Advertisement

Normalization in Human Glioma Tissue

  • Ana Paula Santin Bertoni
  • Isabele Cristiana Iser
  • Rafael Paschoal de Campos
  • Márcia Rosangela Wink
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 2065)

Abstract

For tissues obtained from glioma samples with/without nonneoplastic brain there is no consensus for universal reference gene but there are some potential genes that might have good stability, under certain conditions. Considering all points described in this work, the care with tissue collection, until gene amplification, directly impacts on the reliable characterization of its mRNA levels. Moreover, it is clear the importance of selecting the most appropriate reference genes for each experimental situation, to allow the accurate normalization of target genes, especially for genes that are subtly regulated.

Key words

Reference gene Quantitative PCR Glioma Preanalytical handling 

References

  1. 1.
    Cohen AL, Colman H (2015) Glioma biology and molecular markers. Cancer Treat Res 163:15–30CrossRefGoogle Scholar
  2. 2.
    Hanif F, Muzaffar K, Perveen K, Malhi SM, Simjee SU (2017) Glioblastoma multiforme: a review of its epidemiology and pathogenesis through clinical presentation and treatment. Asian Pac J Cancer Prev 18(1):3–9PubMedPubMedCentralGoogle Scholar
  3. 3.
    Wen PY, Kesari S (2008) Malignant gliomas in adults. N Engl J Med 359(5):492–507CrossRefGoogle Scholar
  4. 4.
    Qazi MA, Vora P, Venugopal C, Sidhu SS, Moffat J, Swanton C et al (2017) Intratumoral heterogeneity: pathways to treatment resistance and relapse in human glioblastoma. Ann Oncol 28(7):1448–1456CrossRefGoogle Scholar
  5. 5.
    Jansen MP, Foekens JA, van Staveren IL, Dirkzwager-Kiel MM, Ritstier K, Look MP et al (2005) Molecular classification of tamoxifen-resistant breast carcinomas by gene expression profiling. J Clin Oncol Off J Am Soc Clin Oncol 23(4):732–740CrossRefGoogle Scholar
  6. 6.
    Verhaak RG, Hoadley KA, Purdom E, Wang V, Qi Y, Wilkerson MD et al (2010) Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17(1):98–110CrossRefGoogle Scholar
  7. 7.
    Ellervik C, Vaught J (2015) Preanalytical variables affecting the integrity of human biospecimens in biobanking. Clin Chem 61(7):914–934CrossRefGoogle Scholar
  8. 8.
    Hennig G, Gehrmann M, Stropp U, Brauch H, Fritz P, Eichelbaum M et al (2010) Automated extraction of DNA and RNA from a single formalin-fixed paraffin-embedded tissue section for analysis of both single-nucleotide polymorphisms and mRNA expression. Clin Chem 56(12):1845–1853CrossRefGoogle Scholar
  9. 9.
    Gouveia GR, Ferreira SC, Ferreira JE, Siqueira SA, Pereira J (2014) Comparison of two methods of RNA extraction from formalin-fixed paraffin-embedded tissue specimens. Biomed Res Int 2014:151724CrossRefGoogle Scholar
  10. 10.
    Birdsill AC, Walker DG, Lue L, Sue LI, Beach TG (2011) Postmortem interval effect on RNA and gene expression in human brain tissue. Cell Tissue Bank 12(4):311–318CrossRefGoogle Scholar
  11. 11.
    Choi S, Ray HE, Lai SH, Alwood JS, Globus RK (2016) Preservation of multiple mammalian tissues to maximize science return from ground based and spaceflight experiments. PLoS One 11(12):e0167391CrossRefGoogle Scholar
  12. 12.
    Cronin M, Pho M, Dutta D, Stephans JC, Shak S, Kiefer MC et al (2004) Measurement of gene expression in archival paraffin-embedded tissues: development and performance of a 92-gene reverse transcriptase-polymerase chain reaction assay. Am J Pathol 164(1):35–42CrossRefGoogle Scholar
  13. 13.
    Yu K, Xing J, Zhang J, Zhao R, Zhang Y, Zhao L (2017) Effect of multiple cycles of freeze-thawing on the RNA quality of lung cancer tissues. Cell Tissue Bank 18(3):433–440CrossRefGoogle Scholar
  14. 14.
    Nouaille S, Mondeil S, Finoux AL, Moulis C, Girbal L, Cocaign-Bousquet M (2017) The stability of an mRNA is influenced by its concentration: a potential physical mechanism to regulate gene expression. Nucleic Acids Res 45(20):11711–11724CrossRefGoogle Scholar
  15. 15.
    Alkallas R, Fish L, Goodarzi H, Najafabadi HS (2017) Inference of RNA decay rate from transcriptional profiling highlights the regulatory programs of Alzheimer’s disease. Nat Commun 8(1):909CrossRefGoogle Scholar
  16. 16.
    Weber CF, Kuske CR (2012) Comparative assessment of fungal cellobiohydrolase I richness and composition in cDNA generated using oligo(dT) primers or random hexamers. J Microbiol Methods 88(2):224–228CrossRefGoogle Scholar
  17. 17.
    Lekanne Deprez RH, Fijnvandraat AC, Ruijter JM, Moorman AF (2002) Sensitivity and accuracy of quantitative real-time polymerase chain reaction using SYBR green I depends on cDNA synthesis conditions. Anal Biochem 307(1):63–69CrossRefGoogle Scholar
