

4 The main risk factors for this disease are diabetes, hypertension, smoking, hyperlipidemia, and obesity, 5 and its most common complications are myocardial infarction, heart failure, stroke, and death. The main manifestations of CAD are stable or unstable angina pectoris and identifiable or unrecognized myocardial infarction. 1–3 CAD shows high prevalence and is associated with a high fatality rate among cardiovascular diseases. Keywords: coronary artery disease, diagnosis, gene expression, classifierĬoronary artery disease (CAD) is a complex pathology associated with behavioral and environmental factors. Using these genes, we defined four diagnostic classifiers using multiple methods. Conclusion: We identified a set of genes specific for CAD whose expression can be measured non-invasively. Furthermore, GSEA showed autophagy and the proteasome to be major pathways involving the DEGs. A GSVA score was also established using the top 20 significant DEGs, which showed an AUC of 0.971 in the training set and 0.989 in the test set. Using the 20 DEGs, SVM and random forest classifiers were also generated and showed high diagnostic efficacy, with respective AUCs of 0.997 and 1.00 against the training set. The AUC for the classifier was 1.00 in the training set and 0.997 in the test set.

Twenty genes were identified as optimal features and used to generate the logistic classifier based on LASSO. The DEGs were involved in some pathways associated with CAD, such as pathways involving calcium and interleukin-17 signaling. Results: In the training set, we found 135 up-regulated genes and 104 down-regulated genes in CAD patients compared with controls. The performance of the models was evaluated in terms of the area under receiver operating characteristic curves (AUC). Gene set variation analysis (GSVA) score and gene set enrichment analysis (GSEA) were also conducted. Then, least absolute shrinkage and selection operator (LASSO) logistic regression, random forest, and support vector machine (SVM) models were created. To create a robust CAD classifier, DEGs were identified by feature selection using the principal component analysis. DEGs were analyzed for functional enrichment. We identified differentially expressed genes (DEGs) in peripheral blood mononuclear cells between CAD samples and healthy controls. Methods: We downloaded a CAD-related data set (GSE113079) from the Gene Expression Omnibus (GEO) database. We defined four classifiers based on gene expression profiles in peripheral blood mononuclear cells and determined their potential for CAD detection. Current diagnostic methods for CAD involve risk to the patient and are costly, so better diagnostic tools are needed. Objective: Coronary artery disease (CAD) is a serious global health concern. The Fifth Affiliated Hospital of Guangxi Medical University, 89 Qixing Road, Nanning, Guangxi, 530022, People’s Republic of China *These authors contributed equally to this work Jie Liu,1,2,* Xiaodong Wang,1,2,* Junhua Lin,1,* Shaohua Li,1 Guoxiong Deng,1,2 Jinru Wei1,2ġDepartment of Cardiology, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530022, People’s Republic of China 2Department of Cardiology, The First People’s Hospital of Nanning, Nanning, Guangxi, 530022, People’s Republic of China Classifiers for Predicting Coronary Artery Disease Based on Gene Expression Profiles in Peripheral Blood Mononuclear Cells
