Plasma microrna expression profile for reduced ejection fraction in dilated cardiomyopathy

被引:0
作者
Maria Calderon-Dominguez
Thalía Belmonte
Maribel Quezada-Feijoo
Mónica Ramos
Juan Calderon-Dominguez
Oscar Campuzano
Alipio Mangas
Rocio Toro
机构
[1] Puerta del Mar University Hospital,Biomedical Research and Innovation Institute of Cadiz (INiBICA), Research Unit
[2] Cruz Roja Hospital,Cardiology Department
[3] Universidad Alfonso X,Cardiovascular Genetics Center
[4] University of Girona-IDIBGI,Medical Science Department, School of Medicine
[5] University of Girona,Centro de Investigación Biomédica en Red
[6] Enfermedades Cardiovasculares (CIBERCV),Internal Medicine Department, Puerta del Mar University Hospital, School of Medicine
[7] University of Cadiz,Medicine Department, School of Medicine
[8] University of Cadiz,undefined
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Scientific Reports | / 11卷
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摘要
The left ventricular (LV) ejection fraction (EF) is key to prognosis in dilated cardiomyopathy (DCM). Circulating microRNAs have emerged as reliable biomarkers for heart diseases, included DCM. Clinicians need improved tools for greater clarification of DCM EF categorization, to identify high-risk patients. Thus, we investigated whether microRNA profiles can categorize DCM patients based on their EF. 179-differentially expressed circulating microRNAs were screened in two groups: (1) non-idiopathic DCM; (2) idiopathic DCM. Then, 26 microRNAs were identified and validated in the plasma of ischemic-DCM (n = 60), idiopathic-DCM (n = 55) and healthy individuals (n = 44). We identified fourteen microRNAs associated with echocardiographic variables that differentiated idiopathic DCM according to the EF degree. A predictive model of a three-microRNA (miR-130b-3p, miR-150-5p and miR-210-3p) combined with clinical variables (left bundle branch block, left ventricle end-systolic dimension, lower systolic blood pressure and smoking habit) was obtained for idiopathic DCM with a severely reduced-EF. The receiver operating characteristic curve analysis supported the discriminative potential of the diagnosis. Bioinformatics analysis revealed that miR-150-5p and miR-210-3p target genes might interact with each other with a high connectivity degree. In conclusion, our results revealed a three-microRNA signature combined with clinical variables that highly discriminate idiopathic DCM categorization. This is a potential novel prognostic biomarker with high clinical value.
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