J Cancer 2018; 9(8):1506-1517. doi:10.7150/jca.22797
The Interaction of Smoking with Gene Polymorphisms on Four Digestive Cancers: A Systematic Review and Meta-Analysis
1. Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, College of Life Science, Northwest University, Xi'an 710069, China
2. Institute of Preventive Genomic Medicine, Xi'an 710069, China
Du L, Lei L, Zhao X, He H, Chen E, Dong J, Zeng Y, Yang J. The Interaction of Smoking with Gene Polymorphisms on Four Digestive Cancers: A Systematic Review and Meta-Analysis. J Cancer 2018; 9(8):1506-1517. doi:10.7150/jca.22797. Available from http://www.jcancer.org/v09p1506.htm
The main purpose of this study was to perform a meta-analysis to assess the interaction between smoking and nine genes (GSTM1, GSTT1, GSTP1, CYP1A1, NAT2, SULT1A1, hOGG1, XRCC1 and p53) on colorectal cancer, gastric cancer, liver cancer and oesophageal cancer. Published articles from the PubMed, ISI and EMBASE databases were retrieved. A total of 67 case-control studies or nested case-control studies were identified for the analysis. The pooled jodds ratio (OR) with 95% confidence interval (CI) was calculated using the random effect model. The overall study showed that the GSTM1 polymorphism was associated with the risk of the four digestive cancers among Asian population (OR 1.284, 95% CI: 1.122-1.470, p: 0). Subgroup analyses by cancer site showed that GSTM1 null genotype increased the gastric cancer risk in total population (OR 1.335, 95% CI: 1.145-1.556, p: 0). However, the association of GSTM1 null genotype with the oesophageal cancer risk was found in smokers (OR 1.382, 95% CI: 1.009-1.894, p:0.044), but not in non-smokers (OR 1.250, 95% CI: 0.826-1.891, p:0.290). Moreover, smokers with the CYP1A1 IIe462Val polymorphism were at an increased cancer risk in Asian population (OR=1.585, 95% CI 1.029-2.442, p: 0.037). None of the other gene-smoking interactions was observed in the above cancers. This meta-analysis reveals two potential gene-smoking interactions, one is between smoking and GSTM1 on oesophageal cancer, and the other is between smoking and CYP1A1 IIe462Val on the four cancers in Asian population. Future studies need to be conducted to verify the conclusions.
Keywords: gene polymorphisms, gene-smoking interaction, digestive cancer, meta-analysis
Cancer was the second leading cause of non-communicable diseases deaths worldwide in 2015. Most cancer patients die from digestive cancers between 2005 and 2015, of which the death toll increased to 832,000 for colorectal cancer (CRC), 818.9,000 for gastric cancer (GC), 810.5,000 for liver cancer (LC) and 439,000 for oesophageal cancer (OC). Moreover, the incidence of these four cancers ranks in the top ten over the world, mainly in developing countries . Especially, these cancers are generally recognized as tobacco-related cancers (TRCs) by the International Association of Research in Cancer (IARC) . However, not all individuals exposed to tobacco develop these cancers. Because the etiology of cancer is multifactorial and complicated , cigarette smoking, as a prevalent environment factor, may interact with multiple genetic factors, leading to a higher susceptibility to cancer.
The research on the gene-smoking interaction in cancer risk has been popular . Previously published studies clarified the molecular mechanism of the gene-smoking interaction. Most tobacco carcinogens first form DNA adducts via metabolic activation; persistent DNA adducts induce mutations in some critical genes and initiate carcinogenesis . The elimination of DNA adducts requires DNA repair, implying that variations of the DNA repair genes may be related to different repair efficiencies of DNA damage . Moreover, various detoxification pathways are competitive and different individuals have distinct balances between metabolic activation and detoxification, influencing the cancer risk . Increasing epidemiologic studies and meta-analyses have indicated the interaction between smoking and gene polymorphisms in various cancer types [9-11]. However, most meta-analyses only assessed the interaction between single gene polymorphism and smoking on one or several cancers. Furthermore, the results were inconsistent or even conflicting. Hence, we performed a comprehensive meta-analysis on the interaction of smoking with ten gene polymorphisms in four digestive cancers. The aim was to develop a more powerful evaluation of gene-smoking interaction on major digestive cancers risk.
