Null genotypes of GSTM1 and GSTT1 contribute to risk of cervical neoplasia: an evidence-based meta-analysis.
Null genotypes of GSTM1 and GSTT1 contribute to risk of cervical neoplasia: an evidence-based meta-analysis.
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GSTM1 和 GSTT1 的无效基因型会增加宫颈肿瘤的风险:基于证据的荟萃分析
DOI:
10.1371/journal.pone.0020157
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发表时间:
2011
期刊:
影响因子:
3.7
通讯作者:
Zhang L
中科院分区:
文献类型:
--
作者:
Gao LB;Pan XM;Li LJ;Liang WB;Bai P;Rao L;Su XW;Wang T;Zhou B;Wei YG;Zhang L
Background and Objectives Glutathione S-transferases (GSTs) are multifunctional enzymes that play a key role in the detoxification of varieties of both endogenous products of oxidative stress and exogenous carcinogens. Methods In this meta-analysis, twenty-five studies were identified by searching PubMed, EMBASE, ISI Web of Science and CBM databases: 23 evaluated GSTM1 and 19 evaluated GSTT1. Crude odds ratios with corresponding 95% confidence intervals were used to estimate the association between GSTM1 and GSTT1 polymorphisms and risk of cervical neoplasia. Subgroup analyses were conducted by pathological history, ethnicity, source of DNA for genotyping, quality score, and matching variable. Results The null genotypes of GSTM1 and GSTT1 polymorphisms were associated with a significantly increased risk of cervical neoplasia (for GSTM1: OR = 1.40; 95%CI, 1.19–1.65; for GSTT1: OR = 1.30; 95%CI, 1.05–1.62, respectively). Subgroup analyses showed that the null genotype of GSTM1 increased the risk of cervical neoplasia in Asians, studies with DNA isolation from white blood cells and tissue samples, both high and low quality studies, and matched studies. In GSTM1-GSTT1 interaction analysis, individuals with dual null genotype were associated with a significantly increased risk of cervical neoplasia (OR = 1.72; 95%CI, 1.18–2.51). Conclusion These findings indicate that GSTM1 and GSTT1 polymorphisms, particularly GSTM1-GSTT1 interaction, may play critical roles in the development of cervical neoplasia. A conservative manner should be adopted to interpret these results because of obvious heterogeneity between-study, unadjusted data, and relatively small sample size in this meta-analysis. Well designed studies with larger sample size are of great value to confirm these results.