• 제목/요약/키워드: hage migration inhibitory factor (MIF)

검색결과 1건 처리시간 0.014초

Contribution of Macrophage Migration Inhibitory Factor -173G/C Gene Polymorphism to the Risk of Cancer in Chinese Population

  • Wang, Cheng-Di;Li, Tai-Ming;Ren, Zheng-Ju;Ji, Yu-Lin;Zhi, Liu-Shou
    • Asian Pacific Journal of Cancer Prevention
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    • 제16권11호
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    • pp.4597-4601
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    • 2015
  • Background: Macrophage migration inhibitory factor (MIF) -173G/C (rs755622) gene polymorphism has been associated with cancer risk. Previous studies have revealed that MIF -173G/C gene polymorphism may increase cancer in the Chinese population, while results of individual published studies remain inconsistent and inconclusive.We performed this meta-analysis to derive a more precise estimation of the relationship. Materials and Methods: We conducted a search on PubMed, Embase, MEDLINE, Cochrane Library, Chinese National Knowledge Infrastructure (CNKI), Wanfang, Weipu on Dec 31, 2014.Odds ratio (OR) and 95% confidence interval (95% CI) were used to assess the association. A total of eight studies including 2,186 cases and 2,285 controls were involved in this meta-analysis. Results: The pooled results indicated the significant association between MIF -173G/C polymorphism and the risk of cancer for Chinese population (CC + CG vs GG: OR=1.14, 95%CI=1.02-127, pheterogeneity<0.01; P=0.023; CC vs CG+GG: OR=1.12, 95%CI=1.02-1.23, pheterogeneity<001; P=0.017;CC vs GG: OR=1.18, 95%CI=1.04-1.33, pheterogeneity<001; P=0.008; CG vs GG:OR=1.03, 95%CI=0.91-1.15, pheterogeneity<001; P=0.656; C vs G:OR=1.24, 95%CI=1.14-1.25, pheterogeneity<001; P<001). Subgroup analysis showed that in patients with "solid tumors", heterogeneity was very large (OR=0.94,95%CI=0.83-1.06,pheterogeneity=0.044; p=0.297). Within "non-solid tumors", the association became even stronger (OR=6.62, 95 % CI=4.32-10.14, pheterogeneity<0.001; p<0.001). Conclusions: This study suggested that MIF -173G/C gene polymorphism may increase increase cancer in the Chinese population.Furthermore, more larger sample and representative population-based casees and well-matched controls are needed to validate our results.