Identification of valid reference genes for gene expression studies of human stomach cancer by reverse transcription-qPCR
© Rho et al; licensee BioMed Central Ltd. 2010
Received: 30 November 2009
Accepted: 28 May 2010
Published: 28 May 2010
Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is a powerful method for the analysis of gene expression. Target gene expression levels are usually normalized to a consistently expressed reference gene also known as internal standard, in the same sample. However, much effort has not been expended thus far in the search for reference genes suitable for the study of stomach cancer using RT-qPCR, although selection of optimal reference genes is critical for interpretation of results.
We assessed the suitability of six possible reference genes, beta-actin (ACTB), glyceraldehydes-3-phosphate dehydrogenase (GAPDH), hypoxanthine phosphoribosyl transferase 1 (HPRT1), beta-2-microglobulin (B2M), ribosomal subunit L29 (RPL29) and 18S ribosomal RNA (18S rRNA) in 20 normal and tumor stomach tissue pairs of stomach cancer patients and 6 stomach cancer cell lines, by RT-qPCR. Employing expression stability analyses using NormFinder and geNorm algorithms we determined the order of performance of these reference genes and their variation values.
This RT-qPCR study showed that there are statistically significant (p < 0.05) differences in the expression levels of HPRT1 and 18S rRNA in 'normal-' versus 'tumor stomach tissues'. The stability analyses by geNorm suggest B2M-GAPDH, as best reference gene combination for 'stomach cancer cell lines'; RPL29-HPRT1, for 'all stomach tissues'; and ACTB-18S rRNA, for 'all stomach cell lines and tissues'. NormFinder also identified B2M as the best reference gene for 'stomach cancer cell lines', RPL29-B2M for 'all stomach tissues', and 18S rRNA-ACTB for 'all stomach cell lines and tissues'. The comparisons of normalized expression of the target gene, GPNMB, showed different interpretation of target gene expression depend on best single reference gene or combination.
This study validated RPL29 and RPL29-B2M as the best single reference genes and combination, for RT-qPCR analysis of 'all stomach tissues', and B2M and B2M-GAPDH as the best single reference gene and combination, for 'stomach cancer cell lines'. Use of these validated reference genes should provide more exact interpretation of differential gene expressions at transcription level in stomach cancer.
Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is a powerful tool for validating the observed gene expression differences, because of its greater sensitivity and specificity. In traditional gene expression studies, a 'reference gene', also called 'internal standard' or 'housekeeping gene' is used for the normalization. The expression of beta-actin (ACTB) and glyceraldehydes-3-phosphate dehydrogenase (GAPDH), used in a majority of studies , was reported to vary with experimental conditions  and clinical status of the tissue studied (e.g. asthma), making these genes unsuitable as internal standards for use in normalization of gene expression . Thus, the validity of the reference gene chosen for statistical analysis is crucial for avoiding the hazard of misinterpreting data and invalid conclusions .
It was suggested that at least three considerations should be taken into account in choosing a reference gene: 1) constancy of its expression throughout the intervention, 2) its amplification efficiency and 3) its abundance, which should be similar to that of the genes of interest . In addition, to ensure the relevance, accuracy and correctness of interpretations of RT-qPCR, it is recommended that the precise guidelines for RT-qPCR MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiment) should be adhered to . Several tools for statistical analysis such as NormFinder , geNorm , BestKeeper  have been developed to help in the choice of appropriate reference genes. These tools assess the variations in the expression of a number of potential reference genes and suggest which reference gene(s) is appropriate for normalization of gene expression data in a given study.
