A sequence-based approach to identify reference genes for gene expression analysis
- Raj Chari1Email author,
- Kim M Lonergan1,
- Larissa A Pikor1,
- Bradley P Coe1,
- Chang Qi Zhu2,
- Timothy HW Chan1, 3,
- Calum E MacAulay1,
- Ming-Sound Tsao2,
- Stephen Lam1,
- Raymond T Ng†1, 3 and
- Wan L Lam†1
© Chari et al; licensee BioMed Central Ltd. 2010
Received: 17 February 2010
Accepted: 3 August 2010
Published: 3 August 2010
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.
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.
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.
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.
Gene expression profiling, including quantitative RT-PCR (qPCR) and microarray experimentation, is invaluable for the molecular analysis of biological systems. The interpretation of results from such experiments (i.e., the determination of differential expression for a particular gene among datasets) is strongly influenced by the selection of reference genes for normalization across datasets . Specifically, gene expression is normalized within a given dataset by calculating the transcript abundance of the gene of interest relative to a gene that is constantly expressed across independent datasets (termed a "housekeeping" or a "reference" gene), and differential expression between two datasets or samples is determined by calculating the ratio of the normalized expression levels for the gene of interest between the two datasets. Typically, housekeeping genes satisfy the following criteria: they are highly expressed in the cell, the variability in expression between samples is minimal, and the genes' expression is not influenced by the experimental conditions tested . Hence, problems arise when housekeeping genes are selected that do not meet these criteria, as fluctuations in these genes may erroneously influence the data interpretation.
Historically, beta actin (ACTB), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and 18 S rRNA have been routinely used as reference genes for qPCR and microarray data normalization. However, a number of studies have shown that expression of these genes varies considerably depending on the specific tissue type and disease state of the tissue [3–16]. Attempts to achieve more reliable normalization include the spiking of synthetic poly-A RNAs for the analysis of cDNA arrays and northern blots, and the combined use of an oligo-(dT)n primer with an 18 S specific primer for qPCR analysis [17, 18]. In addition, 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].
Recently, efforts have been made to identify more suitable reference genes for microarray and qPCR studies of lung cancer. Specifically, candidate reference genes have been identified from the mining of microarray gene expression data to identify the least variable genes, followed by validation of expression using qPCR [11, 20, 21]. However, as microarray data do not provide absolute abundance values for transcripts, selection of reference genes from this type of data is inherently problematic. To circumvent this handicap in the utilization of microarray data, we turn to the use of large-scale expression profiling permitted by serial analysis of gene expression (SAGE) experimentation for the identification of novel reference genes optimal for the study of lung cancer. This approach, which we have termed normalization of expression by permutation of SAGE (NEPS), takes advantage of the fact that SAGE is a transcriptome profiling technique that identifies the absolute abundance levels of transcripts by direct enumeration of sequence tag counts, thus allowing the direct comparison of expression levels across multiple profiles without the need for reference or housekeeping genes .
NEPS adopts a permutation test approach designed for analyzing relatively small sample sizes, such as those typically encountered with SAGE. Unlike the conventional T-test, the permutation test is non-parametric . The null hypothesis states that the mean gene expression levels in two groups of SAGE libraries being compared (in this case normal and cancer), are the same. For this analysis, samples from both the normal and the cancer groups are pooled, followed by random sampling to create a simulated Group 1 and a simulated Group 2. For each gene, the difference in expression between these two simulated groups was measured. This exercise was repeated 10,000 times, thus generating a simulated mean μ and a simulated standard deviation σ. The permutation score (PS) of a given gene is defined by , where O is the true difference between the average expression levels in the two groups. Hence, for a given gene, the closer the permutation score is to zero, the more it satisfies the constancy requirement.
Lung NEPS Genes
Average Raw Tag Count 1
protein phosphatase 1, catalytic subunit, beta isoform
cystatin B (stefin B)
ribosomal protein L4
schlafen family member 13
capping protein (actin filament) muscle Z-line, beta
ATP synthase, H+ transporting, mitochondrial F0 complex, subunit F6
RAB5C, member RAS oncogene family
NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 1, 7.5 kDa
ribosomal protein L19
high-mobility group box 1
CD55 molecule, decay accelerating factor for complement (Cromer blood group)
ribosomal protein S18
heat shock 70 kDa protein 1A
eukaryotic translation initiation factor 4A, isoform 2
SAGE library construction
26 normal and 11 lung cancer SAGE libraries were constructed and used in the analysis . The construction of the 26 normal libraries has been previously described [25, 26]. 24 of these libraries were generated from exfoliated bronchial cells obtained from bronchial brushes, and two libraries from normal lung parenchyma (Additional file 1). Conversely, the 11 cancer libraries were generated from biopsied specimens with six libraries representing lung squamous cell carcinoma and five libraries representing carcinoma in situ. This data can be found at the GEO database with the following series accession numbers: GSE3707, GSE5473, and GSE7898. All samples were acquired under approval by the University of British Columbia - British Columbia Cancer Agency Research Ethics Board (UBC-BCCA-REB) and all subjects provided written consent.
