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      Cyclin D1 G870A Polymorphism Contributes to Colorectal Cancer Susceptibility: Evidence from a Systematic Review of 22 Case-Control Studies

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          Abstract

          Background

          Cyclin D1 ( CCND1) plays a vital role in cancer cell cycle progression. Numerous epidemiological studies have evaluated the association between the CCND1 G870A polymorphism and the risk of colorectal cancer. However, these studies have yielded conflicting results. To derive a more precise estimation of this association, we conducted a meta-analysis and systematic review.

          Methodology/Principal Findings

          A comprehensive search was conducted to identify eligible studies of the CCND1 G870A polymorphism and colorectal cancer risk. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were derived from a fixed effect or random effect model. We applied a grading system (Venice criteria) that assessed the epidemiological strength of the association. A total of 22 publications that included 6157 cases and 8198 controls were identified. We found that the CCND1 G870A polymorphism was significantly associated with overall colorectal cancer risk (homozygote genetic model: OR = 1.130, 95% CI = 1.023–1.248, P = 0.016; heterozygote genetic model: OR = 1.124, 95% CI = 1.030–1.226, P = 0.009; dominant genetic model: OR = 1.127, 95% CI = 1.037–1.224, P = 0.005). After further stratified analyses, the increased risk was observed only in the subgroups of hospital-based studies, PCR-RFLP genotyping methods, sporadic colorectal cancer, and Caucasian ethnicity.

          Conclusions

          The available evidence demonstrates that the CCND1 870A allele might be a low-penetrant risk factor for colorectal cancer.

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          Most cited references37

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          Estimates of cancer incidence and mortality in Europe in 2008.

          J Ferlay, D.M. PARKIN, E Steliarova-Foucher (2010)
          Up-to-date statistics on cancer occurrence and outcome are essential for the planning and evaluation of programmes for cancer control. Since the relevant information for 2008 is not generally available as yet, we used statistical models to estimate incidence and mortality data for 25 cancers in 40 European countries (grouped and individually) in 2008. The calculations are based on published data. If not collected, national rates were estimated from national mortality data and incidence and mortality data provided by local cancer registries of the same or neighbouring country. The estimated 2008 rates were applied to the corresponding country population estimates for 2008 to obtain an estimate of the numbers of cancer cases and deaths in Europe in 2008. There were an estimated 3.2 million new cases of cancer and 1.7 million deaths from cancer in 2008. The most common cancers were colorectal cancers (436,000 cases, 13.6% of the total), breast cancer (421,000, 13.1%), lung cancer (391,000, 12.2%) and prostate cancer (382,000, 11.9%). The most common causes of death from cancer were lung cancer (342,000 deaths, 19.9% of the total), colorectal cancer (212,000 deaths, 12.3%), breast cancer (129,000, 7.5%) and stomach cancer (117,000, 6.8%). Copyright 2009 Elsevier Ltd. All rights reserved.
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            Meta-analysis in clinical research.

            K L'Abbé, A S Detsky, K O'Rourke (1987)
            Meta-analysis is the process of combining study results that can be used to draw conclusions about therapeutic effectiveness or to plan new studies. We review important design and statistical issues of this process. The design issues include protocol development, objectives, literature search, publication bias, measures of study outcomes, and quality of the data. The statistical issues include consistency (homogeneity) of study outcomes, and techniques for pooling results from several studies. Guidelines are provided to assess the quality of meta-analyses based on our discussion of the design and statistical issues. Limitations and areas for further development of this approach are discussed; researchers should come to a general agreement on how to conduct meta-analysis. As an explicit strategy for summarizing results, meta-analysis may help clinicians and researchers better understand the findings of clinical studies.
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              A note on graphical presentation of estimated odds ratios from several clinical trials.

              R. F. GALBRAITH (1988)
              To display a number of estimates of a parameter obtained from different studies it is common practice to plot a sequence of confidence intervals. This can be useful but is often unsatisfactory. An alternative display is suggested which represents intervals as points on a bivariate graph, and which has advantages. When the data are estimates of odds ratios from studies with a binary response, it is argued that for either type of plot, a log scale should be used rather than a linear scale.
              Article 0 comments Cited 158 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                : Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, USA )
                ISSN (Electronic): 1932-6203
                Publication date Collection: 2012
                Publication date (Electronic): 11 May 2012
                Volume: 7
                Issue: 5
                Electronic Location Identifier: e36813
                Affiliations
                [1 ]Department of Surgery, Shanghai Tenth People’s Hospital affiliated with Tongji University, Shanghai, People’s Republic of China
                [2 ]Department of Surgery, The Sixth People’s Hospital affiliated with Shanghai Jiao Tong University, Shanghai, People’s Republic of China
                Ohio State University Medical Center, United States of America
                Author notes

                Conceived and designed the experiments: YLM YZY. Performed the experiments: YZY FW. Analyzed the data: CZS YZ. Wrote the paper: YZY HLQ YLM.

                Article
                Publisher ID: PONE-D-12-02938
                DOI: 10.1371/journal.pone.0036813
                PMC ID: 3350479
                PubMed ID: 22606291
                SO-VID: 6dc26d8c-1dc7-4657-8348-14a0d436b090
                Copyright © Yang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                Date received : 27 January 2012
                Date accepted : 6 April 2012
                Page count
                Pages: 9
                Categories
                Subject: Research Article
                Subject: Biology
                Subject: Computational Biology
                Subject: Population Genetics
                Subject: Genetic Polymorphism
                Subject: Evolutionary Biology
                Subject: Population Genetics
                Subject: Genetic Polymorphism
                Subject: Genetics
                Subject: Population Genetics
                Subject: Genetic Polymorphism
                Subject: Cancer Genetics
                Subject: Population Biology
                Subject: Population Genetics
                Subject: Genetic Polymorphism
                Subject: Medicine
                Subject: Clinical Genetics
                Subject: Genetic Testing
                Subject: Clinical Research Design
                Subject: Case-Control Studies
                Subject: Systematic Reviews

                ScienceOpen disciplines: Uncategorized
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                ScienceOpen disciplines: Uncategorized

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