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Increased MiR-142 Levels in Plasma and Atherosclerotic Plaques from Peripheral Artery Disease Patients with Post-Surgery Cardiovascular Events

Overview
Journal Int J Mol Sci
Publisher MDPI
Date 2020 Dec 19
PMID 33339419
Citations 12
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Abstract

There is an intensive effort to identify biomarkers to predict cardiovascular disease evolution. We aimed to determine the potential of microRNAs to predict the appearance of cardiovascular events (CVEs) in patients with peripheral artery disease (PAD) following femoral artery bypass surgery. Forty-seven PAD patients were enrolled and divided into two groups, without CVEs ( = 35) and with CVEs ( = 12), during 1 year follow-up. Intra-surgery atherosclerotic plaques from femoral arteries were collected and the levels of miR-142, miR-223, miR-155, and miR-92a of the primary transcripts of these microRNAs (pri-miRNAs), and gene expression of and were determined. Results showed that, in the plaques, miR-142, miR-223, and miR-155 expression levels were significantly increased in PAD patients with CVEs compared to those without CVEs. Positive correlations between these miRNAs and their pri-miRNAs levels and the expression were observed. In the plasma of PAD patients with CVEs compared to those without CVEs, miR-223 and miR-142 were significantly increased. The multiple linear regression analyses revealed significant associations among several plasma lipids, oxidative and inflammatory parameters, and plasma miRNAs levels. Receiver operator characteristic (ROC) analysis disclosed that plasma miR-142 levels could be an independent predictor for CVEs in PAD patients. Functional bioinformatics analyses supported the role of these miRNAs in the regulation of biological processes associated with atherosclerosis. Taken together, these data suggest that plasma levels of miR-142, miR-223, miR-155, and miR-92a can significantly predict CVEs among PAD patients with good accuracy, and that plasma levels of miR-142 can be an independent biomarker to predict post-surgery CVEs development in PAD patients.

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