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Medical Oncology

, Volume 28, Issue 4, pp 1129–1135 | Cite as

TP53 Arg72Pro polymorphism and endometrial cancer risk: a meta-analysis

  • De-Ke Jiang
  • Lei Yao
  • Wei-Hua Ren
  • Wen-Zhang Wang
  • Bo Peng
  • Long Yu
Original Paper

Abstract

Studies investigating the relationship between TP53 Arg72Pro polymorphism and endometrial cancer risk reported conflicting results. To explore a more precise estimate of the effect of this polymorphism on endometrial carcinogenesis, a meta-analysis was performed by searching eligible studies in PubMed. Crude odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the association for codominant model (Arg/Arg vs. Pro/Pro, Arg/Pro vs. Pro/Pro), dominant model (Arg/Arg + Arg/Pro vs. Pro/Pro), and recessive model (Arg/Arg vs. Arg/Pro + Pro/Pro), respectively. Subgroup analyses were performed by Hardy–Weinberg equilibrium (HWE) in controls, the specimen of cases for determining TP53 genotypes, sample size, the source of control and case groups, and ethnicity. We identified 8 case–control studies involving 2,154 subjects for this meta-analysis. Overall, no evidence of association was observed between TP53 genotypes and endometrial cancer risk in all genetic models (Arg/Arg vs. Pro/Pro: OR = 0.98, 95% CI: 0.69–1.39, P = 0.90; Arg/Pro vs. Pro/Pro: OR = 1.00, 95% CI: 0.71–1.42, P = 0.98; dominant model: OR = 0.99, 95% CI: 0.71–1.38, P = 0.95; recessive model: OR = 1.06, 95% CI: 0.80–1.41, P = 0.95). Stratified analyses also detected no significant association in any subgroup, except among those studies with controls deviated from HWE in recessive model (OR = 1.60, 95% CI: 1.07–2.39). In conclusion, we did not observe any evidence for a role of TP53 Arg72Pro polymorphism in endometrial cancer. The reported significant association between this polymorphism and endometrial cancer risk may be due to methodological errors such as selection bias, small sample size, Type I error, and population stratification.

Keywords

TP53 Codon 72 Polymorphism Endometrial cancer Meta-analysis 

Notes

Acknowledgments

This work was supported by the National 973 program of China (2004CB518605), the National 863 project of China (2006AA020501), the National Key Sci-Tech Special Project of China (2008ZX10002-020), the Project of the Shanghai Municipal Science and Technology Commission (03dz14086), and the National Natural Science foundation of China (30024001, 30771188).

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • De-Ke Jiang
    • 1
  • Lei Yao
    • 1
  • Wei-Hua Ren
    • 1
  • Wen-Zhang Wang
    • 1
  • Bo Peng
    • 1
  • Long Yu
    • 1
    • 2
  1. 1.The State Key Laboratory of Genetic EngineeringFudan UniversityShanghaiChina
  2. 2.Institute of Biomedical ScienceFudan UniversityShanghaiChina

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