Stomach cancer is the fourth most common cancer worldwide, with a reported 934,000 cases in 2002 . Survival from stomach cancer is poor since patients are often diagnosed only after the disease has already advanced significantly , which makes early detection very important. Screening aiming at early detection involves endoscopic examination. To confirm the presence of cancer, biopsies are taken from suspected tissues and subjected to RT-qPCR to confirm abnormal expression of cancer related genes. But appropriate reference genes have to be identified for valid comparisons between expressions of normal versus cancer genes. Reference genes have been described for RT-qPCR studies in various cancers of other tissues [1, 12–21]. However there seems to be no consensus on reference genes for gene expression studies in stomach cancer. We therefore searched PubMed with MeSH terms "gastric cancer", "real-time", and "PCR". In an evaluation of 115 articles published from May 2007 to November 2009, we found that GAPDH (53 cases; 46.1%) and ACTB (41 cases; 35.7%) were the most frequently used reference genes in gastric cancer studies; followed by 18S rRNA (8 cases; 7.0%), beta-2-microglobulin (B2M; 3 cases; 2.6%), hypoxanthine phosphoribosyl transferase 1 (HPRT1; 2cases; 1.7%), TATA binding protein (TBP; 1 case; 0.9%), and beta-tubulin (TUBB; 1 case; 0.9%). In five cases (4.3%), external standard curve was used for absolute quantification (AQ) instead of normalized value by reference gene.
Potential reference genes evaluated in this study.
GenBank Accession No.
Cytoskeletal structural protein
Oxidoreductase in glycolysis and gluconeogenesis
Hypoxanthine phosphoribosyl transferase 1
Metabolic salvage of purines
Beta-chain of major histocompatibility complex class I molecules
18S ribosomal RNA
Ribosomal protein L29
Structural constituent of ribosome
Glycoprotein (transmembrane) nmb
Involved in growth delay and reduction of metastatic potential
Cell lines and human tissues
Features of patients who provided stomach cancer tissues.
Number of patients
Number of patients
Age at diagnosis (years)
Mean ± SD
60.8 ± 12.1
RNA extraction and cDNA synthesis
Stomach cancer tissue samples were preserved in RNAlater solution (Qiagen, Hilden, Germany) until use for RNA extraction. Total RNA was extracted with TRIzol regent according to the manufacturer's protocol (Invitrogen), and treated with DNaseI on RNeasy Mini column (Qiagen) to remove residual genomic DNA. Concentration and A260/280 ratio of purified RNA were measured with Nanodrop ND-1000 (Thermo Scientific, Wilmington, DE, USA), and quality was assessed on Agilent 2100 Bioanalyzer using RNA 6000 Nano kit (Agilent Technologies, Santa Clara, CA, USA). Two μg of poly-dT primed total RNA (random hexamer primed total RNA for 18S rRNA amplification) were reverse-transcribed with Transcriptor Reverse Transcriptase according to the manufacturer's protocol (Roche Applied Science, Mannheim, Germany).
Reverse transcription quantitative real-time PCR (RT-qPCR)
Primers for six reference genes and a target gene.
Forward primer [5'→3']
Reverse sequence [5'→3']
In house design2
Statistical analyses were performed with GraphPad Prism V4.03 (GraphPad Software, La Jolla, CA, USA). Normality was assessed according to Kolmogorov-Smirnov (KS), D'Agostino-Pearson (DAP), and Shapiro-Wilk (SW) tests. For the distribution of non-normal distributed groups, non-parametric Mann-Whitney U-test and Wilcoxon signed rank test were performed. P-values with p < 0.05 were considered statistically significant. We applied NormFinder V12  and geNorm™ V3.44  software to determine the expression values of six candidate reference genes.
RNA quality assessment
We assessed the quality of RNA used as starting material in several ways. A260/280 ratio measured by Nanodrop was 2.08 ± 0.09 (mean ± SD) confirming that the RNA was pure and protein-free. RNA quality reported as RNA integrity number (RIN) by RNA 6000 Nano Labchip for cultured cell line was 9.7 ± 0.2 (mean ± SD), and 7.4 ± 1.0 for patient tissue samples. For the matched pairs of stomach tissue samples, we did not find any statistically significant difference in either A260/280 ratio between normal (2.05 ± 0.03) and tumor (2.04 ± 0.05) tissues (paired Student's t-test p-value = 0.214) or RIN values between normal (7.2 ± 0.5) and tumor (7.5 ± 1.4) tissue sample groups (paired Student's t-test p-value = 0.340).
