| Literature DB >> 27009866 |
Yuan Yang1,2, Wenjing Wang1,3, Liangcai Zhang4, Shihua Zhang5, Guiyou Liu6, Yingcui Yu7, Mingzhi Liao1.
Abstract
Many studies have investigated the association between single nucleotide polymorphism (SNP) rs6983267 and the risk of prostate cancer. However, results of these studies are inconsistent. Therefore, we summarised available data and performed a meta-analysis to determine this association. Relevant articles were identified by searching the PubMed, Web of Science and Embase database. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using random effects model. We used dominant model (GG + TG vs TT), recessive model (GG vs TG + TT) and additive model (GG +TT vs TG) to determine the association between the rs6983267 polymorphism and risk of prostate cancer. Summary, 9 studies involving 8726 participants were included in this meta-analysis. Overall, though no association was observed between the rs6983267 polymorphism and risk of prostate cancer, subgroup analysis according to ethnicity showed a significant association between the rs6983267 polymorphism and risk of prostate cancer among white European men [recessive model: GG vs TG + TT, OR=1.21, (95% CI: 1.03, 1.42), P=0.02]. Our results indicate that the GG genotype of the rs6983267 polymorphism will increase individual susceptibility to prostate cancer in white European men.Entities:
Keywords: association; meta-analysis; prostate cancer; risk; rs6983267
Mesh:
Year: 2016 PMID: 27009866 PMCID: PMC5041923 DOI: 10.18632/oncotarget.8186
Source DB: PubMed Journal: Oncotarget ISSN: 1949-2553
Figure 1Flow diagram of the process for study selection
Characteristics of the 9 studies included in the meta-analysis.
| Author (Publication Date) | Country | Case | Control | Susceptibility[ | P value of HWE[ | ||||
|---|---|---|---|---|---|---|---|---|---|
| GG | TG | TT | GG | TG | TT | ||||
| Natalia A. Oskina (2013) | Siberian | 114 | 186 | 89 | 87 | 177 | 77 | N | 0.471 |
| Ana S. Branković (2013) | Serbian | 53 | 80 | 17 | 25 | 49 | 26 | N | 0.842 |
| CKM Ho (2012) | Scottish | 42 | 104 | 70 | 46 | 136 | 66 | N | 0.102 |
| Jae Y. Joung (2012) | Korean | 46 | 92 | 56 | 31 | 86 | 51 | N | 0.618 |
| Jason Yongsheng Chan (2012) | Chinese | 63 | 136 | 89 | 23 | 74 | 47 | N | 0.493 |
| Miao Liu (2009) | Japanese | 25 | 151 | 147 | 59 | 181 | 151 | Y | 0.694 |
| S. Lilly Zheng (2007) | European–American | 495 | 771 | 285 | 142 | 299 | 132 | Y | 0.293 |
| Olivia Fletcher (2008) | English and Scottish | 408 | 734 | 338 | 371 | 653 | 312 | Y | 0.404 |
| Iona Cheng (2008) | European–American | 126 | 215 | 76 | 105 | 206 | 106 | Y | 0.807 |
“Y” indicates an association between the rs6983267 polymorphism and risk of prostate cancer; N indicates no association between the rs6983267 polymorphism and risk of prostate cancer.
HWE, Hardy–Weinberg equilibrium; P > 0.05 indicates that the participants in the control group met the HWE.
Figure 2Forest Plot of different model
A. Dominant model (GG + TG vs TT); B. Recessive model (GG vs TG + TT); C. Additive model (GG +TT vs TG).
Figure 3Funnel Plot of different model
The shapes of the funnel plots show a slight asymmetry. A. Dominant model (GG + TG vs TT); B. Recessive model (GG vs TG + TT); C. Additive model (GG +TT vs TG).