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Replication of GWAS identifies RTEL1, CDKN2A/B, and PHLDB1 SNPs as risk factors in Portuguese gliomas patients

  • Marta Viana-Pereira
  • Daniel Antunes Moreno
  • Paulo Linhares
  • Júlia Amorim
  • Rui Nabiço
  • Sandra Costa
  • Rui Vaz
  • Rui Manuel ReisEmail author
Original Article
  • 52 Downloads

Abstract

Diffuse gliomas are the most common malignant primary brain tumors and remain incurable. A better knowledge of the tumor etiology is required. Specific single nucleotides polymorphisms (SNPs) rs4977756 (CDKN2A/B), rs6010620 (RTEL1), rs498872 (PHLDB1), rs2736100 (TERT), and rs4295627 (CCDC26) have been associated with glioma susceptibility and are potential risk biomarkers. This study aimed to analyze five SNPs associated with glioma susceptibility, in the Portuguese population. SNPs were genotyped using the Sequenom MassARRAY platform in 127 gliomas and 180 controls. Unconditional logistic regression models were used to calculate odds ratio (OR) and 95% confidence intervals. The false-positive report probability was also assessed. The associations between polymorphisms and survival were evaluated using the log-rank test. It was found that the AG and GG genotypes of the rs4977756 (CDKN2A/B) were associated with an increased risk of gliomas (OR 1.85 and OR 2.38) and glioblastomas (OR 2.77 and OR 3.94). The GA genotype of the rs6010620 (RTEL1) was associated with a decreased risk of glioblastomas (OR 0.45). We also observed that the GA genotype of the rs498872 (PHLDB1) was associated with an increased risk of gliomas (OR 2.92) and glioblastomas (OR 2.39). No significant risk associations were found for the rs2736100 (TERT) and rs4295627 (CCDC26). In addition, the genotype AA of the rs498872 (PHLDB1) was associated with poor overall survival of gliomas patients (AA vs. GA, p = 0.037). The rs6010620 (RTEL1), rs4977756 (CDKN2A/B), and rs498872 (PHLDB1) are associated with glioma risk in the Portuguese population and these data may contribute to understanding gliomas etiology.

Keywords

Glioma risk SNPs GWAS RTEL1 CDKN2A/B PHLDB1 

Abbreviations

A

Adenine

C

Cytosine

CCDC26

Coiled-coil domain containing 26

CDKN2A/B

Cyclin-dependent kinase inhibitor 2A/B

CI

Confidence interval

FPRP

False-positive report probability

G

Guanine

GWAS

Genome-wide association studies

OR

Odds ratio

OS

Overall survival

PHLDB1

Pleckstrin Homology-Like Domain Family B member 1

RTEL1

Regulator of telomere elongation helicase 1

SNPs

Single nucleotide polymorphisms

T

Thymine

TERT

Telomerase reverse transcriptase

WHO

World Health Organization

Notes

Author contributions

MV-P: Genotyping of samples, analysis of polymorphisms and patient’s data, tables and figures, manuscript review; DAM: interpreted genotyping, patient’s data, bibliography review, manuscript writing; PL, JA, RN and RV: clinical data collection, interpretation and final review; SC: collections of samples, DNA extraction, genotyping; RMR: study design, scientific support, results analysis, manuscript review, research coordination.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by Fundacão para a Ciência e Tecnologia (FCT), Portugal (www.fct.pt; grant PTDC/SAU-ONC/115513/2009_FCOMP-01-0124-FEDER-015949 to R.M.R.). This article has been developed under the scope of the project NORTE-01-0145-FEDER-000013, supported by the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). M.V.P. was recipient of an FCT Post-Doctoral fellowship (ref: SFRH/BPD/104290/2014).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

Institutional ethics committees of S. João and Braga Hospitals (Approval 29/10/2007).

Informed consent

Not applicable. This is a retrospective study where any information that allows participants identification is not disclosed.

