Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
BMC Cancer
Published in: BMC Cancer 1/2006

Open Access 01-12-2006 | Research article

Selection of suitable reference genes for accurate normalization of gene expression profile studies in non-small cell lung cancer

Authors: Silvia Saviozzi, Francesca Cordero, Marco Lo Iacono, Silvia Novello, Scagliotti V Giorgio, Raffaele A Calogero

Published in: BMC Cancer | Issue 1/2006

Login to get access

Abstract

Background

In real-time RT quantitative PCR (qPCR) the accuracy of normalized data is highly dependent on the reliability of the reference genes (RGs). Failure to use an appropriate control gene for normalization of qPCR data may result in biased gene expression profiles, as well as low precision, so that only gross changes in expression level are declared statistically significant or patterns of expression are erroneously characterized. Therefore, it is essential to determine whether potential RGs are appropriate for specific experimental purposes. Aim of this study was to identify and validate RGs for use in the differentiation of normal and tumor lung expression profiles.

Methods

A meta-analysis of lung cancer transcription profiles generated with the GeneChip technology was used to identify five putative RGs. Their consistency and that of seven commonly used RGs was tested by using Taqman probes on 18 paired normal-tumor lung snap-frozen specimens obtained from non-small-cell lung cancer (NSCLC) patients during primary curative resection.

Results

The 12 RGs displayed showed a wide range of Ct values: except for rRNA18S (mean 9.8), the mean values of all the commercial RGs and ESD ranged from 19 to 26, whereas those of the microarray-selected RGs (BTF-3, YAP1, HIST1H2BC, RPL30) exceeded 26. RG expression stability within sample populations and under the experimental conditions (tumour versus normal lung specimens) was evaluated by: (1) descriptive statistic; (2) equivalence test; (3) GeNorm applet. All these approaches indicated that the most stable RGs were POLR2A, rRNA18S, YAP1 and ESD.

