Evaluation of suitable reference genes for normalization of real-time reverse transcription PCR analysis in colon cancer

  • Lise Aa Sørby1Email author,

    Affiliated with

    • Solveig N Andersen2, 3,

      Affiliated with

      • Ida RK Bukholm3, 4, 5 and

        Affiliated with

        • Morten B Jacobsen1, 3, 6

          Affiliated with

          Journal of Experimental & Clinical Cancer Research201029:144

          DOI: 10.1186/1756-9966-29-144

          Received: 9 September 2010

          Accepted: 8 November 2010

          Published: 8 November 2010

          Abstract

          Background

          Real-time reverse transcription PCR (qRT-PCR) is frequently used for gene expression quantification due to its methodological reproducibility and sensitivity. The gene expression is quantified by normalization to one or more reference genes which are presumed stably expressed throughout a given experiment. The aim of this study was to validate a standardized experimental setup to identifying reference genes for normalization of qRT-PCR in the metastatic and non-metastatic colon cancer.

          Methods

          In this study, expression of 16 commonly used reference genes was quantified in tumour tissue and individual-matched normal mucosa in 18 non-metastatic colon cancer patients and 20 colon cancer patients with distant metastases using TaqMan Low Density Array (TLDA). The expression stability was determined and compared by means of geNorm and NormFinder.

          Results

          Two pairs of genes, HPRT1/PPIA and IPO8/PPIA, were identified to be suitable to normalize gene expression data in metastatic and non-metastatic colon cancer patients, according to geNorm and NormFinder respectively.

          Conclusion

          We propose a standardized approach of finding the most suitable reference gene(s) in every qRT-PCR experiment using TLDA.

          Background

          qRT-PCR is one of the most sensitive methods for mRNA detection and quantification. The method has also become the preferred method for validating results obtained by other techniques, such as microarray [1]. There are differences among different qRT-PCR assays due to biological and technical variations [2, 3]. In order to identify truly gene specific variations it is important to use a suitable normalization method. One of the most commonly used approaches involves relative quantification of target genes against one or more reference genes which are thought to be stably expressed in the examined tissue [4]. There have been a number of reports that demonstrate that the expression levels of putative reference genes vary extensively in different tissues and diseases and thus are unsuitable for normalization purposes [515]. Consequently, each research group has to validate multiple reference genes in their own experimental setup and normalize qRT-PCR data against a few reference genes tested from independent pathways using at least one algorithm. It appears that improvements in methods of identifying reference genes are more important than the identification of the particular reference genes themselves [16].

          It has been argued for use of multiple genes in the normalization of qRT-PCR analysis and several algorithms have been developed [1720]. Vandesompele et al., 2002, used the geometric mean of the most stable genes to improve the accuracy of the analysis in a method called geNorm [19]. This method relies on the principle that the expression ratio of two ideal reference genes is identical in all samples regardless of the experimental conditions. For every reference gene geNorm determine the pairwise variation with all other reference genes. The average pairwise variation of a particular gene is defined as the internal control stability measure; M. Genes with the lowest M values are the most stable ones. Another algorithm in which the expressional stability of genes is evaluated is NormFinder [17]. NormFinder estimates the intra-group and the inter-group expression variation. Both of these sources of variation are combined into a stability value. This method can account for heterogeneity of the tested tissue samples. Genes with the lowest stability value have the most stable expression.

