1. Introduction

Cancer is one of the main reasons for the decline of life quality of people all over the world for its high mortality rate, and it brings great challenges to clinical therapy. A former study demonstrated that the number of patients diagnosed as cancer increased by 19.3 million and cases dying from cancer were up to 10 million worldwide in 2020.[1] Lifestyle factors such as smoking and drinking were considered as extremely influential stimulus of the occurrence of cancer. Polycyclic aromatic hydrocarbon, an emission of tobacco, has been regarded as the major organic pollutants affecting human health. A prospective study in 2018 suggested that smokers diagnosed with cancer might be associated with lower survival rate.[2] Some researchers found that long-term smokers and heavy smokers have a significantly higher risk of cancer than the general population.[3–5] Acetaldehyde is the main toxic and harmful substance in the process of alcohol metabolism, which was classified as the group 1 of human carcinogens in the report of International Agency for Research on Cancer. Studies have shown that alcohol consumption increased cancer susceptibility via compromising human immune system and destroying immune mechanism, and this effect was more obvious in Asian populations.[6,7]

Glutathione S-transferases (GSTs) supergene family, mainly produced by liver, is one of the most important phase II enzymes in biotransformation in vivo. Each member of GSTs is located on different chromosomes and encoded by one or several highly polymorphic genes.[8] GSTs family plays an essential role in the defense mechanism that protecting against cytotoxic electrophilic chemicals, and GSTs can indirectly control some other metabolizing enzyme’ activities.[3,9,10] Former studies have found that GSTs could reduce the cytotoxic effect by regulating chaperone proteins, ubiquitin-proteasome components, inflammation-related proteins, and apoptosis-related proteins.[11,12] In recent years, many studies have reported that lifestyle factors such as smoking and drinking might lead to changes in enzyme activity levels due to mutations of associated genes. Therefore, it is imperative to explore the mutual regulation and interaction between GSTs polymorphisms and cancers among smoking and drinking population. At present, GSTM1, GSTT1 and GSTP1 are the mainstream of research among the members of GSTs. GSTM1 is located on chromosome 1p13.3, encoding the u class of enzymes. GSTM1 can detoxifies cellular electrophilic substances by hormonally controlling under induction by phenobarbital and propylthiouracil.[13]GSTT1 is located on chromosome 22q11.2, encoding for θ class of enzymes. Similar to GSTM1, GSTT1 can be found in almost all eukaryotes and prokaryotes. The homozygous deletion mutations of GSTM1 and GSTT1 might lead to enzyme inactivation and alter the growth activity of certain tumor factors. Due to different coding sites of amino acids, GSTM1-null (GSTM1 -/-) and GSTT1-null (GSTT1 -/-) present the detoxification functional gene deficiency, thus altering susceptibility to some cancers aroused by environmental and lifestyle factors.[14]GSTP1rs1695(AA, AG, GG), located on chromosome 11q13, is the most studied gene encoding the π class of enzymes. GSTP1 polymorphisms were highly associated with alcohol consumption, drug-resistance and the development of cancer.[15] Some studies suggested that cancer risks differs significantly in patients with mutations of GSTs when smoking, drinking, ethnicity and source of controls were taken into account. For example, Katiyar et al, in 2020’ study demonstrated GSTM1 mutations were associated with a higher incidence of cancer among smokers[16]; however, ThekkePurakkal et al, in 2019, explained that there was no statistically significant difference in the effect of GSTM1 gene polymorphisms on cancer among smokers.[17]

Meta-analysis is a robust and scientific statistical analysis method based on huge data, which has incomparable advantages over other research methods, usually having a high credibility. In recent years, the relationship between various cancers and GSTs gene has been studied by scholars worldwide. Xavier et al, in 2017, found that Asian country people with GSTM1--null gene were more easily to develop gastric cancer than European and American.[18] Hernández et al[19] in 2017, demonstrated that GSTM1 and GSTT1 deletion could not be regarded as a separate factor influencing the survival of lung cancer. Lee et al[20] in 2020, showed that GSTP1rs1695 polymorphism was useful for the treatment of chronic myeloid leukemia patients. Hoxhaj et al in 2020, found that GSTM1, GSTT1 and GSTP1 polymorphisms might increase the risk of developing a second primary cancer among head and neck cancer survivors in different degrees.[21] However, because of the bias of language expression, regions, source and number of cases, there is still lack of a consistent conclusion. Therefore, a large scale of samples and suitable model designs are needed to further evaluate the relationship between GSTs gene and cancer development among smokers and drinkers. The aim of this study was to draw a latest conclusion on the relationship between GSTM1, GSTT1 and GSTP1 gene polymorphism and cancer risks among smokers and drinkers. Before us, no similar article has been found to systematically analyze the association between GSTs alone or in combination with smoking or drinking and all kinds of cancers, and we hope that these results will provide some insights into cancer prevention.