Conclusion

These data suggest that POLR2A, rRNA18S, YAP1 and ESD are the most suitable RGs for gene expression profile studies in NSCLC. Furthermore, they highlight the limitations of commercial RGs and indicate that meta-data analysis of genome-wide transcription profiling studies may identify new RGs.
Appendix
Available only for authorised users
Literature
1.
Jemal A, Tiwari RC, Murray T, Ghafoor A, Samuels A, Ward E, Feuer EJ, Thun MJ, American Cancer S: Cancer statistics, 2004. CA: a Cancer Journal for Clinicians. 2004, 54 (1): 8-29.
2.
Mather D, Sullivan SD, Parasuraman TV: Beyond survival: economic analyses of chemotherapy in advanced inoperable NSCLC. Oncology (Huntingt). 1998, 12: 199-209.
3.
Schiller JH, Harrington D, Belani CP, Langer C, Sandler A, Krook J, Zhu J, Johnson DH, Eastern Cooperative Oncology G: Comparison of four chemotherapy regimens for advanced non-small-cell lung cancer.[see comment]. New England Journal of Medicine. 2002, 346 (2): 92-98. 10.1056/NEJMoa011954.CrossRefPubMed
4.
Bepler G, Sharma S, Cantor A, Gautam A, Haura E, Simon G, Sharma A, Sommers E, Robinson L: RRM1 and PTEN as prognostic parameters for overall and disease-free survival in patients with non-small-cell lung cancer. Journal of Clinical Oncology. 2004, 22 (10): 1878-1885. 10.1200/JCO.2004.12.002.CrossRefPubMed
5.
Rosell R, Felip E, Taron M, Majo J, Mendez P, Sanchez-Ronco M, Queralt C, Sanchez JJ, Maestre J: Gene expression as a predictive marker of outcome in stage IIB-IIIA-IIIB non-small cell lung cancer after induction gemcitabine-based chemotherapy followed by resectional surgery. Clinical Cancer Research. 2004, 10 (12 Pt 2): 4215s-4219s. 10.1158/1078-0432.CCR-040006.CrossRefPubMed
6.
Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods (Duluth). 2001, 25 (4): 402-408.CrossRef
7.
Schmittgen TD, Zakrajsek BA: Effect of experimental treatment on housekeeping gene expression: validation by real-time, quantitative RT-PCR. Journal of Biochemical & Biophysical Methods. 2000, 46 (1–2): 69-81. 10.1016/S0165-022X(00)00129-9.CrossRef
8.
Huggett J, Dheda K, Bustin S, Zumla A: Real-time RT-PCR normalisation; strategies and considerations. Genes & Immunity. 2005, 6 (4): 279-284. 10.1038/sj.gene.6364190.CrossRef
9.
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 Research. 2004, 64 (15): 5245-5250. 10.1158/0008-5472.CAN-04-0496.CrossRefPubMed
10.
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.[see comment]. Kidney International. 2003, 64 (1): 356-360. 10.1046/j.1523-1755.2003.00074.x.CrossRefPubMed
11.
Warrington JA, Nair A, Mahadevappa M, Tsyganskaya M: Comparison of human adult and fetal expression and identification of 535 housekeeping/maintenance genes. Physiological Genomics. 2000, 2 (3): 143-147.PubMed
12.
Muller-Tidow C, Diederichs S, Bulk E, Pohle T, Steffen B, Schwable J, Plewka S, Thomas M, Metzger R, Schneider PM, et al: Identification of metastasis-associated receptor tyrosine kinases in non-small cell lung cancer. Cancer Research. 2005, 65 (5): 1778-1782. 10.1158/0008-5472.CAN-04-3388.CrossRefPubMed
13.
Brabender J, Metzger R, Salonga D, Danenberg KD, Danenberg PV, Holscher AH, Schneider PM: Comprehensive expression analysis of retinoic acid receptors and retinoid X receptors in non-small cell lung cancer: implications for tumor development and prognosis. Carcinogenesis. 2005, 26 (3): 525-530. 10.1093/carcin/bgi006.CrossRefPubMed
14.
Yuan A, Yu CJ, Shun CT, Luh KT, Kuo SH, Lee YC, Yang PC: Total cyclooxygenase-2 mRNA levels correlate with vascular endothelial growth factor mRNA levels, tumor angiogenesis and prognosis in non-small cell lung cancer patients. International Journal of Cancer. 2005, 115 (4): 545-555. 10.1002/ijc.20898.CrossRefPubMed
15.
Falleni M, Pellegrini C, Marchetti A, Roncalli M, Nosotti M, Palleschi A, Santambrogio L, Coggi G, Bosari S: Quantitative evaluation of the apoptosis regulating genes Survivin, Bcl-2 and Bax in inflammatory and malignant pleural lesions. Lung Cancer. 2005, 48 (2): 211-216. 10.1016/j.lungcan.2004.10.003.CrossRefPubMed
16.
Parekh K, Ramachandran S, Cooper J, Bigner D, Patterson A, Mohanakumar T: Tenascin-C, over expressed in lung cancer down regulates effector functions of tumor infiltrating lymphocytes. Lung Cancer. 2005, 47 (1): 17-29. 10.1016/j.lungcan.2004.05.016.CrossRefPubMed
17.
Bas A, Forsberg G, Hammarstrom S, Hammarstrom ML: Utility of the housekeeping genes 18S rRNA, beta-actin and glyceraldehyde-3-phosphate-dehydrogenase for normalization in real-time quantitative reverse transcriptase-polymerase chain reaction analysis of gene expression in human T lymphocytes. Scandinavian Journal of Immunology. 2004, 59 (6): 566-573. 10.1111/j.0300-9475.2004.01440.x.CrossRefPubMed
18.
Goidin D, Mamessier A, Staquet MJ, Schmitt D, Berthier-Vergnes O: Ribosomal 18S RNA prevails over glyceraldehyde-3-phosphate dehydrogenase and beta-actin genes as internal standard for quantitative comparison of mRNA levels in invasive and noninvasive human melanoma cell subpopulations. Analytical Biochemistry. 2001, 295 (1): 17-21. 10.1006/abio.2001.5171.CrossRefPubMed
19.
Zhong H, Simons JW: Direct comparison of GAPDH, beta-actin, cyclophilin, and 28S rRNA as internal standards for quantifying RNA levels under hypoxia. Biochemical & Biophysical Research Communications. 1999, 259 (3): 523-526. 10.1006/bbrc.1999.0815.CrossRef
20.
Bustin SA: Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. Journal of Molecular Endocrinology. 2000, 25 (2): 169-193. 10.1677/jme.0.0250169.CrossRefPubMed
21.
Bhattacharjee A, Richards WG, Staunton J, Li C, Monti S, Vasa P, Ladd C, Beheshti J, Bueno R, Gillette M, et al: Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proceedings of the National Academy of Sciences of the United States of America. 2001, 98 (24): 13790-13795. 10.1073/pnas.191502998.CrossRefPubMedPubMedCentral
