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Common Gene Variants in the Tumor Necrosis Factor (TNF) and TNF Receptor Superfamilies and NF-kB Transcription Factors and Non-Hodgkin Lymphoma Risk

Author

Listed:
  • Sophia S Wang
  • Mark P Purdue
  • James R Cerhan
  • Tongzhang Zheng
  • Idan Menashe
  • Bruce K Armstrong
  • Qing Lan
  • Patricia Hartge
  • Anne Kricker
  • Yawei Zhang
  • Lindsay M Morton
  • Claire M Vajdic
  • Theodore R Holford
  • Richard K Severson
  • Andrew Grulich
  • Brian P Leaderer
  • Scott Davis
  • Wendy Cozen
  • Meredith Yeager
  • Stephen J Chanock
  • Nilanjan Chatterjee
  • Nathaniel Rothman

Abstract

Background: A promoter polymorphism in the pro-inflammatory cytokine tumor necrosis factor (TNF) (TNF G-308A) is associated with increased non-Hodgkin lymphoma (NHL) risk. The protein product, TNF-α, activates the nuclear factor kappa beta (NF-κB) transcription factor, and is critical for inflammatory and apoptotic responses in cancer progression. We hypothesized that the TNF and NF-κB pathways are important for NHL and that gene variations across the pathways may alter NHL risk. Methodology/Principal Findings: We genotyped 500 tag single nucleotide polymorphisms (SNPs) from 48 candidate gene regions (defined as 20 kb 5′, 10 kb 3′) in the TNF and TNF receptor superfamilies and the NF-κB and related transcription factors, in 1946 NHL cases and 1808 controls pooled from three independent population-based case-control studies. We obtaineded a gene region-level summary of association by computing the minimum p-value (“minP test”). We used logistic regression to compute odds ratios and 95% confidence intervals for NHL and four major NHL subtypes in relation to SNP genotypes and haplotypes. For NHL, the tail strength statistic supported an overall relationship between the TNF/NF-κB pathway and NHL (p = 0.02). We confirmed the association between TNF/LTA on chromosome 6p21.3 with NHL and found the LTA rs2844484 SNP most significantly and specifically associated with the major subtype, diffuse large B-cell lymphoma (DLBCL) (p-trend = 0.001). We also implicated for the first time, variants in NFKBIL1 on chromosome 6p21.3, associated with NHL. Other gene regions identified as statistically significantly associated with NHL included FAS, IRF4, TNFSF13B, TANK, TNFSF7 and TNFRSF13C. Accordingly, the single most significant SNPs associated with NHL were FAS rs4934436 (p-trend = 0.0024), IRF4 rs12211228 (p-trend = 0.0026), TNFSF13B rs2582869 (p-trend = 0.0055), TANK rs1921310 (p-trend = 0.0025), TNFSF7 rs16994592 (p-trend = 0.0024), and TNFRSF13C rs6002551 (p-trend = 0.0074). All associations were consistent in each study with no apparent specificity for NHL subtype. Conclusions/Significance: Our results provide consistent evidence that variation in the TNF superfamily of genes and specifically within chromosome 6p21.3 impacts lymphomagenesis. Further characterization of these susceptibility loci and identification of functional variants are warranted.

Suggested Citation

  • Sophia S Wang & Mark P Purdue & James R Cerhan & Tongzhang Zheng & Idan Menashe & Bruce K Armstrong & Qing Lan & Patricia Hartge & Anne Kricker & Yawei Zhang & Lindsay M Morton & Claire M Vajdic & The, 2009. "Common Gene Variants in the Tumor Necrosis Factor (TNF) and TNF Receptor Superfamilies and NF-kB Transcription Factors and Non-Hodgkin Lymphoma Risk," PLOS ONE, Public Library of Science, vol. 4(4), pages 1-9, April.
  • Handle: RePEc:plo:pone00:0005360
    DOI: 10.1371/journal.pone.0005360
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    1. Ash A. Alizadeh & Michael B. Eisen & R. Eric Davis & Chi Ma & Izidore S. Lossos & Andreas Rosenwald & Jennifer C. Boldrick & Hajeer Sabet & Truc Tran & Xin Yu & John I. Powell & Liming Yang & Gerald E, 2000. "Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling," Nature, Nature, vol. 403(6769), pages 503-511, February.
    2. Arthur L. Shaffer & N. C. Tolga Emre & Laurence Lamy & Vu N. Ngo & George Wright & Wenming Xiao & John Powell & Sandeep Dave & Xin Yu & Hong Zhao & Yuxin Zeng & Bangzheng Chen & Joshua Epstein & Louis, 2008. "IRF4 addiction in multiple myeloma," Nature, Nature, vol. 454(7201), pages 226-231, July.
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    1. James E Hayes & Gosia Trynka & Joseph Vijai & Kenneth Offit & Soumya Raychaudhuri & Robert J Klein, 2015. "Tissue-Specific Enrichment of Lymphoma Risk Loci in Regulatory Elements," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-14, September.

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