A sequence-based approach to identify reference genes for gene expression analysis.
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.
Chari
Raj
R
Department of Integrative Oncology, British Columbia Cancer Agency Research Centre, Vancouver, BC, Canada. rchari@bccrc.ca
Lonergan
Kim M
KM
Pikor
Larissa A
LA
Coe
Bradley P
BP
Zhu
Chang Qi
CQ
Chan
Timothy H W
TH
MacAulay
Calum E
CE
Tsao
Ming-Sound
MS
Lam
Stephen
S
Ng
Raymond T
RT
Lam
Wan L
WL
D042967:Electron Transport Complex I; D020869:Gene Expression Profiling / Q000592:standards*; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics*; D009245:NADH Dehydrogenase / Q000235:genetics / Q000592:standards; D020411:Oligonucleotide Array Sequence Analysis; D012015:Reference Standards; D020133:Reverse Transcriptase Polymerase Chain Reaction; D012269:Ribosomal Proteins / Q000235:genetics / Q000592:standards; D020696:rab5 GTP-Binding Proteins / Q000235:genetics / Q000592:standards
C489092:RPL19 protein, human; D012269:Ribosomal Proteins; C041184:ribosomal protein S18; D009245:NADH Dehydrogenase; C427755:RAB5C protein, human; D020696:rab5 GTP-Binding Proteins; D042967:Electron Transport Complex I; C118736:NDUFA1 protein, human