Expression of Preselected Genes in Mononuclear Blood Cells Is Associated with the Extent of Coronary Artery Stenosis

The goal of this study was examination of the association between the expression levels of the genes involved in high-density lipoprotein metabolism and atherogenesis and underlying metabolic pathways and the number of stenotic coronary arteries. Expression of 65 preselected genes in the peripheral blood mononuclear cells of the control patients (n = 63) and patients with coronary artery disease (CAD) with one or two (low stenosis group, n = 35) or three or four (high stenosis group, n = 41) stenotic vessels, confirmed by coronary angiography, was measured with real-time PCR. Functional enrichment analysis was applied for annotation of differentially expressed genes. Protein products of the differentially expressed genes (DEGs) in the CAD patients compared to the controls were associated with metabolic pathways related to assembly, remodeling, and clearance of plasma lipoproteins, as well as with signaling and regulation of expression of the genes involved in cholesterol transport and efflux. However, comparison of the gene expression profiles and associated metabolic pathways between the groups with high versus low stenosis revealed specific differences between these groups. Expression of the CETP, PLTP, CD36, IL18, ITGB3, S100A8, S100A12, and VEGFA genes increased with the increase of the number of stenotic vessels, which suggests involvement of these genes in stenosis expansion via lipoprotein metabolism, inflammation, angiogenesis, and innate immunity. The set of genes ITGB3, VEGFA, and CETP was selected as a new gene expression signature of expansion of the coronary artery stenosis, which was validated with the GSE12288 dataset from the Gene Expression Omnibus database, demonstrating an average odds ratio (OR) of 7.49 (95% CI, 2.21 to 25.43). Averaged expression levels of the ITGB3, VEGFA, and CETP genes could be used for diagnosis, prognosis evaluation, and treatment of coronary stenosis with strong predictive power.