Materials and methods
PubMed, ISI and EMBASE databases were searched until Dec. 2017 with combinations of the following keywords: “smoke, cigarette, tobacco, smoking”, “gene, polymorphism”, “colorectal, colon, rectum, colorectum, liver, hepatocellular, oesophageal, oesophagus, gastric, stomach”, and “cancer, carcinoma, adenomas”. No restrictions were placed on language. References of the retrieved and review articles were also screened by hand.
Inclusion and exclusion criteria
Studies that were included in our analysis had to meet all of the following criteria: (1) evaluated the gene-smoking interaction on the risk of digestive cancers; (2) only case-control studies or cohort studies were considered; (3) provided case and control or cohort size by gene-smoking interaction; (4) showed the gene polymorphisms that were evaluated in at least five independent studies on the four digestive cancers and (5) when an author had several studies on the same patient population, only the most recent or largest sample article was included..The following exclusion criteria were used: (1) the full text was not obtained; (2) only case population; and (3) duplicated study.
Data extraction and quality assessment
All data were independently extracted by two investigators according to the above selection criteria. The information collected from each study are as follows: the first author's last name; year of publication; country of origin; ethnicity; study design; total number of cases and controls or cohort; cancer type; gene names; number of cases and controls or cohort by gene polymorphisms; number of cases and controls or cohort by gene-smoking interaction. Smoking habits were categorized as non-smoker and smoker. The number of cases and controls or cohort by gene-smoking interaction was extracted according to four combinations: non-smoker + “no risk” polymorphism; non-smoker + “at risk” polymorphism; smoker + “no risk” polymorphism; and smoker + “at risk” polymorphism. For each gene polymorphism, the “at risk” phenotype was identified based on known biological mechanisms and the classification conducted by most included articles. “At risk” polymorphism for GSTM1/GSTT1 was the null (-/-); for GSTP1, the IIe105Val substitution (Ile/Val+Val/Val); for CYP1A1, the 3801T>C substitution (MspI) (T/C+C/C) and Ile462Val substitution (Ile/Val+Val/Val), for NAT2, the fast + intermediate (at least one *4 or *12) acetylator; for SULT1A1, the slow+intermediate (at least one *2) sulphation, for hOGG1, the Ser326Cys substitution (Ser/Cys+Cys/Cys); for XRCC1, the Arg399Gln substitution (Arg/Gln+Gln/Gln); and for p53, the Arg72Pro substitution (Arg/Pro +Pro/Pro).
The quality of each study was evaluated by the Newcastle-Ottawa Scale (NOS), which is a 9-star system containing the following three dimensions: selection; comparability; and outcome (cohort studies) or exposure (case-control studies) . A study with 7-9 scores was classified as a high-quality study, while those with scores of 4-6 and 0-3 are moderate- and low-quality studies, respectively .
The reference group was identified as “no risk” polymorphism, and the odds ratios (OR) with 95% confidence intervals (CI) were calculated to determine a risk of the association between gene polymorphisms and digestive cancers. To be conservative, the random effects model was applied to calculate the summary risk. In addition, the subgroup analyses were conducted based on the cancer site and ethnicity. Heterogeneity was evaluated among studies by calculating the Q-statistic and I2 value . Publication bias was assessed by constructing the funnel plots (there was no publication bias if the funnel plot was symmetric) and quantified using Begg's test and Egger's test [15, 16], in which a p-value<0.05 indicated the presence of potential publication bias. All statistical analyses were performed using Comprehensive Meta-Analysis Software, version v. 2.0 (CMA, Biostat, Englewood, NJ, USA). For the positive findings, the false-positive report probability and statistical power were calculated by G*Power software [17, 18].
A total of 1979 articles were collected from the 3 databases. As shown in Figure 1, 1491 publications were excluded; 1251 articles were titles, abstracts, systematic reviews, meta-analyses, case reports and irrelevant articles and another 240 papers lacked data on gene-smoking interactions. Finally, a total of 67 studies were included in this meta-analysis. The reason for removing 421 studies from the remaining articles was that they evaluated the gene polymorphisms in less than five independent studies on the four digestive cancers.
