Advertisement

Oral Cancer

, Volume 3, Issue 3–4, pp 49–58 | Cite as

Identification of stably expressed genes for normalization of gene expression data in oral tumors: a preliminary analysis

  • Aniket Parab
  • Sanit Mhatre
  • Sujata Hake
  • Sadhana Kannan
  • Prathamesh Pai
  • Shubhada Kane
  • Narendra JoshiEmail author
Original Article
  • 30 Downloads
Part of the following topical collections:
  1. Basic Science

Abstract

Aim

We sought to identify stably expressed genes in tumors of gingivo-buccal region and tongue from untreated as well as treated patients.

Background

The study was undertaken in view of the ambiguity with regards to the choice of reference genes for normalization of gene expression data from gingivo-buccal region and tongue. This aspect was also examined in tumors from treated patients since it could provide clues for such analyses in the assessment of treatment modalities in the future.

Methods

Expression of ten candidate housekeeping genes, identified in array-based studies, was tested using TaqMan based semi-quantitative real-time PCR. Thirty-five buccal mucosa derived (18 from treated patients) and 15 tongue tumors (8 from treated patients) were studied. Most stable genes were identified based on the consensus between the results of the three methods, Comparative δCt, BestKeeper and NormFinder, used for data analysis.

Results

CHMP2A and VPS29 were identified as the most stably expressed genes suitable for normalization of data from buccal-mucosa tumors, whereas RPS13 and PSMB2 were indicated for similar specimens from treated patients. The same criteria identified stable expression of PSMB2 and PUM1 in tumors from tongue and OAZ1 and RPS13 for the post-treatment tongue tumors.

Conclusion

We have identified stably expressed genes in common oral cancers which can be used for normalization of the gene expression data. Results also established differences in tumors arising at different sites of the oral cavity and highlighted further changes following exposure to therapy.

Keywords

Housekeeping genes Oral cancers Comparative δCt BestKeeper NormFinder 

Notes

Acknowledgements

This work was supported by an intramural grant from the Tata Memorial Centre, Parel, Mumbai 400012 India. The assistance by the ICMR National Tumor Tissue Repository at TMH is gratefully acknowledged. The authors would especially like to thank Mrs. Manisha Kulkarni and Mr. Anand Deshpande from the ICMR National Tumor Tissue Repository at TMH for their assistance in procurement of the specimens. The authors would also like to thank Mr. Jaykumar Kambli for his assistance in the study, Dr Manoj Mahimkar and Dr Milind Vaidya of ACTREC, Tata Memorial Centre, for their valuable suggestions and support during the study as well as in manuscript preparation.

Funding

This study was funded by an Intramural grant from the Tata Memorial Centre, Mumbai 400012 India.

Compliance with ethical standards

Conflict of interest

Dr. Narendra Joshi declares that he has no conflict of interest. Mr. Sanit Mhatre declares that he has no conflict of interest. Mr. Aniket Parab declares that he has no conflict of interest. Mrs. Sadhana Kannan declares that she has no conflict of interest. Mrs. Sujata Hake declares that she has no conflict of interest. Dr. Prathamesh Pai declares that he has no conflict of interest. Dr. Shubhada Kane declares that she has no conflict of interest.

Research involving human participants and/or animals

All the procedures involving human participants were performed in accordance with the ethical standards of the institutional and national research committees (guidelines) and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

41548_2019_20_MOESM1_ESM.docx (114 kb)
Supplementary material 1 (DOCX 114 kb)

