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Year : 2014  |  Volume : 10  |  Issue : 7  |  Page : 201-205

Quantitative assessment of the influence of glutathione S-transferase M1 null variant on ovarian cancer risk

1 Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin 300121; Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China
2 Department of Gynecology, Tianjin Central Hospital of Gynecology Obstetrics, Nankai District, Tianjin, China
3 Department of Anorectal Surgery, Affiliated hospital to Shandong university of traditional Chinese Medicine, Jinan city, Shandong Province, 250011, China
4 Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin 300121, China

Date of Web Publication29-Nov-2014

Correspondence Address:
Dr. Xipeng Zhang
Department of Colorectal Surgery, Tianjin Union Medicine Center (UMC), 190 Jieyuan Road, Hongqiao District, Tianjin 300121
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0973-1482.145872

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 > Abstract 

Objective: Many studies have reported the role of glutathione S-transferase Mu 1 (GST M1) polymorphism with ovary cancer risk, but the results remained controversial. Materials and Methods: To derive a more precise estimation of the relationship, a meta-analysis was performed. Odds ratios (ORs) with 95% confidence intervals (CIs) were estimated to assess the association between GSTM1 polymorphism and ovary cancer risk. A total of 11 studies including 2709 cases and 3599 controls were also involved in this meta-analysis. Results: When all the eligible studies were pooled into this meta-analysis, no significant association between ovary cancer risk and GSTM1 polymorphism was found (OR = 1.010, 95% CI = 0.911-1.121, P heterogeneity = 0.174, P = 0.848). Discussion: Our meta-analysis supports that the GSTM1 polymorphism is not contributed to the risk of ovary cancer from currently available evidence.

Keywords: Glutathione S-transferase Mu 1, meta-analysis, ovarian cancer, polymorphism, susceptibility

How to cite this article:
Xu C, Chen S, Wang T, Zhao K, You X, Wang Y, Zhang X, Li Y. Quantitative assessment of the influence of glutathione S-transferase M1 null variant on ovarian cancer risk. J Can Res Ther 2014;10, Suppl S3:201-5

How to cite this URL:
Xu C, Chen S, Wang T, Zhao K, You X, Wang Y, Zhang X, Li Y. Quantitative assessment of the influence of glutathione S-transferase M1 null variant on ovarian cancer risk. J Can Res Ther [serial online] 2014 [cited 2022 Jan 19];10:201-5. Available from: https://www.cancerjournal.net/text.asp?2014/10/7/201/145872

Chen Xu, Shuo Chen and Tao Wang contributed equally to this article as joint first authors.

 > Introduction Top

Ovarian cancer is one of the most common gynecological malignancies with high mortality, and it is difficult to make an early diagnosis. [1],[2] It is also one of the leading causes of malignant deaths in women in the world. [3] Despite the public health importance of ovarian cancer, its etiology remains unclear. [4],[5] Many studies suggest that the genetic factors play an important role in the etiology of ovarian cancer. [6],[7] Besides, examination of genetic polymorphisms may explain individual differences in the risk of ovarian cancer. [8] Cytosolic glutathione S-transferase (GST) comprises multiple isoenzymes that catalyze reactions between glutathione and lipophilic compounds with electrophilic centers, resulting in the neutralization of toxic compounds, xenobiotics, and products of oxidative stress/reactive oxygen species. [9],[10] With respect to the substrates of GSTs, it is worth noting that GSTM1 participates in the deactivation of carcinogenic intermediates of polycyclic aromatic hydrocarbons. [11],[12] The GSTM1 has been found polymorphic in the population. [13],[14] The designated GSTM1 and GSTT1 null genotypes have demonstrated functional relevance that is, reduced enzyme activity, which seems to denote impaired ability to detoxify carcinogens, a state conferring an increased cancer risk. [10]

In recent years, several studies have been conducted to evaluate the association between GSTM1 polymorphism and ovarian cancer risk, with inconclusive results. Therefore, to derive a more precise estimation of the association between GSTM1 polymorphism and ovarian cancer risk, a meta-analysis was performed.