          Colorectal cancer is among the most frequent malignant diseases worldwide, and is one of the leading causes of cancer-related deaths [21]. The majority of colorectal tumours develop along a well-defined adenoma-carcinoma sequence in which oncogenes are activated and tumour suppressor genes lose their function [22]. Despite a high 5-year survival rate in early colorectal cancer, only 10% of the patients with distant metastases survive after five years [23]. Thus, it is important to elucidate the biology that contributes to this progression, especially those processes that facilitates the switch to invasive and metastatic disease. Biological changes are a result of partly differential gene expression, which can be confirmed by qRT-PCR. It is necessary to validate reference genes in the particular experimental system in order to trust the differential gene expressions which are detected. Previous studies have tried to find universally stable reference genes across several types of cancers, including colon cancer [2426]. Recent reports, however, claim that stably expressed genes in one tumour type may not predict stable expression in another tumour type [12, 27]. Moreover, results in one tumour type, like colorectal cancer, show stably expressed genes in one experimental in which are different from the stably expressed genes in another experimental setup [2830]. Hence, reference genes should be validated and selected in every experiment in any tissue type. Recently, it has been suggested that the focus should be on introducing and validating novel approach for reference gene identification and standardizing experimental setup rather than giving general suggestions for different tissues [16]. Applying TaqMan Low Density Array (TLDA) to examining reference genes is a step towards a more standardized experimental setup. TLDA was evaluated in colorectal cancer by Lü et al., 2008, as a roughly robust and labour-saving method for gene quantification compared with routine qRT-PCR [31]. Well-designed TaqMan probes require little optimization, and TLDA allows simultaneously real-time detection of many gene products in several samples offering higher through put than established single array method [31, 32]. Hence, in the present study we used TLDA to find potential reference genes for data normalization in qRT-PCR experiments in metastatic and non-metastatic colon cancer patients. The gene expression of 16 commonly used reference genes in tumour tissue and individual-matched normal mucosa of metastatic and non-metastatic colon cancer patients were analyzed and the expression stability was determined and compared using geNorm and NormFinder.

          Methods

          Patients and tissue specimens

          RNAlater-stored tumour tissue samples and individual-matched normal mucosa were obtained from 38 patients with colonic adenocarcinoma who underwent resection at Akershus University Hospital Trust between 2004 and 2009. The dissected tissue samples were collected in the operating room and stored immediately in approximately five volumes of RNAlater (Ambion Inc., Austin TX, USA) and frozen at -80°C. Eighteen patients with non-metastatic disease, Dukes B (with a minimum of 12 negative lymph nodes) where no metastases occurred during 5 years follow up, and 20 patients originally staged as Duke C who displayed distant metastases during a 5 year follow-up (Duke C) or patients classified as Dukes D were included in the study. There were 22 women and 16 men with a mean age of 69 +/- 14 years (range 29-92) at surgery. Three sectioned pieces of the tumour samples were made. The central piece was further processed for RNA isolation, while the two end pieces were fixed in formalin and embedded in paraffin (FFPE). Four μm sections of FFPE samples were stained with Hagens Hematoxylin and examined by a pathologist for determination of percentage tumour cells. To avoid bias from necrosis or minimal tumour representation we included tumour tissue samples with more than 70% tumour cells.

          mRNA isolation

          Total RNA isolation was performed using the method of Wei and Khan, 2002, [33] modified according to T. Lüders (unpublished work) to also include miRNA for further analyses. Approximately 60 mg frozen tissue was homogenized in TriReagent (Ambion) using Mixer Mill MM301 (Retch) for 2 × 2 min at 30 Hz. After phase-separation with chloroform, the aqueous phase (containing RNA) was mixed with 1.5 volumes 100% ethanol and transferred to an RNeasy Mini spin column (Qiagen). Further processing was performed following the manufacturer's protocol. A DNase treatment was included in the procedure. RNA was eluted in 60 μl RNase-free water and stored at -80°C. The concentration of each RNA sample was obtained from A260 measurements using the NanoDrop 2000 (Thermo Fischer Scientific Inc.). The RNA integrity number (RIN) was tested by using the Agilent 2100 Bioanalyzer (Agilent Technologies).

          cDNA synthesis

          Complementary DNAs (cDNAs) were produced from 1 μg RNA of each sample using the High Capacity RNA-to-cDNA Master Mix (Applied Biosystems) according to the manufacturer's instructions. The following thermal cycler conditions were used: 5 min at 25°C, 30 min at 42°C and 5 min at 85°C. Three random RNA samples were additionally run in the absence of reverse transcriptase enzyme to assess the degree of contaminating genomic DNA. Real-time PCR with genomic DNA specific assay revealed that RNA was free of genomic DNA (data not shown).

