Identification of key genes in ruptured atherosclerotic plaques by weighted gene correlation network analysis
Identification of key genes in ruptured atherosclerotic plaques by weighted gene correlation network analysis
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加权基因相关网络分析鉴定破裂动脉粥样硬化斑块的关键基因
DOI：
10.1038/s41598-020-67114-2
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发表时间：
2020-07-02
影响因子：
4.6
通讯作者：
Bao, Mei-Hua
中科院分区：
文献类型：
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作者：
Xu, Bao-Feng;Liu, Rui;Bao, Mei-Hua
动脉粥样硬化斑块的破裂是心脑血管事件的关键。识别与斑块破裂相关的关键基因是预测斑块状态、预防临床事件的重要途径。在本研究中，我们从GEO数据库下载了两个与动脉粥样硬化斑块破裂相关的表达谱(GSE41571和GSE120521)。用R软件对GSE41571中的11个样本进行差异表达基因(DEG)筛选，构建加权基因相关网络分析(WGCNA)。利用David网站上的基因肿瘤学(GO)和京都基因和基因组百科全书(KEGG)丰富工具，以及STRING网站上的蛋白质-蛋白质相互作用来预测基因的功能和机制。此外，我们将从WGCNA中提取的HUB基因映射到DEGS上，并使用Cytoscape 3.7.2构建了一个子网络。通过分子复合体检测(MCODE)对关键基因进行了鉴定。使用数据集GSE120521和人类颈动脉内膜切除术(CEA)斑块进行进一步的验证。结果：在我们的研究中，在GSE41571中确定了868个deg。通过WGCNA分析，确定了236个HUB基因的6个模块。在这6个模块中，蓝色和棕色模块与破裂斑块的相关性最高(相关系数分别为0.82和−0.9)。从蓝色和棕色模块中鉴定出72个HUB基因。这72个基因可能与细胞黏附、细胞外基质组织、细胞生长、细胞迁移、白细胞迁移、PI3K-Akt信号转导、焦点黏附和细胞外基质-受体相互作用有关。在72个HUB基因中，有45个基因定位于DEGS(LogFC >； 1.0，p-Value <； 0.05)。这45个HUB基因的子网络和MCODE分析表明，3个簇(13个基因)是关键基因。第1类分别为LOXL1、FBLN5、FMOD、ELN、EFEMP1，第2类为RILP、HLADRA、HLADMB、HLADMA，第3类为sFRP4、FZD6、DKK3。进一步检测EFEMP1、BGN、ELN、FMOD、DKK3、FBLN5、FZD6、HLADRA、HLADMB、HLADMA、RILP可能具有潜在的诊断价值。
The rupture of atherosclerotic plaques is essential for cardiovascular and cerebrovascular events. Identification of the key genes related to plaque rupture is an important approach to predict the status of plaque and to prevent the clinical events. In the present study, we downloaded two expression profiles related to the rupture of atherosclerotic plaques (GSE41571 and GSE120521) from GEO database. 11 samples in GSE41571 were used to identify the differentially expressed genes (DEGs) and to construct the weighted gene correlation network analysis (WGCNA) by R software. The gene oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment tool in DAVID website, and the Protein-protein interactions in STRING website were used to predict the functions and mechanisms of genes. Furthermore, we mapped the hub genes extracted from WGCNA to DEGs, and constructed a sub-network using Cytoscape 3.7.2. The key genes were identified by the molecular complex detection (MCODE) in Cytoscape. Further validation was conducted using dataset GSE120521 and human carotid endarterectomy (CEA) plaques. Results: In our study, 868 DEGs were identified in GSE41571. Six modules with 236 hub genes were identified through WGCNA analysis. Among these six modules, blue and brown modules were of the highest correlations with ruptured plaques (with a correlation of 0.82 and −0.9 respectively). 72 hub genes were identified from blue and brown modules. These 72 genes were the most likely ones being related to cell adhesion, extracellular matrix organization, cell growth, cell migration, leukocyte migration, PI3K-Akt signaling, focal adhesion, and ECM-receptor interaction. Among the 72 hub genes, 45 were mapped to the DEGs (logFC > 1.0, p-value < 0.05). The sub-network of these 45 hub genes and MCODE analysis indicated 3 clusters (13 genes) as key genes. They were LOXL1, FBLN5, FMOD, ELN, EFEMP1 in cluster 1, RILP, HLA-DRA, HLA-DMB, HLA-DMA in cluster 2, and SFRP4, FZD6, DKK3 in cluster 3. Further expression detection indicated EFEMP1, BGN, ELN, FMOD, DKK3, FBLN5, FZD6, HLA-DRA, HLA-DMB, HLA-DMA, and RILP might have potential diagnostic value.