Selection of suitable housekeeping genes for expression analysis in glioblastoma using quantitative RT-PCR
© Valente et al; licensee BioMed Central Ltd. 2009
Received: 21 October 2008
Accepted: 03 March 2009
Published: 03 March 2009
Considering the broad variation in the expression of housekeeping genes among tissues and experimental situations, studies using quantitative RT-PCR require strict definition of adequate endogenous controls. For glioblastoma, the most common type of tumor in the central nervous system, there was no previous report regarding this issue.
Here we show that amongst seven frequently used housekeeping genes TBP and HPRT1 are adequate references for glioblastoma gene expression analysis. Evaluation of the expression levels of 12 target genes utilizing different endogenous controls revealed that the normalization method applied might introduce errors in the estimation of relative quantities. Genes presenting expression levels which do not significantly differ between tumor and normal tissues can be considered either increased or decreased if unsuitable reference genes are applied. Most importantly, genes showing significant differences in expression levels between tumor and normal tissues can be missed. We also demonstrated that the Holliday Junction Recognizing Protein, a novel DNA repair protein over expressed in lung cancer, is extremely over-expressed in glioblastoma, with a median change of about 134 fold.
Altogether, our data show the relevance of previous validation of candidate control genes for each experimental model and indicate TBP plus HPRT1 as suitable references for studies on glioblastoma gene expression.
Methods for the quantification of accurate gene expression have an increasingly important role in studies aiming for the reliable examination of expression profiles generated by high-throughput approaches. Real-time reverse transcription quantitative PCR (qRT-PCR) has emerged as one of the most powerful tools for this purpose. Given the extreme sensitivity of qRT-PCR, a careful and stringent selection of a proper constitutively expressed control gene is required to account for differences in the amount and quality of starting RNA and in cDNA synthesis efficiency. Adequate normalizations presume the use of an internal control, often referred to as a housekeeping or reference gene, whose expression levels should not significantly vary among tissues and experimental situations analyzed [1, 2]. Genes most commonly applied as references in qRT-PCR studies include: beta actin (ACTB), glyceraldeyde-3-phosphate dehydrogenase (GAPDH), beta glucuronidase (GUSB), hypoxanthine guanine phosphoribosyl transferase (HPRT1) and ribosome small subunit (18S) ribosomal RNA [1–3]. However, several reports have mentioned these classical housekeeping genes as showing variable expression levels in different experimental conditions [3–9]. Furthermore, the same gene revealed as almost invariant for certain tissues or cell types or could present highly variable expression levels in other tissues or experimental conditions [2, 9, 10]. Thus, it is clear that suitable control genes are extremely specific for particular sample sets and experimental models, being a crucial component in assessing confident gene expression patterns. It has been strongly suggested that more than one stable expressed reference gene should be used to avoid misinterpretation of gene expression data [6, 7, 11–13].
In this context, the present work aimed to evaluate suitability of selected candidate housekeeping genes for expression analysis in glioblastoma (GBM), the highest-grade malignant astrocytoma . These malignant gliomas are the most common and the major lethal type of tumor in the central nervous system , leading to a mean survival time of 1 year after diagnosis . This discouraging prognosis is decurrent from both the infiltrative nature of the tumor and the resistance of tumor cells to cytotoxic treatments [17–19]. Many therapy modalities based on characterized genetic alterations are already in use or in clinical trials phase, but their efficacy is still below expectation [19–21]. Thus, the need for novel therapeutic targets for GBM treatment becomes urgent. In this direction, several recent studies are dedicated to explore high-throughput expression profiles, using qRT-PCR to produce reliable measurements, in order to identify novel genes differentially expressed in GBM [22–26].
Although the necessity of stringent selection of housekeeping genes is well established, until now it has been no systematic investigation directed to point out adequate control genes for quantitative expression analysis in GBM. The majority of the studies apply one of the most commonly used housekeeping genes, such as ACTB and GAPDH. To get the actual panorama of reference genes used in GBM quantitative expression studies, we performed a Medline search using the terms real-time PCR and glioma. We found 45 available articles, published from January 2007 to July 2008, based on the use of different reference genes. More than 80% of these studies use one of the following genes as internal controls: ACTB (cited 19 times, 42%), GADPH (cited 13 times, 29%) or 18S rRNA (cited 5 times, 11%), without any previous evaluation of their stability within the model. This search revealed that we do not have a consensus in the field and, moreover, a meaningful study on the application of reference genes in glioblastoma gene expression investigation is essential and timely.