SAGE data from public domain
Publicly available SAGE data were also used in this analysis, representing both brain and breast cancer. Specifically, six normal and 12 breast cancer libraries (Additional file 2) and 7 normal and 19 brain cancer libraries were used (Additional file 3). The libraries were obtained from the cancer genome anatomy project (CGAP) database http://cgap.nci.nih.gov[27, 28].
For each gene, select all the gene-specific normalized tag counts from the normal libraries and all the gene-specific normalized tag counts from the cancer libraries.
Randomly select n counts to create a simulated normal set, and calculate the simulated normal mean μ sn .
Similarly, select the remaining c counts form the simulated cancerous set. Calculate the simulated cancer mean μ sc .
Consider the random variable v = μ sc - μ sn , called the simulated difference.
Repeat the steps A to D above m times. Let μ and σ denote the mean and the standard deviation of v.
Now separate the libraries back into their true identity: normal or cancerous. Calculate the true observed difference O = μ rc - μ rn , where μ rc denotes the true mean count of the cancerous libraries, and μ rn denotes the true mean of the normal libraries.
Calculate the Permutation Score PS where .
Repeat all the above steps for each gene. Sort the permutation score in descending order.
The permutation score is one way to measure how likely the actual observed difference occurs by chance. It is based on standardization, i.e., subtracting the mean and then divided by the standard deviation. The more the true observed difference is from the average (expressed as multiples of the standard deviation), the less likely that the true observed difference is a coincidence. That is to say, the larger the permutation score, the more significant is the observed difference between cancerous and normal samples.
On the other hand, for the sake of evaluating the constancy requirement, the ideal reference gene would have a permutation score equal to 0. This means that there is no difference in the distributions of expression levels between cancerous and normal samples. For the results reported here, we used m = 10,000 permutations.
Raw tag counts for each SAGE library were normalized to tags per million (TPM) to facilitate adequate comparison among libraries. Tag-to-gene mapping was performed using the February 5th, 2007 version of SAGEGenie . In cases where multiple SAGE tags mapped to the same gene, the tags were collapsed to capture all potential transcript variants, and a cumulative tag count was utilized for analysis.
Statistical criteria for reference gene selection
The permutation test outlined above was used to identify genes which were statistically similar when comparing the libraries from normal tissue (bronchial epithelium and lung parenchyma) and cancerous tissue of the lung. Three main criteria were used for reference gene selection: permutation score (described above) ≤ 0.15; at least two SAGE tags observed in each library; and an overall average count of ≥ 25 across all samples. For the analysis in brain and breast tissue, the first two criteria were maintained, but due to the lower sequencing depth, an average count of ≥ 10 across all samples was used instead.
Quantitative RT-PCR validation in clinical lung cancer specimens
One microgram of total RNA from 15 lung tumor and matched non-malignant parenchyma samples were converted to cDNA using the High-Capacity cDNA archive kit (Applied Biosystems Inc., Foster City CA). One hundred nanograms of cDNA were utilized for qPCR using the TaqMan Gene Expression Assay (Applied Biosystems Inc). All fifteen lung NEPS genes and six additional reference genes were assayed. All TaqMan probes were pre-optimized by Applied Biosystems. Primer IDs for all genes are provided in Additional file 4. The 30 samples were assayed in triplicate in parallel along with negative (no cDNA template) controls using the 7500 Fast Real-Time PCR System. Appropriate cDNA dilutions were used such that the exponential phase of the amplification curves were within the 40 PCR cycles recommended by the manufacturer (i.e. ranging from 16-36 cycles for the 20 genes and 1-13 cycles for 18SRNA). Cycle thresholds were determined from amplification curves using 7500 Fast System software.
For the analysis of qPCR data, three different methods were used. Within each method, all genes were ranked from best to worst. Subsequently, for each gene, a cumulative ranking across all three methods was determined by summing its rank from each individual method. Two previously published methods, geNorm  and NormFinder , and the variance of cycle threshold difference (dCt) across all 15 tumor/matched non-malignant sample pairs were the approaches used to determine constancy.
Analysis of publicly available microarray datasets
For the Affymetrix data (GSE10072), Raw CEL files were processed through Affymetrix's Microarray Array Suite (MAS) 5.0 algorithm in the "affy" package in Bioconductor [32, 33]. Briefly, MAS 5.0 is a three step process which involves a global background signal correction, correction of the probe value for cross-hybridization and spurious signals using mismatch probes which are off by one base, and finally, scale normalization of each experiment to a fixed median intensity to facilitate inter-experimental comparison http://media.affymetrix.com/support/technical/whitepapers/sadd_whitepaper.pdf. Probes were filtered on MAS 5.0 calls, and those having a "P" or "M" call in at least 50% of samples were retained. This resulted in a dataset of 11440 probes. Of the 15 lung NEPS reference genes, 12 were represented on the array platform. Of those 12 genes, probes which had a "P" call in 100% of the samples were used for the calculation of the scaling factor with only one probe/gene allowed. If two probes met these criteria for one gene, the probe with the highest mean expression was chosen. After employing these criteria, eight probes were used (Additional file 5), which represented genes PPP1CB, B2M, RPL4, CAPZB, ATP5J, RAB5C, NDUFA1, and HSPA1A.
