qRT-PCR has been a breakthrough for the quantification of gene expression in many biological systems. In this study we assume that no single gene stays unaffected under malignant development in colon cancer and therefore identify genes with least variation. We identified two pairs of genes, HPRT1/PPIA and IPO8/PPIA, which may be suitable to normalize gene expression data in studies conducted in metastatic and non-metastatic colon cancer patients. In addition, we found that B2M, ACTB and 18S were unstable in the tissue examined. We propose a standardized approach of finding the most suitable reference gene(s) in every qRT-PCR experiment using TLDA.
Complex signalling pathways have been related to the metastatic progression of colon cancer, hence pathway based gene expression assays, often revealed by qRT-PCR, are significant in cancer biology. Publications dealing with colon cancer have reported gene expression studies in metastatic diseases [34, 35]. However, the stability of the reference gene expression in metastatic and non-metastatic primary tumours remains crucial. Ramaswamy et al., 2003, described a gene expression signature that distinguished primary and metastatic adenocarcinomas, indicating that the metastatic potential of human tumours is encoded in the bulk of the primary tumour , fully in accordance with modern clonal stem cell theories . Hence, one may presume that the metastatic capacity of the primary tumour will influence commonly chosen reference genes.
The most recent study of reference genes in colon cancer was reported by Kheirelseid et al., 2010, where 64 colorectal tumours and tumour associated normal specimens were examined using qRT-PCR followed by three different statistical algorithms, geNorm, NormFinder and qBaseplus . Kheirelseid et al., 2010, found that the combination of two reference genes, B2M and PPIA, more accurately normalized qRT-PCR data in colorectal cancer. This is in concordance with our findings, where PPIA was one of the two genes identified as the most stable pair. In contrast, B2M was identified as one of the most variable genes in the tissue examined. This disparity may be explained by the difference in patient material since Kheirelseid et al., 2010, included all stages of colon cancer and even included rectum tumour samples. Furthermore the percentage of tumour cells in the samples was not addressed. In the study of Kheirelseid et al., 2010, all three algorithms confirmed the selection of the B2M and PPIA pairing as the best combination of reference genes. In the present study however, the geNorm algorithm differs from the results obtained by NormFinder. According to geNorm HPRT1 and PPIA were the most suitable genes for normalization, but NormFinder suggested IPO8 and PPIA. This discrepancy confirms previous results reported by Caradec et al., 2010, concluding that the evaluation of suitable reference genes dramatically differs according to the statistical method used . Caradec et al., 2010, investigated reference genes in four cell lines treated with four different oxygen concentrations, and observed large variations in gene expression results depending of statistical method used. Thus Caradec et al., 2010, recommended Ct coefficients of variation (CtCV%) calculation for each reference gene for validation of the statistical methods. It is defined as the ratio of the standard deviation to the mean. Genes with low CtCV% value indicate more stable expression of those genes. In the present study, IPO8 was the most stable gene on the basis of CtCV% (5.12%), followed by GUSB (5.55%) and HPRT1 (6.04%) as the second and third most stable gene. Using NormFinder IPO8 was one of the genes which were identified as the most stable pair of genes, which may indicate that the CtCV% verifies the NormFinder results. Nevertheless, PPIA, which was suggested by both geNorm and NormFinder as one of the stable pair of genes, was ranked as the tenth most stable gene with a CtCV% of 7.34%. This may be explained by the low Ct mean of this particular gene (18.0), resulting in a relatively high CtCV% despite a low standard deviation. Another aspect which strengthens the results achieved by NormFinder compared with geNorm is the argument that geNorm lacks robustness compared with NormFinder . Reports show that exclusion of one sample in a specific tissue collection led geNorm to change a suggested reference gene (18S) from being an unstable gene to one of the top ranking stable genes . NormFinder also enables estimation of the variation between sample subgroups, like tumour and normal tissue, thus this algorithm can account for heterogeneity in the tested samples, which may be important considering the heterogeneity of the samples studied.
The optimal normalization will vary with study design. The most suitable reference gene in one medical condition may be regulated in other tissues or diseases. Blanquicett et al., 2002, found that 18S, S9 and GUS were the least regulated genes among 15 putative reference genes when examining tumour and normal colorectal and liver tissues . Furthermore, Dydensborg et al., 2006, identified B2M as the most appropriate gene for normalizing colon carcinomas comparing to normal mucosa when they investigated seven colon adenocarcinomas containing both epithelial and stromal cells . B2M was in this study identified as the least stable gene using NormFinder, and the third most variable gene using geNorm. In the present study where the tumour tissue samples consisted of more than 70% tumour cells some of the stromal cells are excluded. This might explain the discrepancies in the ranking of B2M since tumour tissue is heterogeneous and the fraction of different cells may influence the gene expression results. Moreover, different patient groups, including age and clinical background, may also give dissimilarities across studies. Experimental variations may also influence the gene expression results, though using triplicates in the qRT-PCR analysis as used in this study will diminish this variation.
Single assays qRT-PCR are time- and labour-intensive, and require relatively large amounts of cDNA and PCR reagents in multivariate gene expression studies. TLDA overcome these drawbacks since this technique allows for simultaneously detection of expression of up to 384 genes and requires less template cDNA and PCR reagents than routine qRT-PCR [1, 31, 38–40].