Abstract
Background: Studies have shown that glutathione S-transferase M1 (GSTM1) and. glutathione S-transferase T1 (GSTT1) null genotype may increase the risk of cervical cancer (CC) or ovarian cancer (OC), however, the results of published original studies and meta-analyses are inconsistent.
Objectives: To investigate the association between GSTM1 present/null and GSTT1 present/null polymorphisms, with the risk of cervical cancer or ovarian cancer.
Methods: The odds ratios (ORs) and 95% confidence intervals (CIs) were used to assess the association between GSTM1 present/null and GSTT1 present/null polymorphisms and the risk of cervical cancer or ovarian cancer. To assess the confidence of statistically significant associations, we applied false positive reporting probability (FPRP) and bayesian false discovery probability (BFDP) tests.
Results: Overall analysis showed that GSTM1 null was associated with an increased risk of cervical cancer, and subgroup analysis showed a significant increase in cervical cancer risk in Indian and Chinese populations; GSTT1 was not found null genotype are significantly associated with cervical cancer. Overall analysis showed that GSTM1 and GSTT1 null were not associated with the risk of ovarian cancer, subgroup analysis showed that GSTM1 null was associated with an increased risk of OC in East Asia, and GSTT1 null was associated with an increased risk of OC in South America. However, when we used false positive reporting probability and bayesian false discovery probability to verify the confidence of a significant association, all positive results showed “low confidence” (FPRP > .2, BFDP > .8).
Conclusion: Overall, this study strongly suggests that all positive results should be interpreted with caution and are likely a result of missing plausibility rather than a true association.
Introduction
Gynecological cancers have different degrees of negative impact on women’s health around the world. Among them, with CC the highest incidence and OC with the highest mortality have attracted much attention. According to the 2020 global cancer incidence and mortality statistics released by the World Health Organization, about 604,000 women were diagnosed with CC, and about 342,000 women died of CC, witch has become the most common cancer in 23 countries and 36. The number one cause of cancer death in 100 countries. According to the data survey released by the national cancer center of my country, in recent years, the incidence of CC has increased at an average annual rate of 8.7% (Zhao and Song, 2021). According to global statistics in 2020, about 310,000 women were diagnosed with OC, and about 210,000 women died of OC. The analysis of the incidence and death data of OC in the “China cancer registry annual report” shows that from 2005 to 2016, OC in China incidence and mortality are rapidly increasing, and most OC occur in people over the age of 50 (Huang et al., 2022). Although the main pathogenic factors of the two cancers are different, epidemiological studies have shown that the occurrence of both cancers is related to individual genetic susceptibility, and studies have shown that the genetic polymorphism of cancer susceptibility genes is associated with high cancer risk. There may be associations; therefore, finding true gene associations will help people to further understand the pathogenesis of CC and OC, and actively exploring the multi-pathway pathogenesis of CC and OC is of great significance for cancer prevention, diagnosis, and treatment (Ueda et al., 2008).
Glutathione s-transferase system (GSTs: Glutathione s -transferases), as the first line of defense in cell protection, participates in the detoxification process of exogenous toxins in vivo, making reduced glutathione and electrophilic substances combine to convert toxic substances in the body into hydrophilic substances, which are excreted through urine or bile to complete the detoxification process (Board and Menon, 2013; Zou, 2013). Currently, eight glutathione s-transferases have been identified in mammals, including alpha, kappa, mu, omega, pi, sigma, theta, and zeta. Among them, mu (µ)-type GSTM1 and theta (θ)-type GSTT1 is the most studied genes in the relationship between gynecological tumors and glutathione transferase, GSTM1 is located on chromosome 1 (1p13.3), GSTT1 is located on chromosome 22 (p11.23), its function is to link various parent electrochemical compounds (such as drugs, environmental toxins, oxidation chain products, etc.) combine with glutathione to enter the next metabolic step, allowing the toxic substances to be easily excreted from the body. The GST gene has polymorphisms at multiple loci, among which GSTM1 and GSTT1 share a common zero allele. The most common mutation of these two genes is the whole null genotype, and the mutation of the gene will change the activation or inactivation of the corresponding enzyme. The ability to source substrates, thereby affecting the detoxification of carcinogens, exposing cells in the body to toxic substances, causing DNA damage, potentially increasing somatic mutations that increase an individual by 39%, risk of developing tumors (Abbas et al., 2018; Sharma et al., 2019). Therefore individuals with homozygous null genotype polymorphisms are considered potential risk factors for the development of various malignancies in humans. At present, the correlation of GSTM1 and GSTT1 present/null polymorphisms with CC and OC is still unclear. Therefore, studying the glutathione metabolic pathway involving glutathione-s-transferase may be useful for early warning and early warning of gynecological malignancies. Prevention as well as treatment options and prognosis for cancer patients are of great importance.
So far, there have been 31 articles (Warwick A. P et al., 1994; Warwick A et al., 1994; Chen et al., 1999; Kim et al., 2000; Sierra-Torres et al., 2003; Lee et al., 2004; Sharma et al., 2004; Niwa et al., 2005; Huang, 2006; Joseph et al., 2006; Sobti et al., 2006; Zhou et al., 2006; de Carvalho et al., 2008; Nishino et al., 2008; Singh et al., 2008; Song et al., 2008; Ueda et al., 2008; Liu et al., 2009; Settheetham-Ishida et al., 2009; Kiran et al., 2010; Palma et al., 2010; Ueda et al., 2010; Stosic et al., 2014; Hasan et al., 2015; Nunobiki et al., 2015; Sharma et al., 2015; Satinder et al., 2017; Tacca et al., 2018; Wang et al., 2018; Zhang et al., 2019; Wongpratate et al., 2020) on the individual and combined effects of GSTM1 and/or GSTT1 present/null polymorphisms and CC risk, and nine meta-analyses (Economopoulos et al., 2010a; Gao et al., 2011; Sui et al., 2011; Wang et al., 2011; Liu and Xu, 2012; Zhang et al., 2012; Zhen et al., 2013; Sun and Song, 2016; Tian et al., 2019) reporting GSTM1 and/or GSTT1 present/null polymorphisms associated with CC risk. 14 articles investigated the individual impact of GSTM1 and/or GSTT1 present/null polymorphisms and OC risk (Sarhanis et al., 1996; Esteller et al., 1997; Hengstler et al., 1998; Goodman et al., 2000; Baxter et al., 2001; Spurdle et al., 2001; Morari et al., 2006; Chunhua, 2008; Gates et al., 2008; Ueda et al., 2008; Khokhrin et al., 2012; Oliveira et al., 2012; Cai et al., 2016; Pljesa et al., 2017), and five meta analyses (Economopoulos et al., 2010b; Yin et al., 2013; Han et al., 2014; Jin and Hao, 2014; Xu et al., 2014) reported individual effects of GSTM1 and/or GSTT1 present/null polymorphisms and OC risk. However, the conclusions of all studies were inconsistent and even contradictory. Furthermore, no study has examined the correlation between the corresponding positive results. Correlations are assessed for reliability. Newer original studies have recently been published investigating these associations, and therefore, an updated meta-analysis should be performed to explore these questions. Two methods FPRP and BFDP tests were used to assess the confidence of these findings. We aim to provide a real link to these questions and to discuss the positive findings identified in terms of biological mechanisms involved in CC and OC.
Material and methods
Literature search strategy
This meta-analysis was conducted based on the priority reported entries of systematic reviews and meta-analyses (PRISMA). Pubmed, Embase, Scopus, Chinese biomedical medical databases (CBM), China national knowledge infrastructure (CNKI), and Wanfang databases and so on in both Chinese and English (up to 15 September 2021) were searched to identify eligible studies that analyzed the GSTM1 present/null and GSTT1 present/null, with CC and OC risk. The following keywords were used: (GSTT1 OR glutathione s-transferase T1 OR GSTM1 OR glutathione s-transferase M1) AND (polymorphism OR variant OR mutation) AND (ovarian cancer OR oophoroma OR carcinoma of ovary OR cervical cancer OR carcinoma of uterine cerxix OR cervical malignancy). The search strategy was designed to be sensitive and broad. We first carefully reviewed the title and abstract of the search results, and then downloaded full articles to identify possible articles. These were evaluated in detail to identify relevant articles. The reference lists of identified articles and reviews was also examined as appropriate. The corresponding author may be contacted by e-mail if only the abstract was available online or the data was incomplete.
