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miRNA 表达研究:数据归一化中可靠参考控制的选择挑战。

MicroRNA expression studies: challenge of selecting reliable reference controls for data normalization.

机构信息

Department of Pharmaceutical Biotechnology, School of Pharmacy, Hamadan University of Medical Sciences, Hamadan, Iran.

Department of Tumor Biology, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany.

出版信息

Cell Mol Life Sci. 2019 Sep;76(18):3497-3514. doi: 10.1007/s00018-019-03136-y. Epub 2019 May 14.

Abstract

Accurate determination of microRNA expression levels is a prerequisite in using these small non-coding RNA molecules as novel biomarkers in disease diagnosis and prognosis. Quantitative PCR is the method of choice for measuring the expression levels of microRNAs. However, a major obstacle that affects the reliability of results is the lack of validated reference controls for data normalization. Various non-coding RNAs have previously been used as reference controls, but their use may lead to variations and lack of comparability of microRNA data among the studies. Despite the growing number of studies investigating microRNA profiles to discriminate between healthy and disease stages, robust reference controls for data normalization have so far not been established. In the present article, we provide an overview of different reference controls used in various diseases, and highlight the urgent need for the identification of suitable reference controls to produce reliable data. Our analysis shows, among others, that RNU6 is not an ideal normalizer in studies using patient material from different diseases. Finally, our article tries to disclose the challenges to find a reference control which is uniformly and stably expressed across all body tissues, fluids, and diseases.

摘要

准确测定 microRNA 表达水平是将这些小非编码 RNA 分子用作疾病诊断和预后新型生物标志物的前提。定量 PCR 是测量 microRNA 表达水平的首选方法。然而,影响结果可靠性的一个主要障碍是缺乏用于数据归一化的经过验证的参考对照。先前已经使用各种非编码 RNA 作为参考对照,但它们的使用可能导致研究中 microRNA 数据的变化和缺乏可比性。尽管越来越多的研究调查 microRNA 谱以区分健康和疾病阶段,但到目前为止,尚未建立用于数据归一化的稳健参考对照。在本文中,我们概述了各种疾病中使用的不同参考对照,并强调了迫切需要确定合适的参考对照以产生可靠的数据。我们的分析表明,在使用来自不同疾病的患者材料的研究中,RNU6 不是理想的归一化因子。最后,我们的文章试图揭示寻找一种在所有身体组织、体液和疾病中均匀且稳定表达的参考对照的挑战。

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