Human Genetics

, Volume 122, Issue 5, pp 525–533 | Cite as

One-carbon metabolism gene polymorphisms and risk of non-Hodgkin lymphoma in Australia

  • Kyoung-Mu LeeEmail author
  • Qing Lan
  • Anne Kricker
  • Mark P. Purdue
  • Andrew E. Grulich
  • Claire M. Vajdic
  • Jennifer Turner
  • Denise Whitby
  • Daehee Kang
  • Stephen Chanock
  • Nathaniel Rothman
  • Bruce K. Armstrong
Original Investigation


Dysregulation of the one-carbon metabolic pathway, which controls nucleotide synthesis and DNA methylation, may promote lymphomagenesis. We evaluated the association between polymorphisms in one-carbon metabolism genes and risk of non-Hodgkin lymphoma (NHL) in a population-based case-control study in Australia. Cases (n = 561) and controls (n = 506) were genotyped for 14 selected single-nucleotide polymorphisms in 10 genes (CBS, FPGS, FTHFD, MTHFR, MTHFS, MTR, SHMT1, SLC19A1, TCN1, and TYMS). We also conducted a meta-analysis of all studies of Caucasian populations investigating the association between MTHFR Ex5+79C > T (a.k.a., 677C>T) and NHL risk. A global test of 13 genotypes was statistically significant for diffuse large B-cell lymphoma (DLBCL; = 0.008), but not for follicular lymphoma (FL; = 0.27) or all NHL (= 0.17). The T allele at MTHFR Ex5+79 was marginally significantly associated with all NHL (OR = 1.25, 95% CI = 0.98–1.59) and DLBCL (1.36, 0.96–1.93). The T allele at TYMS Ex8+157 was associated with a reduced risk of FL (0.64, 0.46–0.91). An elevated risk of NHL was also observed among carriers of the G allele at FTHFD Ex21+31 (all NHL, 1.31, 1.02–1.69; DLBCL, 1.50, 1.05–2.14). A meta-analysis of 11 studies conducted in Caucasian populations of European origin (4,121 cases and 5,358 controls) supported an association between the MTHFR Ex5+79 T allele and increased NHL risk (additive model, = 0.01). In conclusion, the results of this study suggest that genetic polymorphisms of one-carbon metabolism genes such as MTHFR and TYMS may influence susceptibility to NHL.


Acute Lymphoblastic Leukemia False Discovery Rate Australian Capital Territory False Positive Report Probability Uracil Misincorporation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We gratefully acknowledge the individuals who participated in the research, the clinicians who gave permission for us to approach their patients, and staff at the NSW Central Cancer Registry and the Hunter Valley Research Foundation. Special thanks to Melisa Litchfield, Maria Agaliotis, and Chris Goumas for data collection and data entry and to Jackie Turner for telephone follow-up. We also thank Robert Welch and Sunita Yadavalli at the NCI Core Genotyping Facility for their work in the specimen handling and laboratory analysis of genotyping data. This research was supported by the Intramural Research Program of the NIH and the National Cancer Institute. Bruce Armstrong’s research is supported by a University of Sydney Medical Foundation Program Grant. The principal investigator of the population-based case-control study in New South Wales, Australia is B.K.A.; the recruitment of subjects and sample collection were performed by A.K., A.E.G., C.M.V, and J.T.; the one-carbon metabolism project was initiated and conducted by Q.L., N.R., M.P.P., and D.K.; D.W.’s laboratory performed the DNA extraction and genotyping at the NCI Core Genotyping Facility was supervised by S.C.; the statistical analysis was performed by K.M.L. and M.P.P.; the paper was drafted and revised by K.M.L., M.P.P., Q.L., N.R., and B.K.A.; and all authors reviewed and approved the paper.

Supplementary material

439_2007_431_MOESM1_ESM.doc (288 kb)
Supplementary Table I Main effect of SNPs in one-carbon metabolism genes on NHL risk in Australia: SNPs for which little indication of a statistically significant association with NHL (DOC 288 kb)


