Identification of a blood-based 12-gene signature that predicts the severity of coronary artery stenosis: An integrative approach based on gene network construction, Support Vector Machine algorithm, and multi-cohort validation

Background and aims: We aimed to identify a blood-based gene expression score (GES) to predict the severity of coronary artery stenosis in patients with known or suspected coronary artery disease (CAD) by integrative use of gene network construction, Support Vector Machine (SVM) algorithm, and multi-cohort validation. Methods: In the discovery phase, a public blood-based microarray dataset of 110 patients with known CAD was analyzed by weighted gene coexpression network analysis and protein-protein interaction network analysis to identify candidate hub genes. In the training set with 151 CAD patients, bioinformatically identified hub genes were experimentally verified by real-time polymerase chain reaction, and statistically filtered with the SVM algorithm to develop a GES. Internal and external validation of GES was performed in patients with suspected CAD from two validation cohorts (n = 209 and 206). Results: The discovery phase screened 15 network-centric hub genes significantly correlated with the Duke CAD Severity Index. In the training cohort, 12 of 15 hub genes were filtered to construct a blood-based GES12, which showed good discrimination for higher modified Gensini scores (AUC: 0.798 and 0.812), higher Sullivan Extent scores (AUC: 0.776 and 0.778), and the presence of obstructive CAD (AUC: 0.834 and 0.792) in two validation cohorts. A nomogram comprising GES12, smoking status, hypertension status, low density lipoprotein cholesterol level, and body mass index further improved performance, with respect to discrimination, risk classification, and clinical utility, for prediction of coronary stenosis severity. Conclusions: GES12 is useful in predicting the severity of coronary artery stenosis in patients with known or suspected CAD.