          TLDA design and preparation

          TaqMan Endogenous Control Assays (Applied Biosystems) are 384-well microfluidic cards containing 16 preoptimized human TaqMan Gene Expression Assays commonly used as endogenous controls and genes that exhibit minimal differential expression across different tissues (Table 1). The assay was performed in triplicates. 50 μl cDNA (1 μg mRNA) was used as a template. Matched samples from 4 patients where loaded on each card. NTC (no template control) was added in one loading port. PCR amplification was performed using the ABI Prism 7900 HT Real Time PCR System (Perkin-Elmer Applied Biosystems, Foster City, California, USA). Thermal cycling conditions were used as follows: 2 min at 50°C, 10 min at 94.5°C, 30 sec at 97°C, and 1 min at 59.7°C for 40 cycles.
          Table 1

          Candidate reference genes included in the TaqMan Endogenous Control Assay.

          Gene name

          Gene symbol

          Assay ID

          Size (bp)

          Function

          18S Ribosomal RNA

          18S

          Hs99999901_s1

          187

          Part of a ribosomal subunit

          Phosphoglycerate kinase-1

          PGK1

          Hs99999906_m1

          75

          Key enzyme in glycolysis

          Β-Actin

          ACTB

          Hs99999903_m1

          171

          Cytoskeletal structural protein

          Polymerase (RNA) II polypeptide A

          POLR2A

          Hs00172187_m1

          61

          Catalyzes the RNA synthesis from DNA

          Beta-2-microglobulin

          B2M

          Hs99999907_m1

          75

          Beta-chain of major histocompatibility complex class I molecules

          Peptidyl-prolyl isomerase/cyclophilin A

          PPIA

          Hs99999904_m1

          98

          Catalyzes the cis-trans isomerization of proline imidic peptide bonds in oligopeptides, accelerating folding

          Glyceraldehyd-3-phosphate dehydrogenase

          GAPDH

          Hs99999905_m1

          122

          Dehydrogenase, oxidoreductase in glycolysis and gluconeogenesis

          Acidic ribosomal phosphoprotein P0

          RPLP0

          Hs99999902_m1

          105

          Ribosome biogenesis and assembly

          Β-Glucuronidase

          GUSB

          Hs99999908_m1

          81

          Glycoprotein, degradation of dermatan and keratin sulfates

          Transcription factor IID, TATA box binding protein

          TBP

          Hs99999910_m1

          127

          TATA binding protein, general RNA polymerase II transcription factor

          Hydromethylbilane synthase

          HMBS

          Hs00609297_m1

          64

          Heme synthesis, porphyrin metabolism

          Transferrin receptor (p90, CD71)

          TFRC

          Hs99999911_m1

          105

          Cellular uptake of iron

          Hypoxanthine-phosphoribosyl-transferase 1

          HPRT1

          Hs99999909_m1

          100

          Glycosyltransferase, purine synthesis in salvage pathway

          Ubiquitin C

          UBC

          Hs00824723_m1

          71

          Protein degradation

          Importin 8

          IPO8

          Hs00183533_m1

          71

          Function in nuclear protein import

          Tyrosine 3 monooxygenase activation protein, zeta polypeptide

          YWHAZ

          Hs00237047_m1

          70

          Signal transduction by binding to phosphorylated serine residues on a variety of signalling molecules

          TLDA analysis and Statistical analysis

          RealTime Statminer 3.0 Software (Integromics, Madrid, Spain) was used for implementation of quality controls in addition to calculation of optimal endogenous controls. This program uses the comparative Ct method for relative quantification analysis, and the results are expressed as a fold change of expression levels (DDCt values). The mean value of triplicates was applied for all calculations. Medians were used to replace missing values that occurred due to inconsistencies between replicates rather than from low expression. The detectability threshold was set to 36, meaning failing detectors are those with a Ct greater than or equal to 36. To measure the expressional stability of the candidate endogenous control genes, two commonly used programs were employed: geNorm [19] and NormFinder [17], both of which available in RealTime Statminer. Ct coefficients of variations (CtCV%) were calculated for every reference gene across all samples. All data are expressed as means ± SD. Except from the analyses in RealTime Statminer, all other calculations were performed using SPSS (version 14.0; SPSS, Chicago, IL, USA).

