A New Approach For Coronary Atherosclerotic Heart Disease Diagnosis By LncRNA Profiling Of Peripheral Blood Mononuclear Cells -Derived Small Extracellular Vesicles

Abstract

Abstract Background: Long noncoding RNAs (lncRNAs) are involved in many physiological processes and have also been reported to play an essential role in cardiovascular diseases. However, lncRNAs have not been used as a serological marker to diagnose coronary artery disease (CAD) in clinics. Methods: We employed a lncRNA microarray to analyse lncRNA expression in monocyte small extracellular vesicles (sEVs) from three CAD patients and three healthy controls. We validated the differential expression of lncRNA in both plasma and monocyte sEVs by quantitative real-time PCR (RT-qPCR). We also evaluated the ability of lncRNA to diagnose CAD by receiver operating characteristic curve (ROC) analysis in plasma and monocytes sEVs. Combined with lncRNA expression, a diagnostic prediction model of CAD was constructed using the Random Forest and nomogram analysis. Result: The results showed 89 upregulated lncRNAs and 211 downregulated lncRNAs in patients with coronary atherosclerotic heart disease relative to the control group. SNAR-E upregulation and RPL34-AS1 downregulation were the most evident findings. SNAR-E expression was associated with diabetes mellitus (DM), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol. RP34L-AS1 expression was associated with age combined with DM, TC, HDL-C, and lipoprotein (a). Moreover, whether in the plasma or sEVs, SNAR-E and RP34L-AS1 diagnosed CAD with high sensitivity and specificity. The prediction model showed arobust diagnosticc ability and stability. Conclusion: SNAR-E and RPL34-AS1 in sEVs or plasma have higher sensitivity and specificity in diagnosing CAD than conventional electrocardiogram (ECG), dynamic ECG, or the treadmill exercise tests. The diagnosis model comprising these two molecules showed considerable accuracy and stability, and may assist in the early diagnosis of CAD and support clinical guidance.