Genetic polymorphisms of XRCC3 Thr241Met (C18067T, rs861539) and bladder cancer risk: a meta-analysis of 18 research studies

Ma, Qing-Tong; Zhao, Yumei; Wang, Shoufeng; Zhang, Xiaoyan; Zhang, Jinling; Du, Mei; Li, Liang; Zhang, Yun

doi:10.1007/s13277-013-1203-3

Cited by 11 publications

(7 citation statements)

References 24 publications

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“…29 An interaction with smoking may explain some of the heterogeneity observed among prior studies of the X-ray repair complementing defective in chinese hamster 3 (XRCC3) SNP. 30 SLC6A3 variations lead to stress-induced cigarette craving. 31 While data on SNP associations are lacking, growth differentiation factor 15 (GDF15) is being promoted as a biomarker of urothelial cell cancer.…”

Section: Resultsmentioning

confidence: 99%

A Screening-Testing Approach for Detecting Gene-Environment Interactions Using Sequential Penalized and Unpenalized Multiple Logistic Regression

2014

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Gene-environment (G×E) interactions are biologically important for a wide range of environmental exposures and clinical outcomes. Because of the large number of potential interactions in genomewide association data, the standard approach fits one model per G×E interaction with multiple hypothesis correction (MHC) used to control the type I error rate. Although sometimes effective, using one model per candidate G×E interaction test has two important limitations: low power due to MHC and omitted variable bias. To avoid the coefficient estimation bias associated with independent models, researchers have used penalized regression methods to jointly test all main effects and interactions in a single regression model. Although penalized regression supports joint analysis of all interactions, can be used with hierarchical constraints, and offers excellent predictive performance, it cannot assess the statistical significance of G×E interactions or compute meaningful estimates of effect size. To address the challenge of low power, researchers have separately explored screening-testing, or two-stage, methods in which the set of potential G×E interactions is first filtered and then tested for interactions with MHC only applied to the tests actually performed in the second stage. Although two-stage methods are statistically valid and effective at improving power, they still test multiple separate models and so are impacted by MHC and biased coefficient estimation. To remedy the challenges of both poor power and omitted variable bias encountered with traditional G×E interaction detection methods, we propose a novel approach that combines elements of screening-testing and hierarchical penalized regression. Specifically, our proposed method uses, in the first stage, an elastic net-penalized multiple logistic regression model to jointly estimate either the marginal association filter statistic or the gene-environment correlation filter statistic for all candidate genetic markers. In the second stage, a single multiple logistic regression model is used to jointly assess marginal terms and G×E interactions for all genetic markers that pass the first stage filter. A single likelihood-ratio test is used to determine whether any of the interactions are statistically significant. We demonstrate the efficacy of our method relative to alternative G×E detection methods on a bladder cancer data set.

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Section: Resultsmentioning

confidence: 99%

A Screening-Testing Approach for Detecting Gene-Environment Interactions Using Sequential Penalized and Unpenalized Multiple Logistic Regression

2014

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“…Based on a similar hypothesis of influencing radiotoxicity, the association of XRCC3 polymorphisms with cancer risk has also been extensively evaluated. Evidence from meta-analysis supports a positive association between the XRCC3 p.Thr241Met polymorphism and the risk of bladder cancer [ 56 – 57 ], breast cancer [ 58 – 59 ], cervical cancer [ 60 – 61 ] and hepatocellular carcinoma [ 62 – 63 ]. However, in glioma [ 64 – 65 ], NSCLC [ 66 – 67 ], colorectal cancer [ 68 – 69 ] and gastric cancer [ 70 – 71 ] patients, the associations were not significant.…”