          Research Ethics

          This project was approved by the Regional Committee for Medical Research Ethics, Eastern Norway. The Norwegian Social Science Data Service has approved the collection and analysis of data.

          Results

          RNA quality control

          mRNAs of 16 potential reference genes were quantified by qRT-PCR using equal amounts of RNA templates from every tissue samples. The integrity of RNA (RIN) was ranged from 7.2 to 10.0, with a mean value of 8.9, which indicates good preservation of the RNA; thus suitable for RNA studies.

          Range of expression of candidate endogenous control genes

          Table 2 presents the mean Ct and SD values obtained across the candidate endogenous control genes in normal and tumour tissue separated into the two groups of patients; non-metastatic and metastatic colon cancer patients. Additional calculations across all patients are shown in Table 3. Mean Ct values ranged from 8.71 (± 1.31 SD) (18S) across all samples to 26.70 (± 1.69 SD) (TBP). The gene with the lowest standard deviation across all samples was IPO8 which showed an overall SD of 1.28, while the gene with the highest standard deviation across the samples was PGK1 with an overall SD of 2.49. The reference genes displayed a relatively broad range of expression. PGK1 had the widest range of Ct values between 8.35 and 29.83 (mean 21.03 ± 2.49 SD, range of 21.47), while B2M had the narrowest range of Ct values between 15.25 and 23.59 (mean 17.10 ± 1.31 SD, range of 8.34). During the subsequent analyses using Statminer Ct values above 36 are excluded and imputed, because Ct values above this level are not reliable. This quality control will thus influence the Ct ranges.
          Table 2

          Cycle threshold (Ct) values of candidate reference genes divided in the four tissue groups.

          Gene symbol

          Non-metastatic colon cancer

          Metastatic colon cancer

           

          Tumour

          Normal

          Tumour

          Normal

           

          Mean

          SD

          N

          Mean

          SD

          N

          Mean

          SD

          N

          Mean

          SD

          N

          18S

          8,095

          0,546

          18

          8,440

          1,066

          18

          8,800

          1.066

          20

          9,408

          2,035

          20

          ACTB

          20,003

          0,765

          18

          19,949

          1,209

          18

          20,363

          1.209

          20

          20,578

          2,673

          20

          B2M

          17,050

          0,996

          18

          17,041

          1,002

          18

          17,217

          1.002

          20

          17,085

          1,632

          20

          GAPDH

          18,503

          0,722

          18

          19,502

          1,044

          18

          19,211

          1.044

          20

          20,145

          2,541

          20

          GUSB

          23,274

          0,375

          18

          24,081

          0,865

          18

          23,564

          0.865

          20

          24,060

          1,981

          20

          HMBS

          25,328

          0,736

          18

          26,577

          0,974

          18

          25,963

          0.974

          20

          27,030

          2,436

          20

          HPRT1

          22,795

          0,814

          18

          24,183

          0,750

          18

          23,320

          0.750

          20

          24,264

          1,849

          20

          IPO8

          24,575

          0,469

          18

          25,084

          0,780

          18

          25,099

          0.780

          20

          25,529

          2,108

          20

          PGK1

          20,322

          1,054

          18

          21,151

          1,012

          18

          20,996

          1.011

          20

          21,573

          3,257

          20

          POLR2A

          24,007

          0,634

          18

          24,508

          1,061

          18

          24,933

          1.061

          20

          25,330

          2,590

          20

          PPIA

          17,081

          0,485

          18

          18,241

          0,906

          18

          17,506

          0.906

          20

          18,335

          1,724

          20

          RPLP0

          19,706

          0,637

          18

          20,647

          0,952

          18

          20,319

          0.952

          20

          21,081

          2,002

          20

          TBP

          26,157

          0,577

          18

          26,860

          1,035

          18

          26,649

          1.035

          20

          27,110

          2,797

          20

          TFRC

          21,774

          0,926

          18

          23,334

          1,030

          18

          22,679

          1.030

          20

          23,663

          2,303

          20

          UBC

          21,285

          0,675

          18

          21,771

          1,046

          18

          21,532

          1.046

          20

          22,044

          2,180

          20

          YWHAZ

          23,933

          0,723

          18

          25,041

          1,275

          18

          24,457

          1.275

          20

          25,401

          2,174

          20

          Table 3

          Cycle threshold (Ct) values of candidate endogenous control genes across all tissue samples.

