Genetic polymorphisms modify bladder cancer recurrence and survival in a <scp>USA</scp> population‐based prognostic study

Andrew, Angeline S.; Gui, Jiang; Hu, Ting; Wyszynski, Asaf; Marsit, Carmen J.; Kelsey, Karl T.; Schned, Alan R.; Tanyos, Sam A.; Pendleton, Eben M.; Ekstrom, Rebecca M.; Li, Zhongze; Zens, Michael S.; Borsuk, Mark E.; Moore, Jason H.

doi:10.1111/bju.12641

Cited by 26 publications

(20 citation statements)

References 33 publications

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“…Detailed methods have been described previously in Karagas et al 17 and Andrew et al 18 Briefly, the cases were New Hampshire residents of ages 25 to 74 years, diagnosed with bladder cancer from July 1, 1994 to June 30, 2001 identified via the New Hampshire State Cancer Registry. Controls less than 65 years of age were selected using population lists obtained from the New Hampshire Department of Transportation, while controls aged 65 and older were chosen from data provided by the Centers for Medicare & Medicaid Services (CMS) of New Hampshire.…”

Section: Methodsmentioning

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: Methodsmentioning

confidence: 99%

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

2014

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“…The LIG4 T>C at nt 1977 polymorphism (SNP rs1805386) is significantly associated with a decrease in breast cancer risk and survival [ 157 , 158 ]. Longer survival among patients with non-invasive tumors associated with XRCC4 polymorphism (SNP rs2662238) was found in patients with bladder cancer [ 159 ]. A decreased risk of non-astrocytoma brain cancer is associated with XRCC4 polymorphisms SNP rs7721416 and rs2662242 [ 160 ].…”

Section: Core Nhej Factors In Carcinogenesis and Cancermentioning

confidence: 99%

The Role of the Core Non-Homologous End Joining Factors in Carcinogenesis and Cancer

Sishc

Davis

2017

Cancers

136

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DNA double-strand breaks (DSBs) are deleterious DNA lesions that if left unrepaired or are misrepaired, potentially result in chromosomal aberrations, known drivers of carcinogenesis. Pathways that direct the repair of DSBs are traditionally believed to be guardians of the genome as they protect cells from genomic instability. The prominent DSB repair pathway in human cells is the non-homologous end joining (NHEJ) pathway, which mediates template-independent re-ligation of the broken DNA molecule and is active in all phases of the cell cycle. Its role as a guardian of the genome is supported by the fact that defects in NHEJ lead to increased sensitivity to agents that induce DSBs and an increased frequency of chromosomal aberrations. Conversely, evidence from tumors and tumor cell lines has emerged that NHEJ also promotes chromosomal aberrations and genomic instability, particularly in cells that have a defect in one of the other DSB repair pathways. Collectively, the data present a conundrum: how can a single pathway both suppress and promote carcinogenesis? In this review, we will examine NHEJ’s role as both a guardian and a disruptor of the genome and explain how underlying genetic context not only dictates whether NHEJ promotes or suppresses carcinogenesis, but also how it alters the response of tumors to conventional therapeutics.

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“…Table 1 showed the characteristics of all the eligible studies and genotype frequency distributions of twelve polymorphisms in five XRCC genes ( XRCC1 -rs915927, XRCC1 -rs25489, XRCC1 -rs25487, XRCC1 -rs1799782, XRCC1 -rs3213245, XRCC2 -rs3218536, XRCC3 -rs1799796, XRCC3 -rs861539, XRCC4 -rs6869366, XRCC4 -rs28360071, XRCC4 -rs1805377, XRCC7 -rs7003908) included in current meta-analysis (Agalliu et al, 2010, Andrew et al, 2015, Andrew et al, 2007, Andrew et al, 2006, Arizono et al, 2008, Berhane et al, 2012, Broberg et al, 2005, Chang et al, 2009, Lan et al, 2006, Lavender et al, 2010, Chang et al, 2008, Dhillon et al, 2009, Figueroa et al, 2007a, Figueroa et al, 2007b, Fontana et al, 2008, Gangwar et al, 2009, Hamano et al, 2008, Hirata et al, 2006, Hirata et al, 2007, Huang et al, 2007, Abe et al, 2011, Mittal et al, 2008, Narter et al, 2009, Nowacka-Zawisza et al, 2015, Ramaniuk et al, 2014, Ritchey et al, 2005, Rybicki et al, 2004, Sak et al, 2007, Sanyal et al, 2004, Shen et al, 2003, Stern et al, 2002, Stern et al, 2001, Van Gils et al, 2002, Wang et al, 2010, Wang et al, 2008, Wen et al, 2009, Wen et al, 2013, Wu et al, 2006, Xu et al, 2007, Zhi et al, 2012, Hao et al, 2008, Zhou et al, 2012, Zhu et al, 2014, Zhu et al, 2012, Kelsey et al, 2004, Kuasne et al, 2011, Luedeke et al, 2009, Mandal et al, 2010, Mandal et al, 2011, Matullo, 2005, Matullo et al, 2006, Matullo et al, 2001, Mittal et al, 2012a, Mittal et al, 2012b). The study selection processes were presented in Supplementary Figs.…”

Section: Resultsmentioning

confidence: 99%

Association Between Twelve Polymorphisms in Five X-ray Repair Cross-complementing Genes and the Risk of Urological Neoplasms: A Systematic Review and Meta-Analysis

Zhang¹,

Li²,

Hao³

et al. 2017

EBioMedicine

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Polymorphisms in X-ray repair cross-complementing (XRCC) genes have been implicated in altering the risk of various urological cancers. However, the results of reported studies are controversial. To ascertain whether polymorphisms in XRCC genes are associated with the risk of urological neoplasms, we conducted present updated meta-analysis and systematic review. Summary odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were used to estimate the association. Finally, 54 publications comprising 129 case-control studies for twelve polymorphisms in five XRCC genes were enrolled. We identified that XRCC1-rs25489 polymorphism was associated with an increased risk of urological neoplasms in heterozygote and dominant models. Moreover, in the subgroup analysis by cancer type, we found that XRCC1-rs25489 polymorphism was associated with an increased risk of bladder cancer (BC) in heterozygote model. Although overall analyses suggested a null result for XRCC1-rs25487 polymorphism, in the stratified analysis by ethnicity, an increased risk of urological neoplasms for Asians in allelic and homozygote models was identified. While for other polymorphisms in XRCC genes, no significant association was uncovered. To sum up, our results indicated that XRCC1-rs25489 polymorphism is a risk factor for urological neoplasms, particularly for BC. Further studies with large sample size are needed to validate these findings.

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Genetic polymorphisms modify bladder cancer recurrence and survival in a USA population‐based prognostic study

Cited by 26 publications

References 33 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

The Role of the Core Non-Homologous End Joining Factors in Carcinogenesis and Cancer

Association Between Twelve Polymorphisms in Five X-ray Repair Cross-complementing Genes and the Risk of Urological Neoplasms: A Systematic Review and Meta-Analysis

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