Literature inclusion and exclusion criteria
Inclusion criteria were as listed below: 1) articles on the GSTM1 present/null and GSTT1 present/null, with the risk of CC or OC. 2) The diagnostic criteria for CC and OC meet histological or pathological criteria. 3) case-control studies or cohort studies where the language of the literature is limited to Chinese or English. 4) sufficient genotype data to calculate ORs and 95% CIs. Exclusion criteria were as listed below: 1) no raw data. 2) no control. 3) review articles, case reports, editorials, or animal research. 4) duplicate and insufficient data.
Extraction information
Two investigators independently extracted data using excel. Any disagreement was solved by iteration, discussion, and consensus. The details of the data extraction form included the following: first author, year of publication, country, geographical region, ethnicity, control source, control type, matching, adjusted OR, SNP, sample size, each locus, the number of genotypes, and the literature quality score. Of these, the literature quality score needs to be obtained by calculation.
Quality score assessment
The quality of all studies was assessed independently by two researchers. We supplemented and improved the quality assessment criteria from relevant guidelines and previous meta-analysis, combined with NOS criteria (Aerssens et al., 2000; Moher et al., 2009; Thakkinstian et al., 2011), Supplementary Table S1 lists the quality assessment scales for studies of the association of CC or OC risk. Studies were considered to be of low quality if the quality score was less than 9, whereas in the Meta-analysis, scores ≥ 11 were considered to be of high quality, and studies with scores between 9 and 11 were considered to be of moderate quality. Supplementary Table S1 lists the scoring scale for assessing the quality of the literature with the following entries: 1) source of the experimental group; 2) source of the control group. 3) diagnostic criteria for patients with CC and OC. 4) inclusion criteria for the control group. 5) whether the experimental and control groups were matched. 6) genotype testing. 7) samples used to determine genotype. 8) assessment of the association between genotype and OC and CC. 9) size of sample size.
Statistical analysis
We applied the crude ratio (OR) and its 95% confidence interval (CI) to assess the association effect of the GSTM1 present/null and GSTT1 present/null, with the risk of CC or OC. Q-tests were used to assess heterogeneity between selected studies and statistically, significant heterogeneity was considered if p < .10 and/or I2 > 50%, using a random-effects model (Mantel and Haenszel, 1959), and if heterogeneity was not significant (I2 ≤ 50%), a fixed-effects model (Der Simonian and Laird, 2015) was considered, followed by a search for sources of heterogeneity based on meta-regression analysis. Subgroup analyses were performed for HPV infection, smoking, geographic region, and ethnicity according to CC epidemiology, and for ethnicity and geographic region according to OC epidemiology. Two methods were used to conduct sensitivity analyses: one was to exclude one study at a time. The second was to conduct statistical analyses after excluding low-quality and small-sample studies. Publication bias was confirmed according to Begg’s funnel plot (Begg and Mazumdar, 1994) and Egger’s test (considered significant publication bias if p < .05) (Egger et al., 1997) and if publication bias was observed, non-parametric pruning and padding methods were applied to identify missing studies (Dual and Tweedie, 2000). To assess the confidence of statistically significant associations in the current and previous meta-analyses, we applied the FPRP (Wacholder et al., 2004) and the BFDP test (Ioannidis et al., 2008), and the FPRP was estimated using the excel spreadsheet appendix. All statistical analyses were calculated using Stata version 12.0 (STATA Corporation, college station, TX).
Results
Literature search results
A total of 600 articles were searched (Figure 1). After reading the topic, 413 articles inconsistent with this study (including other genotype studies, reviews, case reports, meta-analyses, and letters) were excluded, 122 duplicate articles were excluded after further reading of the title and abstract, and the remaining articles were read in full of the 66 articles, 22 studies for which complete data were not available were excluded, and the final 44 original articles were included in this study. 31 studies related to CC were included (including 30 for GSTM1 and 22 for GSTT1) (Warwick A. P et al., 1994; Warwick A et al., 1994; Chen et al., 1999; Kim et al., 2000; Sierra-Torres et al., 2003; Lee et al., 2004; Sharma et al., 2004; Niwa et al., 2005; Huang, 2006; Joseph et al., 2006; Sobti et al., 2006; Zhou et al., 2006; de Carvalho et al., 2008; Nishino et al., 2008; Singh et al., 2008; Song et al., 2008; Ueda et al., 2008; Liu et al., 2009; Settheetham-Ishida et al., 2009; Kiran et al., 2010; Palma et al., 2010; Ueda et al., 2010; Stosic et al., 2014; Hasan et al., 2015; Nunobiki et al., 2015; Sharma et al., 2015; Satinder et al., 2017; Tacca et al., 2018; Wang et al., 2018; Zhang et al., 2019; Wongpratate et al., 2020), 14 studies related to OC (including 14 GSTM1 and 11 GSTT1) (Sarhanis et al., 1996; Esteller et al., 1997; Hengstler et al., 1998; Goodman et al., 2000; Baxter et al., 2001; Spurdle et al., 2001; Morari et al., 2006; Chunhua, 2008; Gates et al., 2008; Ueda et al., 2008; Khokhrin et al., 2012; Oliveira et al., 2012; Cai et al., 2016; Pljesa et al., 2017). Table 1 shows the general characteristics of the studies included in this meta-analysis. Among the studies on CC risk, there were 30 articles on GSTM1 present/null polymorphisms (including 3,484 cases and 4,208 controls, see Table 2), 22 articles on GSTT1 present/null polymorphisms (including 2,500 cases), and 3,148 control cases, see Table 3). Among OC risk studies, there were 14 articles on GSTM1 present/null polymorphisms (including 3,035 cases and 3,422 controls, see Table 2), 11 articles on GSTT1 present/null polymorphisms (including 2,543 cases and 3,275 controls, see Table 3). Finally, according to the quality assessment of molecular association studies, among the studies on the association of GSTM1 present/null polymorphisms with CC risk, there were 13 high-quality, 7 medium-quality, and 10 low-quality studies. Among studies on the association between polymorphisms and CC risk, there were 9 high-quality, 7 moderate-quality and 7 low-quality studies, Among the studies on the association between GSTM1 present/null and OC risk, there were 6 high-quality studies. High-quality, 3 moderate-quality, and 5 low-quality studies, among the studies on the association of GSTT1 present/null polymorphisms with OC risk, there were 5 high-quality, 2 moderate-quality, and 4 low-quality studies.