Expression ranges of candidate reference genes and target gene
Human stomach tissues also showed wide variations in Cp values ranging from 13.3 to 29.4 (Figure 1), with highest expression of B2M and 18S rRNA and lowest expression of HPRT1. The expression of target gene GPNMB in Cp ranged from 28.5 to 34.7 in stomach tissues. In normality test, ACTB, HPRT1, and 18S rRNA in 'normal stomach tissues' group and all genes except for GAPDH in 'tumor stomach tissues' followed normal distribution by KS-test. HPRT1, B2M, and RPL29 in tumor stomach tissues passed the normality in DAP- and SW-tests. Thus, for comparing non-normal distributed paired groups, we performed non-parametric Wilcoxon signed rank test. Significant expression increase from normal to tumor stomach tissues (p < 0.05) were observed in HPRT1 (p = 0.011) and 18S rRNA (p = 0.021), but not in ACTB (p = 0.058), GAPDH (p = 0.918), B2M (p = 0.740), or RPL29 (p = 0.208).
Expression stability of candidate reference genes
Ranking of the candidate single reference genes based on their stability values calculated from NormFinder.
Non-stomach cancer cell lines
Stomach cancer cell lines
Normal stomach tissues
Tumor stomach tissues
All stomach tissues
All stomach cell lines + tissues
Gene in ranking order
Gene in ranking order
Gene in ranking order
Gene in ranking order
Gene in ranking order
Gene in ranking order
Target gene expression profiles are influenced by reference genes employed for normalization
Differential gene expression in cancer identified from transcriptome study suggests that some specific genes might be involved in tumorigenesis and metastasis of cancer. RT-qPCR is a robust and specific method for the validation of the identity of candidate genes of stomach cancer, because it detects even very weak signals from extremely small amounts of biopsied samples if the patient is in early stage of cancer. However, in the absence of appropriate reference genes, data obtained are open to question leading to misinterpretation. Prior to this study, no validated reference gene has been identified for 'stomach cancer cell line' or 'stomach cancer tissue', but ACTB and GAPDH have been used most frequently until now without consideration of their inconsistent expressions in different experimental settings and clinical conditions. We examined, in addition to ACTB and GAPDH, four other reference genes, HPRT1, RPL29, 18S rRNA, and B2M that have been evaluated as reference genes in recent studies for other human cancers.
It is evident that choosing appropriate primer set is an important starting point to obtain accurate results. We considered following points in selecting primers. First, we adopted primer sets that were previously reported to have or designed to possess an amplicon length around 200 bp. Second, all of the primer sets were required to span at least two neighboring exons except for 18S rRNA gene which is not an mRNA. The above two points are related to the amplification efficiency. It is necessary that reference gene and target gene maintain similar amplification efficiency . Amplicon length is closely related to amplification efficiency . So one would expect similar efficiency from amplicon of similar length, and higher efficiency from a shorter amplicon. The benefit of shorter amplicon, 70-250 bp, in RT-qPCR is that amplification is "independent" of RNA quality . The amplification efficiency is also affected by gDNA contamination, because competitive binding of primers acts as a limiting factor causing decrease of amplification efficiency . In this context, DNaseI treatment during the RNA purification is crucial to avoid amplification from residual gDNA, but it might not be totally effective. Therefore, our second consideration helps to detect possible contamination with gDNA with different amplicon sizes. We confirmed that each forward and reverse primer is anchored on different exon by BLAT searches on human genome sequences and also that there was no amplified product from contaminating gDNA with extended amplicon length (Table 2). Besides, we also ensured the high quality of RNA, the starting material, in several ways. We also performed the experiments in triplicate for every gene and every sample.