References

  1. 1.
    Thellin O, Zorzi W, Lakaye B, De Borman B, Coumans B, Hennen G et al (1999) Housekeeping genes as internal standards: use and limits. J Biotechnol 75(2–3):291–295CrossRefGoogle Scholar
  2. 2.
    Catalan V, Gomez-Ambrosi J, Rotellar F, Silva C, Rodriguez A, Salvador J et al (2007) Validation of endogenous control genes in human adipose tissue: relevance to obesity and obesity-associated type 2 diabetes mellitus. Horm Metab Res 39(7):495–500CrossRefGoogle Scholar
  3. 3.
    Dheda K, Huggett JF, Chang JS, Kim LU, Bustin SA, Johnson MA et al (2005) The implications of using an inappropriate reference gene for real-time reverse transcription PCR data normalization. Anal Biochem 344(1):141–143CrossRefGoogle Scholar
  4. 4.
    Kolkova Z, Arakelyan A, Casslen B, Hansson S, Kriegova E (2013) Normalizing to GADPH jeopardises correct quantification of gene expression in ovarian tumours—IPO8 and RPL4 are reliable reference genes. J Ovarian Res 6(1):60CrossRefGoogle Scholar
  5. 5.
    Eisenberg E, Levanon EY (2013) Human housekeeping genes, revisited. Trends Genet TIG 29(10):569–574CrossRefGoogle Scholar
  6. 6.
    Zhu J, He F, Hu S, Yu J (2008) On the nature of human housekeeping genes. Trends Genet TIG 24(10):481–484CrossRefGoogle Scholar
  7. 7.
    Chang CW, Cheng WC, Chen CR, Shu WY, Tsai ML, Huang CL et al (2011) Identification of human housekeeping genes and tissue-selective genes by microarray meta-analysis. PLoS One 6(7):e22859CrossRefGoogle Scholar
  8. 8.
    de Jonge HJ, Fehrmann RS, de Bont ES, Hofstra RM, Gerbens F, Kamps WA et al (2007) Evidence based selection of housekeeping genes. PLoS One 2(9):e898CrossRefGoogle Scholar
  9. 9.
    Chen M, Xiao J, Zhang Z, Liu J, Wu J, Yu J (2013) Identification of human HK genes and gene expression regulation study in cancer from transcriptomics data analysis. PLoS One 8(1):e54082CrossRefGoogle Scholar
  10. 10.
    Rubie C, Kempf K, Hans J, Su T, Tilton B, Georg T et al (2005) Housekeeping gene variability in normal and cancerous colorectal, pancreatic, esophageal, gastric and hepatic tissues. Mol Cell Probes 19(2):101–109CrossRefGoogle Scholar
  11. 11.
    Lee PD, Sladek R, Greenwood CM, Hudson TJ (2002) Control genes and variability: absence of ubiquitous reference transcripts in diverse mammalian expression studies. Genome Res 12(2):292–297CrossRefGoogle Scholar
  12. 12.
    Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP (2004) Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: Bestkeeper–Excel-based tool using pair-wise correlations. Biotechnol Lett 26(6):509–515CrossRefGoogle Scholar
  13. 13.
    Lyng MB, Laenkholm AV, Pallisgaard N, Ditzel HJ (2008) Identification of genes for normalization of real-time RT-PCR data in breast carcinomas. BMC Cancer 8:20CrossRefGoogle Scholar
  14. 14.
    Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A et al (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3(7):research0034.1CrossRefGoogle Scholar
  15. 15.
    Bennani-Baiti B, Toegel S, Viernstein H, Urban E, Noe CR, Bennani-Baiti IM (2015) Inflammation modulates RLIP76/RALBP1 electrophile-glutathione conjugate transporter and housekeeping genes in human blood-brain barrier endothelial cells. PLoS One 10(9):e0139101CrossRefGoogle Scholar
  16. 16.
    Song W, Zhang WH, Zhang H, Li Y, Zhang Y, Yin W et al (2016) Validation of housekeeping genes for the normalization of RT-qPCR expression studies in oral squamous cell carcinoma cell line treated by 5 kinds of chemotherapy drugs. Cell Mol Biol 62(13):29–34CrossRefGoogle Scholar
  17. 17.
    Elango JK, Gangadharan P, Sumithra S, Kuriakose MA (2006) Trends of head and neck cancers in urban and rural India. Asian Pac J Cancer Prev APJCP 7(1):108–112PubMedGoogle Scholar
  18. 18.
    Alam H, Sehgal L, Kundu ST, Dalal SN, Vaidya MM (2011) Novel function of keratins 5 and 14 in proliferation and differentiation of stratified epithelial cells. Mol Biol Cell 22(21):4068–4078CrossRefGoogle Scholar
  19. 19.
    Bhosale PG, Cristea S, Ambatipudi S, Desai RS, Kumar R, Patil A et al (2017) Chromosomal alterations and gene expression changes associated with the progression of leukoplakia to advanced gingivobuccal cancer. Transl Oncol 10(3):396–409CrossRefGoogle Scholar
  20. 20.
    Rao RS, Patil S, Ghosh S, Kumari K (2015) Current aspects and future strategies in oral cancer research: a review. J Med Radiol Pathol Surg 1(3):8–13CrossRefGoogle Scholar
  21. 21.
    Rentoft M, Hultin S, Coates PJ, Laurell G, Nylander K (2010) Tubulin alpha-6 chain is a stably expressed reference gene in archival samples of normal oral tissue and oral squamous cell carcinoma. Exp Ther Med 1(3):419–423CrossRefGoogle Scholar
  22. 22.
    Lallemant B, Evrard A, Combescure C, Chapuis H, Chambon G, Raynal C et al (2009) Reference gene selection for head and neck squamous cell carcinoma gene expression studies. BMC Mol Biol 10(1):78CrossRefGoogle Scholar
  23. 23.
