|Year : 2016 | Volume
| Issue : 1 | Page : 12-19
Different effects of the three polymorphisms on 15q25.1 onlung cancer risk: Evidence from published literatures
Huiling Sun1, Yuqin Pan2, Bangshun He2, Qiwen Deng2, Houqun Ying3, Jie Chen1, Xian Liu2, Shukui Wang2
1 Department of Life Sciences, Nanjing Normal University; Central Laboratory, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
2 Central Laboratory, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
3 Central Laboratory, Nanjing First Hospital, Nanjing Medical University; Medical College, Southeast University, Nanjing, Jiangsu, China
|Date of Web Publication||13-Apr-2016|
Central Laboratory of Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing, Jiangsu - 210 006
Source of Support: None, Conflict of Interest: None
Aim of Study: Genetic studies have shown a significant association between lung cancer risk and single nucleotide polymorphisms on chromosome 15q25.1. The potential association of three polymorphisms in the CHRNA3 (rs1051730(G > A)), CHRNA5 (rs16969968(G > A)), and AGPHD1 (rs8034191(A > G)) with the lung cancer risk has been widely investigated, but the results are inconsistent. The aim of meta-analysis was toclarify the link between three polymorphisms and susceptibility to lung cancer.
Materials and Methods: All the relevant data were retrieved by Embase, PubMed, and Web of Science, and then 38 eligible studies were chosen in this meta-analysis.
Results: There was no association of rs16969968 polymorphism with cancer risk in the overall pooled analysis. For the rs1051730 and rs8034191 polymorphisms, the results revealed that the five models were significantlyassociated with elevated risk of cancer. Further stratified analysis indicated that increased risk for lung cancer was foundinthe Caucasian, African American, and Asian population.
Conclusion: In summary, CHRNA3 rs1051730 (G > A) and AGPHD1 rs8034191 (A > G) were more susceptible to lung cancers than noncarriers.
Keywords: AGPHD1, CHRNA3, CHRNA5, lung cancer, polymorphism
|How to cite this article:|
Sun H, Pan Y, He B, Deng Q, Ying H, Chen J, Liu X, Wang S. Different effects of the three polymorphisms on 15q25.1 onlung cancer risk: Evidence from published literatures. J Can Res Ther 2016;12:12-9
|How to cite this URL:|
Sun H, Pan Y, He B, Deng Q, Ying H, Chen J, Liu X, Wang S. Different effects of the three polymorphisms on 15q25.1 onlung cancer risk: Evidence from published literatures. J Can Res Ther [serial online] 2016 [cited 2021 Jan 16];12:12-9. Available from: https://www.cancerjournal.net/text.asp?2016/12/1/12/151863
| > Introduction|| |
Lung cancer is the most common malignancy and the main cause of cancer death, accounting for 120 million incidence and 100 million deaths per year worldwide, which includes non-small cell lung cancer (NSCLC) and small cell lung cancer according to pathology. Although smoking has been proved as a predominant risk factor for lung cancer, the data only showed a minor proportion of smokers werefinally infected with lung cancer during their whole life. The individual susceptibility to lung cancer may be generallyexplained by both genetic variation and gene–environment interactions.,
However, inherited genetic predisposition to the disease has recently become a subject of intense research. Direct evidence for the genetic predisposition to lung cancer was provided by increased risk associated with constitutional TP53 (tumor protein p53) and RB1 (retinoblastoma),, gene mutations, rare Mendelian cancer syndromes for instance Bloom's and Werner's syndromes, and the strongly familial lung cancer.,, Previous genome-wide associationstudies were conducted to verify the universal low-penetrance alleles influencing lung cancer risk. Genome-wide association studies have reported that common polymorphisms of 15q25.1 have a strong association with lung cancer risk and smoking habit.,, The region of chromosome 15q25.1 contains six genes-iron-responsive element-binding protein 2 (IREB2), AGPHD1, PSMA4, cholinergic receptor nicotinicα3 (CHRNA3), cholinergic receptor nicotinicα5(CHRNA5), and cholinergic receptor nicotinic β4 (CHRNB4) that are potential candidates to harbor variantsinfluencing lung cancer risk. CHRNA3 and CHRNA5 have been well studied and known to encode nicotinic acetylcholine receptor subunits (nAChRs), which may contribute lung carcinogenesis. CHRNA3 (rs1051730) maps atan 88kb region, with a C allele to T allele substitution which is shown to be significantly associated with increased lung cancer risk.,,,,CHRNA5 (rs16969968) is a missense variant that results in an aspartic acid (G allele) change to asparagine (A allele) and it has been reported to have a 1.3-fold increase in nicotine dependence susceptibility and a 1.3-fold increase in lung cancer risk., In addition, the AGPHD1 (rs8034191 (T > C)) is a noncoding variant, with one copy of C “risk allele” has a 1.28-fold increase in lung cancer risk. However, based on the fact that individual studies with insufficient sample sizes lack sufficient statistical power to detect common variants with tiny effects on lung cancer, the results were not reproducible. The present study was to evaluate the effect of three polymorphisms of rs1051730, rs16969968, and rs8034191 on the risk of lung cancer to derive a more precise estimation.