Since the development of qPCR, several statistical programs were developed to identify optimal reference genes. We chose geNorm and NormFinder to analyze the stability of the six reference genes we studied. The geNorm program calculates M-values based on the average pair-wise variation of a particular gene compared with all other studied candidate reference genes and ranks them . In comparison, NormFinder adopts a strategy, called 'model-based approach to estimation of expression variation' . These distinct strategies identified for us the best single or combination reference genes in each group of comparisons. GeNorm identified RPL29-HPRT1 as the most optimal combination for 'all stomach tissues', while NormFinder identified RPL29-B2M instead. Although HPRT1 or B2M was in the best combination identified both by geNorm and NormFinder, respectively, they were ranked third in our single reference gene ranking by each analysis (Table 4). With 'stomach cancer cell lines', the rankings from two analyses were identical, i.e. GAPDH-B2M was the most stable reference gene combination followed by RPL29. These results were supported by statistical data, because the highly ranked reference genes have narrower range of variations in expression levels (Figure 1). For example, the most unstable gene, HPRT1, in 'stomach cancer cell lines' has much wider range of expression compared to GAPDH or B2M. This is also true for 'all stomach tissues', since RPL29 and HPRT1 have much narrower range of expression than GAPDH or ACTB.
Some reference genes such as ACTB and B2M are expressed somewhat more in stomach tissues than in cancer cell lines. Cancer cell lines are supposed to be more activated in metabolism, eventually displaying higher transcription activities. However, higher expression of ACTB and B2M was reported in stomach tissues than AGS/SNU-638 stomach cancer cell lines  as well as higher B2M expression in liver tissues than HepG2/Hep3B/SK-HEP-1/SNU-182 liver cancer cell lines . In comparison, in this study, the average expression levels of GAPDH and RPL29 were similar in stomach tissues and cancer cell lines. Thus, it appears that metabolically more activated cancer cells are not always in higher transcriptional activity for every gene and every kinds of cell line.
To determine the best reference genes, we analyzed our results with the six candidate reference genes under the suggested rules . First, in terms of amplification efficiency, all primer sets seem acceptable because they have similar and close to perfect amplification efficiency of 2.0 (Table 2). Second, in terms of constant expression in comparable conditions, HPRT1 and 18S rRNA are to be excluded from the best candidate list for comparing gene expression in normal and tumor stomach tissues', because statistical analysis revealed a significant increase in gene expression in tumor tissues compared to normal tissues. Third, in terms of the abundance of reference genes and target gene, ideally the reference should be almost same in its abundance. However, in reality it is hard to find out genes showing exactly same amount of expression. Therefore, it is advisable to use the lesser different reference gene to give out the more accurate interpretation. In agreement with this, RPL29 seems appropriate because the expression is close to that of GPNMB. Lastly, in selecting the best reference genes from two algorithms, we considered whether selecting multiple reference genes in combination is better than selecting a single reference gene alone, because there is still considerable difference of opinion on the use of multiple reference genes as reported in several studies . Since some of the genes included in combination were shown to be differentially expressed between normal and tumor or stomach cancer cell lines and tissues, it is necessary to take into account the consistency of expression ranges of each reference gene. Although RPL29-HPRT1 combination has been suggested as the best for 'all stomach tissues' by geNorm, it is also evident that HPRT1 expression has increased from normal to tumor and this combination is not considered suitable one. In this context, it is not advisable to accept the best combinations for 'all stomach cancer cell lines and tissues'. Both algorithms suggested combinations that have 18S rRNA showing differences in the levels of expression between normal and tumor stomach tissues. Actually, only RPL29 showed consistent expression range for all stomach cancer cells and tissues, suggesting that using single reference gene may be more appropriate for comparisons. Taking these findings together, B2M seems to be the most suitable single reference gene for 'stomach cancer cell lines' and RPL29 for 'all stomach tissues'. RPL29 is also the best for comparing target gene expressions in stomach cancer cells and tissues. Using GAPDH-B2M combination for comparing gene expressions in 'stomach cancer cell lines' and RPL29-B2M combination for comparing in 'all stomach tissues' is therefore recommended. We recognize the limitation of this study in that we examined a limited number of samples, but we feel that our conclusions and recommendations are supported in part by previously deposited expression data from microarray and reports in the literature. In an asthmatic airway study, ACTB and GAPDH were found to be unsuitable as reference genes . Same conclusion was reported for breast, prostate and pancreatic cancers where transcript levels of GAPDH were found elevated . For stomach tissues, we confirmed this in microarray data deposited in ArrayExpress Gene Expression ATLAS http://www.ebi.ac.uk/arrayexpress. The gene expression profile of advance gastric cancer tissues (E-GEOD-2685) showed elevated expression in ACTB (p-value = 7.73e-3) and GAPDH (p-value = 1.94e-3), but no significant difference with other four candidate reference genes. For the target gene GPNMB, elevation of expression was observed in primary gastric tumors (p-value = 1.11e-8; E-GEOD-15460). Thus, it seems clear that blindly choosing just ACTB or GAPDH without such evaluations should be avoided.