 > Materials and methods Top

0 Search strategies

We sought to identify all epidemiologic studies that investigated the association of GSTM1 genetic polymorphism with ovary cancer. To identify relevant studies, we conducted a comprehensive systematic bibliographic search through PUBMED, EMBASE, ISI Web of Knowledge, Cochrane Library, and other databases without date and language restrictions for all medical published up to October 31, 2013. The following search strategy was performed by consecutively entering the combined free words "prostate," "susceptibility," "GSTM1," "GST," "genetics," "polymorphism," "cancer," "neoplasm," "carcinoma," and "tumor" including all alternative locations and combinations of the terms. Moreover, we also supplemented this search by reviewing the reference lists of all retrieved publications and the most recent review articles to ascertain additional undetected published studies. When more than one studies of the same population were included in several publications, only the most recent or complete study was used in this meta-analysis.

Inclusion criteria

Two investigators independently reviewed abstracts in duplicate to determine whether they met the general inclusion and exclusion criteria; any discrepancies were resolved by discussion between the investigators. For the meta-analysis, the following inclusion criteria were considered: (1) Only case-control studies that had original data of a quantitative assessment of the relationship of GST variants and risk of ovary cancer, concentrating upon polymorphism in GSTM1; (2) an appropriate description of GSTM1 polymorphism in ovary cancer cases and controls; (3) cases with ovary cancer were eligible regardless of whether they had a first-degree relative with ovary cancer or not, regardless of tumor stage; (4) controls were eligible without other ovary diseases, and without other cancers; (5) results expressed as odds ratio (OR); and (6) studies with a 95% confidence intervals (95% CI) for OR, or sufficient data to calculate these numbers.

Exclusion criteria

While for the exclusion criteria, we provided the following: (1) Studies without the raw data of GSTM1 genotype; (2) case-only studies, family-based studies, case reports, editorials, and review articles (including meta-analyses); (3) studies that compare the racial variation of GST variants in healthy population; (4) controls with any type of tumor; (5) studies that used GST polymorphisms to predict survival in prostate cancer; and (6) studies that investigated GST variants as markers for response to therapy. In studies with overlapping cases/controls, the higher quality score or the study with more information on origin of cases/controls was included in the meta-analysis.

Data extraction

Information was carefully extracted from all eligible publications independently by two authors according to the inclusion criteria listed above. Disagreement was resolved by discussion between the two authors. The following data were collected from each study: First author's surname, year of publication, ethnicity, total numbers of cases and controls, and numbers of cases and controls with the GSTM1 genotype. We did not limit the number of patients to include a study in our meta-analysis.

Statistical analysis

We used the crude ORs with their corresponding 95% CI as the metric of choice. Based on the individual ORs, the pooled OR was estimated. To take into account the possibility of heterogeneity across the studies, a statistical test for heterogeneity was performed using the Q statistic. [15] The heterogeneity was assessed using the I 2 statistic, which takes values between 0% and 100% with higher values denoting greater degree of heterogeneity (I 2 =0-25%, no heterogeneity; I 2 =25-50%, moderate heterogeneity; I 2 =50-75%, large heterogeneity; I 2 =75-100%, extreme heterogeneity). [16] The pooled OR was analyzed jointly using both fixed effects (Mantel-Haenszel) and a random effects model (DerSimonian and Laird). [17] A fixed effects model was used when there was no heterogeneity of the results of the studies; otherwise, a random effects model was used. To explore the reasons of heterogeneity, subgroup analyses were performed by grouping studies that showed similar characteristics, such as ethnicity and control source. In addition, sensitivity analyses were also employed. In sensitivity analysis, each study was excluded one at a time to determine the magnitude of influence on the overall summary estimate. [18] For publication bias assessing, inverted funnel plot, Begg's test, [19] and Egger's test [20] were employed. In the funnel plot, the results of the small studies are shown to be more widely scattered than those of the large studies. It was estimated on the basis of the method published by Fleiss et al. [21] Finally, to evaluate whether the overall estimates were different, the analysis was restricted to studies that histological confirmed all ovary cancer cases. Analyses were performed using Stata software (version 12.0, Stata Corporation, College Station, TX, USA), using two-sided P values.

