Original Article


Evaluation of the performance of serum miRNAs as normalizers in microRNA studies focused on cardiovascular disease

Xiaoyan Wang, Xiaoyi Zhang, Jie Yuan, Jian Wu, Xin Deng, Juan Peng, Shijun Wang, Chunjie Yang, Junbo Ge, Yunzeng Zou

Abstract

Background: Few study has been done to evaluate the stability and superiority of normalizers for serum microRNA (miRNA) study in cardiovascular disease. Therefore, the aim of this study is to assess the suitability of several common normalizers (miR-16, SNOU6, 5S, miR-19b, miR-24, miR-15b, let 7i) in cardiovascular disease.
Methods: We evaluated the stability of the seven circulating miRNAs as reference genes in the blood samples from patients with cardiovascular disease [heart failure (HF) and hypertension] and healthy people. Stability was quantified by combining BestKeeper, NormFinder and comparative delta Cq analysis.
Results: A total of 62 subjects were included in this study, of which 25 patients were with HF, 10 patients were with hypertension, and 27 were healthy people. The analysis from both BestKeeper and comparative delta ct analysis demonstrated that let-7i and miR-16 showed the best performance [the standard deviations (SD) in BestKeeper for let-7i and miR-16 were 0.60 and 0.72, and the mean SD in comparative delta ct analysis for let-7i and miR-16 were 1.79 and 1.82, respectively], while SNOU6 and 5S had the highest variability. In NormFinder analysis, miR-15 show best stability (ρ=0.029), followed by miR-19b (ρ=0.037), let-7i (ρ=0.064), SNOU6 (ρ=0.064), 5S (ρ=0.064), miR-16 (ρ=0.064), while miR-24 (ρ=0.075) showed worst stability.
Conclusions: This study pointed out that in the serum studies focused on cardiovascular disease, let-7i and miR-16 had the best performance, while SNOU6 and 5S were not suitable as reference gene. This study indicate that the selection of an optimal reference genes is important to get an accurate result in serum miRNA studies, the findings are of clinical significance to guide the further miRNA studies or tests.

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