| > Materials and Methods|| |
Using the combined words”CHRNA3/CHRNA5/AGPHD1”, “polymorphisms/genetic variation”,and “lung cancer/tumor/carcinomas”search were performed via medical database Embase, PubMed, and Web of Science. The search updated on March 28th, 2014, and the search was restricted to English papers. In addition, more studies were identified by manual search according to the references listed in the retrieved studies.
Inclusion and exclusion criteria
Studies with data were enrolledin this meta-analysis only if studies met all of the following criteria: (1) Case-control studies; (2) available cancer risk and CHRNA3/CHRNA5/AGPHD1 polymorphism data related to population; (3) present sufficient data to estimate the odds ratio (OR) with 95% confidence interval (CI); and (4) available genotype frequency. In addition, the studies were eliminated if there were no raw data, or they were case-only studies, case reports, editorials, and review articles (including the meta-analyses) in the studies.
Information was carefully extracted from all the eligible articles independently by two of the authors (Huiling Sun and Yuqin Pan) based on the above inclusion and exclusion criteria. The enrolled studies with characteristics of information were extracted as below: The first author's last name, publication years, country, ethnicity, type of cancer, the source of controls, genotyping method, matching numbers of genotyped cases and controls and polymorphism site, and P for Hardy–Weinberg equilibrium (HWE) [Table 1]. The discrepancies and differences were resolved by discussion with our team.
Crude OR with 95% CIs were used to assess the strength of association between the polymorphisms in CHRNA3 (rs1051730), CHRNA5 (rs16969968) and AGPHD1(rs8034191) and lung cancer risk. The pooled ORs were estimated for homozygote, heterozygote, dominant, recessive and allelic comparison model in rs1051730, rs16969968 and rs8034191, respectively. Stratified analyses were performed by race. Eligible studies were evaluated by HWE using goodness-of-fit c 2 test. Chi-square-based Q-test was used to evaluate the heterogeneity across the studies. Pheterogeneity (Ph)<0.05 is considered significant. The data were combined using both the random effects (DerSimonian and Laird method) and the fixed-effects (Mantel–Haenszel method) models. The random effects models were used when heterogeneity existed,, otherwise the fixed effects models were usedto pool the results. Moreover, sensitivity analyses were performed to assess the stability of results. The publication bias was assessed graphically by using the funnel plots and statistically using the Egger's linear regression test. All the statistical tests were performed with Stata11.0. All the P values were two-sided.
| > Results|| |
Characteristics of studies
Thirty-two eligible studies were enrolledaccording to the inclusion and exclusion criteria [Figure 1]. And the major characteristics of 32 selected studies are summarized in [Table 1]. For rs1051730 polymorphism, 14 studies with available data were enrolled in the pooled analysis. The studies included Caucasians (seven studies), African Americans (two studies), and Asians (five studies). In addition, the control included both population- and hospital-based, and the main genotyping method was TaqMan [Table 1].
