Selection of DDX5 as a novel internal control for Q-RT-PCR from microarray data using a block bootstrap re-sampling scheme

Sixty-six Affymetrix microarray slides using lung adenocarcinoma tissue RNAs were analyzed by a statistical re-sampling method in order to detect genes with minimal variation in gene expression. By this approach, we identified DDX5 as a novel internal control for Q-RT-PCR. Twenty-three genes, which were differentially expressed between adjacent normal and tumor samples, were selected and analyzed using 24 paired lung adenocarcinoma samples by Q-RT-PCR using two internal controls, DDX5 and GAPDH. The percentage correlation between Q-RT-PCR and microarray were 70% and 48% by using DDX5 and GAPDH as internal controls, respectively.Together, these quantification strategies for Q-RT-PCR data processing procedure, which focused on minimal variation, ought to significantly facilitate internal control evaluation and selection for Q-RT-PCR when corroborating microarray data.Microarrays, by making use of the sequence resources created in genomic projects, are a powerful technology capable of measuring the expression levels of thousands of genes simultaneously and have dramatically expedited comprehensive understanding of gene expression profiles for disease development. For example, microarray technology has been used to compare gene expression profiles between normal and diseased cells and this has led to dramatic advances in the understanding of cellular processes at the molecular level [1]. Several microarray platforms are currently available. The short-oligonucleotide-based Affymetrix GeneChip？ arrays utilize multiple probes for each gene with an automated control for the experimental process from hybridization to quantification and thus provide reliable and comparable data [2]. The multiple probe sets for each gene are typically scattered across the surface of the Affymetrix microarrays. Variations in intensity from probe to probe or chip to chip for samples need to be resolved to obtain a reliable level of expression. Various statistical algorithms are available for pro