Advancing Coronary Artery Disease Diagnosis: LncRNA Profiling of Small Extracellular Vesicles Derived from Peripheral Blood Mononuclear Cells

Abstract

Abstract Background Long noncoding RNAs (lncRNAs) constitute integral components of many physiological processes and exhibit considerable impact on cardiovascular maladies. Nonetheless, the clinical utilization of lncRNAs as serological markers for diagnosing Coronary Artery Disease (CAD) remains unexplored. Methods To scrutinize lncRNA expression, we harnessed a lncRNA microarray to probe monocyte small extracellular vesicles (sEVs) derived from three CAD patients and an equivalent number of healthy controls. Differential lncRNA expression in both plasma and monocyte sEVs was corroborated via quantitative real-time PCR (RT-qPCR). A diagnostic prediction model for CAD, integrating lncRNA expression, was fabricated utilizing Random Forest and nomogram analyses. Results Data elucidated that 89 lncRNAs were upregulated, while 211 were downregulated in CAD patients compared to controls. Most notable were the upregulation of SNAR-E and downregulation of RPL34-AS1. The expression of SNAR-E correlated with Diabetes Mellitus (DM), Total Cholesterol (TC), High-Density Lipoprotein Cholesterol (HDL-C), and Low-Density Lipoprotein Cholesterol. Conversely, RP34L-AS1 expression corresponded to age alongside DM, TC, HDL-C, and Lipoprotein (a). Additionally, both SNAR-E and RP34L-AS1 displayed elevated diagnostic accuracy for CAD in plasma and sEVs, characterized by high sensitivity and specificity. The prediction model manifested robust diagnostic prowess and consistency. Conclusion SNAR-E and RPL34-AS1 in sEVs and 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.