Therefore, we investigated here the suitability of seven frequently used housekeeping genes for real-time RT-PCR analysis in human GBM versus non-neoplastic white matter comparisons. We determined that TBP and HPRT1 are suitable reference genes for expression studies in GBM. The significance of applying adequate normalization methods was demonstrated by the evaluation of the expression levels of 12 target genes upon different normalization approaches. Our data revealed that, depending on the normalization method utilized, genes whose expression levels are similar in normal and tumor tissues could be interpreted as up or down regulated and genes presenting significant differences in expression levels can be missed. These data show the relevance of previous validation of candidate control genes to obtain adequate normalizations in quantitative expression studies.
The Expression Levels of Candidate Housekeeping Genes
Selected housekeeping genes for expression analysis
Cytoskeletal structural protein
Exoglycosidase in lysosomes
Heme biosynthetic pathway
Hypoxanthine guanine phosphoribosyl transferase 1
Metabolic salvage of purines
TATA-box binding protein
General transcription factor
18S ribosomal RNA
Stability of Candidate Housekeeping Genes on Normal and Neoplastic Brain Tissues
Expression stability measures (M) calculated by geNorm for all candidate housekeeping genes analyzed
Best combination of two genes
ACTB + GUSB
Expression stability values calculated by geNorm and NormFinder for the three genes expressed in similar levels between tumor and normal tissues
Best combination of two genes
TBP + HPRT1 = 0.87
TBP + HPRT1 = 0.166
Evaluation of the Expression Profiles of Target Genes Following Different Normalization Approaches
Target genes evaluated in expression analysis
coiled-coil and C2 domain containing 2A
Noor A et al., 2008 
DENN/MADD domain containing 4C
Olsen JV et al., 2006 
NIPA-like domain containing 2
Lefrève, C et al., 2004 
axin interactor, dorsalization associated
Rui Y et al., 2007 
ATPase family, AAA domain containing 5
Douglas, J et al., 2007 
MAGI family member, X-linked transcript variant 1
Ota T et al., 2004 
WD repeat domain 74
Eilbracht J et al., 2004 
Holliday junction recognition protein
Kato T et al., 2007 
mesoderm induction early response 1, family member 3
Mehrle A et al., 2006 
C10 or f88
chromosome 10 open reading frame 88
Gerhard, DS et al., 2004 
transmembrane protein 116
Gerhard, DS et al., 2004 
transmembrane protein 48
Olsen JV et al., 2006 
The present study is the first report of a systematic evaluation of potential reference genes with regard to their usefulness as normalizers in malignant glioma expression studies. Amongst seven commonly used classical housekeeping genes, we found that expression levels of ACTB, GAPDH, GUSB and 18S rRNA, significantly differed between tumor and normal tissues on the basis of the examination of raw CT values (figure 2). It could be concluded that these genes are regulated and thus not indicated for target gene normalizations. It was previously reported that this initial analysis is mandatory in order to exclude highly unstable genes from further calculations using programs based on pairwise variation, such as geNorm and NormFinder [7, 39]. In those studies, Ohl and collaborators have found genes up and down regulated in prostate and bladder cancer samples through comparisons of CT values. We also consider this preliminary exclusion crucial, because the simultaneous analysis of several genes whose expression levels are similarly biased, could lead to the wrong choice on the basis of software evaluation only. This type of misinterpretation can be clearly illustrated by data presented in table 2, where ACTB and GUSB are being indicated as the two best reference genes. ACTB and GUSB selection, based on geNorm analysis including the seven potential housekeeping genes, probably resulted from similarities in the expression pattern of these two genes, which are both significantly up regulated in tumor tissues, as well as GAPDH (figure 2). Although such genes that regulate basic and ubiquitous cellular functions are frequently assumed as almost invariable between different samples, many other studies corroborate our observations and have also demonstrated that their individual expression may vary as a result of neoplastic growth, hypoxia or experimental treatment [1, 3, 5, 6, 11]. These data show the obligatory requirement of prior exclusion of regulated genes based on raw expression data evaluation.