          Gene

          Mean

          ± s.e.m

          Standard deviation (SD)

          Ct min

          Ct max

          Ct Range

          CtCV%

          18S

          8.708

          0.151

          1.314

          6.858

          16.932

          10.073

          14,99

          ACTB

          20.236

          0.187

          1.630

          17.979

          31.018

          13.039

          8,10

          B2M

          17.101

          0.150

          1.306

          15.251

          23.587

          8.336

          7,69

          GAPDH

          19.358

          0.191

          1.661

          17.382

          30.403

          17.382

          8,63

          GUSB

          23.748

          0.150

          1.309

          21.719

          32.338

          10.619

          5,55

          HMBS

          26.239

          0.186

          1.625

          24.315

          36.823

          12.508

          6,23

          HPRT1

          23.649

          0.164

          1.429

          21.852

          31.617

          9.765

          6,04

          IPO8

          25.085

          0.146

          1.276

          23.749

          33.903

          10.154

          5,12

          PGK1

          21.025

          0.285

          2.487

          8.354

          29.829

          21.474

          10,35

          POLR2A

          24.717

          0.184

          1.606

          20.827

          34.874

          14.047

          6,54

          PPIA

          17.978

          0.150

          1.305

          15.980

          25.150

          9.170

          7,34

          RPLP0

          20.452

          0.162

          1.413

          18.682

          29.171

          10.489

          6,93

          TBP

          26.704

          0.194

          1.692

          24.858

          38.656

          13.799

          6,38

          TFRC

          22.878

          0.196

          1.711

          19.907

          32.261

          12.354

          7,53

          UBC

          21.665

          0.163

          1.422

          19.475

          30.387

          10.912

          6,60

          YWHAZ

          24.720

          0.193

          1.685

          22.733

          32.853

          10.120

          6,86

          Note. S.e.m, standard error of mean; CtCV%, Coefficients of variations of candidate reference genes.

          Results of validation programs

          In order to determine the stability of genes and thus find the best endogenous controls, the data were analysed by geNorm and NormFinder. In these analyses, medians were used to replace missing values because they occurred due to inconsistencies between replicates rather than from low expression. The ranking of the gene expression stability values (M) of the tested endogenous control genes using geNorm is illustrated in Figure 1.A. The genes with the highest M, i.e. the least stable genes, gets stepwise excluded until the most stable genes remain. The best two genes are ranked without distinguishing between them. HPRT1 and PPIA were identified as the most stable pair of genes, followed by PGK1 as the third most stable gene. Furthermore, pairwise variation were also calculated using geNorm in order to determine the optimal number of genes required for normalization, Figure 1.B. The analysis showed that HPRT1 and PPIA may be sufficient for calculation of the normalization factor and normalization to genes of interest, since the V2/3 value is in this analysis equal to the cut-off value of 0.15 [19]. However, there is a gradual decrease in the pairwise variability plot and thereby an improvement to the normalization factor by adding additional genes to the calculation. Nevertheless, two or three genes would be satisfactory for normalization according to the cut-off value of 0.15. While geNorm uses a pairwise comparison approach, NormFinder first estimates the intra-group and then the inter-group variability of expression of a control gene [17]. In contrast to the geNorm results, NormFinder ranked RPLP0 as the most stable gene, with TBP and GUSB closely behind as second and third, respectively (Figure 2). However, using this algorithm the combination of IPO8 and PPIA turned out to have a lower stability score than the most stable single gene. Thus this combination is more suitable for normalizing qPCR. There was considerably closer agreement between the geNorm and Normfinder results on the least stable genes, with the order of 4 out of 5 worst ranking genes being identical; ACTB, 18S, B2M and TFRC. These genes had a stability value more than twice so high (geNorm) and more than 3 times so high (NormFinder) as the best ranking genes.
          http://static-content.springer.com/image/art%3A10.1186%2F1756-9966-29-144/MediaObjects/13046_2010_Article_391_Fig1_HTML.jpg
          Figure 1

