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      Association between miR34b/c Polymorphism rs4938723 and Cancer Risk: A Meta-Analysis of 11 Studies including 6169 Cases and 6337 Controls

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          The functional polymorphism rs4938723 in the promoter region of pri-miR-34b/c is potentially associated with susceptibility to several cancers, including hepatocellular carcinoma, colorectal cancer, and breast cancer. Here we conducted a comprehensive meta-analysis to investigate the association between rs4938723 and cancer risk.


          Eligible studies extracted from the databases of PubMed, Web of Science, and Cochrane Library were evaluated. Statistical analysis was performed using Revman 5.2 and STATA 12.0 software.


          By characterizing the extracted data, a total of 11 studies reported in 10 publications including 6169 cases and 6337 controls were selected for further analysis. Our results revealed a significant association between the rs4938723 polymorphism and cancer risk in the codominant model (TC vs. TT: OR=1.10, 95% CI=1.02–1.19, P=0.009) but not in other genetic models. In the stratified analysis of different cancer types, a significant association was found in nasopharyngeal cancer, osteosarcoma, and renal cell cancer. Furthermore, stratified analysis of ethnicity indicated that a highly significant association was shown in the Asian population in a codominant model (TC vs. TT: OR=1.13, 95% CI=1.03–1.24, P=0.007) when compared with African-Americans and Caucasians.


          Overall, the current study suggests that the miR-34b/c rs4938723 polymorphism may be associated with the risk of cancers, including nasopharyngeal cancer, osteosarcoma, and renal cell cancer, and to some extent this polymorphism is closely related to cancer susceptibility in Asians.

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          Most cited references 36

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          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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            An adjusted rank correlation test is proposed as a technique for identifying publication bias in a meta-analysis, and its operating characteristics are evaluated via simulations. The test statistic is a direct statistical analogue of the popular "funnel-graph." The number of component studies in the meta-analysis, the nature of the selection mechanism, the range of variances of the effect size estimates, and the true underlying effect size are all observed to be influential in determining the power of the test. The test is fairly powerful for large meta-analyses with 75 component studies, but has only moderate power for meta-analyses with 25 component studies. However, in many of the configurations in which there is low power, there is also relatively little bias in the summary effect size estimate. Nonetheless, the test must be interpreted with caution in small meta-analyses. In particular, bias cannot be ruled out if the test is not significant. The proposed technique has potential utility as an exploratory tool for meta-analysts, as a formal procedure to complement the funnel-graph.
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                Author and article information

                Department of General Surgery, Ruijin Hospital, affiliated with Shanghai Jiaotong University School of Medicine, Shanghai Institute of Digestive Surgery, Shanghai, China
                Author notes
                Corresponding Authors: Baiyong Shen, e-mail: profshenby@ , Xiaxing Deng, e-mail: dxx888888@

                Study Design


                Data Collection


                Statistical Analysis


                Data Interpretation


                Manuscript Preparation


                Literature Search


                Funds Collection


                Xinjing Wang and Xiongxiong Lu contributed equally to this work

                Med Sci Monit
                Med. Sci. Monit
                Medical Science Monitor
                Medical Science Monitor : International Medical Journal of Experimental and Clinical Research
                International Scientific Literature, Inc.
                19 October 2014
                : 20
                : 1977-1982
                25326793 4213004 10.12659/MSM.892350 892350
                © Med Sci Monit, 2014

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License

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