Among the seven candidate housekeeping genes analyzed, TBP and HPRT1 were indicated as the best combination of reference genes for expression studies in GBM, using three independent methods of analysis: geNorm , NormFinder  and equivalence test . Recently, both HPRT1 and TBP were indicated as suitable reference genes for differential expression studies using qRT-PCR in different type of cancers, moreover HPRT1 was recommended as a universal single reference gene for expression analysis in cancer [3, 7, 39]. However, normalizations based in more than one best-performing reference gene gives more accurate results and has been increasingly suggested [6, 13, 40]. In our study, we observed differences in the significance of comparisons when utilizing TBP and HPRT1 associated or each one separately (see additional file 1). Thus, until further extended analysis becomes available, we suggest the use of TBP plus HPRT1 as the more adequate endogenous controls for target gene normalizations in GBM expression analysis.
The significance of applying different reference genes for the estimation of the relative quantities of gene expression, was demonstrated here by the analysis of 12 target genes, candidates of being over expressed in GBM, following three normalization approaches: i) with geNorm normalization factors calculated for TBP+HPRT1; ii) with GUSB, which is ~4.7 times increased in tumors; and iii) with 18S rRNA, which is ~13% decreased in GBM samples. Our data clearly show that the normalization method applied might introduce errors in the estimation of relative expression levels. Genes, whose expression levels did not significantly vary between tumor and normal tissues, would be considered significantly diminished when normalized with GUSB or increased when normalized with 18S rRNA. Moreover, genes presenting significant differences in the relative quantities between tumor and normal tissues can be missed if these unsuitable endogenous controls are utilized (figure 4). Therefore, we can conclude that for GBM gene expression studies, GUSB along with the most frequently utilized internal controls, ACTB and GAPDH, must be considered inadequate for normalizations due to its significant increase in tumor samples. 18S rRNA also led to erroneous estimation in gene expression levels and proved not to be useful for normalizations. This could be explained by the imbalance between messenger and ribosomal RNA  or, possibly, by the independently regulated rRNA transcription, which is carried out by RNA polymerase I, as previously reported .
In this study, we found five genes whose expression levels significantly differ between tumor and normal samples. Surprisingly, two of them were decreased (TG4 and TG6) and three (TG1, TG8 and TG12) were increased in tumor tissue, and only one confirmed the high expression levels (>25 fold) indicated by the microarray data . This is probably due to the difference in the sizes of the tumor samples analyzed that was five times smaller in the microarray experiments (n = 6) than in the qRT-PCR analysis performed here (n = 30). We also observed that the validation rate of the microarray data obtained in our study (25%) was much lower than previously reported (90%) . The high validation rate reported in the former study can be explained by the criteria of target genes selection, which was biased to genes related to pathways probably altered in cancer. One of the three genes validated here, NM_018410, presents transcription levels extremely elevated in GBM, independently of the reference gene utilized (TG8, figure 3H). Over-expression of TG8 in GBM when compared to pilocytic astrocytoma was previously suggested by microarray data analysis, where a change of about nine fold was observed . This gene was recently annotated as HJURP, the Holliday Junction Recognition Protein. It was demonstrated that HJURP is over expressed in lung cancer and is involved in chromosomal stability, being a competence factor for immortality of cancer cells in culture . The role of HJURP in glioblastoma will be further characterized.
In conclusion, our data show the relevance of previous validation of candidate housekeeping genes for each specific application, especially when small differences are intended to be detected. For glioblastoma, it was demonstrated that TBP plus HPRT1 are suitable reference genes for normalization purposes in gene expression profiling studies. Together, these results highlight the importance of careful reevaluation of glioblastoma gene expression data currently available.
Glioblastoma samples were obtained from 30 patients (mean age 55 years, range 19–79 years) submitted to surgical resection for tumor ablation at the Clinical Hospital of the Faculty of Medicine of Ribeirão Preto, University of São Paulo. Tumor grade was determined according to WHO criteria . Non-neoplastic white matter samples were obtained from patients undergoing temporal lobectomy for epilepsy treatment. The study was approved by the Ethics Committee of the Faculty of Medicine and informed consent was obtained from each patient. Thirty tumors and nine non-neoplastic fresh surgical samples were sectioned and immediately snap-frozen in liquid nitrogen upon surgical removal. All tissue samples were microdissected for exclusion of tissue areas presenting necrosis or not matching to GBM diagnostic prior to RNA extraction. Standardized conditions of storage and microdissection of tumor samples are important steps to guarantee reliability of data and the conclusions derived from them, since GBM are heterogeneous solid tumors often presenting necrosis .