  1. Anguera MC, Field MS, Perry C, Ghandour H, Chiang EP, Selhub J, Shane B, Stover PJ (2006) Regulation of folate-mediated one-carbon metabolism by 10-formyltetrahydrofolate dehydrogenase. J Biol Chem 281(27):18335–18342PubMedCrossRefGoogle Scholar
  2. Begg CB, Mazumdar M (1994) Operating characteristics of a rank correlation test for publication bias. Biometrics 50:1088–1101PubMedCrossRefGoogle Scholar
  3. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B289-B300Google Scholar
  4. Blount BC, Mack MM, Wehr CM, MacGregor JT, Hiatt RA, Wang G, Wickramasinghe SN, Everson RB, Ames BN (1997) Folate deficiency causes uracil misincorporation into human DNA and chromosome breakage: implications for cancer and neuronal damage. Proc Natl Acad Sci USA 94(7):3290–3295PubMedCrossRefGoogle Scholar
  5. Chapman JM, Cooper JD, Todd JA, Clayton DG (2003) Detecting disease associations due to linkage disequilibrium using haplotype tags: a class of tests and the determinants of statistical power. Hum Hered 56(1–3):18–31PubMedCrossRefGoogle Scholar
  6. Curran-Everett D (2000) Multiple comparisons: philosophies and illustrations. Am J Physiol Regul Integr Comp Physiol 279(1):R1–R8PubMedGoogle Scholar
  7. Das PM, Singal R (2004) DNA methylation and cancer. J Clin Oncol 22(22):4632–4642PubMedCrossRefGoogle Scholar
  8. Deligezer U, Akisik EE, Yaman F, Erten N, Dalay N (2006) MTHFR C677T gene polymorphism in lymphoproliferative diseases. J Clin Lab Anal 20:37–41PubMedCrossRefGoogle Scholar
  9. Egger M, Davey Smith G, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315:629–634PubMedGoogle Scholar
  10. Frosst P, Blom HJ, Milos R, Goyette P, Sheppard CA, Matthews RG, Boers GJH, den Heijer M, Kluijtmans LAJ, van den Heuvel LP, Rozen R (1995) A candidate genetic risk factor for vascular disease: a common mutation in methylenetetrahydrofolate reductase. Nat Genet 10:111–113PubMedCrossRefGoogle Scholar
  11. Gemmati D, Ongaro A, Scapoli GL, Porta MD, Tognazzo S, Serino ML, Bona ED, Rodeghiero F, Gilli G, Reverberi R, Garuso A, Pasello M, Pellati A, Mattei MD (2004) Common gene polymorphisms in the metabolic folate and methylation pathway and the risk of acute lymphoblastic leukemia and non-Hodgikin’s lymphoma in adults. Cancer Epidemiol Biomarkers Prev 13(5):787–794PubMedGoogle Scholar
  12. Gonzalez Ordonez AJ, Fernandez Carreira JM, Fernandez Alvarez CR, Martin L, Sanchez Garcia J, Medina Rodriguez JM, Alvarez MV, Coto E (2000) Normal frequencies of the C677T genotypes on the methylenetetrahydrofolate reductase (MTHFR) gene among lymphoproliferative disorders but not in multiple myeloma. Leuk Lymphoma 39(5–6):607–612PubMedGoogle Scholar
  13. Habib EE, Aziz M, Kotb M (2005) Genetic polymorphism of folate and methionine metabolizing enzymes and their susceptibility to malignant lymphoma. J Egypt Natl Canc Inst 17(3):184–192PubMedGoogle Scholar
  14. Hanada M, Delia D, Aiello A, Stadtmauer E, Reed JC (1993) Bcl-2 gene hypomethylation and high-level expression in B-cell chronic lymphocytic leukemia. Blood 82(6):1820–1828PubMedGoogle Scholar
  15. Hartge P, Wang SS (2004) Overview of the etiology and epidemiology of lymphoma. In: Mauch PM, Armitage JO, Coiffier B, Dalla-Favera R, Harris NL (eds) Non-Hodgkin’s lymphomas. Lippincott, Philadelphia, pp 711–727Google Scholar
  16. Hishida A, Matsuo K, Hamajima N, Ito H, Ogura M, Kagami Y, Taji H, Morishima Y, Emi N, Tajima K (2003) Associations between polymorphisms in the thymidylate synthase and serine hydroxymethyltransferase genes and susceptibility to malignant lymphoma. Haematologica 88(2):159–166PubMedGoogle Scholar
  17. Hughes AM, Armstrong BK, Vajdic CM, Turner J, Grulich A, Fritschi L, Milliken S, Kaldor J, Benke G, Kricker A (2004) Pigmentary characteristics, sun sensitivity and non-Hodgkin lymphoma. Int J Cancer 110(3):429–434PubMedCrossRefGoogle Scholar
