Skip to main content

Advertisement

SpringerLink
Log in

Selecting housekeeping genes as references for the normalization of quantitative PCR data in breast cancer

  • Research Article
  • Published:
Clinical and Translational Oncology Aims and scope Submit manuscript

Abstract

Objective

The common reference genes of choice in relative gene expression studies based on quantitative real time polymerase chain reaction, ACTB and B2M, were shown to be regulated differently in respect to tissue type. In this study, the stability of the selected housekeeping genes for normalizing the qPCR data were identified in the tumor and its adjacent tissues in invasive breast cancer, and the variability of their levels according to the stages and the histopathologic subtypes was analyzed.

Methods

Four housekeeping genes: PUM1, RPL13A, B2M, and ACTB were analyzed in 99 surgically excised tissue specimens (50 tumor, 45 tumor adjacent and 4 normal breast tissues). Three of the most common softwares (GeNorm, NormFinder, and BestKeeper) were used for calculation purposes.

Results

When all of the tissue samples were included in analyses, PUM1 was the most stable gene according to calculations made with both NormFinder and BestKeeper; while PUM1/RPL13A combination was the most stable by GeNorm software. The PUM1 gene was also identified as the most stable gene among the four in all sample groups (in both Estrogen Receptor positive and Estrogen Receptor negative subgroups of invasive breast carcinoma and in normal breast tissue) according to calculations made using the NormFinder software.

Conclusion

While suggesting PUM1 is one of the most stable single gene and the PUM1/RPL13A pair as one of the best housekeeping genes for the normalization of expression studies in invasive breast tumor studies, it will be more practical to evaluate stability once more and decide upon the reference gene accordingly within the sample group itself.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Reis-Filho JS, Pusztai L. Gene expression profiling in breast cancer. Classification, prognostication, and prediction. Lancet. 2011;378:1812–23.

    Article  CAS  PubMed  Google Scholar 

  2. Mocellin S, Rossi CR, Pilati P, Nitti D, Marincola FD. Quantitative real-time PCR: a powerful ally in cancer research. Trends Mol Med. 2003;9:189–95.

    Article  CAS  PubMed  Google Scholar 

  3. Yuan JS, Reed A, Chen F, Stewart CN. Statistical analysis of real-time PCR data. BMC Bioinform. 2006;7:85–96.

    Article  Google Scholar 

  4. Jacques BK, Roelofs RW, Giesendorf BA, Pennings JL, Waas ET, Feuth T, et al. Normalization of gene expression measurements in tumor tissues: comparison of 13 endogenous control genes. Lab Invest. 2005;85:154–9.

    Article  Google Scholar 

  5. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCT method. Methods. 2001;25:402–8.

    Article  CAS  PubMed  Google Scholar 

  6. Savonet V, Maenhaut C, Miot F, Pirson I. Pitfalls in the use of several “housekeeping” genes as standards for quantitation of mRNA: the example of thyroid cells. Anal Biochem. 1997;247(1):165–7.

    Article  CAS  PubMed  Google Scholar 

  7. Tricarico C, Pinzani P, Bianchi S, Paglierani M, Distante V, Pazzagli M, et al. 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.

    Article  CAS  PubMed  Google Scholar 

  8. 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. Anal Biochem. 2001;295(1):17–21.

    Article  CAS  PubMed  Google Scholar 

  9. El-Naggar AK, Mackay B, Sneige N, Batsakis JG. Stromal neoplasms of the breast: a comparative flow cytometric study. J Surg Oncol. 1990;44(3):151–6.

    Article  CAS  PubMed  Google Scholar 

  10. Freeman WM, Walker SJ, Vrana KE. Quantitative RT-PCR. pitfalls and potential. Biotechniques. 1999;26(1):112–22.

    CAS  PubMed  Google Scholar 

  11. De Ferrari L, Aitken S. Mining housekeeping genes with a Naive Bayes classifier. BMC Genomics. 2006;7:277.

    Article  PubMed Central  PubMed  Google Scholar 

  12. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002;3:1–11.

    Article  Google Scholar 

  13. Andersen CL, Jensen JL, Ørntoft 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;64:5245–50.

    Article  CAS  PubMed  Google Scholar 

  14. Pfaffl MW, Tichopad 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. Biotechnol Lett. 2004;26:509–15.

    Article  CAS  PubMed  Google Scholar 

  15. Çavuşoğlu AÇ, Saydam S, Alakavuklar M, Canda T, Kılıç Y, Harmancıoğlu O, et al. A pilot study for human tumor/DNA banking: returned more questions than answers. Med Oncol. 2008;25(4):471–3.

    Article  PubMed  Google Scholar 

  16. Chomczynski P. A reagent for the single-step simultaneous isolation of RNA, DNA and proteins from cell and tissue samples. Biotechniques. 1993;15:532–4.

    CAS  PubMed  Google Scholar 

  17. Mestdagh P, Van Vlierberghe P, De Weer A, Muth D, Westermann F, Speleman F, et al. A novel and universal method for microRNA RT-qPCR data normalization. Genome Biol. 2009;10:R64.