Section: Discussionmentioning

confidence: 99%

Association between Single Nucleotide Polymorphisms in XRCC3 and Radiation-Induced Adverse Effects on Normal Tissue: A Meta-Analysis

et al. 2015

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The X-ray repair cross-complementing group 3 (XRCC3) protein plays an important role in the repair of DNA double-strand breaks. The relationship between XRCC3 polymorphisms and the risk of radiation-induced adverse effects on normal tissue remains inconclusive. Thus, we performed a meta-analysis to elucidate the association between XRCC3 polymorphisms and radiation-induced adverse effects on normal tissue. All eligible studies up to December 2014 were identified through a search of the PubMed, Embase and Web of Science databases. Seventeen studies involving 656 cases and 2193 controls were ultimately included in this meta-analysis. The pooled odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were calculated to evaluate the association between XRCC3 polymorphisms and the risk of radiation-induced normal tissue adverse effects. We found that the XRCC3 p.Thr241Met (rs861539) polymorphism was significantly associated with early adverse effects induced by radiotherapy (OR = 1.99, 95%CI: 1.31–3.01, P = 0.001). A positive association lacking statistical significance with late adverse effects was also identified (OR = 1.28, 95%CI: 0.97–1.68, P = 0.08). In addition, the rs861539 polymorphism was significantly correlated with a higher risk of adverse effects induced by head and neck area irradiation (OR = 2.41, 95%CI: 1.49–3.89, p = 0.0003) and breast irradiation (OR = 1.41, 95%CI: 1.02–1.95, p = 0.04), whereas the correlation was not significant for lung irradiation or pelvic irradiation. Furthermore, XRCC3 rs1799794 polymorphism may have a protective effect against late adverse effects induced by radiotherapy (OR = 0.47, 95%CI: 0.26–0.86, P = 0.01). Well-designed large-scale clinical studies are required to further validate our results.

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“…Although the functional relevance of the XRCC3 Thr241Met variation is unknown, some studies have reported that this polymorphism is associated with increased risk of breast cancer [ 20 ]. A meta-analysis has revealed that the XRCC3 gene rs861539 polymorphism also increases the risk of developing hepatocellular carcinoma, as well as head and neck, bladder, and breast cancer [ 11 , 32 , 33 , 60 – 62 ]. However, this polymorphism has also been shown to have a weak association with the risk of breast cancer as well as lung cancer [ 63 , 64 ] and no connection with the development of ovarian, cervical, or colorectal cancer or renal cell carcinoma [ 9 , 65 – 67 ].…”

Section: Discussionmentioning

confidence: 99%

Polymorphisms of Homologous RecombinationRAD51,RAD51B,XRCC2, andXRCC3Genes and the Risk of Prostate Cancer

Nowacka-Zawisza

Wiśnik

Wasilewski

et al. 2015

Analytical Cellular Pathology

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Genetic polymorphisms in DNA repair genes may induce individual variations in DNA repair capacity, which may in turn contribute to the risk of cancer developing. Homologous recombination repair (HRR) plays a critical role in maintaining chromosomal integrity and protecting against carcinogenic factors. The aim of the present study was to evaluate the relationship between prostate cancer risk and the presence of single nucleotide polymorphisms (SNPs) in the genes involved in HRR, that is, RAD51 (rs1801320 and rs1801321), RAD51B (rs10483813 and rs3784099), XRCC2 (rs3218536), and XRCC3 (rs861539). Polymorphisms were analyzed by PCR-RFLP and Real-Time PCR in 101 patients with prostate adenocarcinoma and 216 age- and sex-matched controls. A significant relationship was detected between the RAD51 gene rs1801320 polymorphism and increased prostate cancer risk. Our results indicate that the RAD51 gene rs1801320 polymorphism may contribute to prostate cancer susceptibility in Poland.

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Genetic polymorphisms of XRCC3 Thr241Met (C18067T, rs861539) and bladder cancer risk: a meta-analysis of 18 research studies

Cited by 11 publications

References 24 publications

A Screening-Testing Approach for Detecting Gene-Environment Interactions Using Sequential Penalized and Unpenalized Multiple Logistic Regression

A Screening-Testing Approach for Detecting Gene-Environment Interactions Using Sequential Penalized and Unpenalized Multiple Logistic Regression

Association between Single Nucleotide Polymorphisms in XRCC3 and Radiation-Induced Adverse Effects on Normal Tissue: A Meta-Analysis

Polymorphisms of Homologous RecombinationRAD51,RAD51B,XRCC2, andXRCC3Genes and the Risk of Prostate Cancer

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