Integrative Identification of Hub Genes Associated With Immune Cells in Atrial Fibrillation Using Weighted Gene Correlation Network Analysis.
Integrative Identification of Hub Genes Associated With Immune Cells in Atrial Fibrillation Using Weighted Gene Correlation Network Analysis.
复制标题
使用加权基因相关网络分析综合鉴定与心房颤动中免疫细胞相关的中心基因
DOI：
10.3389/fcvm.2020.631775
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发表时间：
2020
影响因子：
3.6
通讯作者：
Guo C
中科院分区：
文献类型：
--
作者：
Yan T;Zhu S;Zhu M;Wang C;Guo C
背景：心房颤动（AF）是临床上最常见的快速性心律失常，导致高发病率和死亡率。虽然对AF进行了许多研究，但AF的分子机制尚未完全阐明。本研究旨在通过生物信息学的整合分析，探讨AF的分子机制，为AF的病理生理学研究提供新的思路。方法：下载GSE 115574数据集，应用Cibersort对22种免疫细胞的相对表达量进行估计。通过R语言的limma软件包进行差异表达基因（DEG）的鉴定。采用加权基因相关网络分析（WGCNA）将DEG聚类为不同的模块，并探讨模块与免疫细胞类型之间的关系。在显著性模块中对DEG进行功能富集分析，并基于蛋白质-蛋白质相互作用（PPI）网络鉴定枢纽基因。然后使用定量实时聚合酶链反应（qRT-PCR）验证Hub基因。结果：共识别出2,350个DEG，并利用WGCNA聚类为11个模块。与246个基因的洋红色模块被确定为与M1巨噬细胞相关的关键模块，具有最高的相关系数。三个枢纽基因（CTSS，CSF 2 RB和NCF 2）进行了鉴定。用其他三个数据集和qRT-PCR验证的结果表明，这三个基因在AF患者中的表达水平显著高于SR患者，与生物信息学分析结果一致。结论：通过全面的生物信息学分析发现了3个新基因，这些基因可能在房颤的病理生理机制中发挥重要作用，为房颤的治疗和早期发现提供了新的思路和潜在的治疗靶点。
Background: Atrial fibrillation (AF) is the most common tachyarrhythmia in the clinic, leading to high morbidity and mortality. Although many studies on AF have been conducted, the molecular mechanism of AF has not been fully elucidated. This study was designed to explore the molecular mechanism of AF using integrative bioinformatics analysis and provide new insights into the pathophysiology of AF. Methods: The GSE115574 dataset was downloaded, and Cibersort was applied to estimate the relative expression of 22 kinds of immune cells. Differentially expressed genes (DEGs) were identified through the limma package in R language. Weighted gene correlation network analysis (WGCNA) was performed to cluster DEGs into different modules and explore relationships between modules and immune cell types. Functional enrichment analysis was performed on DEGs in the significant module, and hub genes were identified based on the protein-protein interaction (PPI) network. Hub genes were then verified using quantitative real-time polymerase chain reaction (qRT-PCR). Results: A total of 2,350 DEGs were identified and clustered into eleven modules using WGCNA. The magenta module with 246 genes was identified as the key module associated with M1 macrophages with the highest correlation coefficient. Three hub genes (CTSS, CSF2RB, and NCF2) were identified. The results verified using three other datasets and qRT-PCR demonstrated that the expression levels of these three genes in patients with AF were significantly higher than those in patients with SR, which were consistent with the bioinformatic analysis. Conclusion: Three novel genes identified using comprehensive bioinformatics analysis may play crucial roles in the pathophysiological mechanism in AF, which provide potential therapeutic targets and new insights into the treatment and early detection of AF.