  18. Kim YI (2005) 5,10-Methylenetetrahydrofolate reductase polymorphisms and phamacogenetics: a new role of single nucleotide polymorphisms in the folate metabolic pathway in human health and disease. Nutr Rev 63(11):398–407PubMedCrossRefGoogle Scholar
  19. Krupenko SA, Oleinik NV (2002) 10-formyltetrahydrofolate dehydrogenase, one of the major folate enzymes, is down-regulated in tumor tissues and possesses suppressor effects on cancer cells. Cell Growth Differ 13(5):227–236PubMedGoogle Scholar
  20. Kuppers R (2005) Mechanisms of B-cell lymphoma pathogenesis. Nat Rev Cancer 5(4):251–262PubMedCrossRefGoogle Scholar
  21. Ladner RD (2001) The role of dUTPase and uracil-DNA repair in cancer chemotherapy. Curr Protein Pept Sci 2(4):361–370PubMedCrossRefGoogle Scholar
  22. Lightfoot TJ, Skibola CF, Willett EV, Skibola DR, Allan JM, Coppede F, Adason PJ, Morgan GJ, Roman E, Smith MT (2005) Risk of non-Hodgkin lymphoma associated with polymorphisms in folate-metabolizing genes. Cancer Epidemiol Biomarkers Prev 14(12):2999–3003PubMedCrossRefGoogle Scholar
  23. Lim U, Wang SS, Hartge P, Cozen W, Kelemen LE, Chanock S, Davis S, Blair A, Schenk M, Rothman N, Lan Q (2007) Gene-nutrient interactions among determinants of folate and one-carbon metabolism on the risk of non-Hodgkin lymphoma: NCI-SEER Case-Control Study. Blood 109(7):3050–3059PubMedGoogle Scholar
  24. Lincz LF, Scorgie FE, Kerridge I, Potts R, Spencer A, Enno A (2003) Methionine synthase genetic polymorphism MS A2756G alters susceptibility to follicular but not diffuse large B-cell non-Hodgkin’s lymphoma or multiple myeloma. Br J Haematol 120(6):1051–1054PubMedCrossRefGoogle Scholar
  25. Linnebank M, Schmidt S, Kolsch H, Linnebank A, Heun R, Schmidt-Wolf IGH, Glasmacher A, Fliessbach K, Klockgether T, Schlegel U, Pels H (2004) The methionine synthase polymorphism D919G alters susceptibility to primary central nervous system lymphoma. Br J Cancer 90:1969–1971PubMedCrossRefGoogle Scholar
  26. Mandola MV, Stoehlmacher J, Zhang W, Groshen S, Yu MC, Iqbal S, Lenz HJ, Ladner RD (2004) A 6 bp polymorphism in the thymidylate synthase gene causes message instability and is associated with decreased intratumoral TS mRNA levels. Pharmacogenetics 14(5):319–327PubMedCrossRefGoogle Scholar
  27. Matsuo K, Suzuki R, Hamajima N, Ogura M, Kagami Y, Taji H, Kondoh E, Maeda S, Asakura S, Kaba S, Nakamura, Seto M, Morishima Y, Tajima K (2001) Association between polymorphisms of folate- and methionine-metabolizing enzymes and susceptibility to malignant lymphoma. Blood 97(10):3205–3209PubMedCrossRefGoogle Scholar
  28. Matsuo K, Hamajima N, Suzuki R, Ogura M, Kagami Y, Faji H, Yasue T, Mueller NE, Nakamura S, Seto M, Morishima Y, Jajima K (2004) Methylenetetrahydrofolate reductase gene (MTHFR) polymorphisms and reduced risk of malignant lymphoma. Am J Hematol 77:351–357PubMedCrossRefGoogle Scholar
  29. Niclot S, Pruvot Q, Besson C, Savoy D, Macintyre E, Salles G, Brousse N, Varet B, Landais P, Taupin P, Junien C, Baudry-Bluteau D (2006) Implication of the folate-methionine metabolism pathways in susceptibility to follicular lymphomas. Blood 108(1):278–285PubMedCrossRefGoogle Scholar
  30. Packer BR, Yeager M, Burdett L, Welch R, Beerman M, Qi L, Sicotte H, Staats B, Acharya M, Crenshaw A, Eckert A, Puri V, Gerhard DS, Chanock SJ (2006) SNP500Cancer: a public resource for sequence validation, assay development, and frequency analysis for genetic variation in candidate genes. Nucleic Acids Res 34:D617-D621PubMedCrossRefGoogle Scholar
  31. Pereira TV, Rudnicki M, Pereira AC, Pombo-de-Oliveira MS, Franco RF (2006) 5,10-Methylenetetrahydrofolate reductase polymorphisms and acute lymphoblastic leukemia risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev 15(10):1956–1963PubMedCrossRefGoogle Scholar