FIGURE 1
TABLE 1
| First author/year | Country | Geographic region | Ethnicity | Tumor classification | Source of controls | Matching | Adjustments | SNP | Quality score |
|---|---|---|---|---|---|---|---|---|---|
| Warwick A. P. Warwick A. P et al. (1994)/1994 | United Kingdom | Europe | Caucasian | CC | HB | NA | NA | GSTM1 | 7 |
| Warwick A, Warwick A et al. (1994)/1994 | United Kingdom | Europe | Caucasian | CC | HB | NA | NA | GSTT1 | 8 |
| Chen C Chen et al. (1999)/1999 | United States | North America | Caucasian | CC | PB | Age | Age | GSTM1, T1 | 15 |
| Kim JW Kim et al. (2000)/2000 | Korea | East Asia | Asian | CC | PB | Age | Age | GSTM1, T1 | 14 |
| S-T CH Sierra-Torres et al. (2003)/2003 | United States | North America | Caucasian | CC | PB | Age | Smoking | GSTM1 | 12 |
| Lee SA Lee et al. (2004)/2004 | India | South Asia | Indian | CC | PB | NA | NA | GSTM1, T1 | 10 |
| Sharma A Sharma et al. (2004)/2004 | Korea | East Asia | Asian | CC | HB | NA | NA | GSTM1, T1 | 8 |
| Niwa Y Niwa et al. (2005)/2005 | Japan | East Asia | Asian | CC | HB | NA | Age | GSTM1, T1 | 13 |
| Zhou Q Zhou et al. (2006)/2006 | India | South Asia | Indian | CC | HB | NA | NA | GSTM1, T1 | 9 |
| Joseph T Joseph et al. (2006)/2006 | China | East Asia | Asian | CC | HB | NA | Age | GSTM1, T1 | 11 |
| Huang YK Huang (2006)/2006 | China | East Asia | Asian | CC | HB | NA | NA | GSTM1 | 9 |
| Sobti RC Sobti et al. (2006)/2006 | India | South Asia | Indian | CC | PB | Age | NA | GSTM1, T1 | 16 |
| Nishino K Nishino et al. (2008)/2008 | Japan | East Asia | Asian | CC | PB | NA | Age | GSTM1, T1 | 11 |
| De C CR de Carvalho et al. (2008)/2008 | Brazil | South America | Mixed | CC | HB | NA | Age | GSTM1, T1 | 9 |
| S-I W Settheetham-Ishida et al. (2009)/2009 | Thailand | Southeast Asia | Asian | CC | PB | Age | Age | GSTM1, T1 | 14 |
| Song GY Song et al. (2008)/2008 | China | East Asia | Asian | CC | PB | NA | NA | GSTM1 | 12 |
| Singh H Singh et al. (2008)/2008 | India | South Asia | Indian | CC | PB | NA | NA | GSTM1, T1 | 11 |
| Liu Y Liu et al., (2009)/2009 | China | East Asia | Asian | CC | HB | NA | NA | GSTM1 | 4 |
| Palma S Palma et al. (2010)/2010 | Italy | Europe | Caucasian | CC | PB | Age | Age | GSTM1, T1 | 14 |
| Ueda M Ueda et al. (2010)/2010 | Japan | East Asia | Asian | CC | PB | NA | NA | GSTM1, T1 | 11 |
| Kiran B Kiran et al. (2010)/2010 | Turkey | West Asia | Caucasian | CC | HB | NA | NA | GSTM1, T1 | 10 |
| Stosic I Stosic et al. (2014)/2014 | Serbia | Europa | Serbian | CC | PB | NA | NA | GSTM1, T1 | 11 |
| Natphopsuk S Nunobiki et al. (2015)/2015 | Thailand | Southeast Asia | Asian | CC | HB | Age | Age | GSTM1 | 13 |
| Hasan S Hasan et al. (2015)/2015 | Pakistan | South Asia | Caucasian | CC | PB | NA | NA | GSTM1, T1 | 8 |
| Sharma A Sharma et al. (2015)/2015 | India | South Asia | Indian | CC | HB | NA | NA | GSTM1, T1 | 7 |
| Satinder K Satinder et al. (2017)/2017 | India | South Asia | Indian | CC | HB | Age | Age | GSTM1, T1 | 15 |
| Wang J Wang et al. (2018)/2018 | China | East Asia | Asian | CC | HB | NA | NA | GSTM1 | 9 |
| Tacca A.L.M Tacca et al. (2018)/2019 | Brazil | South America | Mixed | CC | HB | Age | NA | GSTM1, T1 | 13 |
| Zhang Y Zhang et al. (2019)/2019 | China | East Asia | Asian | CC | PB | NA | NA | GSTM1 | 12 |
| Wongpratate M Wongpratate et al. (2020)/2020 | Thailand | Southeast Asia | Asian | CC | PB | Age | Age | GSTM1,T1 | 14 |
| Ueda M Ueda et al. (2008)/2008 | Japan | East Asia | Asian | CC/OC | PB | NA | NA | GSTM1, T1 | 8 |
| Sarhanis P Sarhanis et al. (1996)/1996 | United Kingdom | Europe | Caucasian | OC | HB | NA | NA | GSTM1, T1 | 9 |
| Hengstler JG Hengstler et al. (1998)/1998 | Germany | Europe | Caucasian | OC | HB | NA | NA | GSTM1,T1 | 9 |
| Goodman JE Goodman et al. (2000) 2000 | Germany | Europe | Caucasian | OC | HB | NA | Age | GSTM1, T1 | 16 |
| Lallas TA Esteller et al. (1997)/2000 | United States | North America | Caucasian | OC | PB | NA | NA | GSTM1 | 10 |
| Spurdle AB Spurdle et al. (2001)/2001 | Australia | Europe | Caucasian | OC | HB | Age | Age | GSTM1, T1 | 12 |
| Baxter SW Baxter et al. (2001)/2001 | United Kingdom | Europe | Caucasian | OC | PB | NA | NA | GSTM1 | 12 |
| Morari EC Morari et al. (2006)/2006 | Brazil | South America | Mixed | OC | PB | NA | Age | GSTM1, T1 | 18 |
| Gates M A Gates et al. (2008)/2008 | United States | North America | Caucasian | OC | PB | Age | Age | GSTM1, T1 | 12 |
| Chunhua Z Chunhua, (2008)/2008 | China | East Asia | Asian | OC | BD | Age | Age | GSTM1, T1 | 11 |
| Oliveira C Oliveira et al. (2012)/2012 | Brazil | South America | Caucasian | OC | HB | NA | Age | GSTM1, T1 | 11 |
| Khokhrin DV Khokhrin et al. (2012)/2012 | Russia | Europe | Caucasian | OC | PB | NA | NA | GSTM1, T1 | 12 |
| Cai Q Cai et al. (2016)/2016 | China | East Asia | Asian | OC | PB | NA | NA | GSTM1 | 9 |
| Pljesa I Pljesa et al. (2017)/2017 | Serbia | Europe | Serbian | OC | HB | NA | Age | GSTM1, T1 | 9 |
General situation and quality evaluation of the included study.
SNP, single nucleotide polymorphism; OC, ovarian cancer; CC, cervical cancer.
TABLE 2
| First author/year | Geographic region | Ethnicity | Tumor classification | Sample size | Genotypes distribution of GSTM1 genotype | |||
|---|---|---|---|---|---|---|---|---|
| Cases | Controls | |||||||
| Positive | Null | Positive | Null | |||||
| Warwick AP Wang et al. (2018)/1994 | Europe | Caucasian | CC | 77/190 | 37 | 40 | 96 | 94 |
| Chen C Zhang et al. (2019)/1999 | North America | Caucasian | CC | 190/206 | 89 | 101 | 88 | 118 |
| Kim JW Wongpratate et al. (2020)/2000 | East Asia | Asian | CC | 181/181 | 86 | 95 | 85 | 96 |
| S-T CH Ueda et al. (2008)/2003 | North America | Caucasian | CC | 69/72 | 34 | 35 | 43 | 29 |
| Sharma A Economopoulos et al. (2010a)/2004 | South Asia | Indian | CC | 142/96 | 61 | 81 | 63 | 33 |
| Lee SA Sui et al. (2011)/2004 | East Asia | Asian | CC | 81/86 | 39 | 42 | 44 | 42 |
| Niwa Y Gao et al. (2011)/2005 | East Asia | Asian | CC | 131/320 | 61 | 70 | 136 | 184 |
| Sobti RC Wang et al. (2011)/2006 | South Asia | Indian | CC | 103/103 | 61 | 42 | 65 | 38 |
| Zhou Q Liu and Xu, (2012)/2006 | East Asia | Asian | CC | 125/125 | 52 | 73 | 71 | 54 |
| Huang YK Zhang et al. (2012)/2006 | East Asia | Asian | CC | 47/78 | 17 | 30 | 46 | 32 |
| Joseph T Sun and Song, (2016)/2006 | South Asia | Indian | CC | 147/165 | 68 | 79 | 111 | 54 |
| Song GY Tian et al. (2019)/2008 | East Asia | Asian | CC | 130/130 | 53 | 77 | 73 | 57 |
| Singh H Zhen et al. (2013)/2008 | South Asia | Indian | CC | 150/168 | 86 | 64 | 122 | 46 |
