Selection and validation of novel stable reference genes for qPCR analysis in EMT and MET

Abstract

Quantitative real-time polymerase chain reaction is a powerful tool for quantifying gene expression. The relative quantification relies on normalizing the data to reference genes or internal controls not modulated by the experimental conditions. The most widely used internal controls occasionally show changed expression patterns in different experimental settings, such as the mesenchymal to epithelial transition. Thus, identifying appropriate internal controls is of utmost importance. We analyzed multiple RNA-Seq datasets using a combination of statistical approaches such as percent relative range and coefficient of variance to define a list of candidate internal control genes, which was then validated experimentally and by using in silico analyses as well. We identified a group of genes as strong internal control candidates with high stability compared to the classical ones. We also presented evidence for the superiority of the percent relative range method for calculating expression stability in data sets with larger sample sizes. We used multiple methods to analyze data collected from several RNA-Seq datasets; we identified Rbm17 and Katna1 as the most stable reference genes in EMT/MET studies. The percent relative range approach surpasses other methods when analyzing datasets of larger sample sizes.