  18. 18.
    Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5(7):621–628CrossRefGoogle Scholar
  19. 19.
    Zeka F, Vanderheyden K, De Smet E, Cuvelier CA, Mestdagh P, Vandesompele J (2016) Straightforward and sensitive RT-qPCR based gene expression analysis of FFPE samples. Sci Rep 6:21418CrossRefGoogle Scholar
  20. 20.
    Stahlberg A, Hakansson J, Xian X, Semb H, Kubista M (2004) Properties of the reverse transcription reaction in mRNA quantification. Clin Chem 50(3):509–515CrossRefGoogle Scholar
  21. 21.
    Huggett J, Dheda K, Bustin S, Zumla A (2005) Real-time RT-PCR normalisation; strategies and considerations. Genes Immun 6(4):279–284CrossRefGoogle Scholar
  22. 22.
    Rohn G, Koch A, Krischek B, Stavrinou P, Goldbrunner R, Timmer M (2018) ACTB and SDHA are suitable endogenous reference genes for gene expression studies in human astrocytomas using quantitative RT-PCR. Technol Cancer Res Treat 17:1533033818802318CrossRefGoogle Scholar
  23. 23.
    Grube S, Gottig T, Freitag D, Ewald C, Kalff R, Walter J (2015) Selection of suitable reference genes for expression analysis in human glioma using RT-qPCR. J Neuro-Oncol 123(1):35–42CrossRefGoogle Scholar
  24. 24.
    Aithal MG, Rajeswari N (2015) Validation of housekeeping genes for gene expression analysis in glioblastoma using quantitative real-time polymerase chain reaction. Brain Tumor Res Treat 3(1):24–29CrossRefGoogle Scholar
  25. 25.
    Gresner SM, Golanska E, Kulczycka-Wojdala D, Jaskolski DJ, Papierz W, Liberski PP (2011) Selection of reference genes for gene expression studies in astrocytomas. Anal Biochem 408(1):163–165CrossRefGoogle Scholar
  26. 26.
    Kreth S, Heyn J, Grau S, Kretzschmar HA, Egensperger R, Kreth FW (2010) Identification of valid endogenous control genes for determining gene expression in human glioma. Neuro Oncol 12(6):570–579CrossRefGoogle Scholar
  27. 27.
    Valente V, Teixeira SA, Neder L, Okamoto OK, Oba-Shinjo SM, Marie SK, Scrideli CA, Paco-Larson ML, Carlotti CG Jr (2009) Selection of suitable housekeeping genes for expression analysis in glioblastoma using quantitative RT-PCR. BMC Mol Biol 10:17CrossRefGoogle Scholar
  28. 28.
    Chow RD, Guzman CD, Wang G, Schmidt F, Youngblood MW, Ye L et al (2017) AAV-mediated direct in vivo CRISPR screen identifies functional suppressors in glioblastoma. Nat Neurosci 20(10):1329–1341CrossRefGoogle Scholar
  29. 29.
    Iser IC, de Campos RP, Bertoni AP, Wink MR (2015) Identification of valid endogenous control genes for determining gene expression in C6 glioma cell line treated with conditioned medium from adipose-derived stem cell. Biomed Pharmacother 75:75–82CrossRefGoogle Scholar
  30. 30.
    Sharan RN, Vaiphei ST, Nongrum S, Keppen J, Ksoo M (2015) Consensus reference gene(s) for gene expression studies in human cancers: end of the tunnel visible? Cell Oncol 38(6):419–431CrossRefGoogle Scholar
  31. 31.
    Ceccarelli M, Barthel FP, Malta TM, Sabedot TS, Salama SR, Murray BA et al (2016) Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma. Cell 164(3):550–563CrossRefGoogle Scholar
  32. 32.
    Mallawaaratchy DM, Hallal S, Russell B, Ly L, Ebrahimkhani S, Wei H et al (2017) Comprehensive proteome profiling of glioblastoma-derived extracellular vesicles identifies markers for more aggressive disease. J Neurooncol 131(2):233–244CrossRefGoogle Scholar
  33. 33.
    Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M et al (2009) The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem 55(4):611–622CrossRefGoogle Scholar
  34. 34.
    Green MR, Sambrook J (2018) Optimizing primer and probe concentrations for use in real-time polymerase chain reaction (PCR) assays. Cold Spring Harb Protoc 2018(10):pdb.prot095018CrossRefGoogle Scholar
  35. 35.
    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):RESEARCH0034CrossRefGoogle Scholar
  36. 36.
    Andersen CL, Jensen JL, Orntoft 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.
    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 7:33CrossRefGoogle Scholar
  38. 38.
    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

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Ana Paula Santin Bertoni
    • 1
  • Isabele Cristiana Iser
    • 1
  • Rafael Paschoal de Campos
    • 1
    • 2
  • Márcia Rosangela Wink
    • 1
  1. 1.Laboratório de Biologia Celular, Departamento de Ciências Básicas da Saúde (DCBS)Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA)Porto AlegreBrazil
  2. 2.Laboratório de Sinalização e Plasticidade Celular, Departamento de Biofísica, Instituto de BiociênciasUniversidade Federal do Rio Grande do Sul (UFRGS)Porto AlegreBrazil

Personalised recommendations