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

TLDR

Microarray technology enables genome‑wide transcriptome profiling, and Q‑RT‑PCR is the gold‑standard for validating these measurements, yet probe‑level normalization variability and the lack of a reliable internal control complicate accurate gene quantification. Using a block bootstrap resampling scheme on 66 Affymetrix lung‑adenocarcinoma microarray slides, the authors identified low‑variance genes, then validated 23 differentially expressed genes by Q‑RT‑PCR in 24 paired samples with DDX5 and GAPDH as internal controls. The approach identified DDX5 as a superior internal control, yielding a 70 % correlation between Q‑RT‑PCR and microarray data versus 48 % with GAPDH, demonstrating that minimal‑variation strategies improve internal control selection for Q‑RT‑PCR validation.

Abstract

Abstract Background The development of microarrays permits us to monitor transcriptomes on a genome-wide scale. To validate microarray measurements, quantitative-real time-reverse transcription PCR (Q-RT-PCR) is one of the most robust and commonly used approaches. The new challenge in gene quantification analysis is how to explicitly incorporate statistical estimation in such studies. In the realm of statistical analysis, the various available methods of the probe level normalization for microarray analysis may result in distinctly different target selections and variation in the scores for the correlation between microarray and Q-RT-PCR. Moreover, it remains a major challenge to identify a proper internal control for Q-RT-PCR when confirming microarray measurements. Results 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. Conclusion 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.