    Article  PubMed Central  PubMed  Google Scholar 

  18. Bustin SA. Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J Mol Endocrinol. 2000;25(2):169–93.

    Article  CAS  PubMed  Google Scholar 

  19. Huggett J, Dheda K, Bustin S, Zumla A. Real-time RT-PCR normalisation; strategies and considerations. Genes Immun. 2005;6(4):279–84.

    Article  CAS  PubMed  Google Scholar 

  20. Bustin SA, Nolan T. Pitfalls of quantitative real-time reverse-transcription polymerase chain reaction. J Biomol Tech. 2004;15(3):155–66.

    PubMed Central  PubMed  Google Scholar 

  21. Antonov J, Goldstein DR, Oberli A, Baltzer A, Pirotta M, Fleismann A, et al. Reliable gene expression measurements from degraded RNA by quantitative real-time PCR depend on short amplicons and a proper normalization. Lab Invest. 2005;85(8):1040–50.

    Article  CAS  PubMed  Google Scholar 

  22. Lyng MB, Laenkholm AV, Pallisgaard N, Ditzel HJ. Identification of genes for normalization of real-time RT-PCR data in breast carcinomas. BMC Cancer. 2008;8:20–30.

    Article  PubMed Central  PubMed  Google Scholar 

  23. Majidzadeh-A K, Esmaeili R, Abdoli N. TFRC and ACTB as the best reference genes to quantify Urokinase Plasminogen Activator in breast cancer. BMC Res Notes. 2011;4:215.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  24. Shah KN, Faridi JS. Estrogen, tamoxifen, and Akt modulate expression of putative housekeeping genes in breast cancer cells. J Steroid Biochem Mol Biol. 2011;125(3–5):219–25.

    Article  CAS  PubMed  Google Scholar 

  25. McNeill RE, Miller N, Kerin MJ. Evaluation and validation of candidate endogenous control genes for real-time quantitative PCR studies of breast cancer. BMC Mol Biol. 2007;27:107–19.

    Article  Google Scholar 

  26. Tutt A, Wang A, Rowland C, Gillett C, Lau K, Chew K, et al. Risk estimation of distant metastasis in node-negative, estrogen receptor-positive breast cancer patients using an RT-PCR based prognostic expression signature. BMC Cancer. 2008;8:339.

    Article  PubMed Central  PubMed  Google Scholar 

  27. Popovici V, Goldstein DR, Antonov J, Jaggi R, Delorenzi M, Wirapati P, et al. Selecting control genes for RT-QPCR using public microarray data. BMC Bioinformatics. 2009;10:42.

    Article  PubMed Central  PubMed  Google Scholar 

  28. Khoshnoud R, He Q, Sylván M, Khoshnoud A, Ivarsson M, Fornander T, et al. The impact of RNA standardization and heterogeneous gene expression on the results of cDNA array of human breast carcinoma. Int J Mol Med. 2010;25(5):735–41.

    Article  CAS  PubMed  Google Scholar 

  29. Rienzo M, Schiano C, Casamassimi A, Grimaldi V, Infante T, Napoli C. Identification of valid reference housekeeping genes for gene expression analysis in tumor neovascularization studies. Clin Transl Oncol. 2013;15:211–8.

    Article  CAS  PubMed  Google Scholar 

  30. Zhang B. RefFinder Tool, Cotton EST Database, East Carolina, Department of Biology East Carolina University Greenville, NC, USA. 2012. http://www.leonxie.com/referencegene.php. Accessed April 2012.

Download references

Acknowledgments

The study was supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK). The corresponding author, YK can be reached at +90-505-5985678 or yk@genodigm.com. The author AC was at the İzmir Training and Research Hospital, Department of Biochemistry at the time this project started. The authors would like to thank Dr. Kıvılcım Kılıç for her expertise in preparing the figures.

Conflict of interest

None declared.

Ethical standard

This study was performed under the ethical considerations of the Dokuz Eylül University Faculty of Medicine, Clinical and Laboratory Studies Ethical Council and with permission declared and numbered 03022006/03.

Author information

Authors and Affiliations

  1. Department of Medical Biology and Genetics, Faculty of Medicine, Dokuz Eylül University, Konak, İzmir, Turkey

    Y. Kılıç & M. Sakızlı

  2. Department of Biochemistry, Faculty of Medicine, İzmir University, İzmir, Turkey

    A. Ç. Çelebiler

Authors
  1. Y. Kılıç
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Ç. Çelebiler
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Sakızlı
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Y. Kılıç.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kılıç, Y., Çelebiler, A.Ç. & Sakızlı, M. Selecting housekeeping genes as references for the normalization of quantitative PCR data in breast cancer. Clin Transl Oncol 16, 184–190 (2014). https://doi.org/10.1007/s12094-013-1058-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12094-013-1058-5

Keywords

Search

Navigation