| Nishino K Sarhanis et al. (1996)/2008 | East Asia | Asian | CC | 124/125 | 47 | 77 | 66 | 59 |
| De C CR Hengstler et al. (1998)/2008 | South America | Mixed | CC | 43/86 | 15 | 28 | 37 | 49 |
| S-I W Goodman et al. (2000)/2009 | Southeast Asia | Asian | CC | 90/94 | 36 | 54 | 38 | 56 |
| Liu Y Esteller et al. (1997)/2009 | East Asia | Asian | CC | 21/45 | 14 | 29 | 30 | 15 |
| Kiran B Spurdle et al. (2001)/2010 | West Asia | Caucasian | CC | 46/52 | 21 | 25 | 22 | 30 |
| Palma S Baxter et al. (2001)/2010 | Europe | Caucasian | CC | 25/111 | 10 | 15 | 53 | 58 |
| Ueda M Morari et al. (2006)/2010 | East Asia | Asian | CC | 83/158 | 42 | 41 | 86 | 72 |
| Stosic I Gates et al. (2008)/2014 | Europa | Serbian | CC | 32/50 | 10 | 22 | 22 | 28 |
| Hasan S Chunhua, (2008)/2015 | South Asia | Caucasian | CC | 50/50 | 13 | 37 | 33 | 17 |
| Natphopsuk S Oliveira et al. (2012)/2015 | Southeast Asia | Asian | CC | 198/198 | 68 | 130 | 73 | 125 |
| Sharma A Khokhrin et al. (2012)/2015 | South Asia | Indian | CC | 135/457 | 56 | 79 | 297 | 160 |
| Satinder K Cai et al. (2016)/2017 | South Asia | Indian | CC | 150/150 | 87 | 63 | 98 | 52 |
| Wang J Pljesa et al. (2017)/2018 | East Asia | Asian | CC | 116/116 | 47 | 69 | 78 | 38 |
| Tacca A.L.M Economopoulos et al. (2010b)/2019 | South America | Mixed | CC | 135/100 | 105 | 30 | 55 | 45 |
| Wongpratate M Yin et al. (2013)/2020 | Southeast Asia | Asian | CC | 198/198 | 68 | 130 | 73 | 125 |
| Zhang Y Jin and Hao, (2014)/2019 | East Asia | Asian | CC | 184/203 | 78 | 106 | 103 | 100 |
| Ueda M Xu et al. (2014)/2008 | East Asia | Asian | CC/OC | 259/95 | 129 | 130 | 56 | 39 |
| Sarhanis P Han et al. (2014)/1996 | Europe | Caucasian | OC | 84/312 | 37 | 47 | 120 | 192 |
| Hengstler JG Capoluongo et al. (2006)/1998 | Europe | Caucasian | OC | 103/115 | 56 | 47 | 81 | 44 |
| Lallas TA Aerssens et al. (2000)/2000 | North America | Caucasian | OC | 138/77 | 68 | 70 | 32 | 45 |
| Baxter SW Moher et al. (2009)/2001 | Europe | Caucasian | OC | 108/106 | 56 | 47 | 59 | 40 |
| Goodman JE Thakkinstian et al. (2011) 2000 | Europe | Caucasian | OC | 293/219 | 120 | 173 | 112 | 107 |
| Spurdle AB Mantel and Haenszel, (1959)/2001 | Europe | Caucasian | OC | 285/299 | 126 | 159 | 135 | 162 |
| Morari EC Der Simonian and Laird, (2015)/2006 | South America | Mixed | OC | 69/222 | 31 | 38 | 122 | 100 |
| Gates M A Begg and Mazumdar, (1994)/2008 | North America | Caucasian | OC | 1175/1202 | 573 | 594 | 567 | 628 |
| Chunhua Z Egger et al. (1997)/2008 | East Asia | Asian | OC | 89/49 | 58 | 31 | 43 | 6 |
| Khokhrin DV Dual and Tweedie, (2000)/2012 | Europe | Caucasian | OC | 104/298 | 57 | 47 | 164 | 134 |
| Oliveira C Wacholder et al. (2004)/2012 | South America | Caucasian | OC | 132/132 | 84 | 48 | 90 | 42 |
| Pljesa I Ioannidis et al. (2008)/2017 | Europa | Serbian | OC | 85/178 | 44 | 41 | 89 | 89 |
| Cai Q Theodoratou et al. (2012)/2016 | East Asia | Asian | OC | 124/124 | 64 | 60 | 71 | 53 |
Basic characteristics of GSTM1 gene polymorphism.
SNP, single nucleotide polymorphism; OC, ovarian cancer; CC, cervical cancer.
TABLE 3
| First author/year | Geographic region | Ethnicity | Tumor classification | Sample size (case/control) | Genotypes distribution of GSTT1 genotype | |||
|---|---|---|---|---|---|---|---|---|
| Cases | Controls | |||||||
| Positive | Null | Positive | Null | |||||
| Warwick A Tacca et al. (2018)/1994 | Europe | Caucasian | CC | 70/167 | 61 | 9 | 141 | 27 |
| Chen C Zhang et al. (2019)/1999 | East Asia | Asian | CC | 181/181 | 61 | 120 | 89 | 92 |
| Kim JW Wongpratate et al. (2020)/2000 | South Asia | Indian | CC | 142/96 | 114 | 28 | 84 | 12 |
| Sharma A Economopoulos et al. (2010a)/2004 | East Asia | Asian | CC | 81/86 | 43 | 38 | 32 | 54 |
| Lee SA Sui et al. (2011)/2004 | East Asia | Asian | CC | 131/320 | 68 | 63 | 175 | 145 |
| Niwa Y Gao et al. (2011)/2005 | South Asia | Indian | CC | 103/103 | 87 | 16 | 77 | 26 |
| Sobti RC Wang et al. (2011)/2006 | East Asia | Asian | CC | 125/125 | 58 | 67 | 70 | 55 |
| Zhou Q Liu and Xu, (2012)/2006 | South Asia | Indian | CC | 147/165 | 123 | 24 | 149 | 16 |
| Joseph T Sun and Song, (2016)/2006 | South Asia | Indian | CC | 150/168 | 110 | 40 | 150 | 18 |
| Singh H Zhen et al. (2013)/2008 | East Asia | Asian | CC | 124/125 | 68 | 56 | 67 | 58 |
| Nishino K Sarhanis et al. (1996)/2008 | South America | Mixed | CC | 43/86 | 21 | 22 | 70 | 16 |
| De C CR Hengstler et al. (1998)/2008 | Southeast Asia | Asian | CC | 90/94 | 48 | 42 | 56 | 38 |
| S-I W Goodman et al. (2000)/2009 | West Asia | Caucasian | CC | 46/52 | 31 | 15 | 36 | 16 |
| Kiran B Spurdle et al. (2001)/2010 | Europe | Caucasian | CC | 25/111 | 17 | 8 | 89 | 22 |
| Palma S Baxter et al. (2001)/2010 | East Asia | Asian | CC | 83/158 | 25 | 58 | 78 | 80 |
| Ueda M Morari et al. (2006)/2010 | Europa | Serbian | CC | 32/50 | 20 | 12 | 30 | 20 |
| Stosic I Gates et al. (2008)/2014 | South Asia | Caucasian | CC | 50/50 | 36 | 14 | 32 | 18 |
| Hasan S Chunhua, (2008)/2015 | South Asia | Indian | CC | 135/457 | 109 | 26 | 392 | 65 |
| Sharma A Khokhrin et al. (2012)/2015 | South Asia | Indian | CC | 150/150 | 128 | 22 | 113 | 37 |
| Satinder K Cai et al. (2016)/2017 | South America | Mixed | CC | 135/100 | 69 | 66 | 44 | 56 |
| Tacca A. Economopoulos et al. (2010b)/2019 | Southeast Asia | Asian | CC | 198/198 | 134 | 64 | 137 | 71 |
| Wongpratate M Yin et al. (2013)/2020 | East Asia | Asian | CC/OC | 259/95 | 108 | 151 | 44 | 51 |
| Ueda M Xu et al. (2014)/2008 | Europe | Caucasian | OC | 84/312 | 68 | 13 | 264 | 61 |
| Sarhanis P Han et al. (2014)/1996 | Europe | Caucasian | OC | 103/115 | 87 | 16 | 99 | 16 |
| Hengstler JG Capoluongo et al. (2006)/1998 | Europe | Caucasian | OC | 108/106 | 87 | 16 | 87 | 12 |
| Goodman JE Thakkinstian et al. (2011) 2000 | Europe | Caucasian | OC | 285/299 | 228 | 57 | 239 | 56 |
| Spurdle AB Mantel and Haenszel, (1959)/2001 | South America | Mixed | OC | 69/222 | 26 | 129 | 45 | 123 |
| Morari EC Der Simonian and Laird, (2015)/2006 | North America | Caucasian | OC | 1175/1202 | 919 | 247 | 938 | 257 |
| Gates M A Begg and Mazumdar, (1994)/2008 | East Asia | Asian | OC | 89/49 | 28 | 42 | 153 | 222 |
| Chunhua Z Egger et al. (1997)/2008 | Europe | Caucasian | OC | 104/298 | 86 | 18 | 254 | 44 |
| Khokhrin DV Dual and Tweedie, (2000)/2012 | South America | Caucasian | OC | 132/132 | 93 | 39 | 98 | 34 |
| Oliveira C Wacholder et al. (2004)/2012 | Europe | Serbian | OC | 85/178 | 72 | 13 | 131 | 47 |
Basic characteristics of GSTT1 gene polymorphism.