2. Methods 2.1. Literature search and selection criteria

Electronic literatures were searched by using the following databases: Web of Science, PubMed, WANFANG and CNKI. The keywords included (GSTM1 or GSTT1 or GSTP1 polymorphisms) and (smoking or cigarettes or tobacco) or (drinking or alcohol) and cancer by different combinations. The databases were searched in chronological order, from January, 2001 to the latest publication due date November, 2022. Only the case-controls about the association between cancers related to GSTM1, GSTT1 and GSTP1 gene polymorphisms among smokers or drinkers that had been published in English or Chinese journals were kept. To reduce omissions as possible, we also searched and consulted references of relevant review articles and meta-analyses. To further investigate the relationship between the degree of smoking and cancer risks, smokers diagnosed with cancers were classified as light smokers (<20 pack-year) and heavy smokers (>20 pack-year).

Articles that confirmed with the following criteria were included: Case-control study; Detailed data on the association of GSTM1, GSTT1 or GSTP1 polymorphisms with smoking and alcohol consumption were available for calculating the odds ratios (ORs) and estimating the 95% confidence interval (95% CI); The disease studied was clinically diagnosed cancer; and Full text available.

The reasons for exclusion were as follows: Review articles and meta-analyses as well as repeated articles in different databases; Minutes of meeting and clinical trials; No detailed data of case group and control group; and No original data on the association of GSTM1, GSTT1 or GSTP1 gene polymorphisms with smoking or drinking status.

2.2. Data extraction

After screening all articles according to the exclusion and inclusion criteria, we performed detailed data extraction for the articles including first author’s last name, year of publication, ethnicity, country, source of control, cancer type, smoking or drinking status and genotype. For case-control studies on the same cancer published by the same author in different years, we kept the latest articles or the maximum sample size in principle. Two researchers used the same keywords to search articles independently. When an article contained unextractable data or some doubts, the 2 researchers would discuss together whether to keep this article. Subgroup analyses were conducted on the ethnicity (Caucasian, Asian, and mixed groups), the source of control group (hospital-based group and population-based group), and the types of cancer (lung cancer, liver cancer, bladder cancer, and so on) to calculate the differences in the prevalence of cancers, respectively.

2.3. Statistical analysis

The Stata software was applied to calculate the GSTM1-null/presence, GSTT1 -null/presence, and GSTP1rs1695 GG + AG/AA of the case group and control group among different smoking and drinking status. OR value and 95% CI were calculated to evaluate the association between GSTM1, GSTT1 and GSTP1 polymorphisms and cancer risks among smokers and drinkers. I-square and P value were used for the assessment of heterogeneity. If the I-square of the heterogeneity test was less than 50% (P > .05), it showed that the heterogeneity between studies was not statistically significant, and the fixed model should be used for calculation; otherwise, the random model would be applied.

In this study, year, ethnicity, and population origin were considered to be the sources that could influence heterogeneity, and the meta-regression analysis were used to find these variables. At the same time, combined with the use of sensitivity analysis, any study that had an impact on the overall results of the study could be identified. We also used the Begg and Egger’s test to calculate possible bias between studies. All P values calculations were two-sided, and when P value < .05, it was considered to be statistically significant.

In order to improve the accuracy and credibility of the results of this experiment, we calculated the false positive reporting probability (FPRP) and the Bayesian false discovery probability (BFDP). As in previous studies, the threshold of FPRP was set to 0.2, and the prior probability was set to 0.25, 0.1, 0.01, 0.001 and 0.0001, to detect an OR of 1.5 associated with cancer risks in the study; and results with FPRP values less than 0.2 should be of concern.[22] Likewise, the BFDP results should be noted when the P value was less than 0.8.[23]

