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Meta-Analysis
. 2009 Sep 1;69(17):6857-64.
doi: 10.1158/0008-5472.CAN-09-1091. Epub 2009 Aug 25.

Polymorphisms in DNA repair genes, smoking, and bladder cancer risk: findings from the international consortium of bladder cancer

Collaborators, Affiliations
Meta-Analysis

Polymorphisms in DNA repair genes, smoking, and bladder cancer risk: findings from the international consortium of bladder cancer

Mariana C Stern et al. Cancer Res. .

Abstract

Tobacco smoking is the most important and well-established bladder cancer risk factor and a rich source of chemical carcinogens and reactive oxygen species that can induce damage to DNA in urothelial cells. Therefore, common variation in DNA repair genes might modify bladder cancer risk. In this study, we present results from meta-analyses and pooled analyses conducted as part of the International Consortium of Bladder Cancer. We included data on 10 single nucleotide polymorphisms corresponding to seven DNA repair genes from 13 studies. Pooled analyses and meta-analyses included 5,282 cases and 5,954 controls of non-Latino white origin. We found evidence for weak but consistent associations with ERCC2 D312N [rs1799793; per-allele odds ratio (OR), 1.10; 95% confidence interval (95% CI), 1.01-1.19; P = 0.021], NBN E185Q (rs1805794; per-allele OR, 1.09; 95% CI, 1.01-1.18; P = 0.028), and XPC A499V (rs2228000; per-allele OR, 1.10; 95% CI, 1.00-1.21; P = 0.044). The association with NBN E185Q was limited to ever smokers (interaction P = 0.002) and was strongest for the highest levels of smoking dose and smoking duration. Overall, our study provides the strongest evidence to date for a role of common variants in DNA repair genes in bladder carcinogenesis.

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Figures

Figure 1
Figure 1
Meta-plots for DNA repair SNPs that showed a statistically significant association with bladder cancer risk in pooled analyses. The I2 statistics provides an estimate of the heterogeneity across studies.
Figure 2
Figure 2
Meta-plots for NBN E185Q stratified by smoking status (ever versus never). The I2 statistics provides an estimate of the heterogeneity across studies.

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