OC, ovarian cancer; CC, cervical cancer.
Quantitative synthesis
Association of GSTM1 present/null with the risk of CC development
A total of 30 studies on GSTM1 present/null polymorphisms and the risk of CC were included. Regarding the comparison of the distribution of positive vs. null in the case group and the control group, the heterogeneity test results showed that the Q test p = .000 and I2 = 69.8%, the random effect model is used, and the forest diagram: OR [95% CI] is 1.47 (1.23–1.75), see Figure 2 and Table 4 shows the results of the association between GSTM1 present/null polymorphisms and CC risk. In the overall analysis, individuals with GSTM1 null genotype had a significantly increased risk of CC (OR = 1.47, 95% CI:1.23–1.75). Further subgroup analysis for race, country and geographical region showed that a significantly increased risk of CC was observed in Indians (OR = 1.96, 95% CI:1.51–2.55) and Asians (OR = 1.44, 95% CI:1.18–1.75), a significantly increased risk of CC was observed in East Asia (OR = 1.56, 95% CI:1.23–2.00) and South Asia (OR = 2.12, 95% CI: 1.58–2.85), a subgroup analysis of Asian countries showed that a significantly increased risk of CC was observed only in the Chinese population (OR = 2.10, 95% CI: 1.56–2.82).
FIGURE 2
TABLE 4
| n | Cases/controls | Test of association | Test of heterogeneity | Egger’s test | ||
|---|---|---|---|---|---|---|
| OR (95% CI) | Ph | I2 (%) | PE | |||
| Overall | 30 | 3484/4,208 | 1.47 (1.23–1.75)* | .000 | 69.8 | .233 |
| Ethnicity | ||||||
| Indian | 6 | 827/1139 | 1.96 (1.51–2.55) | .104 | 45.2 | |
| Asian | 15 | 1990/2152 | 1.44 (1.18–1.75)* | .003 | 56.9 | |
| Caucasian | 6 | 457/681 | 1.37 (.85–2.21)* | .006 | 69.4 | |
| Mixed | 2 | 178/186 | .69 (.18–2.69)* | .004 | 88.0 | |
| Geographic region | ||||||
| East Asia | 12 | 1504/1662 | 1.56 (1.23–2.00)* | .002 | 61.8 | |
| Europe | 3 | 134/351 | 1.26 (.84–1.90) | .699 | 0.0 | |
| South Asia | 7 | 877/1189 | 2.12 (1.58–2.85)* | .027 | 57.9 | |
| North America | 2 | 259/278 | 1.07 (.61–1.88)* | .136 | 54.9 | |
| Southeast Asia | 3 | 486/490 | 1.10 (.85–1.42) | .963 | 0.0 | |
| South America | 2 | 178/186 | .69 (.18–2.69)* | .004 | 88.0 | |
| Country | ||||||
| China | 6 | 645/697 | 2.10 (1.56–2.82) | .134 | 40.6 | |
| Japan | 4 | 597/698 | 1.25 (.89–1.75)* | .109 | 50.5 | |
| Korea | 2 | 262/267 | 1.02 (.73–1.44) | .703 | .0 | |
| Thailand | 3 | 486/490 | 1.10 (.85–1.42) | .963 | .0 | |
Pooled estimates of the association of GSTM1 polymorphism with risk of cervical cancer.
*A random-effect model was used when p < .10 and/or I2 > 50%; otherwise, a fixed-effects model was used.
Bold values means the statistical significance.
Association of GSTT1 present/null with the risk of CC development
A total of 22 studies on GSTT1 present/null polymorphisms and risk of CC were included, and the heterogeneity test showed Q-test p = .000 and I2 = 66.0%, and the random-effects model was selected, and the forest plot showed that the OR [95% CI] was 1.21 (.97–1.50), as shown in Figure 3. Table 5 shows the results of the association between GSTT1 present/null polymorphisms and CC risk. In the overall analysis, no association was observed between GSTT1 null genotype and CC risk, and no association with CC risk was observed in further subgroup analysis.
FIGURE 3
TABLE 5
| n | Cases/controls | Test of association | Test of heterogeneity | Egger’s test | ||
|---|---|---|---|---|---|---|
| OR (95% CI) | Ph | I2 (%) | PE | |||
| Overall | 22 | 2500/3148 | 1.21 (.97–1.50)* | .000 | 66.0 | .937 |
| Ethnicity | ||||||
| Indian | 6 | 827/1139 | 1.25 (.72–2.20)* | .000 | 79.4 | |
| Asian | 9 | 1272/1392 | 1.21 (.94–1.56)* | .012 | 59.0 | |
| Caucasian | 4 | 191/381 | .98 (.64–1.51) | .409 | .0 | |
| Mixed | 2 | 178/186 | 1.81 (.31–10.61)* | .000 | 92.7 | |
| Geographic region | ||||||
| East Asia | 7 | 984/1090 | 1.25 (.91–1.72)* | .008 | 65.6 | |
| South Asia | 7 | 877/1189 | 1.17 (.70–1.94)* | .000 | 77.0 | |
| Southeast Asia | 2 | 288/302 | 1.03 (.74–1.45) | .358 | .0 | |
| South America | 2 | 178/186 | 1.81 (.31–10.61)* | .000 | 92.7 | |
| Europe | 3 | 127/329 | 1.05 (.62–1.79) | .342 | 6.7 | |
Pooled estimates of the association of GSTT1 polymorphism with risk of cervical cancer.
*A random-effect model was used when p < .10 and/or I2 > 50%; otherwise, a fixed-effects model was used.
Bold values means the statistical significance.
Association of GSTM1 present/null with the risk of OC development
A total of 14 studies on GSTM1 present/null polymorphisms and risk of OC were included. The heterogeneity test showed Q-test p = .050 and I2 = 41.8%, and a fixed-effects model was selected, and the forest plot showed that the OR [95% CI] was 1.15 (.99–1.34), as shown in Figure 4 and Table 6 shows the results of the association between GSTM1 present/null polymorphisms and OC risk. In the overall analysis, GSTM1 null was not significantly associated with increased OC risk, and further subgroup analysis showed that GSTM1 null genotype was associated with increased OC risk in East Asia (OR = 1.65, 95% CI:1.00–2.73).
FIGURE 4
TABLE 6
| n | Cases/controls | Test of association | Test of heterogeneity | Egger’s test | ||
|---|---|---|---|---|---|---|
| OR (95% CI) | Ph | I2 (%) | PE | |||
| Overall | 14 | 3035/3422 | 1.15 (.99–1.34) | .050 | 41.8 | .044 |
| Ethnicity | ||||||
| Asian | 3 | 472/268 | 1.65(1.00–2.73)* | .123 | 52.2 | |
| Caucasian | 9 | 2409/2754 | 1.07 (.91–1.25) | .177 | 30.2 | |
| Geographic region | ||||||
| East Asia | 3 | 472/268 | 1.65(1.00–2.73)* | .123 | 52.2 | |
| Europe | 7 | 1057/1528 | 1.14 (.95–1.36) | .323 | 14.0 | |
| North America | 2 | 1305/1272 | .92 (.79–1.07) | .411 | .0 | |
| South America | 2 | 201/354 | 1.35 (.93–1.95) | .599 | .0 | |
Pooled estimates of the association of GSTM1 polymorphism with risk of ovarian cancer.
*A random-effect model was used when p < .10 and/or I2 > 50%; otherwise, a fixed-effects model was used.
Bold values means the statistical significance.
Association of GSTT1 present/null with the risk of OC development
A total of 11 studies were included regarding the GSTT1 present/null polymorphisms and the risk of OC, and the results of the heterogeneity test showed Q-test p = .039 and I2 = 5.6%, and the fixed-effects model was chosen, and the forest plot showed that the OR [95% CI] was 1.05 (.92–1.19), as shown in Figure 5 and Table 7 shows the results of the association between GSTT1 present/null polymorphisms and OC risk. In the overall analysis, The GSTT1 null genotype was not significantly associated with OC risk, but subgroup analysis showed that the GSTT1 null genotype was associated with an increased risk of OC in South America (OR = 1.48, 95% CI:1.01–2.17).