 > Results Top

0 Characteristics of eligible studies

All studies relevant to the searching words according to the inclusion and exclusion criteria were retrieved originally. As a result, a total of 11 eligible studies, including 2709 cases and 3599 controls were included in our work. [22],[23],[24],[25],[26],[27],[28],[29],[30],[31] All the included studies were case-control studies. Of the 11 studies, there were 2 with UK ethnicity, 2 with Germany ethnicity, 3 with USA ethnicity 2 with Brazil ethnicity, 1 with Australia ethnicity and 1 with Russia ethnicity. All the cases were diagnosed as ovary cancer while controls were mainly healthy populations. The main characteristics of the studies included in this meta-analysis related to this polymorphism and ovary cancer susceptibility were listed in [Table 1].
Table 1: Characteristics of the eligible studies in present study

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Meta-analysis results

All the eligible studies were pooled into this meta-analysis, there was no statistically significant association between GSTM1 genotype [OR = 1.010, 95% CI = 0.911-1.121, P heterogeneity = 0.174, P = 0.848, [Figure 1] and ovary cancer risk.
Figure 1: Forest plot for meta-analysis of the association between the glutathione S-transferase Mu 1 polymorphism and ovarian cancer risk

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Sensitivity analyses

One-way sensitivity analyses of the pooled ORs and 95% CIs for GSTM1 was performed. The pooled ORs were calculated by means of a random effects model. When omitting each data set in the meta-analysis, the pooled ORs were always persistent. The analysis for carriers of the GSTM1 genotype was shown in [Figure 2]. There is no single study that influenced the pooled ORs qualitatively as indicated by sensitivity analyses, suggesting that the results of this meta-analysis are stable.
Figure 2: One-way sensitivity analysis of the pooled odds ratios and 95% confidence interval for glutathione S-transferase Mu 1, omitting each dataset in the meta-analysis

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Publication bias

The shapes of the funnel plots seemed symmetrical for all analyses. Begg's test was employed and did not suggest publication bias [Figure 3]. Egger's test also confirmed the same result about publication bias [Figure 4]. In addition, no evidence of publication bias was found in any subgroup analyses under different ethnic decent models.
Figure 3: Begg's funnel plot analysis to detect potential publication bias

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Figure 4: Egger's funnel plot analysis to detect potential publication bias

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 > Discussion Top

Ovarian cancer is the leading cause of death from gynecologic malignancies. It is mostly asymptomatic at early-stage, and most of the cases are diagnosed when the tumor has established regional or distant metastases. [32] Therefore, it is important to clarify the molecular mechanism of its development which can help to detect it at an early stage, and studies on gene polymorphism that affects the pathways known to influence the neoplastic process may be particularly relevant. Single nucleotide polymorphisms is the most common form of human genetic variation and may contribute to an individual's susceptibility to cancer; however, the underlying molecular mechanism is unknown. Previous studies suggested that some variants may affect either the expression or activity levels of enzymes and therefore may be mechanistically associated with cancer risk. [33],[34],[35] It has been hypothesized that GSTM1 polymorphism is associated with risk of ovarian cancer, and many reports have been published but no clear consensus has been reached. This led us to undertake the present meta-analysis, which could quantify the synthesis of all the available data and might help us to explore a more robust estimate of the role of this polymorphism with ovarian carcinogenesis.

Regarding the association between GSTM1 polymorphism and ovarian cancer susceptibility, a total of 11 case-control studies were found by searching databases, with inconclusive results. Since single study may have been underpowered in clarifying this polymorphism with ovarian cancer risk, we performed a meta-analysis for better understanding of the association between GSTM1 polymorphism and ovarian cancer risk. The results strongly suggested that this polymorphism was not associated with ovarian cancer risk.

Some limitations of this meta-analysis should be acknowledged. Firstly, as an observational study, there is potential for recall bias from case-control studies. Second, a potential source of bias in studies of genotypes might be the inclusion of individuals from different ethnic backgrounds. Third, our results were based on unadjusted estimates, while a more precise analysis should be conducted adjusted by other factors like smoking, drinking status, and environmental factors.

In summary, the results of our meta-analysis indicate that the GSTM1 polymorphism is not contributed to the risk of ovary cancer. The finding provides more information on screening the high-risk group of ovary cancer, and a new strategy to prevent its occurrence. Large studies with the pooling of individual data should be considered in future association studies to verify results from this meta-analysis and to further evaluate the effect of gene-environment interactions on the GSTM1 polymorphism-associated ovary cancer risk.

 > References Top

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  [Figure 1], [Figure 2], [Figure 3], [Figure 4]

  [Table 1]


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