RNA Isolation and Quality Evaluation
Total RNA was isolated using the TRIzol Reagent (Invitrogen) following the manufacturer's instructions with an additional phenol/clorophorm extraction to improve protein exclusion. The concentration and purity of isolated RNA were assessed by absorbance (A) readings on a UV spectrophotometer (Hitachi) at the wavelengths of 260 and 280 nm. The mean ratio value of A260/280 for all RNA samples was 1.81 (± 0.06), reflecting high purity and protein absence. RNA integrity was evaluated by the ratio of 28S/18S ribosomal RNA bands after eletrophoresis in denaturing 1% agarose gel. To guarantee the quality necessary for expression analysis all samples used in this study presented a 28S/18S rRNA ratio ≥1.7.
DNAse Treatment and cDNA synthesis
One microgram of total RNA from each sample was treated with DNAse I enzyme (Invitrogen) in the presence of 40 U of RNAse inhibitor (RNAseOUT, Invitrogen), following the instructions of the manufacturer. Treated RNA was reverse transcribed using the HighCapacity kit (Applied Biosystems) in 20 μL of final volume, according to fabricant's recommendations and with addition of 250 ng of oligo(dT)18–24 per reaction.
Quantitative Real-Time RT-PCR
Primer sequences and amplification summary
Primer Sequence [5' → 3']
Amplicon size (bp)
Intervening sequence size (bp)
Amplification efficiency (%)
Amplification of specific PCR products was detected using the SYBR Green PCR Master Mix (PE Applied Biosystems) according to the manufacturer's protocol. All primers employed were synthesized by MWG Biotech Inc or Invitrogen. Amplification efficiency of each primer pair was evaluated by the standard curve method using serial dilutions of pooled cDNA. All primer pairs utilized in this study presented amplification efficiency between 87–110% (table 5). Reactions without template were run in parallel for all plates to verify purity of measurements within each experiment. Each run was completed with a melting curve analysis to confirm the specificity of amplification and lack of primer dimers. The 2-ΔΔCT equation  was applied to calculate the relative expression of tumor samples and non-neoplasic brain tissues. Mean CT of non-neoplastic brain tissues was used as the calibrator sample.
Statistical data analysis
The normality test was performed by the Kolmogorov and Smirnov method and significance between differences in mean CT values was measured by unpaired test t, using GraphPad InStat software. The differences in gene expression levels were analyzed by the Mann-Withney test, using the SPSS 15.0 software. P values < 0.05 were considered statistically significant. For evaluation of expression stability of the candidate reference genes, we applied the softwares geNorm  and NormFinder , and the equivalence test , as previously described. The geNorm and NormFinder programs are Visual Basic application tools for Microsoft Excel available on internet upon request to developers. CT values were converted into raw relative quantities considering the PCR efficiency 2.
We gratefully thank Dr. Paulo Henrique Aguiar, Dr. Flávio Keimiura and Dr. Benedito Oscar Colli for neurosurgical assistance in sample collection. This work was financed by the Brazilian governmental agencies: FAPESP (grant n° 04/12133-6), CNPq (grant n° 485342/2006) and FAEPA-FMRP. VV is supported by a FAPESP fellowship (06/57602-9).
