Application of Weighted Gene Coexpression Network Analysis to Identify Key Modules and Hub Genes in Systemic Juvenile Idiopathic Arthritis.
Application of Weighted Gene Coexpression Network Analysis to Identify Key Modules and Hub Genes in Systemic Juvenile Idiopathic Arthritis.
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DOI：
10.1155/2021/9957569
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发表时间：
2021
影响因子：
--
通讯作者：
Lu L
中科院分区：
文献类型：
--
作者：
Zhou M;Guo R;Wang YF;Yang W;Li R;Lu L
系统性幼年特发性关节炎（systemic juvenile idiopathic arthritis，sJIA）是一种严重的自身炎症性疾病，其发病机制尚不清楚。为了更好地了解这种疾病，我们使用了来自公共领域的分散数据集，并进行了加权基因共表达网络分析（WGCNA），以确定sJIA发病机制的关键模块和枢纽基因。两个基因表达数据集GSE 7753和GSE 13501用于构建WGCNA。将基因本体论（GO）和京都基因和基因组百科全书（KEGG）富集分析应用于sJIA模块中的基因和枢纽基因。Cytoscape用于筛选和可视化枢纽基因。我们进一步比较了枢纽基因与全基因组关联研究（GWAS）基因，并使用共识WGCNA来验证我们的结论在多个独立数据集上是保守的和可重复的。WGCNA共获得5，414个基因，其中高度相关的基因被划分为17个模块。红色模块与sJIA模块的相关性最高（r = 0.8，p = 3e−29），而绿黄色模块与非sJIA模块的相关性最高（r = 0.62，p = 1e−14）。功能富集分析表明，红色模块主要富集在免疫应答、感染、核小体和红细胞的激活中，而绿黄色模块主要富集在免疫应答和炎症中。此外，红色模块中的枢纽基因在红细胞分化中高度富集，包括ALAS2、AHSP、TRIM10、TRIM58和KLF1。来自绿黄模块的枢纽基因主要与免疫反应相关，例如基因KLRB 1、KLRF 1、CD 160和KIR。我们确定了sJIA相关模块和几个可能与sJIA发展相关的枢纽基因。特别是，这些模块可能有助于理解sJIA的机制，枢纽基因可能成为未来sJIA的生物标志物和治疗靶点。
Systemic juvenile idiopathic arthritis (sJIA) is a severe autoinflammatory disorder with a still not clearly defined molecular mechanism. To better understand the disease, we used scattered datasets from public domains and performed a weighted gene coexpression network analysis (WGCNA) to identify key modules and hub genes underlying sJIA pathogenesis. Two gene expression datasets, GSE7753 and GSE13501, were used to construct the WGCNA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were applied to the genes and hub genes in the sJIA modules. Cytoscape was used to screen and visualize the hub genes. We further compared the hub genes with the genome-wide association study (GWAS) genes and used a consensus WGCNA to verify that our conclusions were conservative and reproducible across multiple independent datasets. A total of 5,414 genes were obtained for WGCNA, from which highly correlated genes were divided into 17 modules. The red module demonstrated the highest correlation with the sJIA module (r = 0.8, p = 3e−29), whereas the green-yellow module was found to be closely related to the non-sJIA module (r = 0.62, p = 1e−14). Functional enrichment analysis demonstrated that the red module was mostly enriched in the activation of immune responses, infection, nucleosomes, and erythrocytes, and the green-yellow module was mostly enriched in immune responses and inflammation. Additionally, the hub genes in the red module were highly enriched in erythrocyte differentiation, including ALAS2, AHSP, TRIM10, TRIM58, and KLF1. The hub genes from the green-yellow module were mainly associated with immune responses, as exemplified by the genes KLRB1, KLRF1, CD160, and KIRs. We identified sJIA-related modules and several hub genes that might be associated with the development of sJIA. Particularly, the modules may help understand the mechanisms of sJIA, and the hub genes may become biomarkers and therapeutic targets of sJIA in the future.
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WGCNA：用于加权相关网络分析的 R 包。
影响因子：
3
作者：
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通讯作者：
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DOI：
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发表时间：
2017-11-06
期刊：
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