Study characteristics and quality assessment
Study characteristics are summarized in Table 1. These studies were case-control or nested case-control studies, including 21,954 cases and 30,341 controls. Forty-three studies were performed in Asia, 11 studies were performed in Europe, 10 studies were performed in the Americas, and 3 studies were performed in Africa. Among all identified articles, 30 evaluated GSTM1 polymorphism [19-48], 18 evaluated GSTT1 polymorphism [20-24, 30-32, 34, 35, 40, 42-48], 12 evaluated GSTP1 polymorphism [11, 22, 30, 32, 34, 35, 42, 49-53], 8 evaluated CYP1A1 IIe462Val polymorphism [9, 27, 28, 54-58], 7 evaluated CYP1A1 MspI polymorphism [26, 28, 45, 54, 57, 58], 8 evaluated NAT2 polymorphism [24, 28, 36, 38, 46, 59-61], 6 evaluated SULT1A1 polymorphism [24, 45, 62-65], 8 evaluated hOGG1 polymorphism [66-73], 7 evaluated XRCC1 polymorphism [52, 67, 69, 74-77], and 6 evaluated p53 polymorphism [78-83].
Flow diagram of study selection in this meta-analysis. This flowchart indicates that the process of screening relevant studies based on the inclusion/exclusion criteria. A total of 67 studies were included in this meta-analysis.(Click on the image to enlarge.)
As shown in Table 1, the quality scores of studies ranged from 6 to 9. Therefore, 91% of the studies (n=61) were high-quality studies (studies with a score≥7).
Tobacco metabolizing related genes
Among 30 studies on the GSTM1 polymorphism in Table 2, the results showed the GSTM1 null genotype increased the four digestive cancers risk (OR=1.118, 95% CI 1.022-1.222). No significant publication bias was found using Begg's test (p=0.10), while there was publication bias by Egger's test (p=0.045). According to the trim and fill analysis, the adjusted estimated effect was OR 1.054 (95% CI: 0.954-1.163) based on the random-effects model. Substantial heterogeneity was observed in this analysis (Q=70.248, p=0.000, I2= 53.024 %), which suggested that GSTM1 polymorphisms have different effects on the risk of four cancers, depending on the cancer type and ethnicity. Subgroup analysis based on ethnicity revealed that such an association was observed among both African (OR=1.614, 95% CI 1.038-2.51; I2=0%, p for heterogeneity=1) and Asian (OR=1.284, 95% CI 1.122-1.47; I2=57.181%, p for heterogeneity=0.001) populations; further subgroup analysis based on the cancer type showed that the GSTM1 null genotype were associated with an increased risk of oesophageal cancer (OR=1.406, 95% CI 1.124-1.759; I2=63.644%, p for heterogeneity=0.027) and gastric cancer (OR=1.335, 95% CI 1.145-1.556; I2=52.921%, p for heterogeneity=0.019). Stratified analysis by smoking status showed the association of the GSTM1 null genotype with the four cancers risk was significant among smokers (OR=1.179, 95% CI 1.030-1.349; I2=57.328%, p for heterogeneity=0). In subgroup analyses among smokers, there was publication bias (p Begg =0.004; p Egger =0.029). According to the trim and fill analysis, the adjusted estimated effect was OR 1.012 (95%CI: 0.867-1.181) based on the random-effects model. However, the effect size was only found in Asian population (OR=1.355, 95% CI 1.089-1.686; I2=39.566%, p for heterogeneity=0.044). Smokers with the GSTM1 null genotype had an increased risk of oesophageal cancer (OR=1.382, 95% CI 1.009-1.894, I2=55.082, p for heterogeneity=0.064) and gastric cancer (OR=1.690, 95% CI 1.298-2.201, I2=69.955%, p for heterogeneity=0). Moreover, subgroup analyses in non-smokers showed that the GSTM1 null genotype also increased the gastric cancer risk (OR=1.344, 95% CI 1.054-1.715; I2=51.576%, p for heterogeneity=0.024). The GSTM1 null genotype was associated with the four cancers risk in Asian population (OR=1.237, 95% CI 1.020-1.500; I2=44.307%, p for heterogeneity=0.023), no publication bias was observed (p>0.05).