References

  1. 1.
    Alcantara Llaguno SR, Parada LF (2016) Cell of origin of glioma: biological and clinical implications. Br J Cancer 115(12):1445–1450.  https://doi.org/10.1038/bjc.2016.354 CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Ohgaki H, Kleihues P (2005) Epidemiology and etiology of gliomas. Acta Neuropathol 109(1):93–108.  https://doi.org/10.1007/s00401-005-0991-y CrossRefPubMedGoogle Scholar
  3. 3.
    Paolillo M, Boselli C, Schinelli S (2018) Glioblastoma under Siege: an overview of current therapeutic strategies. Brain Sci.  https://doi.org/10.3390/brainsci8010015 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Wen PY, Reardon DA (2016) Neuro-oncology in 2015: progress in glioma diagnosis, classification and treatment. Nat Rev Neurol 12(2):69–70.  https://doi.org/10.1038/nrneurol.2015.242 CrossRefPubMedGoogle Scholar
  5. 5.
    Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK et al (2016) The 2016 World Health Organization classification of tumors. Acta Neuropathol 131(6):803–820.  https://doi.org/10.1007/s00401-016-1545-1 CrossRefGoogle Scholar
  6. 6.
    Batista R, Cruvinel-Carloni A, Vinagre J, Peixoto J, Catarino TA, Campanella NC et al (2016) The prognostic impact of TERT promoter mutations in glioblastomas is modified by the rs2853669 single nucleotide polymorphism. Int J Cancer 139(2):414–423.  https://doi.org/10.1002/ijc.30057 CrossRefPubMedGoogle Scholar
  7. 7.
    Eckel-Passow JE, Lachance DH, Molinaro AM, Walsh KM, Decker PA, Sicotte H et al (2015) Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors. N Engl J Med 372:2499–2508.  https://doi.org/10.1056/NEJMoa1407279 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Wesseling P, Capper D (2018) WHO 2016 classification of gliomas. Neuropathol Appl Neurobiol 44(2):139–150.  https://doi.org/10.1111/nan.12432 CrossRefPubMedGoogle Scholar
  9. 9.
    Brat DJ, Verhaak RG, Aldape KD, Yung WK, Salama SR, Cooper LA et al (2015) Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N Engl J Med 372(26):2481–2498.  https://doi.org/10.1056/NEJMoa1402121 CrossRefPubMedGoogle Scholar
  10. 10.
    Noroxe DS, Poulsen HS, Lassen U (2016) Hallmarks of glioblastoma: a systematic review. ESMO Open 1(6):e000144.  https://doi.org/10.1136/esmoopen-2016-000144 CrossRefPubMedGoogle Scholar
  11. 11.
    Ohgaki H (2009) Epidemiology of brain tumors. Methods Mol Biol 472:323–342.  https://doi.org/10.1007/978-1-60327-492-0_14 CrossRefPubMedGoogle Scholar
  12. 12.
    Schwartzbaum JA, Fisher JL, Aldape KD, Wrensch M (2006) Epidemiology and molecular pathology of glioma. Nat Clin Pract Neurol 2(9):494–503.  https://doi.org/10.1038/ncpneuro0289 CrossRefPubMedGoogle Scholar
  13. 13.
    Deng N, Zhou H, Fan H, Yuan Y (2017) Single nucleotide polymorphisms and cancer susceptibility. Oncotarget 8(66):110635–110649.  https://doi.org/10.18632/oncotarget.22372 CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Adel Fahmideh M, Schwartzbaum J, Frumento P, Feychting M (2014) Association between DNA repair gene polymorphisms and risk of glioma: a systematic review and meta-analysis. Neuro Oncology 6(6):807–814.  https://doi.org/10.1093/neuonc/nou003 CrossRefGoogle Scholar
  15. 15.
    Liu Y, Scheurer ME, El-Zein R, Cao Y, Do KA, Gilbert M et al (2009) Association and interactions between DNA repair gene polymorphisms and adult glioma. Cancer Epidemiol Biomarkers Prev 18(1):204–214.  https://doi.org/10.1158/1055-9965.EPI-08-0632 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    McKean-Cowdin R, Barnholtz-Sloan J, Inskip PD, Ruder AM, Butler M, Rajaraman P et al (2009) Associations between polymorphisms in DNA repair genes and glioblastoma. Cancer Epidemiol Biomarkers Prev 18(4):1118–1126.  https://doi.org/10.1158/1055-9965.EPI-08-1078 CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Zawlik I, Kita D, Vaccarella S, Mittelbronn M, Franceschi S, Ohgaki H (2009) Common polymorphisms in the MDM2 and TP53 genes and the relationship between TP53 mutations and patient outcomes in glioblastomas. Brain Pathol 19(2):188–194.  https://doi.org/10.1111/j.1750-3639.2008.00170.x CrossRefPubMedGoogle Scholar
  18. 18.
    Costa BM, Ferreira P, Costa S, Canedo P, Oliveira P, Silva A et al (2007) Association between functional EGF + 61 polymorphism and glioma risk. Clin Cancer Res 13(9):2621–2626.  https://doi.org/10.1158/1078-0432.CCR-06-2606 CrossRefPubMedGoogle Scholar