  32. Purdue MP, Lan Q, Kricker A, Grulich AE, Vajdic CM, Turner J, Whitby D, Chanock S, Rothman N, Armstrong BK (2007) Polymorphisms in immune function genes and risk of non-Hodgkin lymphoma: findings from the New South Wales non-Hodgkin lymphoma study. Carcinogenesis 28(3):704–712PubMedCrossRefGoogle Scholar
  33. Reuland SN, Vlasov AP, Krupenko SA (2006) Modular organization of FDH: Exploring the basis of hydrolase catalysis. Protein Sci 15(5):1076–1084PubMedCrossRefGoogle Scholar
  34. Rossi D, Capello D, Gloghini A, Franceschetti S, Paulli M, Bhatia K, Saglio G, Vitolo U, Pileri SA, Esteller M, Carbone A, Gaidano G (2004) Aberrant promoter methylation of multiple genes throughout the clinico-pathologic spectrum of B-cell neoplasia. Haematologica 89(2):154–164PubMedGoogle Scholar
  35. Schaid DJ, Rowland CM, Tines DE, Jacobson RM, Poland GA (2002) Score tests for association between traits and haplotypes when linkage phase is ambiguous. Am J Hum Genet 70(2):425–434PubMedCrossRefGoogle Scholar
  36. Shen M, Rothman N, Berndt SI, He X, Yeager M, Welch R, Chanock S, Caporaso N, Lan Q (2005) Polymorphisms in folate metabolic genes and lung cancer risk in Xuan Wei, China. Lung Cancer 49(3):299–309PubMedCrossRefGoogle Scholar
  37. Skibola CF, Forrest MS, Coppede F, Agana L, Hubbard A, Smith MT, Bracci PM, Holly EA (2004) Polymorphisms and haplotypes in folate-metabolizing genes and risk of non-Hodgkin lymphoma. Blood 104(7):2155–2162PubMedCrossRefGoogle Scholar
  38. Sterne JA, Bradburn MJ, Egger M (2001) Meta-analysis in Stata. In: Egger M, Davey Smith G, Altman D (eds) Systematic reviews in health care, 2nd edn, vol 2. Blackwell BMJ Books, Boston, pp 347–369Google Scholar
  39. Timuragaoglu A, Dizlek S, Uysalgil N, Tosun O, Yamac K (2006) Methylenetetrahydrofolate reductase C677T polymorphism in adult patients with lymphoproliferative disorders and its effect on chemotherapy. Ann Hematol 85(12):863–868PubMedCrossRefGoogle Scholar
  40. Toffoli G, Rossi D, Gaidano G, Cecchin E, Boiocchi M, Carbone A (2003) Methylenetetrahydrofolate reductase genotype in diffuse large B-cell lymphomas with and without hypermethylation of the DNA repair gene O6-methylguanine DNA methyltransferase. Int J Biol Markers 18(3):218–221PubMedGoogle Scholar
  41. Turner JJ, Hughes AM, Kricker A, Milliken S, Grulich A, Kaldor J, Armstrong B (2004) Use of the WHO lymphoma classification in a population-based epidemiological study. Ann Oncol 15(4):631–637PubMedCrossRefGoogle Scholar
  42. Wacholder S, Chanock S, Garcia-Closas M, El Ghormli L, Rothman N (2004) Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J Natl Cancer Inst 96(6):434–442PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Kyoung-Mu Lee
    • 1
    Email author
  • Qing Lan
    • 1
  • Anne Kricker
    • 2
  • Mark P. Purdue
    • 1
  • Andrew E. Grulich
    • 3
  • Claire M. Vajdic
    • 3
  • Jennifer Turner
    • 4
  • Denise Whitby
    • 5
  • Daehee Kang
    • 6
  • Stephen Chanock
    • 7
  • Nathaniel Rothman
    • 1
  • Bruce K. Armstrong
    • 2
  1. 1.Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and GeneticsNational Cancer Institute, NIH, DHHSBethesdaUSA
  2. 2.School of Public HealthThe University of SydneySydneyAustralia
  3. 3.National Centre for HIV Epidemiology and Clinical ResearchSydneyAustralia
  4. 4.St. Vincent’s HospitalSydneyAustralia
  5. 5.NCI-FrederickFrederickUSA
  6. 6.Department of Preventive MedicineSeoul National University College of MedicineSeoulSouth Korea
  7. 7.Core Genotyping Facility, Advanced Technology Center, Division of Cancer Epidemiology and Genetics and Pediatric Oncology BranchCenter for Cancer Research, NCI, NIH, DHHSBethesdaUSA

Personalised recommendations