Characteristics of included case-control studies
|First author, year||NOS||Country/Ethnicity||Cancer site||Genes||Genotype distribution (cases/controls)||Genotype distribution by smoking status (cases/controls)|
|No risk*||At risk$||Non-smoker||Smoker|
|No risk*||At risk$||No risk*||At risk$|
|Yu,1999 a||7||China/ Asia||Liver||GSTM1||38/151||42/177||25/94||22/104||13/57||20/73|
|Van der Hel,2003 a||8||Netherlands/Europe||Colorectum||GSTM1||124/396||88/369||73/271||65/257||51/125||23/112|
|Van der Hel,2003 b||7||Netherlands/Europe||Colorectum||NAT2||146/495||112/362||99/341||63/249||42/153||45/113|
|Little, 2006||8||Northeast Scotland/Europe||Colorectum||CYP1A1#||235/372||16/24||75/128||5/5||84/142||7/10|
|Yu, 1999 c||9||China/Asia||Liver||CYP1A1#||46/239||35/170||33/147||15/97||13/92||20/73|
Aberrations: NOS, the Newcastle-Ottawa-Scale.
^Number of cases and controls.
*The wild type of each gene.
$The mutant type of each gene.
# For CYP1A1, the IIe462Val substitution (IIe/Val+Val/Val).
& For CYP1A1, the 3801T>C substitution (MspI) (T/C+C/C).
Meta-analysis of the association between GSTM1, GSTT1 polymorphisms and the four digestive cancers risk
|Stratified analysis||Subgroup analysis||No. of studies||OR (95% CI)||Heterogeneity test||Publication bias||False-positive report probability||Statistical power|
|GSTM1 total population||Overall cancer||30||1.118(1.022-1.222)||70.248||0||53.024||0.100*||0.050||0.659|
|GSTM1 non-smokers||Overall cancer||30||1.071(0.948-1.210)||54.333||0.011||39.263||0.486*|
|GSTM1 smokers||Overall cancer||30||1.179(1.030-1.349)||77.335||0||57.328||0.004*||0.050||0.728|
|GSTT1 total population||Overall cancer||18||0.970(0.863-1.092)||38.800||0.010||45.876||0.150*|
|GSTT1 non-smokers||Overall cancer||18||0.979(0.838-1.143)||28.943||0.115||27.443||0.554*|
|GSTT1 smokers||Overall cancer||18||0.977(0.843-1.132)||31.747||0.062||33.852||0.888*|
The bold letters show statistically significant results.
* Begg's test for publication bias.
$ Egger's test for publication bias.
Among 18 studies on the GSTT1 polymorphism in Table 2, we found that the GSTT1 null genotype could increase the oesophageal cancer risk in non-smokers (OR=1.845, 95% CI 1.204-2.829; I2=26.196%, p for heterogeneity=0.255). By subgroup analysis in non-smokers, Only one study showed the GSTT1 polymorphisms were related to the risk of four cancers in African population (OR=3.034, 95% CI 1.564-5.889). No publication bias was detected in this analysis (p>0.05).
Among 12 studies on the GSTP1 polymorphism in Supplementary Table S1, no significant correlations were found except one study on liver cancer in non-smokers (OR=7.364, 95% CI 1.671-32.440). There was no publication bias (p>0.05).
Eight papers provided data on the CYP1A1 IIe462Val polymorphism in Table 3. The results indicated that smokers with the CYP1A1 Ile462Val polymorphisms were at an increased risk of four cancers in Asian population (OR=1.585, 95%CI 1.029-2.442; I2=41.870%, p for heterogeneity=0.142). Seven articles were about CYP1A1 MspI polymorphism in Supplementary Table S1. The CYP1A1 MspI polymorphisms were not associated with the risk of four cancers in stratified analysis and subgroup analysis.
In Table 3, the SULT1A1 slow/intermediate phenotypes were associated with a 31.5% increase in the risk of four cancers (OR=1.315, 95% CI 1.009-1.715) from 6 studies. However, such an association was not observed in stratified analysis and subgroup analysis. Only one paper showed the association was significant in Asian population (OR=3.104, 95% CI 1.923-5.011).
Eight papers provided data on the NAT2 polymorphism, as shown in Table 4. Two studies indicated that the NAT2 polymorphism was associated with the risk of four cancers in Asian population (OR=1.701, 95% CI 1.019-2.838) [28, 60]. Moreover, the association was also observed in smokers (OR=2.513, 95% CI 1.156-5.462).
DNA repair genes
Neither hOGG1 gene nor XRCC1 gene polymorphism was not associated with the risk of four cancers, as shown in Supplementary Table S1.
Tumour suppressor gene
We also found no significant association of p53 polymorphism with the risk of four cancers (Supplementary Table S1).
