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      Association between cytochrome P450 1A1 (CYP1A1) gene polymorphisms and the risk of renal cell carcinoma: a meta-analysis

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          Abstract

          Cytochrome P450 1A1 (CYP1A1) usually metabolizes carcinogens to their inactive derivatives but occasionally converts the chemicals to more potent carcinogens. To date, many studies have evaluated the association between the CYP1A1 MspI and Ile462Val polymorphisms and renal cell carcinoma (RCC) risk, but the results have been conflicting. To more precisely evaluate the potential association, we carried out a meta-analysis of seven published case-control studies. The meta-analysis indicated that the MspI polymorphism was associated with an increased RCC risk (allele model: OR = 1.49, 95%CI 1.03–2.16; homozygous model: OR = 1.64, 95%CI 1.13–2.40; dominant model: OR = 1.72, 95%CI 1.07–2.76). No significant associations were found for the Ile462Val polymorphism for all genetic models. When stratified by smoking status, smokers carrying the variant Vt and Val allele were more susceptible to RCC (Vt allele: OR = 3.37, 95%CI = 2.24–5.06; Val allele: OR = 2.07, 95%CI = 1.34–3.19). These data indicate that the CYP1A1 MspI polymorphism significantly increased RCC risk, while the Ile462Val polymorphism was not associated with RCC. Among smokers, individuals with the CYP1A1 Vt allele and Val allele showed a significantly increased risk of RCC. More well-designed studies with larger samples are warranted to show the underlying mechanisms of CYP1A1 in the development of RCC.

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          Most cited references 24

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          Measuring inconsistency in meta-analyses.

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            Bias in meta-analysis detected by a simple, graphical test.

            Funnel plots (plots of effect estimates against sample size) may be useful to detect bias in meta-analyses that were later contradicted by large trials. We examined whether a simple test of asymmetry of funnel plots predicts discordance of results when meta-analyses are compared to large trials, and we assessed the prevalence of bias in published meta-analyses. Medline search to identify pairs consisting of a meta-analysis and a single large trial (concordance of results was assumed if effects were in the same direction and the meta-analytic estimate was within 30% of the trial); analysis of funnel plots from 37 meta-analyses identified from a hand search of four leading general medicine journals 1993-6 and 38 meta-analyses from the second 1996 issue of the Cochrane Database of Systematic Reviews. Degree of funnel plot asymmetry as measured by the intercept from regression of standard normal deviates against precision. In the eight pairs of meta-analysis and large trial that were identified (five from cardiovascular medicine, one from diabetic medicine, one from geriatric medicine, one from perinatal medicine) there were four concordant and four discordant pairs. In all cases discordance was due to meta-analyses showing larger effects. Funnel plot asymmetry was present in three out of four discordant pairs but in none of concordant pairs. In 14 (38%) journal meta-analyses and 5 (13%) Cochrane reviews, funnel plot asymmetry indicated that there was bias. A simple analysis of funnel plots provides a useful test for the likely presence of bias in meta-analyses, but as the capacity to detect bias will be limited when meta-analyses are based on a limited number of small trials the results from such analyses should be treated with considerable caution.
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              Meta-analysis in clinical trials.

              This paper examines eight published reviews each reporting results from several related trials. Each review pools the results from the relevant trials in order to evaluate the efficacy of a certain treatment for a specified medical condition. These reviews lack consistent assessment of homogeneity of treatment effect before pooling. We discuss a random effects approach to combining evidence from a series of experiments comparing two treatments. This approach incorporates the heterogeneity of effects in the analysis of the overall treatment efficacy. The model can be extended to include relevant covariates which would reduce the heterogeneity and allow for more specific therapeutic recommendations. We suggest a simple noniterative procedure for characterizing the distribution of treatment effects in a series of studies.
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                Author and article information

                Affiliations
                [1 ]Molecular Oncology Department of Cancer Research Institution, The First Hospital of China Medical University , Shenyang 110001, China
                Author notes
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                29 January 2015
                2015
                : 5
                25630554 4309971 srep08108 10.1038/srep08108
                Copyright © 2015, Macmillan Publishers Limited. All rights reserved

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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