Department of Internal Medicine-Cardiovascular, Rizhao People's Hospital, Rizhao, Shandong 276826, China.
Department of Respiratory, Rizhao People's Hospital, Rizhao, Shandong 276826, China.
Gene. 2019 Aug 20;710:122-130. doi: 10.1016/j.gene.2019.05.010. Epub 2019 May 7.
Coronary artery disease (CAD) is one of the most common causes of morbidity and mortality globally. This work aimed to investigate the specific modules and feature genes associated with CAD.
Three microarray datasets were downloaded from the Gene Expression Omnibus database, which included CAD and healthy samples. WGCNA was applied to identify highly preserved modules across the three datasets. MetaDE method was used to select differentially expressed genes (DEGs) with significant consistency. Protein-protein interaction (PPI) network was constructed using the overlapping genes amongst the DEGs with significant consistency and in the preserved modules. Moreover, a combined machine learning of support vector machine and recursive feature elimination was used to further investigate the feature genes and pathways.
Nine highly preserved modules were detected in the WGCNA network, and 961 DEGs with significant consistency across the three datasets were selected using the metaDE method. A PPI network was constructed with the 158 overlapping genes. Ten genes were found to be involved in these KEGG pathways directly, including genes CD22, CD79B, CD81, CR1, IKBKE, MAP3K3, MAPK14, MMP9, NCF4, and SPP1.
The present work might provide novel insight into the underlying molecular mechanism of CAD.
冠状动脉疾病（CAD）是全球发病率和死亡率最高的疾病之一。本研究旨在探讨与 CAD 相关的特定模块和特征基因。
从基因表达综合数据库（GEO）中下载了 3 个微阵列数据集，包括 CAD 和健康样本。使用 WGCNA 识别 3 个数据集之间高度保守的模块。MetaDE 方法用于选择具有显著一致性的差异表达基因（DEGs）。使用具有显著一致性的 DEGs 和保存模块中的重叠基因构建蛋白质-蛋白质相互作用（PPI）网络。此外，还使用支持向量机和递归特征消除的组合机器学习方法进一步研究特征基因和途径。
在 WGCNA 网络中检测到 9 个高度保守的模块，使用 metaDE 方法选择了 3 个数据集之间具有显著一致性的 961 个 DEGs。构建了一个包含 158 个重叠基因的 PPI 网络。有 10 个基因直接参与了这些 KEGG 途径，包括 CD22、CD79B、CD81、CR1、IKBKE、MAP3K3、MAPK14、MMP9、NCF4 和 SPP1 基因。
本研究可能为 CAD 的潜在分子机制提供新的见解。
