4.3 Article

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

期刊

CARDIOLOGY JOURNAL
卷 23, 期 6, 页码 694-703

出版社

VIA MEDICA
DOI: 10.5603/CJ.2016.0109

关键词

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

资金

  1. National Natural Science Foundation of China [81302883]
  2. Natural Science Foundation of Hunan Province [14JJ4056]

向作者/读者索取更多资源

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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.3
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据