open access

Vol 23, No 6 (2016)
BASIC SCIENCE AND EXPERIMENTALNTAL CARDIOLOGY - ORIGINAL ARTICLES
Published online: 2016-12-14
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Identification and validation of microRNAs as endogenous controls for quantitative polymerase chain reaction in plasma for stable coronary artery disease

Yuejuan Zhang, Wenxian Tang, Ling Peng, Jinqiang Tang, Zhaokai Yuan
DOI: 10.5603/CJ.2016.0109
·
Pubmed: 27976798
·
Cardiol J 2016;23(6):694-703.

open access

Vol 23, No 6 (2016)
BASIC SCIENCE AND EXPERIMENTALNTAL CARDIOLOGY - ORIGINAL ARTICLES
Published online: 2016-12-14

Abstract

Background: Circulating microRNAs (miRNAs) have been proved to serve as biomarkers for diagnosis and assessment of prognosis of coronary artery disease (CAD). Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is a widely-used technique to estimate expression levels of circulating miRNAs. Selection of optimal endogenous control (EC) remains critical to obtain reliable qPCR data of miRNAs expression. However, reference controls for normalization of circulating miRNA in CAD are still lacking. The purpose of this study was to identify stably expressed miRNAs to normalize RT-qPCR data derived from plasma in stable CAD.
Methods: We identified 10 stably expressed candidate ECs by combining miRNA microarray screening and literature screening. These 10 candidate ECs were estimated by RT-qPCR and the data were analyzed by NormFinder and BestKeeper algorithm.
Results: Two most stable ECs were identified as EC candidates and they were subsequently validated in another larger cohort. The 2 candidates were also validated by normalizing the expression levels of miR-21. In general, they were superior to the commonly used reference gene RNU6 in quantification cycle (Cq) value, stability value and normalization effect.
Conclusions: Our results demonstrated that miR-6090 and miR-4516 can be used as reference genes for plasma miRNA analysis in stable CAD.

Abstract

Background: Circulating microRNAs (miRNAs) have been proved to serve as biomarkers for diagnosis and assessment of prognosis of coronary artery disease (CAD). Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is a widely-used technique to estimate expression levels of circulating miRNAs. Selection of optimal endogenous control (EC) remains critical to obtain reliable qPCR data of miRNAs expression. However, reference controls for normalization of circulating miRNA in CAD are still lacking. The purpose of this study was to identify stably expressed miRNAs to normalize RT-qPCR data derived from plasma in stable CAD.
Methods: We identified 10 stably expressed candidate ECs by combining miRNA microarray screening and literature screening. These 10 candidate ECs were estimated by RT-qPCR and the data were analyzed by NormFinder and BestKeeper algorithm.
Results: Two most stable ECs were identified as EC candidates and they were subsequently validated in another larger cohort. The 2 candidates were also validated by normalizing the expression levels of miR-21. In general, they were superior to the commonly used reference gene RNU6 in quantification cycle (Cq) value, stability value and normalization effect.
Conclusions: Our results demonstrated that miR-6090 and miR-4516 can be used as reference genes for plasma miRNA analysis in stable CAD.

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Keywords

stable coronary artery disease; circulating microRNA; reverse transcription quantitative polymerase chain reaction (RT-qPCR); reference genes; normalization

About this article
Title

Identification and validation of microRNAs as endogenous controls for quantitative polymerase chain reaction in plasma for stable coronary artery disease

Journal

Cardiology Journal

Issue

Vol 23, No 6 (2016)

Pages

694-703

Published online

2016-12-14

DOI

10.5603/CJ.2016.0109

Pubmed

27976798

Bibliographic record

Cardiol J 2016;23(6):694-703.

Keywords

stable coronary artery disease
circulating microRNA
reverse transcription quantitative polymerase chain reaction (RT-qPCR)
reference genes
normalization

Authors

Yuejuan Zhang
Wenxian Tang
Ling Peng
Jinqiang Tang
Zhaokai Yuan

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