FIGURE 5
TABLE 7
| n | Cases/controls | Test of association | Test of heterogeneity | Egger’s test | ||
|---|---|---|---|---|---|---|
| OR (95% CI) | Ph | I2 (%) | PE | |||
| Overall | 11 | 2543/3275 | 1.05 (.92–1.19) | .039 | 5.6 | .615 |
| Ethnicity | ||||||
| Asian | 2 | 340/420 | 1.13 (.79–1.60) | .667 | .0 | |
| Caucasian | 7 | 1986/2545 | 1.03 (.89–1.20) | .940 | .0 | |
| Geographic region | ||||||
| East Asia | 2 | 329/470 | 1.13 (.79–1.60)* | .667 | .0 | |
| Europe | 6 | 761/1310 | .97 (.76–1.24) | .378 | .0 | |
| South America | 2 | 287/300 | 1.48 (1.01–2.17) | .298 | 7.7 | |
Pooled estimates of the association of GSTT1 polymorphism with risk of ovarian cancer.
*A random-effect model was used when p < .10 and/or I2 > 50%; otherwise, a fixed-effects model was used.
Heterogeneity test
Due to the sources of potential heterogeneity in the individual original studies, we applied meta-regression analysis to test for heterogeneity, as shown in Table 8. In the study of GSTM1 present/null polymorphisms and CC risk, there was heterogeneity in control matching and literature quality (p < .05), where matching explained 27.93% of the sources of heterogeneity and literature quality explained 18.96% of the sources of heterogeneity (not specifically reported), considering that the two types of covariates may be the main source of heterogeneity in the relevant studies. In the study of GSTM1 present/null polymorphisms and OC risk, there was heterogeneity in sample size (p < .05), showing that it could explain 31.75% of the sources of heterogeneity (not specifically reported), considering that sample size could be the main source of heterogeneity in the relevant studies. No covariates were identified as a source of heterogeneity in studies of GSTT1 present/null and risk of CC or OC.
TABLE 8
| (A) GSTM1 | GSTT1 | |
|---|---|---|
| Logor | P >|t| [95% Conf. interval] | |
| year | .78 (−.04 to −.06) | .34 (−.11 to −.04) |
| Sample size | .37 (−.64 to −.24) | .94 (−.55 to −.60) |
| matching | .01 (−.85 to −.14) | .71 (−.61 to −.43) |
| adjustments | .21 (−.10 to −.45) | .83 (−.44 to −.36) |
| Quality score | .03 (.04 to −.79) | .51 (−.68 to −.35) |
| Geographic region | .71 (−.11 to −.08) | .78 (−.18 to −.14) |
| ethnicity | .81 (−.20 to −.16) | .81 (−.19 to −.24) |
| Source of controls | .07 (−.71 to −.03) | .68 (−.44 to −.66) |
| (B) GSTM1 | GSTT1 | |
|---|---|---|
| Logor | P >|t| [95% Conf. interval] | |
| year | .87 (−.08 to −.09) | .49 (−.05 to −.02) |
| Sample size | .03 (−2.35 to −.13) | .59 (−.76 to −.46) |
| matching | .51 (−.48 to −.25) | .57 (−.58 to −.34) |
| adjustments | .71 (−.36 to −.25) | .73 (−.35 to −.48) |
| Quality score | .80 (−.44 to −.35) | .46 (−.29 to −.59) |
| Geographic region | .32 (−.29 to −.10) | .44 (−.12 to −.26) |
| ethnicity | .31 (−.39 to −.13) | .24 (−.41 to −.12) |
| Source of controls | .90 (−.39 to −.35) | .72 (−.50 to −.36) |
A) Meta-regression analysis of GSTM1, GSTT1 gene polymorphisms, and risk of cervical cancer. (B) Meta-regression analysis of GSTM1, GSTT1 gene polymorphisms, and risk of ovarian cancer.
Sensitivity analysis
Sensitivity analysis was performed using two methods for meta-analysis. First, in evaluating the stability of the current meta-analysis, the results of each study were not changed after deleting them. Second, considering that studies with low quality and small sample size may be more likely to have positive results, we performed sensitivity analysis after excluding low-quality and small sample studies, and the results showed that GSTM1 null was not associated with CC risk in the overall study (OR = 1.24, 95% CI:0.99–1.57), GSTT1 null genotype was associated with CC risk in East Asia (OR = 1.45, 95% CI:1.07–1.96), GSTM1 null genotype was not significantly associated with OC risk in East Asia, and the remaining results were not significantly changed (as shown in Tables 9).
TABLE 9
| Cases/controls | Test of association | Test of heterogeneity | |||
|---|---|---|---|---|---|
| OR (95% CI) | Ph | I2 (%) | |||
| GSTM1with risk of cervical cancer | Overall | 2126/2427 | 1.24 (.99–1.57)* | .000 | 72.6 |
| Ethnicity | |||||
| Indian | 447/483 | 1.86 (1.35–2.57) | .236 | 30.7 | |
| Asian | 1354/1638 | 1.27 (1.05–1.53) | .119 | 37.5 | |
| Geographic region | |||||
| East Asia | 958/1242 | 1.33 (1.04–1.70)* | .062 | 50.0 | |
| South Asia | 447/483 | 1.86 (1.35–2.57) | .236 | 30.7 | |
| Southeast Asia | 396/396 | 1.12 (.84–1.49) | 1.000 | .0 | |
| Country | |||||
| China | 439/458 | 1.64(1.26–2.14) | .588 | .0 | |
| Japan | 338/603 | 1.16(.88–1.52)* | .067 | 63.0 | |
| GSTT1 with risk of cervical cancer | Overall | 1424/1700 | 1.28(.94–1.75)* | .000 | 73.1 |
| Ethnicity | |||||
| Indian | 447/483 | 1.42(.49–4.11)* | .000 | 88.6 | |
| Asian | 842/1117 | 1.33(1.00–1.78)* | .039 | 57.4 | |
| Geographic region | |||||
| East Asia | 644/909 | 1.45(1.07–1.96)* | .082 | 51.6 | |
| South Asia | 447/483 | 1.42(.49–4.11)* | .000 | 88.6 | |
| GSTM1with risk of ovarian cancer | Overall | 2238/2640 | 1.05 (.94–1.18) | .286 | 18.2 |
| Ethnicity | |||||
| Caucasian | 2084/2240 | 1.04 (.92–1.18) | .243 | 25.4 | |
| Geographic region | |||||
| Europe | 870/1091 | 1.16 (.96–1.39) | .464 | .0 | |
| South America | 201/354 | 1.35 (.93–1.95) | .599 | .0 | |
| GSTT1with risk of ovarian cancer | Overall | 2030/2356 | 1.04 (.90–1.21) | .138 | 38.2 |
| Ethnicity | |||||
| Caucasian | 1790/2019 | 1.04 (.89–1.22) | .869 | .0 | |
| Geographic region | |||||
| Europe | 577/870 | .98 (.74–1.29) | .179 | 38.9 | |
| South America | 287/300 | 1.48(1.01–2.17) | .298 | 7.7 | |
Pooled estimates of the association of GSTM1, GSTT1 polymorphism with risk of cervical cancer or ovarian cancer. Exclude low-quality and small sample-studies.
A random-effect model was used when P < 0.10 and/or I2 > 50%.
Bold balues means the statistical significance.
Publication bias
Publication bias was assessed by Begg’s funnel plot and Egger’s test, which showed no evidence of publication bias in the studies of both the GSTM1 present/null and GSTT1 present/null, with the CC risk (see Figure 6). No data showed publication bias between GSTT1 present/null polymorphisms and OC risk (see Figure 7B). The data analysis showed a bias between GSTM1 present/null polymorphisms and OC risk (p = .044), as shown in Figure 7A. Further adjusted for publication bias using a non-parametric “trim and fill” approach, the results remained the same (as shown in Figure 8), indicating that the addition of studies does not affect the overall combined results.
