A sequence-based approach to identify reference genes for gene expression analysis

Abstract

Background: 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. 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.

Methods: Serial 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.

Results: We 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.

Conclusions: Our 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.

Figure 1

Enhanced performance of the lung-NEPS genes (red; see Table 1) relative to previously reported and standard reference genes traditionally used in qPCR and microarray normalization (blue). The x-axis represents the permutation score of a defined gene and the y-axis represents the average raw (non-normalized) tag count for the same gene. Data used in the graph are given in Additional file 7. Lung NEPS genes are stable and highly expressed as compared to the traditionally used genes. B2 M appears to perform the best with respect to high average tag count and low permutation score. Notably, the gene that performs the poorest is GAPDH.

Figure 2

Tissue-specificity of reference genes. Comparison of the permutation scores for reference genes generated in one tissue type with permutation scores for the same genes in the other two tissue types. (A) Performance of lung-NEPS genes in breast and brain tissues, (B) Performance of breast-NEPS genes in lung and brain tissues, and (C) Performance of brain-NEPS genes in lung and breast tissues.

Figure 3

SAM and pathway analysis of a dataset normalized with and without lung NEPS genes. (A) Number of probes identified as differentially over and underexpressed between cancer and normal using SAM on the dataset with and without NEPS normalization. Venn diagram illustrates the overlap in the genes identified as well as those which are different between the two analyses. (B) Canonical pathway analysis using Ingenuity Pathway Analysis. Dark blue bars represent the results from the dataset normalized with MAS 5.0 + NEPS and light blue bars represent the results from normalization using MAS 5.0 alone. The pathways which are the most significant are the most significant in both analyses. Note that key pathways such as Neuregulin signaling and JAK/Stat are identified with higher significance when normalized using the lung NEPS genes. Such differences illustrate the impact of reference gene selection and normalization on differential gene expression analysis.