    Gemenetzidis E, Bose A, Riaz AM, Chaplin T, Young BD, Ali M et al (2009) FOXM1 upregulation is an early event in human squamous cell carcinoma and it is enhanced by nicotine during malignant transformation. PLoS One 4(3):e4849CrossRefGoogle Scholar
  24. 24.
    Lu J, Ma H, Lian S, Huang D, Lian M, Zhang Y, et al (2017) Clinical significance and prognostic value of the expression of LAMP3 in oral squamous cell carcinoma. Dis Markers 2017;2017:1218254PubMedPubMedCentralGoogle Scholar
  25. 25.
    Egloff AM, Liu X, Davis ALG, Trevelline BK, Vuga M, Siegfried JM et al (2013) Elevated gastrin-releasing peptide receptor mRNA expression in buccal mucosa: association with head and neck squamous cell carcinoma. Head Neck 35(2):270–279CrossRefGoogle Scholar
  26. 26.
    Spivack SD, Hurteau GJ, Jain R, Kumar SV, Aldous KM, Gierthy JF et al (2004) Gene-environment interaction signatures by quantitative mRNA profiling in exfoliated buccal mucosal cells. Cancer Res 64(18):6805–6813CrossRefGoogle Scholar
  27. 27.
    Hirano C, Nagata M, Noman AA, Kitamura N, Ohnishi M, Ohyama T et al (2009) Tetraspanin gene expression levels as potential biomarkers for malignancy of gingival squamous cell carcinoma. Int J Cancer 124(12):2911–2916CrossRefGoogle Scholar
  28. 28.
    Kurokawa A, Nagata M, Kitamura N, Noman AA, Ohnishi M, Ohyama T et al (2008) Diagnostic value of integrin α3, β4, and β5 gene expression levels for the clinical outcome of tongue squamous cell carcinoma. Cancer Interdiscip Int J Am Cancer Soc 112(6):1272–1281Google Scholar
  29. 29.
    Clatot F, Gouérant S, Mareschal S, Cornic M, Berghian A, Choussy O et al (2014) The gene expression profile of inflammatory, hypoxic and metabolic genes predicts the metastatic spread of human head and neck squamous cell carcinoma. Oral Oncol 50(3):200–207CrossRefGoogle Scholar
  30. 30.
    Sun Y, Li Y, Luo D, Liao DJ (2012) Pseudogenes as weaknesses of ACTB (Actb) and GAPDH (Gapdh) used as reference genes in reverse transcription and polymerase chain reactions. PLoS One 7(8):e41659CrossRefGoogle Scholar
  31. 31.
    Sikand K, Singh J, Ebron JS, Shukla GC (2012) Housekeeping gene selection advisory: glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and β-actin are targets of miR-644a. PLoS One 7(10):e47510CrossRefGoogle Scholar
  32. 32.
    Chen D, Pan X, Xiao P, Farwell MA, Zhang B (2011) Evaluation and identification of reliable reference genes for pharmacogenomics, toxicogenomics, and small RNA expression analysis. J Cell Physiol 226(10):2469–2477CrossRefGoogle Scholar
  33. 33.
    Haller F, Kulle B, Schwager S, Gunawan B, von Heydebreck A, Sültmann H et al (2004) Equivalence test in quantitative reverse transcription polymerase chain reaction: confirmation of reference genes suitable for normalization. Anal Biochem 335(1):1–9CrossRefGoogle Scholar
  34. 34.
    Schmittgen TD, Zakrajsek BA (2000) Effect of experimental treatment on housekeeping gene expression: validation by real-time, quantitative RT-PCR. J Biochem Biophys Methods 46(1–2):69–81CrossRefGoogle Scholar
  35. 35.
    Silver N, Best S, Jiang J, Thein SL (2006) Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR. BMC Mol Biol 7(1):33CrossRefGoogle Scholar
  36. 36.
    Andersen CL, Jensen JL, Ørntoft TF (2004) 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(15):5245–5250CrossRefGoogle Scholar
  37. 37.
    Tilli TM, Castro Cda S, Tuszynski JA, Carels N (2016) A strategy to identify housekeeping genes suitable for analysis in breast cancer diseases. BMC Genomics 17(1):639CrossRefGoogle Scholar
  38. 38.
    Hildyard JCW, Finch AM, Wells DJ (2019) Identification of qPCR reference genes suitable for normalizing gene expression in the mdx mouse model of Duchenne muscular dystrophy. PLoS One 14(1):e0211384CrossRefGoogle Scholar
  39. 39.
    Mukhopadhyay R, Ray PS, Arif A, Brady AK, Kinter M, Fox PL (2008) DAPK-ZIPK-L13a axis constitutes a negative-feedback module regulating inflammatory gene expression. Mol Cell 32(3):371–382CrossRefGoogle Scholar
  40. 40.
    Folgueira K, Azevedo MA, Brentani H, Carraro DM, De Filho MCB, Katayama MLH et al (2009) Gene expression profile of residual breast cancer after doxorubicin and cyclophosphamide neoadjuvant chemotherapy. Oncol Rep 22(4):805–813Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Aniket Parab
    • 1
  • Sanit Mhatre
    • 1
  • Sujata Hake
    • 1
  • Sadhana Kannan
    • 2
  • Prathamesh Pai
    • 3
  • Shubhada Kane
    • 4
  • Narendra Joshi
    • 1
    • 5
    Email author
  1. 1.Cancer Research InstituteAdvanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial CentreNavi MumbaiIndia
  2. 2.Epidemiology and Clinical Trials Unit, Clinical Research CentreAdvanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial CentreNavi MumbaiIndia
  3. 3.Department of Surgical OncologyTata Memorial HospitalMumbaiIndia
  4. 4.Department of PathologyTata Memorial HospitalMumbaiIndia
  5. 5.ThaneIndia

Personalised recommendations