Abstract
BackgroundAn important consideration when analyzing both microarray and quantitative PCR expression data is the selection of appropriate genes as endogenous controls or reference genes. This step is especially critical when identifying genes differentially expressed between datasets. Moreover, reference genes suitable in one context (e.g. lung cancer) may not be suitable in another (e.g. breast cancer). Currently, the main approach to identify reference genes involves the mining of expression microarray data for highly expressed and relatively constant transcripts across a sample set. A caveat here is the requirement for transcript normalization prior to analysis, and measurements obtained are relative, not absolute. Alternatively, as sequencing-based technologies provide digital quantitative output, absolute quantification ensues, and reference gene identification becomes more accurate.MethodsSerial analysis of gene expression (SAGE) profiles of non-malignant and malignant lung samples were compared using a permutation test to identify the most stably expressed genes across all samples. Subsequently, the specificity of the reference genes was evaluated across multiple tissue types, their constancy of expression was assessed using quantitative RT-PCR (qPCR), and their impact on differential expression analysis of microarray data was evaluated.ResultsWe show that (i) conventional references genes such as ACTB and GAPDH are highly variable between cancerous and non-cancerous samples, (ii) reference genes identified for lung cancer do not perform well for other cancer types (breast and brain), (iii) reference genes identified through SAGE show low variability using qPCR in a different cohort of samples, and (iv) normalization of a lung cancer gene expression microarray dataset with or without our reference genes, yields different results for differential gene expression and subsequent analyses. Specifically, key established pathways in lung cancer exhibit higher statistical significance using a dataset normalized with our reference genes relative to normalization without using our reference genes.ConclusionsOur analyses found NDUFA1, RPL19, RAB5C, and RPS18 to occupy the top ranking positions among 15 suitable reference genes optimal for normalization of lung tissue expression data. Significantly, the approach used in this study can be applied to data generated using new generation sequencing platforms for the identification of reference genes optimal within diverse contexts.
Highlights
An important consideration when analyzing both microarray and quantitative PCR expression data is the selection of appropriate genes as endogenous controls or reference genes
Identification of reference genes for gene expression analysis in lung cancer From our normalization of expression by permutation of SAGE (NEPS) analysis [with an imposed permutation score (PS) threshold ≤ 0.15, and an average expression of ≥ 25 raw tag counts across all samples], 15 genes were identified as the most consistently expressed across normal and cancerous lung tissue (Table 1)
The 18 S and 28 S rRNAs have previously served as reference genes [4,7,11,12,16], and here we show that the ribosomal protein genes can provide this service
Summary
An important consideration when analyzing both microarray and quantitative PCR expression data is the selection of appropriate genes as endogenous controls or reference genes. This step is especially critical when identifying genes differentially expressed between datasets. The main approach to identify reference genes involves the mining of expression microarray data for highly expressed and relatively constant transcripts across a sample set. Re-mining of large microarray datasets for the identification of novel, highly stable genes, as well as use of a combination of reference genes instead of a single gene for normalization, are some of the other approaches taken to address this problem [11,13,19]
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