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Association of plasma MiR-17-92 with dyslipidemia in patients with coronary artery disease.

Medicine | 2014

Circulating microRNAs (miRNAs) have already been proposed as sensitive and informative biomarkers for the diagnosis of multiple diseases. We investigated the miRNA expression patterns in plasma samples of patients with coronary artery disease (CAD) and explored the potential functions of certain miRNAs.Deep sequencing analysis was performed to determine the miRNA expression profiles using RNA samples isolated from 20 healthy subjects and 20 patients with CAD. Quantitative reverse transcription polymerase chain reaction was applied to confirm the differential expression of the miR-17-92 cluster in 81 patients and 50 healthy volunteers. The association between the miR-17-92 cluster and clinical characteristics of patients with CAD were analyzed using SPSS16.0, SPSS Inc, Chicago, IL.Hundreds of miRNAs were detected and most members from the miR-17-92 cluster and its paralogs, including miR-18a, miR-92a, miR-106b, and miR-17, exhibited differential expression in the plasma of patients with CAD compared with controls. Moreover, these miRNAs were found widely related to the blood lipids in the patients with CAD, as miR-17 was positively correlated with total cholesterol, low-density lipoprotein cholesterol, and apolipoprotein B, while miR-92a was found positively related to high-density lipoprotein cholesterol (HDL-C) but negatively related to lipoprotein-a. Additionally, miR-106b was positively related to HDL-C and apolipoprotein A-I.Taken together with existing evidence from mechanistic studies, the current results of our study support a relationship between the miR-17-92 family and lipid metabolism, which merits further study.

Pubmed ID: 25415674 RIS Download

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Gene Expression Omnibus (GEO) (tool)

RRID:SCR_005012

Functional genomics data repository supporting MIAME-compliant data submissions. Includes microarray-based experiments measuring the abundance of mRNA, genomic DNA, and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. Array- and sequence-based data are accepted. Collection of curated gene expression DataSets, as well as original Series and Platform records. The database can be searched using keywords, organism, DataSet type and authors. DataSet records contain additional resources including cluster tools and differential expression queries.

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