          GeNorm analysis of the candidate reference genes. (A) Average expression stability values of reference genes. Genes are presented in an increasing order of stability from left to right with ACTB being the least stable gene and HPRT1 and PPIA the most stable genes. (B) Determination of optimal number of control genes for normalization. Every bar represents change in normalization accuracy when stepwise adding more reference genes according to ranking in Figure 1. A. To meet the recommended cut-off value of 0.15 two pr three genes would be satisfactory for normalization.

          http://static-content.springer.com/image/art%3A10.1186%2F1756-9966-29-144/MediaObjects/13046_2010_Article_391_Fig2_HTML.jpg
          Figure 2

          NormFinder analysis of the candidate reference genes. Genes are presented in an increasing order of stability from left to right with B2M as the least stable gene and RPLP0 as the most stable gene.

          Due to different ranking by geNorm and NormFinder of the most stable genes, cycle threshold coefficient of variation (CtCV%) was calculated for each of them. This calculation was recommended by Caradec et al., 2010, in order to validate the NormFinder and geNorm results [12]. According to the CtCV% calculation, one of the NormFinder pairing genes, IPO8, was ranked as the most stable gene with a CtCV% of 5.12%, which supports the NormFinder result. GUSB (5.5%) and HPRT1 (6.04%) are ranked as the second and third respectively, which do not give identical ranking of results obtain using geNorm and NormFinder. The least stable gene using CtCV% was 18S (14.99%), which was according to geNorm and NormFinder ranked as the second and fifth least stable gene, respectively. The summary of the best ranking genes as determined by each of these calculations is illustrated in Table 4.
          Table 4

          Ranking and best combination of reference genes determined by geNorm, NormFinder and CtCV%.

          Rank

          GeNorm

          NormFinder

          CtCV%

          1

          HPRT1

          RPLP0

          IPO8 (5.12)

          2

          PPIA

          TBP

          GUSB (5.55)

          3

          PGK1

          GUSB

          HPRT1 (6.04)

          4

          RPLP0

          POLR2A

          HMBS (6.23)

          5

          HMBS

          IPO8

          TBP (6.38)

          6

          GAPDH

          GAPDH

          POLR2A (6.54)

          7

          GUSB

          PPIA

          UBC (6.60)

          8

          IPO8

          HPRT1

          YWHAZ (6.86)

          Best gene/combination

          HPRT1/PP1A

          IPO8/PPIA

          IPO8

          Discussion

          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 [36], fully in accordance with modern clonal stem cell theories [37]. 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 [30]. 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 [12]. 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 [32]. 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 [32]. 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 [28]. 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 [29]. 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, 3840].

          Conclusions

          In this study we applied TaqMan Low Density Array in order to identify reference genes in metastatic and non-metastatic colon cancer. The genes often used for normalization of gene expression data may be unstable and thus not suited for use, and therefore identifying stable reference genes in the specific experiment is vital for the results. The approach described herein can serve as a template to identify valid reference genes in any disease state. However, the optimal statistical approach to identify the best reference gene(s) remains to be determined. In the present study NormFinder and geNorm identified two different pairs of the most stable genes. The use of CTCV% might be a good validation of the two results. Nevertheless, the expression of target genes should be evaluated and a comparison of the effect of each pair of reference genes should be determined.

          Declarations

          Acknowledgements

          We thank Department of Clinical Molecular Biology (EpiGen), Akershus University Hospital, Norway, for laboratory facilities, and Randi Bjørseth at Department of Pathology, Akershus University Hospital, Norway, for guidance and technical assistance. The authors declare that they have no conflicts of interest. This study was supported by grants from South Eastern Norway Regional Health Authority, Norway.

          Authors’ Affiliations

          (1)
          Quality and Research Department, Ostfold Hospital Trust
          (2)
          Dep. of Pathology, Akershus University Hospital
          (3)
          Institute of Clinical Medicine, University of Oslo
          (4)
          Dep. of Surgery, Akershus University Hospital
          (5)
          Dep. of Health Promotion, Akershus University Hospital
          (6)
          Dep. of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences

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          © Sørby et al; licensee BioMed Central Ltd. 2010

          This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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