- Radonic A, Thulke S, Mackay IM, Landt O, Siegert W, Nitsche A: Guideline to reference gene selection for quantitative real-time PCR. Biochem Biophys Res Commun. 2004, 313 (4): 856-862. 10.1016/j.bbrc.2003.11.177View ArticlePubMedGoogle Scholar
- Dheda K, Huggett JF, Bustin SA, Johnson MA, Rook G, Zumla A: Validation of housekeeping genes for normalizing RNA expression in real-time PCR. Biotechniques. 2004, 37 (1): 112-114.PubMedGoogle Scholar
- de Kok JB, Roelofs RW, Giesendorf BA, Pennings JL, Waas ET, Feuth T, Swinkels DW, Span PN: Normalization of gene expression measurements in tumor tissues: comparison of 13 endogenous control genes. Lab Invest. 2005, 85 (1): 154-159.View ArticlePubMedGoogle Scholar
- Selvey S, Thompson EW, Matthaei K, Lea RA, Irving MG, Griffiths LR: Beta-actin – an unsuitable internal control for RT-PCR. Mol Cell Probes. 2001, 15 (5): 307-311. 10.1006/mcpr.2001.0376View 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 (9): 765-770. 10.1136/thorax.57.9.765PubMed CentralView ArticlePubMedGoogle Scholar
- Tricarico C, Pinzani P, Bianchi S, Paglierani M, Distante V, Pazzagli M, Bustin SA, Orlando C: Quantitative real-time reverse transcription polymerase chain reaction: normalization to rRNA or single housekeeping genes is inappropriate for human tissue biopsies. Anal Biochem. 2002, 309 (2): 293-300. 10.1016/S0003-2697(02)00311-1View ArticlePubMedGoogle Scholar
- Ohl F, Jung M, Xu C, Stephan C, Rabien A, Burkhardt M, Nitsche A, Kristiansen G, Loening SA, Radonic A, et al: Gene expression studies in prostate cancer tissue: which reference gene should be selected for normalization?. J Mol Med. 2005, 83 (12): 1014-1024. 10.1007/s00109-005-0703-zView ArticlePubMedGoogle Scholar
- Toegel S, Huang W, Piana C, Unger FM, Wirth M, Goldring MB, Gabor F, Viernstein H: Selection of reliable reference genes for qPCR studies on chondroprotective action. BMC Mol Biol. 2007, 8: 13- 10.1186/1471-2199-8-13PubMed CentralView ArticlePubMedGoogle Scholar
- Silver N, Cotroneo E, Proctor G, Osailan S, Paterson KL, Carpenter GH: Selection of housekeeping genes for gene expression studies in the adult rat submandibular gland under normal, inflamed, atrophic and regenerative states. BMC Mol Biol. 2008, 9: 64-PubMed CentralView ArticlePubMedGoogle Scholar
- Calcagno AM, Chewning KJ, Wu CP, Ambudkar SV: Plasma membrane calcium ATPase (PMCA4): a housekeeper for RT-PCR relative quantification of polytopic membrane proteins. BMC Mol Biol. 2006, 7: 29- 10.1186/1471-2199-7-29PubMed CentralView ArticlePubMedGoogle Scholar
- Zhong H, Simons JW: Direct comparison of GAPDH, beta-actin, cyclophilin, and 28S rRNA as internal standards for quantifying RNA levels under hypoxia. Biochem Biophys Res Commun. 1999, 259 (3): 523-526. 10.1006/bbrc.1999.0815View ArticlePubMedGoogle Scholar
- Hamalainen HK, Tubman JC, Vikman S, Kyrola T, Ylikoski E, Warrington JA, Lahesmaa R: Identification and validation of endogenous reference genes for expression profiling of T helper cell differentiation by quantitative real-time RT-PCR. Anal Biochem. 2001, 299 (1): 63-70. 10.1006/abio.2001.5369View ArticlePubMedGoogle Scholar
- Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002, 3 (7): RESEARCH0034- 10.1186/gb-2002-3-7-research0034PubMed CentralView ArticlePubMedGoogle Scholar
- Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P: The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol. 2007, 114 (2): 97-109. 10.1007/s00401-007-0243-4PubMed CentralView ArticlePubMedGoogle Scholar
- Behin A, Hoang-Xuan K, Carpentier AF, Delattre JY: Primary brain tumours in adults. Lancet. 2003, 361 (9354): 323-331. 10.1016/S0140-6736(03)12328-8View ArticlePubMedGoogle Scholar