In this study we systematically explored the suitability of potential candidate reference genes for normalization of gene expression in stomach cancer cell lines and tissues. We propose B2M and RPL29 as the best single reference genes for exploring gene expression in 'stomach cell lines' and 'all stomach tissues', respectively. In addition we suggest that GAPDH-B2M combination for normalizing expression in 'stomach cancer cell lines' and RPL29-B2M combination for comparison between normal and tumor in 'all stomach tissues'. RPL29 is also suitable for the comparison in pooled stomach cancer cell lines and tissue samples. The choice of reference genes should depend on the cell lines and/or tissues under study, and there is no single, universal, common optimal reference gene.
List of abbreviations
reverse transcription quantitative real-time polymerase chain reaction
hypoxanthine guanine phosphoribosyl transferase 1
- 18s rRNA:
18S ribosomal RNA
ribosomal protein large subunit 29
gene stability value
RNA integrity number
This study was supported in part by research grant #0810160 from National Cancer Center. We thank Dr. J. Sri Ram for editorial assistance.
- Jung M, Ramankulov A, Roigas J, Johannsen M, Ringsdorf M, Kristiansen G, Jung K: In search of suitable reference genes for gene expression studies of human renal cell carcinoma by real-time PCR. BMC Mol Biol. 2007, 8: 47-10.1186/1471-2199-8-47.View ArticlePubMedPubMed CentralGoogle Scholar
- Winer J, Jung CK, Shackel I, Williams PM: Development and validation of real-time quantitative reverse transcriptase-polymerase chain reaction for monitoring gene expression in cardiac myocytes in vitro. Anal Biochem. 1999, 270: 41-49. 10.1006/abio.1999.4085.View ArticlePubMedGoogle Scholar
- Glare EM, Divjak M, Bailey MJ, Walters EH: Beta-actin and GAPDH housekeeping gene expression in asthmatic airways is variable and not suitable for normalising mRNA levels. Thorax. 2002, 57: 765-770. 10.1136/thorax.57.9.765.View ArticlePubMedPubMed CentralGoogle Scholar
- Rhinn H, Marchand-Leroux C, Croci N, Plotkine M, Scherman D, Escriou V: Housekeeping while brain's storming Validation of normalizing factors for gene expression studies in a murine model of traumatic brain injury. BMC Mol Biol. 2008, 9: 62-10.1186/1471-2199-9-62.View ArticlePubMedPubMed CentralGoogle Scholar
- Mahoney DJ, Carey K, Fu MH, Snow R, Cameron-Smith D, Parise G, Tarnopolsky MA: Real-time RT-PCR analysis of housekeeping genes in human skeletal muscle following acute exercise. Physiol Genomics. 2004, 18: 226-231. 10.1152/physiolgenomics.00067.2004.View ArticlePubMedGoogle Scholar
- Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, Mueller R, Nolan T, Pfaffl MW, Shipley GL, Vandesompele J, Wittwer CT: The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009, 55: 611-622. 10.1373/clinchem.2008.112797.View ArticlePubMedGoogle Scholar
- Andersen CL, Ledet-Jensen J, Ørntoft T: Normalization of real-time quantitative RT-PCR data: a model based variance estimation approach to identify genes suited for normalization - applied to bladder- and colon-cancer data-sets. Cancer Res. 2004, 64: 5245-5250. 10.1158/0008-5472.CAN-04-0496.View ArticlePubMedGoogle Scholar
