Advertisement

Breast Cancer Research and Treatment

, Volume 122, Issue 2, pp 489–493 | Cite as

XRCC3 5′-UTR and IVS5-14 polymorphisms and breast cancer susceptibility: a meta-analysis

  • Li-Xin Qiu
  • Chen Mao
  • Lei Yao
  • Ke-Da Yu
  • Ping Zhan
  • Bo Chen
  • Hai-Guang Liu
  • Hui Yuan
  • Jian Zhang
  • Kai Xue
  • Xi-Chun Hu
Epidemiology

Abstract

Published data on the association between XRCC3 5′-UTR and IVS5-14 polymorphisms and breast cancer risk are inconclusive. In order to derive a more precise estimation of the relationship, a meta-analysis was performed. Crude ORs with 95% CIs were used to assess the strength of association between these polymorphisms and breast cancer risk. The pooled ORs were performed for codominant model, dominant model, and recessive model, respectively. A total of four studies were involved in the meta-analysis with 6,303 cases and 6,563 controls for XRCC3 5′-UTR polymorphism and with 6,270 cases and 6,682 controls for XRCC3 IVS5-14 polymorphism. For XRCC3 5′-UTR A/G polymorphism, significantly elevated breast cancer risk was associated with variant genotype when all studies were pooled into the meta-analysis (AG vs. AA: OR = 1.11, 95% CI = 1.03–1.19; dominant model: OR = 1.09, 95% CI = 1.01–1.17). For XRCC3 IVS5-14 A/G polymorphism, significantly decreased breast cancer risk was associated with variant genotype (GG vs. AA: OR = 0.86, 95% CI = 0.77–0.96). In conclusion, this meta-analysis suggests that the variant G allele of XRCC3 5′-UTR polymorphism is a low-penetrant risk factor for developing breast cancer, while the variant G allele of XRCC3 IVS5-14 polymorphism has a protective effect on breast cancer development.

Keywords

XRCC3 Polymorphism Breast cancer Susceptibility Meta-analysis 

Notes

Acknowledgments

The authors are fully responsible for all content and editorial decisions and did not receive financial support or other form of compensation related to the development of the manuscript.

References

  1. 1.
    Parkin DM, Bray F, Ferlay J, Pisani P (2005) Global cancer statistics 2002. CA Cancer J Clin 55:74–108CrossRefPubMedGoogle Scholar
  2. 2.
    Lichtenstein P, Holm NV, Verkasalo PK (2000) Environmental and heritable factors in the causation of cancer. N Engl J Med 343:78–85CrossRefPubMedGoogle Scholar
  3. 3.
    Brenneman MA, Weiss AE, Nickoloff JA, Chen DJ (2000) XRCC3 is required for efficient repair of chromosome breaks by homologous recombination. Mutat Res 459:89–97CrossRefPubMedGoogle Scholar
  4. 4.
    Breast Cancer Association Consortium (2006) Commonly studied single-nucleotide polymorphisms and breast cancer: results from the Breast Cancer Association Consortium. J Natl Cancer Inst 98:1382–1396CrossRefGoogle Scholar
  5. 5.
    Lee SA, Lee KM, Park SK, Choi JY, Kim B, Nam J, Yoo KY, Noh DY, Ahn SH, Kang D (2007) Genetic polymorphism of XRCC3 Thr241Met and breast cancer risk: case–control study in Korean women and meta-analysis of 12 studies. Breast Cancer Res Treat 103:71–76CrossRefPubMedGoogle Scholar
  6. 6.
    Manuguerra M, Saletta F, Karagas MR, Berwick M, Veglia F, Vineis P, Matullo G (2006) XRCC3 and XPD/ERCC2 single nucleotide polymorphisms and the risk of cancer: a HuGE review. Am J Epidemiol 164:297–302CrossRefPubMedGoogle Scholar
  7. 7.
    Economopoulos KP, Sergentanis TN (2009) XRCC3 Thr241Met polymorphism and breast cancer risk: a meta-analysis. Breast Cancer Res Treat. doi: 10.1007/s10549-009-0562-3
  8. 8.
    Han J, Hankinson SE, Ranu H, De Vivo I, Hunter DJ (2004) Polymorphisms in DNA double-strand break repair genes and breast cancer risk in the Nurses’ Health Study. Carcinogenesis 25:189–195CrossRefPubMedGoogle Scholar
  9. 9.
    Kuschel B, Auranen A, McBride S, Novik KL, Antoniou A, Lipscombe JM, Day NE, Easton DF, Ponder BA, Pharoah PD (2002) Variants in DNA double-strand break repair genes and breast cancer susceptibility. Hum Mol Genet 11:1399–1407CrossRefPubMedGoogle Scholar
  10. 10.
    Garcia-Closas M, Egan KM, Newcomb PA, Brinton LA, Titus-Ernstoff L, Chanock S, Welch R, Lissowska J, Peplonska B, Szeszenia-Dabrowska N (2006) Polymorphisms in DNA double-strand break repair genes and risk of breast cancer: two population-based studies in USA and Poland, and meta-analyses. Hum Genet 119:376–388CrossRefPubMedGoogle Scholar
  11. 11.
    Cochran WG (1954) The combination of estimates from different experiments. Biometrics 10:101–129CrossRefGoogle Scholar
  12. 12.
    Mantel N, Haenszel W (1959) Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst 22:719–748PubMedGoogle Scholar
  13. 13.
    DerSimonian R, Laird N (1986) Meta-analysis in clinical trials. Control Clin Trials 7:177–188CrossRefPubMedGoogle Scholar
  14. 14.
    Egger M, Davey Smith G, Schneider M, Minder C (1997) Bias in metaanalysis detected by a simple, graphical test. BMJ 315:629–634CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2010

Authors and Affiliations

  • Li-Xin Qiu
    • 1
    • 2
  • Chen Mao
    • 3
  • Lei Yao
    • 4
  • Ke-Da Yu
    • 2
    • 5
  • Ping Zhan
    • 6
  • Bo Chen
    • 7
  • Hai-Guang Liu
    • 8
  • Hui Yuan
    • 9
  • Jian Zhang
    • 1
    • 2
  • Kai Xue
    • 1
    • 2
  • Xi-Chun Hu
    • 1
    • 2
  1. 1.Department of Medical OncologyCancer Hospital, Fudan UniversityShanghaiChina
  2. 2.Department of OncologyShanghai Medical College, Fudan UniversityShanghaiChina
  3. 3.Department of EpidemiologySchool of Public Health and Tropical Medicine, Southern Medical UniversityGuangzhouChina
  4. 4.State Key Laboratory of Genetic Engineering, Institute of Genetics, School of Life SciencesFudan UniversityShanghaiChina
  5. 5.Breast Cancer Institute, Department of Breast SurgeryCancer Hospital, Fudan UniversityShanghaiChina
  6. 6.Department of Respiratory MedicineNanjing Chest HospitalNanjingChina
  7. 7.Department of GeriatricsFirst Affiliated Hospital, Nanjing Medical UniversityNanjingChina
  8. 8.Department of OncologyThe First Affiliated Hospital of Wenzhou Medical CollegeWenzhouChina
  9. 9.Department of Epidemiology and Biostatistics, School of Public HealthAnhui Medical UniversityAnhuiChina

Personalised recommendations