Circulating plasma microRNA expression profile: potential minimally invasive biomarker for early detection of coronary artery disease development.

BACKGROUND

The prevalence of coronary artery disease (CAD) is increasing among young adults. To improve CAD diagnosis, microRNAs are being explored as potential minimally invasive biomarkers. The aim of this study was to evaluate circulating microRNA (miRNA) expression profiles and assess their value in predicting the development of early-onset CAD.

METHODS AND RESULTS

A total of 108 patients with early- and late-onset CAD and 29 individuals without CAD were included, and their miRNA expression was evaluated. The diagnostic value of differentially expressed miRNAs across the subgroups was tested by logistic regression models and ROC curve analysis. A total of 287 different circulating miRNAs were analysed following sequencing and preprocessing. Seven miRNAs (miR-10b-5p, miR-29c-3p, miR-142-5p, miR-320b, miR-451a, miR-486-3p, and miR-625-3p) were found to be differentially expressed across all the study groups, four of which (miR-142-5p, miR-29c-3p, miR-451a, and miR-486-3p) were significantly downregulated in the late-onset CAD group compared with the control group. ROC analysis demonstrated that the combination of the seven miRNAs had high diagnostic accuracy, with an AUC of 0.9924 for distinguishing late-onset CAD from the other groups, and moderate accuracy, with an AUC of 0.8235 for distinguishing early-onset CAD from the other groups.

CONCLUSIONS

A combination of seven circulating miRNAs (miR-10b-5p, miR-29c-3p, miR-142-5p, miR-320b, miR-451a, miR-486-3p, and miR-625-3p) is a promising biomarker panel for CAD diagnosis, distinguishing between early-onset and late-onset disease. While the panel demonstrated high accuracy in classifying late-onset CAD, its ability to predict early-onset CAD requires further validation. Larger, independent populations are needed to validate the predictive ability of the panel for early disease detection, confirm these findings, and improve generalizability.