FIGURE 6
FIGURE 7
FIGURE 8
Reliability of positive results of current and previous meta-analyses
FPRP and BFDP can assess the likelihood of a genuine association between genetic associations and disease. We, therefore, used FPRP and BFDP to validate the credibility of the current and previous meta-analyses. An excel spreadsheet was applied to calculate FPRP and BFDP. critical values of .2 and .8 for FPRP and BRDP, respectively, were used to assess whether they were significantly associated. We determined that significant associations meeting the following statistical criteria were classified as “positive results” (Theodoratou et al., 2012): 1) p < .05 was observed in at least one of the two genetic models (individual the GSTM1 present/null and GSTT1 present/null polymorphisms, with the risk of CC or OC did not need to meet this condition, as they were only used null vs. present). 2) FPRP < .2 and BFDP < .8 at a p-value level of .05. 3) statistical efficacy > .8 and 4) I2 < 50%. If the above criteria were not met, the association was considered a “positive result with low confidence”. Tables 10, 11, present the statistical significance associations, I2 values, statistical efficacy, and FPRP and BFDP values for the current and previous meta-analyses, respectively. Based on these criteria, the results show that the positive results in the current study and the positive results of the previous meta-analysis showed “low confidence” (FPRP > .2 and BFDP > .8).
TABLE 10
| (A) Variables | OR (95% CI) | I2 (%) | Statistical power | The prior probability of .001 | ||
|---|---|---|---|---|---|---|
| 0R = 1.2 | OR = 1.5 | FPRP | BFDP | |||
| GSTM1 (null vs. present) | ||||||
| Overall | 1.47 (1.23–1.75) | 69.8 | .011 | .590 | .568 | .404 |
| Asian | 1.44 (1.18–1.75) | 56.9 | .033 | .659 | .881 | .895 |
| Indian | 1.96 (1.51–2.55) | 45.2 | .000 | .023 | .806 | .029 |
| East Asia | 1.56 (1.23–2.00) | 61.8 | .019 | .379 | .959 | .929 |
| South Asia | 2.12 (1.58–2.85) | 57.9 | .000 | .011 | .887 | .036 |
| China | 2.10 (1.56–2.82) | 40.6 | .000 | .013 | .891 | .043 |
| (B) Variables | OR (95% CI) | I2 (%) | Statistical power | The prior probability of .001 | ||
|---|---|---|---|---|---|---|
| 0R = 1.2 | OR = 1.5 | 0R = 1.2 | OR = 1.5 | |||
| GSTM1 (null vs. present) | ||||||
| Asian | 1.65 (1.00–2.73) | 52.2 | .108 | .355 | .998 | .998 |
| East Asia | 1.65 (1.00–2.73) | 52.2 | .108 | .355 | .998 | .998 |
| GSTT1 (null vs. present) | ||||||
| South America | 1.48 (1.01–2.17) | 7.7 | .141 | .527 | .997 | .998 |
(A) Cervical cancer false-positive report probability values for the current meta-analysis. (B) Ovarian cancer false-positive report probability values for the current meta-analysis.
TABLE 11
| Author | Gene | Variable | OR (95% CI) | I2 (%) | Statistical power | The prior probability of .001 | ||
|---|---|---|---|---|---|---|---|---|
| 0R = 1.2 | OR = 1.5 | FPRP | BFDP | |||||
| Tian Stosic et al. (2014) 2019 | GSTT1 | Overall | 1.78 (1.17–2.72) | 30 | .034 | .214 | .996 | .992 |
| Sun Ueda et al. (2010) 2016 | GSTM1 | Overall | 2.31 (1.57–3.40) | 4.72 | .000 | .014 | .980 | .498 |
| HB | 2.65 (1.51–4.62) | 4.00 | .003 | .022 | .996 | .953 | ||
| Chinese | 1.85 (1.30–2.63) | .0 | .008 | .121 | .987 | .941 | ||
| Mainland | 2.33 (1.39–3.89) | 4.56 | .006 | .046 | .995 | .970 | ||
| Zhen Song et al. (2008) 2013 | GSTM1 | Overall | 1.56 (1.39–1.75) | 67 | .000 | .252 | .000 | .000 |
| smokers | 2.27 (1.46–3.54) | .0 | .002 | .034 | .992 | .906 | ||
| Chinese | 2.51 (1.73–3.65) | 38 | .000 | .004 | .963 | .087 | ||
| Indians | 2.07 (1.49–2.88) | 41.4 | .001 | .028 | .963 | .402 | ||
| Greece | 1.82 (1.11–2.99) | — | .050 | .223 | .997 | .996 | ||
| HPV | 2.25 (1.27–3.15) | 61.8 | .000 | .009 | .949 | .113 | ||
| Zhang de Carvalho et al. (2008) 2012 | GSTM1 | Overall | 1.50 (1.21–1.85) | — | .019 | .500 | .891 | .839 |
| Chinese | 2.12 (1.43–3.15) | — | .002 | .043 | .988 | .866 | ||
| Indians | 2.07 (1.49–2.88) | — | .001 | .028 | .963 | .402 | ||
| smokers | 1.85 (1.07–3.20) | — | .061 | .227 | .998 | .997 | ||
| GSTT1 | Brazil | 4.58 (2.04–5.28) | — | .001 | .003 | .997 | .000 | |
| Liu Nishino et al. (2008) 2012 | GSTM1 | Overall | 1.54 (1.18–2.00) | — | .031 | .422 | .975 | .968 |
| Chinese | 1.85 (1.30–2.63) | — | .008 | .121 | .987 | .941 | ||
| Indians | 2.07 (1.49–2.88) | — | .001 | .028 | .963 | .402 | ||
| Thailand | 1.02 (1.18–2.00) | — | .682 | .869 | .999 | .999 | ||
| smokers | 1.56 (1.01–2.41) | — | .119 | .430 | .997 | .998 | ||
| Wang Sobti et al. (2006) 2011 | GSTM1 | Overall | 1.32 (1.06–1.66) | 58.8 | .208 | .863 | .988 | .997 |
| Chinese | 2.01 (1.46–2.79) | 32.6 | .001 | .040 | .967 | .541 | ||
| Indians | 1.84 (1.37–2.48) | 48.5 | .003 | .090 | .961 | .686 | ||
| GSTT1 | Latinos | 4.58 (2.04–5.28) | — | .001 | .003 | .997 | .000 | |
| Gao Huang, (2006) 2011 | GSTM1 | Cervical cancer | 1.54 (1.16–2.04) | 61.2 | .041 | .427 | .985 | .983 |
| GSTT1 | Cervical cancer | 1.49 (1.02–2.19) | 69.9 | .135 | .514 | .997 | .998 | |
| Latinos | 4.58 (2.04–5.28) | — | .001 | .003 | .997 | .000 | ||
Confidence analysis of positive results from previously published meta-analyses.
HB, hospital-based; HPV, human papillomavirus.
Discussion
CC and OC, as common gynecological cancers, not only impose a heavy physical and psychological burden on women worldwide but also an economic burden on their families and society. Research on genetic susceptibility in their pathogenesis has been long-standing, glutathione transferase, as one of the phase II detoxification enzymes, can catalyze the binding of glutathione to a variety of exogenous organisms and increase the water solubility and excretion of the molecule, and this detoxification ability plays a crucial role in the detoxification of glutathione S-transferase into drugs, carcinogens and reactive oxygen species. Both GSTM1 and GSTT1 have null genotype, which can lead to the deletion of their expression and loss of enzymatic activity, which may impair the ability of individuals to inactivate carcinogens and increase the risk of cancer. However, the results of studies related to the risk of CC or OC by GSTM1 and GSTT1 are inconsistent or even contradictory, so we performed a new statistical analysis of previous and newly published studies to obtain more accurate evidence-based medical conclusion.
