Selection of suitable reference genes for gene expression studies in normal human ovarian tissues, borderline ovarian tumours and ovarian cancer

Ofinran,Olumide; Bose,Ujjal; Hay,Daniel; Abdul,Summi; Tufatelli,Cristina; Khan,Raheela

doi:10.3892/mmr.2016.5933

Selection of suitable reference genes for gene expression studies in normal human ovarian tissues, borderline ovarian tumours and ovarian cancer

Authors:
- Olumide Ofinran
- Ujjal Bose
- Daniel Hay
- Summi Abdul
- Cristina Tufatelli
- Raheela Khan
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Affiliations: Division of Medical Sciences and Graduate Entry Medicine, School of Medicine, The University of Nottingham, Royal Derby Hospital, Derby DE22 3DT, UK, Department of Gynaecological Oncology, Royal Derby Hospital, Derby DE22 3NE, UK
Published online on: November 8, 2016 https://doi.org/10.3892/mmr.2016.5933
Pages: 5725-5731

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Abstract

The use of reference genes is the most common method of controlling the variation in mRNA expression during quantitative polymerase chain reaction, although the use of traditional reference genes, such as β‑actin, glyceraldehyde‑3‑phosphate dehydrogenase or 18S ribosomal RNA, without validation occasionally leads to unreliable results. Therefore, the present study aimed to evaluate a set of five commonly used reference genes to determine the most suitable for gene expression studies in normal ovarian tissues, borderline ovarian and ovarian cancer tissues. The expression stabilities of these genes were ranked using two gene stability algorithms, geNorm and NormFinder. Using geNorm, the two best reference genes in ovarian cancer were β‑glucuronidase and β‑actin. Hypoxanthine phosphoribosyltransferase‑1 and β‑glucuronidase were the most stable in ovarian borderline tumours, and hypoxanthine phosphoribosyltransferase‑1 and glyceraldehyde‑3‑phosphate dehydrogenase were the most stable in normal ovarian tissues. NormFinder ranked β‑actin the most stable in ovarian cancer, and the best combination of two genes was β‑glucuronidase and β‑actin. In borderline tumours, hypoxanthine phosphoribosyltransferase‑1 was identified as the most stable, and the best combination was hypoxanthine phosphoribosyltransferase‑1 and β‑glucuronidase. In normal ovarian tissues, β‑glucuronidase was recommended as the optimum reference gene, and the most optimum pair of reference genes was hypoxanthine phosphoribosyltransferase‑1 and β‑actin. To the best of our knowledge, this is the first study to investigate the selection of a set of reference genes for normalisation in quantitative polymerase chain reactions in different ovarian tissues, and therefore it is recommended that β‑glucuronidase, β‑actin and hypoxanthine phosphoribosyltransferase‑1 are the most suitable reference genes for such analyses.