- Louis DN: Molecular pathology of malignant gliomas. Annu Rev Pathol. 2006, 1: 97-117. 10.1146/annurev.pathol.1.110304.100043View ArticlePubMedGoogle Scholar
- Omuro AM, Delattre JY: Editorial: what is new in the treatment of gliomas?. Curr Opin Neurol. 2007, 20 (6): 704-707. 10.1097/WCO.0b013e3282f1beefView ArticlePubMedGoogle Scholar
- Omuro AM, Faivre S, Raymond E: Lessons learned in the development of targeted therapy for malignant gliomas. Mol Cancer Ther. 2007, 6 (7): 1909-1919. 10.1158/1535-7163.MCT-07-0047View ArticlePubMedGoogle Scholar
- Dent P, Yacoub A, Park M, Sarkar D, Shah K, Curiel DT, Grant S: Searching for a cure: Gene therapy for glioblastoma. Cancer Biol Ther. 2008, 7 (9): 1335-1340.View ArticlePubMedGoogle Scholar
- Idbaih A, Omuro A, Ducray F, Hoang-Xuan K: Molecular genetic markers as predictors of response to chemotherapy in gliomas. Curr Opin Oncol. 2007, 19 (6): 606-611. 10.1097/CCO.0b013e3282f075f3View ArticlePubMedGoogle Scholar
- Colman H, Aldape K: Molecular predictors in glioblastoma: toward personalized therapy. Arch Neurol. 2008, 65 (7): 877-883. 10.1001/archneur.65.7.877View ArticlePubMedGoogle Scholar
- Maier MS, Legare ME, Hanneman WH: The aryl hydrocarbon receptor agonist 3, 3', 4, 4', 5-pentachlorobiphenyl induces distinct patterns of gene expression between hepatoma and glioma cells: chromatin remodeling as a mechanism for selective effects. Neurotoxicology. 2007, 28 (3): 594-612. 10.1016/j.neuro.2007.01.002View ArticlePubMedGoogle Scholar
- Schmitz M, Temme A, Senner V, Ebner R, Schwind S, Stevanovic S, Wehner R, Schackert G, Schackert HK, Fussel M, et al: Identification of SOX2 as a novel glioma-associated antigen and potential target for T cell-based immunotherapy. Br J Cancer. 2007, 96 (8): 1293-1301. 10.1038/sj.bjc.6603696PubMed CentralView ArticlePubMedGoogle Scholar
- Haque T, Faury D, Albrecht S, Lopez-Aguilar E, Hauser P, Garami M, Hanzely Z, Bognar L, Del Maestro RF, Atkinson J, et al: Gene expression profiling from formalin-fixed paraffin-embedded tumors of pediatric glioblastoma. Clin Cancer Res. 2007, 13 (21): 6284-6292. 10.1158/1078-0432.CCR-07-0525View ArticlePubMedGoogle Scholar
- Bozinov O, Kohler S, Samans B, Benes L, Miller D, Ritter M, Sure U, Bertalanffy H: Candidate genes for the progression of malignant gliomas identified by microarray analysis. Neurosurg Rev. 2008, 31 (1): 83-89. discussion 89–90 10.1007/s10143-007-0107-3View ArticlePubMedGoogle Scholar
- Scrideli CA, Carlotti CG, Okamoto OK, Andrade VS, Cortez MA, Motta FJ, Lucio-Eterovic AK, Neder L, Rosemberg S, Oba-Shinjo SM, et al: Gene expression profile analysis of primary glioblastomas and non-neoplastic brain tissue: identification of potential target genes by oligonucleotide microarray and real-time quantitative PCR. J Neurooncol. 2008, 88 (3): 281-291. 10.1007/s11060-008-9579-4View ArticlePubMedGoogle Scholar
- Andersen CL, Jensen JL, Orntoft TF: Normalization of real-time quantitative reverse transcription-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 (15): 5245-5250. 10.1158/0008-5472.CAN-04-0496View ArticlePubMedGoogle Scholar
- Haller F, Kulle B, Schwager S, Gunawan B, von Heydebreck A, Sultmann H, Fuzesi L: Equivalence test in quantitative reverse transcription polymerase chain reaction: confirmation of reference genes suitable for normalization. Anal Biochem. 2004, 335 (1): 1-9. 10.1016/j.ab.2004.08.024View ArticlePubMedGoogle Scholar
- Noor A, Windpassinger C, Patel M, Stachowiak B, Mikhailov A, Azam M, Irfan M, Siddiqui ZK, Naeem F, Paterson AD, et al: CC2D2A, encoding a coiled-coil and C2 domain protein, causes autosomal-recessive mental retardation with retinitis pigmentosa. Am J Hum Genet. 2008, 82 (4): 1011-1018. 10.1016/j.ajhg.2008.01.021PubMed CentralView ArticlePubMedGoogle Scholar