Overall, in the current meta-analysis, statistically significant null of the GSTM1 increased the risk of CC, and based on the biochemical characteristics of GSTM1 present/null polymorphisms. We estimated that individual effects of these genes were associated with an increased risk of CC in all ethnic groups. However, the risk was not consistent across populations, and studies showed that only in Indian and Chinese populations was the risk of CC significantly the increased risk was observed only in Indian and Chinese populations, and no risk correlation was observed in Caucasian and mixed populations, etc., Which may be due to the association of CC development with environmental factors. In addition, in studies related to OC risk, GSTM1 null was shown to be associated with an increased risk of OC in East Asia. GSTT1 null genotype was associated with an increased risk of OC in South America; while no correlation was found in other regions and populations. These results suggest, that the same genes may play different roles in cancer susceptibility across ethnicities and geographic regions. Because cancer is a complex polygenic disease and different genetic backgrounds and environmental factors (economic conditions or lifestyle) may contribute to such differences. Furthermore, random errors and biases are often found in some small-sample, low-quality studies in control groups, so the results of these original studies are not credible, especially in studies of genetic polymorphisms and disease susceptibility. In addition, small sample studies with positive results may be more likely to be reported, however, when they tend to achieve positive results, their studies may be less rigorous and often of lower quality (Attia et al., 2003). Therefore, we assessed the sensitivity analysis to see if there was any variation in the results by including only high-quality and large sample studies, and finally used FPRP and BFDP tests to assess the association between the positive findings from the current meta-analysis and the results of previous relevant studies, as FPRP is considered an appropriate method to assess the probability of significant results in multiple hypothesis testing of genetic polymorphisms and disease susceptibility studies, and In turn, Wacholder et al. (2004) provided a more precise genetic test, and the two methods together further strengthen the confidence of the conclusions, the results of the test on the current study showed that in GSTM1 null may be associated with an increased risk of CC and GSTM1 and GSTT1 null may be associated with an increased risk of OC, but the associated positive results showed “low confidence” (FPRP > .2, BFDP > .8).
A total of nine previous studies have been published on the association between individual GSTM1 and/or GSTT1 present/null polymorphisms and CC risk (Economopoulos et al., 2010a; Gao et al., 2011; Sui et al., 2011; Wang et al., 2011; Liu and Xu, 2012; Zhang et al., 2012; Zhen et al., 2013; Sun and Song, 2016; Tian et al., 2019), Economopoulos et al. (2010a) published a meta-analysis showing that GSTM1 null increases the risk of CC in non-Chinese, while Sui et al. (2011) showed in a published study that GSTM1, GSTT1 null was not associated with CC risk, Gao et al. (2011) suggested in a published study that individual GSTM1 and GSTT1 null increased the risk of CC in the entire study population, in a meta-analysis published by Wang et al. (2011), Liu and Xu (2012), Zhang et al. (2012), Zheng et al. (2013) and Sun and Song (2016) all concluded that GSTM1 null increased the risk of CC in the overall study, smokers, Indians and Chinese, but not in Koreans, while in the Japanese population or other ethnic groups, such as Caucasians, Wang et al. (2011), and Zhang et al. (2012) also performed a combined analysis of GSTT1 null genotype and CC risk, and all results showed no significant association with CC risk. Although the results of the latest meta-analysis published by Tian et al. (2019) were not fully consistent with the previous results, the analysis of results observed that a single GSTM1 null genotype was not associated with an increased risk of CC, whereas GSTT1 null increased the risk of CC in the whole study. Five previous papers have summarized the association between individual GSTM1 and/or GSTT1 present/null polymorphisms and OC risk, concluding that none of the studies observed any association with OC risk except for the finding by Jin et al. (Xu et al., 2014) showing that GSTT1 null increases OC risk in Asian populations. In addition, previously published studies had several shortcomings, I2 values were not shown in two meta-analyses (Liu and Xu, 2012; Zhang et al., 2012). Ten meta-analyses did not assess the quality of eligible studies (Capoluongo et al., 2006; Economopoulos et al., 2010a; Gao et al., 2011; Sui et al., 2011; Wang et al., 2011; Liu and Xu, 2012; Yin et al., 2013; Han et al., 2014; Jin and Hao, 2014; Xu et al., 2014), all meta-analyses did not look for sources of heterogeneity, and the probability and statistical significance of false positive reports were not assessed (Capoluongo et al., 2006; Economopoulos et al., 2010a; Gao et al., 2011; Sui et al., 2011; Wang et al., 2011; Liu and Xu, 2012; Zhang et al., 2012; Yin et al., 2013; Zhen et al., 2013; Han et al., 2014; Jin and Hao, 2014; Xu et al., 2014; Sun and Song, 2016; Tian et al., 2019). Therefore, by assessing the degree of association between positive results, the results showed that their meta-analysis results may not be credible (all meta-analyses FPRP> .2, BFDP> .8) (as shown in Table 11).
Compared with previous meta-analyses, this meta-analysis has several advantages: First, in addition to the inclusion of newly published original studies, the sample size was larger, including 30 studies of GSTM1 gene polymorphism (3,484 cases and 4,208 controls) and 22 studies of GSTT1 present/null polymorphisms (2,500 cases and 3,148 controls) associated with the risk of CC, and OC risk included 14 studies of GSTM1 present/null polymorphisms (3,035 cases and 3,422 controls) and 11 studies of GSTT1 present/null polymorphisms (2,543 cases and 3,275 controls). Second, we performed a quality assessment of the included eligible studies. Third, we applied FPRP and BFDP tests to assess false positive associations to estimate positive findings from this meta-analysis and previous relevant studies. Fourth, meta-regression analysis was applied to explore the sources of heterogeneity. Fifth, important sensitivity analyses were performed for studies with high-quality and large samples. However, our meta-analysis has some limitations: First, some potential covariates were not controlled for, such as age. Second, in the subgroup analysis, although some population studies showed positive results, for example, in the study on the association between GSTM1 and/or GSTT1 present/null polymorphisms and CC risk, the results on South American countries showed that GSTT1 null genotype reduced the risk of CC, and in studies on the association between GSTM1 and/or GSTT1 null genotype and OC risk, GSTT1 null genotype was found to increase the risk of OC in mixed ethnic and Serbian populations. However, the positive results of the above studies corresponded to only one study each (not specifically reported) and the sample size was small enough to explore the true association between them and confirm the validity of their results, so a large sample size and sufficiently large studies would help to validate our findings. Third, the current meta-analysis included only published articles, so there may be publication bias, as shown in Figure 8; known positive results are more likely to be published than negative results, so the genetic effect of GSTM1 and GSTT1 null genotype may be underestimated. Fourth, we did not consider whether the genotype distribution in the controls was in Hardy–Weinberg equilibrium (HWE). Under normal circumstances, the HWE in the meta-analysis of genetic polymorphisms must be calculated to assess the quality, genotyping errors, and selection bias in the study (Hosking et al., 2004; Thakkinstian et al., 2011). However, we cannot calculate or extract the relevant data in the original studies. Fifth, for CC, data on other risk factors such as HPV infection, age and smoking were not extracted, while for ovarian cancer, data on age, obesity and tumor pathological classification were not extracted.
Conclusion
The results of this meta-analysis study suggest that the positive results of GSTM1 null genotype associated with increased risk of CC, and GSTM1 and GSTT1 null genotype associated with increased risk of OC in Chinese and Indian populations may be results with missing credibility rather than true associations, and therefore we should interpret these positive results with caution. In conclusion, due to the small sample size of the relevant studies and the limitations of this study, the GSTM1 present/null and/or GSTT1 present/null polymorphisms with risk of CC or OC still needs to be further explored in depth, and we need more original studies with larger samples for validation.
Statements
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.
Author contributions
JY: designed research, performed research, collected data, analyzed data, wrote paper. Y-YM: check and analyzed the data. JW and X-FH: designed research and revised article.
Acknowledgments
We would like to acknowledge the authors of all the original studies included in the meta-analysis. At the same time, I would like to thank X-FH and JW for their guidance.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2022.1074570/full#supplementary-material
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Summary
Keywords
GSTT1, GSTM1, cervical cancer, ovarian cancer, BFDP, FPRP
Citation
Ye J, Mu Y-Y, Wang J and He X-F (2023) Individual effects of GSTM1 and GSTT1 polymorphisms on cervical or ovarian cancer risk: An updated meta-analysis. Front. Genet. 13:1074570. doi: 10.3389/fgene.2022.1074570
Received
19 October 2022
Accepted
29 December 2022
Published
12 January 2023
Volume
13 - 2022
Edited by
Mohammed S. Mustak, Mangalore University, India
Reviewed by
Rocio Gomez, Center for Research and Advanced Studies (CINVESTAV), Mexico
Rasa Ugenskiene, Lithuanian University of Health Sciences, Lithuania
Updates
Copyright
© 2023 Ye, Mu, Wang and He.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Xiao-Feng He, 393120823@qq.com
This article was submitted to Cancer Genetics and Oncogenomics, a section of the journal Frontiers in Genetics
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