3. Results 3.1. Study characteristics

By using keywords, a total of 2437 related reports were found in digital databases. After scanning the titles and abstracts firstly, 2086 articles were excluded, including reviews, meta-analyses, clinical trials, irrelevant reports and duplicate reports. By carefully reading the whole text of remaining studies, we further removed 266 articles for the following reasons: lack of the information for number of samples; lack of research content on the association between genetic polymorphisms and cancers among smoking or drinking population. Finally, 85 articles (19,604 cases and 23,710 controls) were kept (Fig. 1), of which 70 articles (16,131 cases and 19,696 controls) were about the relationship between GSTM1 and cancers[4,5,16,17,24–89] (Table 1); 49 articles (11,555 cases and 14,606 controls) were about the relationship between GSTT1 and cancers[4,5,16,24,25,27–31,33,34,36–39,41,42,44–49,51,52,56,58,60,61,63,65–68,70,74–76,79,80,82–85,87,88,90–93] (Table 2); and 31 articles (8518 cases and 9884 controls) were about GSTP1 and cancers[5,16,17,24,30,31,34,36,42,44,45,47,49,52,59,66,67,70,79,88,94–103] (Table 3) among smokers. Among these researches, 8 articles mentioned the classification of smoking levels (20 pack-year). For alcohol consumption, 12 articles (4238 cases and 5394 controls) were about the association between GSTM1 and cancers, 8 articles (2949 cases and 4025 controls) were about GSTT1 and cancers and 5 articles (1898 cases and 2527 controls) were about GSTP1 and cancer among drinkers.

Table 1 - Characteristics of the eligible studies for GSTM1 polymorphisms. Author Year Ethnicity Cancer type Source of control Case (N) Control (N) Case (present/null) Control (present/null) Smoking Nonsmoking Drinking Non-drinking Smoking Nonsmoking Drinking Non-drinking Firigato[89] 2022 Mixed Head and neck cancer HB 234 422 205/16 13/1 272/31 94/17 Chorfi[25] 2022 Caucasian Liver cancer NA 132 141 29/58 19/26 36/38 37/32 Deek[24] 2021 Mixed Lung cancer NA 200 200 42/60 75/23 59/40 94/7 Avirmed[26] 2021 Asian Bladder cancer HB 60 60 19/24 6/11 2/12 15/31 Pathak[27] 2021 Caucasian Lung cancer NA 237 212 109/26 69/28 82/11 106/13 Tcheandjieu[28] 2020 Mixed Thyroid cancer PB 660 734 116/153 133/213 173/236 76/130 134/168 163/207 222/290 75/85 Katiyar[16] 2020 Caucasian Head and neck cancer HB 1250 1250 421/424 260/145 165/274 516/295 262/105 603/280 205/119 660/266 Ritambhara[34] 2019 Caucasian Lung cancer HB 120 100 30/82 4/4 11/15 54/20 Singh[29] 2019 Caucasian Nasopharyngeal cancer PB 123 189 23/37 18/45 31/15 76/67 Rostami[30] 2019 Caucasian Chronic myeloid leukemia NA 104 104 19/38 15/32 17/14 36/37 Yamashita[31] 2019 Asian Hypopharyngeal cancer NA 61 71 22/8 22/9 6/7 41/17 Thekkepaurakkal[17] 2019 Caucasian Head and neck cancer HB 389 429 151/170 27/60 142/169 51/65 Li[32] 2019 Asian Lung cancer NA 217 198 49/71 41/56 40/23 64/71 Kalacas[33] 2019 Asian Breast cancer NA 136 136 6/6 52/72 8/10 50/68 4/7 39/86 8/22 35/71 He[35] 2018 Asian Lung cancer PB 313 330 66/48 113/86 87/41 122/93 50/28 167/85 61/36 156/77 Rodrigues-Fleming[36] 2018 Caucasian Colorectal cancer NA 232 738 48/54 52/78 44/56 56/76 154/119 231/234 179/164 206/189 Peddireddy[37] 2016 Caucasian Lung cancer PB 246 250 106/42 76/22 54/21 133/42 Boccia[38] 2015 Caucasian Liver cancer HB 221 290 62/57 31/48 48/69 91/81 Sharma[39] 2015 Caucasian Cervical cancer HB 135 482 9/13 47/66 26/17 290/153 Maurya[40] 2015 Caucasian Head and neck cancer PB 750 750 105/103 64/28 29/96 140/40 159/61 356/173 141/89 374/145 Pan[41] 2014 Asian Lung cancer PB 523 523 104/135 114/170 137/102 162/122 Silva[42] 2014 Mixed Upper aerodigestive tract cancer PB 116 224 47/42 4/2 54/34 69/45 Wang[43] 2014 Asian Bladder cancer NA 358 434 73/124 61/100 75/103 106/150 Jiang[44] 2014 Asian Lung cancer NA 322 456 65/151 48/58