- Olsen JV, Blagoev B, Gnad F, Macek B, Kumar C, Mortensen P, Mann M: Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell. 2006, 127 (3): 635-648. 10.1016/j.cell.2006.09.026View ArticlePubMedGoogle Scholar
- Lefevre C, Bouadjar B, Karaduman A, Jobard F, Saker S, Ozguc M, Lathrop M, Prud'homme JF, Fischer J: Mutations in ichthyin a new gene on chromosome 5q33 in a new form of autosomal recessive congenital ichthyosis. Hum Mol Genet. 2004, 13 (20): 2473-2482. 10.1093/hmg/ddh263View ArticlePubMedGoogle Scholar
- Rui Y, Xu Z, Xiong B, Cao Y, Lin S, Zhang M, Chan SC, Luo W, Han Y, Lu Z, et al: A beta-catenin-independent dorsalization pathway activated by Axin/JNK signaling and antagonized by aida. Dev Cell. 2007, 13 (2): 268-282. 10.1016/j.devcel.2007.07.006View ArticlePubMedGoogle Scholar
- Douglas J, Cilliers D, Coleman K, Tatton-Brown K, Barker K, Bernhard B, Burn J, Huson S, Josifova D, Lacombe D, et al: Mutations in RNF135, a gene within the NF1 microdeletion region, cause phenotypic abnormalities including overgrowth. Nat Genet. 2007, 39 (8): 963-965. 10.1038/ng2083View ArticlePubMedGoogle Scholar
- Ota T, Suzuki Y, Nishikawa T, Otsuki T, Sugiyama T, Irie R, Wakamatsu A, Hayashi K, Sato H, Nagai K, et al: Complete sequencing and characterization of 21, 243 full-length human cDNAs. Nat Genet. 2004, 36 (1): 40-45. 10.1038/ng1285View ArticlePubMedGoogle Scholar
- Eilbracht J, Reichenzeller M, Hergt M, Schnolzer M, Heid H, Stohr M, Franke WW, Schmidt-Zachmann MS: NO66, a highly conserved dual location protein in the nucleolus and in a special type of synchronously replicating chromatin. Mol Biol Cell. 2004, 15 (4): 1816-1832. 10.1091/mbc.E03-08-0623PubMed CentralView ArticlePubMedGoogle Scholar
- Kato T, Sato N, Hayama S, Yamabuki T, Ito T, Miyamoto M, Kondo S, Nakamura Y, Daigo Y: Activation of Holliday junction recognizing protein involved in the chromosomal stability and immortality of cancer cells. Cancer Res. 2007, 67 (18): 8544-8553. 10.1158/0008-5472.CAN-07-1307View ArticlePubMedGoogle Scholar
- Mehrle A, Rosenfelder H, Schupp I, del Val C, Arlt D, Hahne F, Bechtel S, Simpson J, Hofmann O, Hide W: The LIFEdb database in 2006. Nucleic Acids Res. 2006, D415-418. 34 Database
- Gerhard DS, Wagner L, Feingold EA, Shenmen CM, Grouse LH, Schuler G, Klein SL, Old S, Rasooly R, Good P, et al: The status, quality, and expansion of the NIH full-length cDNA project: the Mammalian Gene Collection (MGC). Genome Res. 2004, 14 (10B): 2121-2127. 10.1101/gr.2596504View ArticlePubMedGoogle Scholar
- Ohl F, Jung M, Radonic 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 (5): 1915-1920. 10.1016/S0022-5347(05)00919-5View ArticlePubMedGoogle Scholar
- Schmid H, Cohen CD, Henger A, Irrgang S, Schlondorff D, Kretzler M: Validation of endogenous controls for gene expression analysis in microdissected human renal biopsies. Kidney Int. 2003, 64 (1): 356-360. 10.1046/j.1523-1755.2003.00074.xView ArticlePubMedGoogle Scholar
- Solanas M, Moral R, Escrich E: Unsuitability of using ribosomal RNA as loading control for Northern blot analyses related to the imbalance between messenger and ribosomal RNA content in rat mammary tumors. Anal Biochem. 2001, 288 (1): 99-102. 10.1006/abio.2000.4889View ArticlePubMedGoogle Scholar
- Marie SK, Okamoto OK, Uno M, Hasegawa AP, Oba-Shinjo SM, Cohen T, Camargo AA, Kosoy A, Carlotti CG, Toledo S, et al: Maternal embryonic leucine zipper kinase transcript abundance correlates with malignancy grade in human astrocytomas. Int J Cancer. 2008, 122 (4): 807-815. 10.1002/ijc.23189View ArticlePubMedGoogle Scholar
- Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001, 25 (4): 402-408. 10.1006/meth.2001.1262View ArticlePubMedGoogle Scholar
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.