22.
23.
24.
Raychaudhuri S, Stuart JM, Altman RB: Principal components analysis to summarize microarray experiments: application to sporulation time series. Pacific Symposium on Biocomputing. 2000, 455-466.
25.
Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M, et al: TM4: a free, open-source system for microarray data management and analysis. Biotechniques. 2003, 34 (2): 374-378.PubMed
26.
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 Biology. 2002, 3 (7): RESEARCH0034-10.1186/gb-2002-3-7-research0034.CrossRefPubMedPubMedCentral
27.
28.
Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003, 4: 249-264. 10.1093/biostatistics/4.2.249.CrossRefPubMed
29.
Bolstad BM, Irizarry RA, Astrand M, Speed TP: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003, 19 (2): 185-193. 10.1093/bioinformatics/19.2.185.CrossRefPubMed
30.
Parmigiani G, Garrett-Mayer ES, Anbazhagan R, Gabrielson E: A cross-study comparison of gene expression studies for the molecular classification of lung cancer. Clinical Cancer Research. 2004, 10 (9): 2922-2927. 10.1158/1078-0432.CCR-03-0490.CrossRefPubMed
31.
Stahlberg A, Hakansson J, Xian X, Semb H, Kubista M: Properties of the reverse transcription reaction in mRNA quantification. Clinical Chemistry. 2004, 50 (3): 509-515. 10.1373/clinchem.2003.026161.CrossRefPubMed
32.
Curry J, McHale C, Smith MT: Low efficiency of the Moloney murine leukemia virus reverse transcriptase during reverse transcription of rare t(8;21) fusion gene transcripts. Biotechniques. 2002, 32 (4): 768-775.PubMed
33.
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.PubMed
34.
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. Analytical Biochemistry. 2004, 335 (1): 1-9. 10.1016/j.ab.2004.08.024.CrossRefPubMed
35.
McBride GB: Equivalence tests can enhance enviromental, science, and management. Austral & New Zeland J Statist. 1999, 4 (1): 19-29. 10.1111/1467-842X.00058.CrossRef
36.
37.
Amplification efficiency of TaqMan Gene Expression assays. Application Note Applied Biosystems.
38.
Bustin SA: Absolute quantification of mRNA using real-time reverse transcription chain reaction assay. J Mol Endocrinol. 2000, 25: 169-193. 10.1677/jme.0.0250169.CrossRefPubMed
39.
Quantification strategies in real-time PCR. Chapter 3 of "A-Z of quantitative PCR".
40.
Radonic A, Thulke S, Mackay IM, Landt O, Siegert W, Nitsche A: Guideline to reference gene selection for quantitative real-time PCR. Biochemical & Biophysical Research Communications. 2004, 313 (4): 856-862. 10.1016/j.bbrc.2003.11.177.CrossRef
41.
Thellin O, Zorzi W, Lakaye B, De Borman B, Coumans B, Hennen G, Grisar T, Igout A, Heinen E: Housekeeping genes as internal standards: use and limits. Journal of Biotechnology. 1999, 75 (2–3): 291-295. 10.1016/S0168-1656(99)00163-7.CrossRefPubMed
42.
Liu DWCST, Liu HP: Choice of endogenous control for gene expression in nonsmall cell lung cancer. Eur Respir J. 2005, 26 (6): 1002-1008. 10.1183/09031936.05.00050205.CrossRefPubMed
43.
Revillion F, Pawlowski V, Hornez L, Peyrat JP: Glyceraldehyde-3-phosphate dehydrogenase gene expression in human breast cancer. European Journal of Cancer. 2000, 36 (8): 1038-1042. 10.1016/S0959-8049(00)00051-4.CrossRefPubMed
44.
Rondinelli RHEDE, Tricoli JV: Increased glyceraldehyde-3-phosphate dehydrogenase gene expression in late pathological stage human prostate cancer. Prostate Cancer Prostatic Dis. 1997, 1 (2): 66-72. 10.1038/sj.pcan.4500208.CrossRefPubMed
45.
Schek NHBL, Finn OJ: Increased glyceraldehyde-3-phosphate dehydrogenase gene expression in human pancreatic adenocarcinoma. Cancer Research. 1988, 48 (22): 6354-6359.PubMed
46.
Tokunaga K, Nakamura Y, Sakata K, Fujimori K, Ohkubo M, Sawada K, Sakiyama S: Enhanced expression of a glyceraldehyde-3-phosphate dehydrogenase gene in human lung cancers. Cancer Research. 1987, 47 (21): 5616-5619.PubMed
47.
Dheda K, Huggett JF, Chang JS, Kim LU, Bustin SA, Johnson MA, Rook GA, Zumla A: The implications of using an inappropriate reference gene for real-time reverse transcription PCR data normalization. Analytical Biochemistry. 2005, 344 (1): 141-143. 10.1016/j.ab.2005.05.022.CrossRefPubMed
Metadata
Title
Selection of suitable reference genes for accurate normalization of gene expression profile studies in non-small cell lung cancer
Authors
Silvia Saviozzi
Francesca Cordero
Marco Lo Iacono
Silvia Novello
Scagliotti V Giorgio
Raffaele A Calogero
Publication date
01-12-2006
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2006
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/1471-2407-6-200

Other articles of this Issue 1/2006

BMC Cancer 1/2006 Go to the issue

Research article

Alteration of proliferation and apoptotic markers in normal and premalignant tissue associated with prostate cancer

Research article

Polymorphisms of the XRCC1, XRCC3 and XPDgenes and risk of colorectal adenoma and carcinoma, in a Norwegian cohort: a case control study

Research article

Genetic dissection of apoptosis and cell cycle control in response of colorectal cancer treated with preoperative radiochemotherapy

Research article

Single nucleotide polymorphisms of the APC gene and colorectal cancer risk: a case-control study in Taiwan

Research article

Anthropometric measures in relation to Basal Cell Carcinoma: a longitudinal study

Research article

Evaluation of NTHL1, NEIL1, NEIL2, MPG, TDG, UNG and SMUG1genes in familial colorectal cancer predisposition
Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras
Prof. Fabrice Barlesi
Developed by: Springer Medicine
Image Credits