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1	Ferlay J, Soerjomataram I, Ervik M, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D and Bray F: GLOBOCAN 2012 v1.0, cancer incidence and mortality worldwide: IARC Cancerbase no. 11. Lyon, France: International Agency for Research on Cancer; 2013
2	National Cancer Intelligence Network, . Cancer e-Atlas. http://www.ncin.org.uk/cancer_information_tools/eatlas/accessed January 2014.
3	Cancer Statistics Registrations, . England (Series MB1): No. 43. 2012.
4	Nossov V, Amneus M, Su F, Lang J, Janco JMT, Reddy ST and Farias-Eisner R: The early detection of ovarian cancer: From traditional methods to proteomics. Can we really do better than serum CA-125? Am J Obstet Gynecol. 199:215–223. 2008.PubMed/NCBI
5	Kurman RJ and Shih IeM: The origin and pathogenesis of epithelial ovarian cancer: A proposed unifying theory. Am J Surg Pathol. 34:433–443. 2010. View Article : Google Scholar : PubMed/NCBI
6	Evan GI and Vousden KH: Proliferation, cell cycle and apoptosis in cancer. Nature. 411:342–348. 2001. View Article : Google Scholar : PubMed/NCBI
7	Bustin SA: Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J Mol Endocrinol. 25:169–193. 2000. View Article : Google Scholar : PubMed/NCBI
8	Huggett J, Dheda K, Bustin S and Zumla A: Real-time RT-PCR normalisation; Strategies and considerations. Genes Immun. 6:279–284. 2005. View Article : Google Scholar : PubMed/NCBI
9	Bustin SA: Tenth annual nucleic acid-based technologies: Time to stop and think. Washington, DC, USA, 24–26 June, 2002. Expert Rev Mol Diagn. 2:405–408. 2002. View Article : Google Scholar : PubMed/NCBI
10	Fu J, Bian L, Zhao L, Dong Z, Gao X, Luan H, Sun Y and Song H: Identification of genes for normalization of quantitative real-time PCR data in ovarian tissues. Acta Biochim Biophys Sin (Shanghai). 42:568–574. 2010. View Article : Google Scholar : PubMed/NCBI
11	Dheda K, Huggett JF, Bustin SA, Johnson MA, Rook G and Zumla A: Validation of housekeeping genes for normalizing RNA expression in real-time PCR. Biotechniques. 37:112–119. 2004.PubMed/NCBI
12	Schmittgen TD and Zakrajsek BA: Effect of experimental treatment on housekeeping gene expression: Validation by real-time, quantitative RT-PCR. J Biochem Biophys Methods:. 46:69–81. 2000. View Article : Google Scholar : PubMed/NCBI
13	Eisenberg E and Levanon EY: Human housekeeping genes, revisited. Trends Genet. 29:569–574. 2013. View Article : Google Scholar : PubMed/NCBI
14	Li YL, Ye F, Hu Y, Lu WG and Xie X: Identification of suitable reference genes for gene expression studies of human serous ovarian cancer by real-time polymerase chain reaction. Anal Biochem. 394:110–116. 2009. View Article : Google Scholar : PubMed/NCBI
15	Pfaffl MW: Quantification strategies in real-time PCR. AZ of quantitative PCR. 1:89–113. 2004.
16	Ohl F, Jung M, Xu C, Stephan C, Rabien A, Burkhardt M, Nitsche A, Kristiansen G, Loening SA, Radonić A and Jung K: Gene expression studies in prostate cancer tissue: Which reference gene should be selected for normalization? J Mol Med. 83:1014–1024. 2005. View Article : Google Scholar : PubMed/NCBI
17	Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A and Speleman F: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 3:RESEARCH00342002. View Article : Google Scholar : PubMed/NCBI
18	Radonić A, Thulke S, Mackay IM, Landt O, Siegert W and Nitsche A: Guideline to reference gene selection for quantitative real-time PCR. Biochem Biophys Res Commun. 313:856–862. 2004. View Article : Google Scholar : PubMed/NCBI
19	Bustin SA and Nolan T: Pitfalls of quantitative real-time reverse-transcription polymerase chain reaction. J Biomol Tech. 15:155–166. 2004.PubMed/NCBI
20	Prat J: FIGO Committee on Gynecologic Oncology: Staging classification for cancer of the ovary, fallopian tube, and peritoneum. Int J Gynaecol Obstet. 124:1–5. 2014. View Article : Google Scholar : PubMed/NCBI
21	Tan SC, Carr CA, Yeoh KK, Schofield CJ, Davies KE and Clarke K: Identification of valid housekeeping genes for quantitative RT-PCR analysis of cardiosphere-derived cells preconditioned under hypoxia or with prolyl-4-hydroxylase inhibitors. Mol Biol Rep. 39:4857–4867. 2012. View Article : Google Scholar : PubMed/NCBI
22	Żyżyńska-Granica B and Koziak K: Identification of suitable reference genes for real-time PCR analysis of statin-treated human umbilical vein endothelial cells. PLoS One. 7:e515472012. View Article : Google Scholar : PubMed/NCBI
23	Andersen CL, Jensen JL and 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. 64:5245–5250. 2004. View Article : Google Scholar : PubMed/NCBI
24	Saha P and Blumwald E: Assessing reference genes for accurate transcript normalization using quantitative real-time PCR in pearl millet (Pennisetum glaucum (L.) R. Br). PLoS One. 9:e1063082014. View Article : Google Scholar : PubMed/NCBI
25	Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, Mueller R, Nolan T, Pfaffl MW, Shipley GL, et al: The MIQE guidelines: Minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 55:611–622. 2009. View Article : Google Scholar : PubMed/NCBI
26	Radonic A, Thulke S, Mackay IM, Landt O, Siegert W and Nitsche A: Guideline to reference gene selection for quantitative realtime PCR. Biochem Biophys Res Commun. 313:856–862. 2004. View Article : Google Scholar : PubMed/NCBI
27	Pérez R, Tupac-Yupanqui I and Dunner S: Evaluation of suitable reference genes for gene expression studies in bovine muscular tissue. BMC Mol Biol. 9:792008. View Article : Google Scholar : PubMed/NCBI
28	Tang R, Dodd A, Lai D, McNabb WC and Love DR: Validation of zebrafish (Danio rerio) reference genes for quantitative real-time RT-PCR normalization. Acta Biochim Biophys Sin (Shanghai). 39:384–390. 2007. View Article : Google Scholar : PubMed/NCBI
29	Cruz F, Kalaoun S, Nobile P, Colombo C, Almeida J, Barros LM, Romano E, Grossi-de-Sá MF, Vaslin M and Alves-Ferreira M: Evaluation of coffee reference genes for relative expression studies by quantitative real-time RT-PCR. Mol Breed. 23:607–616. 2009. View Article : Google Scholar
30	Kurman RJ and Shih IM: The origin and pathogenesis of epithelial ovarian cancer: A proposed unifying theory. Am J Surg Pathol. 34:433–443. 2010. View Article : Google Scholar : PubMed/NCBI