  19. 19.
    Costa BM, Viana-Pereira M, Fernandes R, Costa S, Linhares P, Vaz R et al (2011) Impact of EGFR genetic variants on glioma risk and patient outcome. Cancer Epidemiol Biomarkers Prev 20(12):2610–2617.  https://doi.org/10.1158/1055-9965.EPI-11-0340 CrossRefPubMedGoogle Scholar
  20. 20.
    Pandey JP, Kaur N, Costa S, Amorim J, Nabico R, Linhares P et al (2014) Immunoglobulin genes implicated in glioma risk. Oncoimmunology 3:e28609.  https://doi.org/10.4161/onci.28609 CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Rajaraman P, Melin BS, Wang Z, McKean-Cowdin R, Michaud DS, Wang SS et al (2012) Genome-wide association study of glioma and meta-analysis. Hum Genet 131(12):1877–1888.  https://doi.org/10.1007/s00439-012-1212-0 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Shete S, Hosking FJ, Robertson LB, Dobbins SE, Sanson M, Malmer B et al (2009) Genome-wide association study identifies five susceptibility loci for glioma. Nat Genet 41(8):899–904.  https://doi.org/10.1007/s00439-012-1212-0 CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Bondy ML, Scheurer ME, Malmer B, Barnholtz-Sloan JS, Davis FG, Il’yasova D et al (2008) Brain tumor epidemiology: consensus from the Brain Tumor Epidemiology Consortium. Cancer 113(7 Suppl):1953–1968.  https://doi.org/10.1002/cncr.23741 CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Melin B, Jenkins R (2013) Genetics in glioma- lessons learned from genome wide association studies. Curr Opin Neurol 26(6):688–692.  https://doi.org/10.1097/WCO.0000000000000033 CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Ostrom QT, Gittleman H, Stetson L, Virk S, Barnholtz-Sloan JS (2018) Epidemiology of intracranial gliomas. Prog Neurol Surg 30:1–11.  https://doi.org/10.1159/000464374 CrossRefPubMedGoogle Scholar
  26. 26.
    Chen X, Yang G, Zhang D, Zhang W, Zou H, Zhao H et al (2014) Association between the epidermal growth factor +61 G/A polymorphism and glioma risk: a meta-analysis. PLoS ONE 9(4):e95139.  https://doi.org/10.1371/journal.pone.0095139 CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Ghasimi S, Wibom C, Dahlin AM, Brännström T, Golovleva I, Andersson U et al (2016) Genetic risk variants in the CDKN2A/B, RTEL1 and EGFR genes are associated with somatic biomarkers in glioma. J Neurooncol 127(3):483–492.  https://doi.org/10.1007/s11060-016-2066-4 CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Sanson M, Hosking FJ, Shete S, Zelenika D, Dobbins SE, Ma Y (2011) Chromosome 7p11.2 (EGFR) variation influences glioma risk. Hum Mol Genet 20(14):2897–2904.  https://doi.org/10.1093/hmg/ddr192 CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Linhares P, Viana-Pereira M, Ferreira M, Amorim J, Nabicxo R, Pinto F et al (2018) Genetic variants of vascular endothelial growth factor predict risk and survival of gliomas. Tumor Biol 40(3):1–11.  https://doi.org/10.1177/1010428318766273 CrossRefGoogle Scholar
  30. 30.
    Cui T (2015) CCDC26 rs4295627 polymorphism and glioma risk: a meta-analysis. Int J Clin Exp Med 8(3):3862–3868PubMedPubMedCentralGoogle Scholar
  31. 31.
    Du SL, Geng TT, Feng T, Chen CP, Jin TB, Chen C (2014) The RTEL1 rs6010620 polymorphism and glioma risk: a meta-analysis based on 12 case-control studies. Asian Pac J Cancer Prev 15(23):10175–10179.  https://doi.org/10.7314/apjcp.2014.15.23.10175 CrossRefPubMedGoogle Scholar
  32. 32.
    Li H, Xu Y, Mei H, Peng L, Li X, Tang J (2017) The TERT rs2736100 polymorphism increases cancer risk: a meta-analysis. Oncotarget 8(24):38693–38705.  https://doi.org/10.18632/oncotarget.16309 CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Walsh KM, Codd V, Smirnov IV, Rice T, Decker PA, Hansen HM et al (2014) Variants near TERT and TERC influencing telomere length are associated with high-grade glioma risk. Nat Genet 46(7):731–735.  https://doi.org/10.1038/ng.3004 CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Pestana A, Vinagre J, Sobrinho-Simões M, Soares P (2017) TERT biology and function in cancer: beyond immortalisation. J Mol Endocrinol 58(2):R129–R146.  https://doi.org/10.1530/JME-16-0195 CrossRefPubMedGoogle Scholar
  35. 35.