A total of 67 case-control studies on the interaction of gene-smoking on the risk of four digestive cancers were identified in this review. This study included six tobacco metabolizing genes (GSTM1, GSTT1, GSTP1, CYP1A1, SULT1A1, and NAT2), two DNA repair genes (hOGG1 and XRCC1) and one tumour suppressor gene (p53). To the best of our knowledge, this is the first meta-analysis that investigated the joint effect of the most gene polymorphisms and smoking on four digestive cancers. Our data indicated the GSTM1 polymorphism was associated with the risk of four digestive cancers among Asian population (OR 1.284, 95% CI: 1.122-1.470). The GSTM1 null genotype could increase the gastric cancer risk (OR 1.335, 95% CI: 1.145-1.556) in total population. However, the association of the GSTM1 null genotype with the oesophageal cancer risk was found in smokers (OR 1.382, 95% CI: 1.009-1.894), not in non-smokers (OR 1.250, 95% CI: 0.826-1.891). Interestingly, we found the GSTT1 null genotype could increase the oesophageal cancer risk among non-smokers in only 3 studies (OR 1.845, 95% CI: 1.204-2.829). The SULT1A1 polymorphism was related to the risk of four digestive cancers (OR 1.315, 95% CI: 1.009-1.715), but such an association was not observed in stratified analysis and subgroup analysis except one study in Asian population (OR=3.104, 95% CI 1.923-5.011). Two studies indicated that the NAT2 polymorphism was associated with the risk of four cancers in Asian population (OR=1.701, 95% CI 1.019-2.838), and the association was also observed in smokers (OR=2.513, 95% CI 1.156-5.462). Moreover, smokers with the CYP1A1 Ile462Val polymorphism were at an increased cancer risk in Asian population (OR=1.585, 95% CI 1.029-2.442). None of the other gene-smoking interactions was observed in the above cancers.
Increasing studies investigated the gene-smoking interaction on the risk of cancer during these years. Two previously published studies indicated smokers with GSTM1 null genotype were at an increased oesophageal cancer risk [19, 21]. Moreover, the significant association was found between CYP1A1 IIe462Val and liver cancer risk among the cigarette smoking subjects in a meta-analysis (OR = 1.40, 95% CI 1.06-1.85) . These results were similar to our findings. Zhang et al indicated the NAT2 polymorphisms were correlated to an increased liver cancer risk in smokers . Whereas our study only provided two studies to support this conclusion. The SULT1A1 Arg213His polymorphism was associated with an increased oesophageal cancer risk , but such an association was not founded in our subgroup analysis. We also found no interaction of smoking with other genetic polymorphisms on four digestive cancers. Several reasons account for the null results.
First, the association between gene polymorphism and cancer risk could be modified by various smoking habits, including the age of initiating smoking, duration of smoking, pack-years of smoking, the method of tobacco use and cigarette categories. One study showed that lifetime exposure to tobacco increased the risk of upper aero-digestive tract (UADT) cancers. Furthermore, chewing tobacco was more likely to increase the risk of UADT cancers (OR=7.61; 95% CI 4.65-12.45) compared to smoking . The categories of cigarette also play a role in cancer progression and affect the association of gene polymorphisms with cancer susceptibility . Remarkably, Liang et al reported on the significant interactions of smoking pack years with HEL308 genotypes (Pinteraction=0.026) and ADH1B genotypes (Pinteraction=0.0016) in the head and neck squamous cell carcinoma (HNSCC) risk, respectively . Most of the included studies only provided data to evaluate the smoking status and we could not verify the findings in our study. Moreover, the age of initiating smoking is rarely measured in published studies, but this factor could be related to genetic polymorphisms in subgroups. Second, many other genes could be relevant to the metabolism of harmful compounds in tobacco except for the included genes, and the gene-gene interaction also existed in cancer susceptibility [89, 90]. It is probable that combinations of multiple gene polymorphisms are more significant as risk factors than a single gene polymorphism.
Interestingly, we found the GSTT1 null genotype could increase oesophageal cancer risk among non-smokers, but not among smokers. It was conflictive with the recognized conclusion on tobacco use increasing the cancer risk. However, this result also suggested not all the smokers with high-risk genetic variants were at an increased cancer risk. Because other benefical environmental factors, such as dietary habits, play an important role in cancer prevention . A previous study indicated that regular tea consumption decreased the OC (OR: 0.38, 95% CI: 0.17-0.87) and GC (OR: 0.30, 95% CI: 0.14-0.66) risk among those with GSTT1 null genotype . Ko et al also showed soy product consumption was associated with lower breast cancer risk in BRCA mutation carriers (HR: 0.39; 95% CI: 0.19-0.79) . It was resonalble to assume that a protective factor also interacted with the GSTT1 null genotype among smokers. Moreover, our finding was based on only 3 papers, and needed to be further verified by more studies.
