytoscape 鉴定了 7 个枢纽基因,并通过外部数据集进行了验证。对这些枢纽基因的 miRNA 和 TFs 进行预测,揭示了它们的调控作用。免疫细胞浸润和相关性分析表明,CCL22 表达与激活的树突状细胞数量之间存在显著正相关。鉴定的枢纽基因代表了 AD 免疫学发病机制中的潜在诊断和治疗靶点。