- Vandesompele J, Preter KD, Pattyn F, Poppe B, Roy NV, Paepe AD, Speleman F: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002, 3: research0034.1-0034.11. 10.1186/gb-2002-3-7-research0034.View ArticleGoogle Scholar
- Pfaffl MW, Tichopád A, Prgomet C, Neuvians TP: Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper - Excel-based tool using pair-wise correlations. Biotechnology Lett. 2004, 26: 509-515. 10.1023/B:BILE.0000019559.84305.47.View ArticleGoogle Scholar
- Parkin DM, Bray F, Ferlay J, Pisani P: Global cancer statistics, 2002. CA Cancer J Clin. 2005, 55: 74-108. 10.3322/canjclin.55.2.74.View ArticlePubMedGoogle Scholar
- Boyle P, Levin B: World Cancer Report 2008. 2008, Lyon, France International Agency for Research on CancerGoogle Scholar
- Huang D, Chen W, He R, Yu F, Zhang Z, Qiu W: Different cDNA microarray patterns of gene expression reflecting changes during metastatic progression in adenoid cystic carcinoma. World J Surg Oncol. 2003, 1: 28-10.1186/1477-7819-1-28.View ArticlePubMedPubMed CentralGoogle Scholar
- Lyng MB, Lænkholm AV, Pallisgaard N, Ditzel HJ: Identification of genes for normalization of real-time RT-PCR data in breast carcinomas. BMC Cancer. 2008, 8: 20-10.1186/1471-2407-8-20.View ArticlePubMedPubMed CentralGoogle Scholar
- Ohl F, Jung M, Radonić A, Sachs M, Loening SA, Jung K: Identification and validation of suitable endogenous reference genes for gene expression studies of human bladder cancer. J Urol. 2006, 175: 1915-1920. 10.1016/S0022-5347(05)00919-5.View ArticlePubMedGoogle Scholar
- Silvia S, Francesca C, Marco LI, Silvia N, Giogio VS, Raffaele CA: Selection of suitable reference genes for accurate normalization of gene expression profile studies in non-small cell lung cancer. BMC Cancer. 2006, 6: 200-10.1186/1471-2407-6-200.View ArticlePubMed CentralGoogle Scholar
- Li YL, Ye F, Hu Y, Lu WG, Xie X: Identification of suitable reference genes for gene expression studies of human serous ovarian cancer by real-time polymerase chain reaction. Anal Biochem. 2009, 394: 110-116. 10.1016/j.ab.2009.07.022.View ArticlePubMedGoogle Scholar
- Cicinnati VR, Shen Q, Sotiropoulos GC, Radtke A, Gerken G, Beckebaum S: Validation of putative reference genes for gene expression studies in human hepatocellular carcinoma using real-time quantitative RT-PCR. BMC Cancer. 2008, 8: 350-10.1186/1471-2407-8-350.View ArticlePubMedPubMed CentralGoogle Scholar
- Fu LY, Jia HL, Dong QZ, Wu JC, Zhao Y, Zhou HJ, Ren N, Ye QH, Qin LX: Suitable reference genes for real-time PCR in human HBV-related hepatocellular carcinoma with different clinical prognoses. BMC Cancer. 2009, 9: 49-10.1186/1471-2407-9-49.View ArticlePubMedPubMed CentralGoogle Scholar
- Gao Q, Wang XY, Fan J, Qiu SJ, Zhou J, Shi YH, Xiao YS, Xu Y, Huang XW, Sun J: Selection of reference genes for real-time PCR in human hepatocellular carcinoma tissues. J Cancer Res Clin Oncol. 2008, 134: 979-986. 10.1007/s00432-008-0369-3.View ArticlePubMedGoogle Scholar