Regarding the interaction between smoking and GSTM1 and CYP1A1 IIe462Val on digestive cancers risk, evidence regarding the molecular mechanism also supported the results of this meta-analysis. Tobacco smoke contains various carcinogens, for example, polycyclic aromatic hydrocarbons (PAHs) and tobacco specific nitrosamines (TSNA) . These carcinogens are first metabolically activated by phase I enzymes, e.g., cytochrome P4501A1 (CYP1A1), into their final forms and then combine with DNA, forming aromatic-DNA adducts that are considered as an early stage in carcinogenesis. Moreover, these activated forms are detoxified by phase II enzymes, especially glutathione S-transferases (GSTs). Thus, the susceptibility to cancer determined by genetic factors may depend on the metabolic balance between phase I and phase II enzymes. Because the CYP and GST genetic polymorphisms regulate the metabolism of xenobiotics, they are thought to affect individual's sensitivity to environmental factors and susceptibility to cancer. Although this meta-analysis suggested that there was no significant interaction between smoking and other gene polymorphisms, several related molecular mechanisms remain biologically plausible. Except for the CYP and GST family genes, the carcinogens in tobacco smoke can be activated by SULT1A1 and NAT2 [95, 96]. DNA repair genes, e.g, hOGG1 and XRCC, are involved in the elimination of DNA adducts, which suggests that the DNA repair genes polymorphisms may be associated with different repair efficiencies of DNA damage . Moreover, the p53 is a tumour suppressor gene and plays a key role in regulating the cell cycle and maintaining genomic integrity . Thus, it may modify individual's susceptibility to various carcinogens.
Compared with a single study that investigated the role of some metabolic gene polymorphisms in cancer risk, we evaluated the interaction between ten gene polymorphisms and smoking for four digestive cancers, and this is the first such report to date. Therefore, we could provide more comprehensive information on the gene-smoking interaction in main digestive cancers. However, there are several limitations in this meta-analysis. First, there is strong heterogeneity in the risk estimates for most gene polymorphisms and stratified analyses. Second, the ORs were only adjusted for the cancer type and ethnicity. A more precise analysis should be performed based on the data adjusted for confounding factors including the age, sex, family history, environmental factors, cancer stage, and lifestyle. In addition, we were not able to evaluate the interaction of genes with genes or other environmental factors, which should be assessed in future studies.
Meta-analysis of the association between CYP1A1, SULT1A1 polymorphisms and the four digestive cancers risk
|Stratified analysis||Subgroup analysis||No. of studies||OR (95% CI)||Heterogeneity test||False-positive|
|CYP1A1 IIe462Val total population||Overall cancer||8||1.102(0.911-1.332)||13.969||0.052||49.888|
|CYP1A1 IIe462Val non-smokers||Overall cancer||8||0.973(0.827-1.145)||6.539||0.478||0|
|SULT1A1 total population||Overall cancer||6||1.315(1.009-1.715)||17.371||0.004||71.216||0.048||0.993|
The bold letters show statistically significant results.
Meta-analysis of the association between NAT2 polymorphism and the four digestive cancers risk
|Stratified analysis||Subgroup analysis||No. of studies||OR (95% CI)||Heterogeneity test||False-positive report probability||Statistical power|
|total population||Overall cancer||8||0.990(0.872-1.125)||11.662||0.112||39.978|
The bold letters show statistically significant results.
In summary, our meta-analysis provides the evidence of two potential gene-smoking interactions, one is between smoking and GSTM1 on oesophageal cancer, and the other is between smoking and CYP1A1 Ile462Val on the four cancers in Asian populations. None of the other gene-smoking interactions was observed in the above cancer. Future studies need to be conducted to verify the conclusions.
Supplementary table S1.
We would like to thank our laboratory members Ziqing Zhu, Qiqi Li and Ying Yang for discussion. Le Du thanks Jingchuan Li for consistent support during this research. This work was not supported by any grant.
The authors have declared that no competing interest exists.
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Corresponding author: Jin Yang, email: yangjinedu.cn; telephone: 86-13572177146