Integrative in Silico and in vitro validation suggest LINC00963 and SNHG15 as candidate biomarkers for coronary artery disease - Mohammadreza Saberiyan; Parisa Noorabadi; Ozra Kahourian; Ahmad Golestanifar; Negar Jafari; Venus Shahabi Rabori; Pegah Mousavi (Scientific Reports)
Abstract Despite notable advancements in prevention, medication, and treatment approaches, coronary artery disease (CAD) remains a significant challenge for healthcare systems and the economy. In relation to CAD, long non-coding RNAs (lncRNAs) can impact its development by influencing immune responses, affecting the functions of endothelial and vascular smooth muscle cells, and modifying lipid metabolism. Through the analysis of the GEO dataset (GSE42148), we identified differentially expressed genes (DEGs) and lncRNAs (DELs) in CAD patients. We performed functional enrichment and pathway analyses to clarify the roles of these DEGs. To investigate the interactions between DEGs and DELs, we created the lncRNA-mRNA interaction network. To investigate the interactions between DEGs and DELs, we constructed an lncRNA–mRNA interaction network. Candidate lncRNAs were validated by real-time PCR using peripheral blood from CAD patients. Our in vitro study confirmed that LINC00963 and SNHG15 were upregulated in CAD patients compared to the control group. Notably, LINC00963 levels were significantly elevated in patients with a positive family history, hyperlipidemia, hypertension, and diabetes, while SNHG15 expression was higher in smokers. Additionally, a significant negative correlation was found between the expressions of LINC00963 and SNHG15 and the age of the individuals. ROC curve analysis indicated that both lncRNAs have high sensitivity and specificity as biomarkers. Furthermore, this study suggests that LINC00963 and SNHG15 could serve as valuable markers for the early detection of CAD, particularly in younger individuals. It is proposed that these lncRNAs are associated with inflammatory conditions in CAD. Overall, LINC00963 and SNHG15 may act as promising early detection markers for CAD based on bioinformatics and peripheral blood-based validation.