- Ohl F, Jung M, Xu C, Stephan C, Rabien A, Burkhardt M, Nitsche A, Kristiansen G, Loening SA, Radonić A, Jung K: Gene expression studies in prostate cancer tissue: which reference gene should be selected for normalization?. J Mol Med. 2005, 83: 1014-1024. 10.1007/s00109-005-0703-z.View ArticlePubMedGoogle Scholar
- Coulson DT, Brockbank S, Quinn JG, Murphy S, Ravid R, Irvine GB, Johnston JA: Identification of valid reference genes for the normalization of qPCR gene expression data in human brain tissue. BMC Molecular Biology. 2008, 9: 46-10.1186/1471-2199-9-46.View ArticlePubMedPubMed CentralGoogle Scholar
- Metz RL, Yehia G, Fernandes H, Donnelly RJ, Rameshwar P: Cloning and characterization of the 5' flanking region of the HGFIN gene indicate a cooperative role among p53 and cytokine-mediated transcription factors. Cell Cycle. 2005, 4: 315-322.View ArticlePubMedGoogle Scholar
- Metz RL, Patel PS, Hameed M, Bryan M, Rameshwar P: Role of human HGFIN/nmb in breast cancer. Breast Cancer Res. 2007, 9: R58-10.1186/bcr1764.View ArticlePubMedPubMed CentralGoogle Scholar
- Piyamongkol W, Bermúdez MG, Harper JC, Wells D: Detailed investigation of factors influencing amplification efficiency and allele drop-out in single cell PCR: implications for preimplantation genetic diagnosis. Mol Hum Reprod. 2003, 9: 411-420. 10.1093/molehr/gag051.View ArticlePubMedGoogle Scholar
- Fleige S, Pfaffl MW: RNA integrity and the effect on the real-time qRT-PCR performance. Mol Aspects Med. 2006, 27: 126-139. 10.1016/j.mam.2005.12.003.View ArticlePubMedGoogle Scholar
- Kwon MJ, Oh E, Lee S, Roh MR, Kim SE, Lee Y, Choi YL, In YH, Park T, Koh SS, Shin YK: Identification of novel reference genes using multiplatform expression data and their validation for quantitative gene expression analysis. PLoS One. 2009, 4: e6162-10.1371/journal.pone.0006162.View ArticlePubMedPubMed CentralGoogle Scholar
- Sobin LH, Wittekind C: TNM Classification of Malignant Tumours (UICC). 2002, New York, NY; Wiley, 6Google Scholar
- Zhang X, Ding L, Sandford AJ: Selection of reference genes for gene expression studies in human neutrophils by real-time PCR. BMC Mol Biol. 2005, 6: 4-10.1186/1471-2199-6-4.View ArticlePubMedPubMed CentralGoogle Scholar
- Poulin H, Labelle Y: The PLAGL1 gene is down-regulated in human extraskeletal myxoid chondrosarcoma tumors. Cancer Lett. 2005, 227: 185-191. 10.1016/j.canlet.2004.12.007.View ArticlePubMedGoogle Scholar
- Susztak K, Ciccone E, McCue P, Sharma K, Böttinger EP: Multiple metabolic hits converge on CD36 as novel mediator of tubular epithelial apoptosis in diabetic nephropathy. PLoS Med. 2005, 2: e45-10.1371/journal.pmed.0020045.View ArticlePubMedPubMed CentralGoogle Scholar
- Schmittgen TD, Zakrajsek BA: Effect of experimental treatment on housekeeping gene expression: validation by real-time, quantitative RT-PCR. J Biochem Biophys Methods. 2000, 46: 69-81. 10.1016/S0165-022X(00)00129-9.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/10/240/prepub
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.