For the Agilent microarray data (GSE12428), all lung NEPS genes were represented on this microarray platform. Data was processed as described previously . In the cases where lung NEPS genes were represented with multiple probes, the probe with the maximum average intensity across the dataset was used. A list of the probes used is given in Additional file 6. Since each sample had at least two replicate experiments, the average across replicate experiments was used for each probe.
To determine the scaling factor, for each sample, linear regression analysis was performed comparing the values for the reference gene (x) versus the average values for the reference genes across the sample set (y). The slope of the line based on least-squares fitting was then multiplied to each value in the experiment.
Next, Significance Analysis of Microarrays (SAM) was performed to determine differentially expressed genes between non-malignant and malignant samples for both microarray datasets using the "samr" package in R . Unpaired analysis was performed using the normal samples versus tumor samples and the delta parameter set to 0.4. Probes which had a Q-value% ≤ 5 were considered significant. For the Affymetrix dataset, results were compared between the dataset normalized with MAS 5.0 alone and MAS 5.0 + NEPS scaling and for the Agilent dataset, the comparison was done between median normalization alone and NEPS scaling followed by median normalization.
Results and Discussion
Identification of reference genes for gene expression analysis in lung cancer
From our 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). Here we identified beta-2-microglobulin (B2M), components of the large ribosomal subunit such as ribosomal protein L19 (RPL19) and ribosomal protein L4 (RPL4), components of the small ribosomal subunit such as ribosomal protein S18 (RPS18), and electron transport chain constituents such as NADH dehydrogenase (ubiquinone) 1 alpha subcomplex 1 (NDUFA1), to rate highly in our permutation analysis, thereby suggesting their potential as reliable reference genes. B2 M has previously been utilized as a reference gene [10, 15], providing validity to the approach used here. 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 also provide this service.
Performance of standard and previously reported reference genes
Demonstrating tissue specificity of reference genes
Breast NEPS Genes
Average Raw Tag Count
eukaryotic translation initiation factor 5A
eukaryotic translation initiation factor 3, subunit 2 beta, 36 kDa
ribosomal protein S8
ribosomal protein L28
ferritin, light polypeptide
tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, theta polypeptide
transmembrane protein 49
family with sequence similarity 39, member B
ribosomal protein L30
phosphodiesterase 6B, cGMP-specific, rod, beta
tubulin, alpha 1a
myosin, light chain 9, regulatory
myosin, heavy chain 9, non-muscle
nucleophosmin (nucleolar phosphoprotein B23, numatrin)
major histocompatibility complex, class I, A
ribosomal protein S2
Brain NEPS Genes
Average Raw Tag Count
nuclear casein kinase and cyclin-dependent kinase substrate 1
congenital dyserythropoietic anemia, type I
poly(A) binding protein, cytoplasmic, pseudogene 2
general transcription factor II, i
zinc finger, AN1-type domain 5
fission 1 (mitochondrial outer membrane) homolog (S. cerevisiae)
glutamate receptor, ionotropic, N-methyl D-aspartate 2C
ribosomal protein S27a
cytochrome c oxidase subunit IV isoform 1
CXXC finger 5
Quantitative RT-PCR validation of identified reference genes in lung cancer samples
Using a secondary set of 15 tumor and matched non-malignant samples, qPCR was used to validate consistency of expression for all lung-NEPS genes. Additionally, we performed qPCR for previously identified, commonly used housekeeping genes ACTB, GAPDH, HPRT1, and TBP. In addition, two genes out of 13 identified (CUL1 and TRIM27) as suitable reference genes from a previously published study , were selected here based on high NEPS performance (see above), for qPCR analysis.
Quantitative RT-PCR analysis of lung NEPS genes and select previously identified genes
NormFinder Stability Value
Effect of reference genes on differential gene expression analysis
In this study we present a methodology based upon permutation test analysis of SAGE data, to identify reference genes that more stringently satisfy the constancy requirements crucial for accurate normalization between samples utilized in gene expression experiments. Specifically, we have identified reference genes more effective for normalization than the traditional and previously reported housekeeping genes for lung, breast, and brain cancer gene expression profiling. Furthermore, we strongly emphasize that reference genes utilized for expression profiling should be selected in a tissue specific manner. Given that this methodology utilizes sequence-based data, its utility will increase as data generated from new next-generation sequencing platforms accumulate. The usage of more appropriate reference genes will have an impact on the interpretation of existing microarray data as well as expression data generated in future studies, and potentially will shed new insight into the molecular biology of cancer.
We thank Drs. William W. Lockwood and Ian M. Wilson for useful discussion and editing. This work was supported by funds from Canadian Institutes of Health Research. RC is supported by scholarships from the Michael Smith Foundation for Health Research and Canadian Institutes of Health Research.
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