    Wrensch M, Jenkins RB, Chang JS, Yeh RF, Xiao Y, Decker PA et al (2009) Variants in the CDKN2B and RTEL1 regions are associated with high-grade glioma susceptibility. Nat Genet 41(8):905–908.  https://doi.org/10.1038/ng.408 CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Schoemaker MJ, Robertson L, Wigertz A, Jones ME, Hosking FJ, Feychting M et al (2010) Interaction between 5 genetic variants and allergy in glioma risk. Am J Epidemiol 171:1165–1173.  https://doi.org/10.1093/aje/kwq075 CrossRefPubMedGoogle Scholar
  37. 37.
    Wu Y, Tong X, Tang LL, Zhou K, Zhong CH, Jiang S (2014) Associations between the rs6010620 polymorphism in RTEL1 and risk of glioma: a meta-analysis of 20,711 participants. Asian Pac J Cancer Prev 15(17):7163–7167.  https://doi.org/10.7314/apjcp.2014.15.17.7163 CrossRefPubMedGoogle Scholar
  38. 38.
    Zhao W, Bian Y, Zhu W, Zou P, Tang G (2014) Regulator of telomere elongation helicase 1 (RTEL1) rs6010620 polymorphism contribute to increased risk of glioma. Tumour Biol 35(6):5259–5266.  https://doi.org/10.1007/s13277-014-1684-8 CrossRefPubMedGoogle Scholar
  39. 39.
    Coetzee SG, Rhie SK, Berman BP, Coetzee GA, Noushmehr H (2012) FunciSNP: an R/bioconductor tool integrating functional non-coding data sets with genetic association studies to identify candidate regulatory SNPs. Nucleic Acids Res 40(18):e139.  https://doi.org/10.1093/nar/gks542 CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Hirano T (2016) The role of the CCDC26 long noncoding RNA as a tumor suppressor. RNA Dis 3:e1022Google Scholar
  41. 41.
    Enciso-Mora V, Hosking FJ, Kinnersley B, Wang Y, Shete S, Zelenika D et al (2013) Deciphering the 8q24.21 associations for glioma. Hum Mol Genet 22(11):2293–2302CrossRefGoogle Scholar
  42. 42.
    Jenkins RB, Wrensch MR, Johnson D, Fridley BL, Decker PA, Xiao Y et al (2011) Distinct germ line polymorphisms underlie glioma morphologic heterogeneity. Cancer Genet 204(1):13–18.  https://doi.org/10.1016/j.cancergencyto.2010.10.002 CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Qi X, Wan Y, Zhan Q, Yang S, Wang Y, Cai X (2016) Effect of CDKN2A/B rs4977756 polymorphism on glioma risk: a meta-analysis of 16 studies including 24077 participants. Mamm Genome 27(1–2):1–7.  https://doi.org/10.1007/s00335-015-9612-9 CrossRefPubMedGoogle Scholar
  44. 44.
    Lu H, Yang Y, Wang J, Liu Y, Huang M, Sun X et al (2015) The CDKN2A-CDKN2B rs4977756 polymorphism and glioma risk: a meta-analysis. Int J Clin Exp Med 8(10):17480–17488PubMedPubMedCentralGoogle Scholar
  45. 45.
    Yang TH, Kon M, Hung JH, Delisi C (2011) Combinations of newly confirmed glioma-associated loci link regions on chromosomes 1 and 9 to increased disease risk. BMC Med Genomics 4:63.  https://doi.org/10.1186/1755-8794-4-63 CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Nagai MA (2016) Pleckstrin homology-like domain, family A, member 1 (PHLDA1) and cancer. Biomed Rep 4(3):275–281.  https://doi.org/10.3892/br.2016.580 CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Gao X, Mi Y, Yan A, Sha B, Guo N, Hu Z et al (2015) The PHLDB1 rs498872 (11q23.3) polymorphism and glioma risk: a meta-analysis. Asia Pac J Clin Oncol 11(4):e13–e21.  https://doi.org/10.1111/ajco.12211 CrossRefPubMedGoogle Scholar
  48. 48.
    Novembre J, Johnson T, Bryc K, Kutalik Z, Boyko AR, Auton A, Indap A, King KS, Bergmann S, Nelson MR, Stephens M, Bustamante CD (2008) Genes mirror geography within Europe. Nature 456(7218):98–101.  https://doi.org/10.1038/nature07331 CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Wacholder S, Chanock S, Garcia-Closas M, El GL, Rothman N (2004) Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J Natl Cancer Inst 96:434–442.  https://doi.org/10.1093/jnci/djh075 CrossRefPubMedGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of Medicine, Life and Health Sciences Research Institute (ICVS)University of MinhoBragaPortugal
  2. 2.ICVS/3B’s - PT Government Associate LaboratoryMinhoPortugal
  3. 3.Barretos Cancer HospitalMolecular Oncology Research CenterBarretosBrazil
  4. 4.Department of NeurosurgeryHospital S. JoãoPortoPortugal
  5. 5.Faculty of MedicineUniversity of PortoPortoPortugal
  6. 6.Department of OncologyHospital de BragaBragaPortugal

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