Second Department of Cardiovascular Medicine, The First People's Hospital of Shangqiu, Shangqiu City, China.
Department of Endocrinology and Metabolism, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China.
FEBS Open Bio. 2022 Oct;12(10):1814-1827. doi: 10.1002/2211-5463.13469. Epub 2022 Aug 7.
The mortality rate of patients with coronary artery disease (CAD) increases year by year, and the age of onset is decreasing, primarily because of the lack of an efficient and convenient diagnostic method for CAD. In the present study, we aimed to detect CAD-correlated biomarkers and the regulatory pathways involved through weighted co-expression network analysis. The microarray data originated from 93 CAD patients and 48 controls within the Gene Expression Omnibus (GEO) database. The gene network was implemented by weighted gene co-expression network analysis, and the genes were observed to fall into a range of modules. We took the intersection of genes in the modules most correlated with CAD with the differentially expressed genes of CAD, which were identified by applying the limma package. Lasso regression and support vector machine recursive feature elimination algorithms were used to determine CAD candidate signature genes. The biomarkers for diagnosing CAD were detected by validating candidate signature gene diagnostic capabilities (receiver operating characteristic curves) based on data sets from GEO. Three modules were selected, and 26 vital genes were identified. Eight of these genes were reported as the optimal candidate features in terms of CAD diagnosis. Through receiver operating characteristic curve analysis, we identified three genes (ERCC5, HES6 and RORA; area under the curve > 0.8) capable of distinguishing CAD from the control, and observed that these genes are correlated with the immune response. In summary, ERCC5, HES6 and RORA may have potential for diagnosis of CAD.
冠心病患者的死亡率逐年上升，发病年龄呈下降趋势，主要是因为缺乏有效的、便捷的冠心病诊断方法。本研究旨在通过加权共表达网络分析检测冠心病相关生物标志物及相关调控通路。微阵列数据源自 GEO 数据库中 93 名冠心病患者和 48 名对照者。基因网络通过加权基因共表达网络分析实现，观察到基因落入一系列模块中。我们将与 CAD 相关性最大的模块中的基因与 CAD 的差异表达基因（通过 limma 包识别）取交集。Lasso 回归和支持向量机递归特征消除算法用于确定 CAD 候选特征基因。通过基于 GEO 数据集验证候选特征基因诊断能力（接收者操作特征曲线）来检测用于诊断 CAD 的生物标志物。选择了三个模块，确定了 26 个重要基因。其中 8 个基因被报道为 CAD 诊断的最佳候选特征。通过接收者操作特征曲线分析，我们鉴定出三个基因（ERCC5、HES6 和 RORA；曲线下面积>0.8）可用于区分 CAD 和对照组，且这些基因与免疫反应相关。总之，ERCC5、HES6 和 RORA 可能具有用于诊断 CAD 的潜力。
