D'haene Barbara, Mestdagh Pieter, Hellemans Jan, Vandesompele Jo
Biogazelle, Zwijnaarde, Belgium.
Methods Mol Biol. 2012;822:261-72. doi: 10.1007/978-1-61779-427-8_18.
MicroRNAs (miRNAs) are an important class of gene regulators, acting on several aspects of cellular function such as differentiation, cell cycle control, and stemness. These master regulators constitute an invaluable source of biomarkers, and several miRNA signatures correlating with patient diagnosis, prognosis, and response to treatment have been identified. Within this exciting field of research, whole-genome RT-qPCR-based miRNA profiling in combination with a global mean normalization strategy has proven to be the most sensitive and accurate approach for high-throughput miRNA profiling (Mestdagh et al., Genome Biol 10:R64, 2009). In this chapter, we summarize the power of the previously described global mean normalization method in comparison to the multiple reference gene normalization method using the most stably expressed small RNA controls. In addition, we compare the original global mean method to a modified global mean normalization strategy based on the attribution of equal weight to each individual miRNA during normalization. This modified algorithm is implemented in Biogazelle's qbasePLUS software and is presented here for the first time.
微小RNA（miRNA）是一类重要的基因调控因子，作用于细胞功能的多个方面，如分化、细胞周期控制和干性。这些主要调控因子构成了生物标志物的宝贵来源，并且已经鉴定出了几种与患者诊断、预后和治疗反应相关的miRNA特征。在这个令人兴奋的研究领域中，基于全基因组逆转录定量聚合酶链反应（RT-qPCR）的miRNA谱分析与全局均值归一化策略相结合，已被证明是高通量miRNA谱分析最灵敏、最准确的方法（梅斯达赫等人，《基因组生物学》10:R64，2009年）。在本章中，我们总结了与使用最稳定表达的小RNA对照的多参考基因归一化方法相比，上述全局均值归一化方法的优势。此外，我们将原始全局均值方法与一种改进的全局均值归一化策略进行比较，该策略在归一化过程中对每个单独的miRNA赋予相等的权重。这种改进算法在Biogazelle公司的qbasePLUS软件中实现，并且在此首次展示。