|Figure 1: Flow chart of studies identified according to inclusion and exclusion criteria|
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For rs16969968 polymorphism, eight studies which covered Caucasians (seven studies) and Asian (one study) were included in the pooled analysis. What's more, these studies contained six of hospital-based controls and two of population-based controls [Table 2] and [Table 3].
|Table 2: Meta-analysis of the association between rs16969968 (G>A), rs8034191 (A>G), and rs1051730 (G>A) polymorphisms and cancer risk|
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|Table 3: Meta-analysis of the association between rs16969968, rs8034191, and rs1051730 polymorphisms and cancer risk by recessive and dominant models|
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For rs8034191 polymorphism, 10 studies provided available data. At the same time, the studies with data of seven studies of Caucasians, two studies of African Americans, and one study of Asians. To analyze polymorphisms, TaqMan was performed in most studies, amongwhich seven were hospital-based studies and three population-based studies [Table 1].
For the rs1051730 polymorphism, results of pooled analysis revealed that therewas a significant increased risk between rs1051730 (G > A) polymorphism and lung cancer risk, which observed for the comparison of homozygote model (AA vs GG: OR = 1.661, 95% CI: 1.508–1.830, Ph = 0.254), heterozygote model (GA vsGG: OR = 1.314, 95% CI: 1.234–1.399, Ph = 0.192), allele model (A vs G: OR = 0.764, 95% CI = 0.710–0.822, Ph = 0.045), recessive model (AA vs GG + AG: OR = 1.463, 95% CI: 1.337–1.602, Ph = 0.287), and dominant model (AA + GA vs GG: OR = 1.383, 95% CI: 1.303–1.468, Ph = 0.198) [Figure 2]a as well as Caucasian (homozygote: OR = 1.727, 95% CI: 1.556–1.918, Ph = 0.575; heterozygote: OR = 1.285, 95% CI: 1.197–1.379, Ph = 0.942; allele: OR = 0.788, 95% CI: 0.753–0.824, Ph = 0.067; recessive: OR = 1.506, 95% CI: 1.368–1.659, Ph = 0.450, and dominant: OR = 1.374, 95% CI: 1.285–1.469, Ph = 0.929), African Americans (heterozygote: OR = 1.276, 95% CI: 1.089–1.495, Ph = 0.102 and dominant: OR = 1.281, 95% CI: 1.103–1.487, Ph = 0.061), and Asian (heterozygote: OR = 1.926, 95% CI: 1.469–2.526, Ph = 0.344; allele: OR = 0.528, 95% CI: 0.406–0.686, Ph = 0.067; and dominant: OR = 1.932, 95% CI: 1.476–2.528, Ph = 0.34) [Table 2] and [Table 3].
|Figure 2: (a) Forest plots of effect estimates for rs1051730 (G>A) polymorphism (AA+GA vs GG). For each of the studies, the estimate of OR and its 95% CI is plotted with a box and a horizontal line, filled diamond pooled OR and its 95% CI. (b) Forest plots of effect estimates for rs8034191 (A>G) polymorphism (GG+GA vs AA). For each of the studies, the estimate of OR and its 95% CI is plotted with a box and a horizontal line, filled diamond pooled OR and its 95% CI. OR = Odds ratio, CI = confidence interval|
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For rs16969968 polymorphism, the overallconsequences for the rs16969968 (G > A) and cancer risk are shown in [Table 2] and [Table 3]. Results of the pooled analysis indicated that no significant associations were found between rs16969968 (G > A) polymorphism and overall cancer risk as well as ethnicity subgroup analysis.
For rs8034191 polymorphism, results of pooled analysis revealed that there was a significantly increased risk between rs1051730 (G > A) polymorphism and lung cancer, which observed for the comparison of homozygote model (GGvsAA: OR = 1.525, 95% CI: 1.246–1.866, Ph = 0.014) [Figure 2]b, heterozygote model (GA vs AA: OR = 1.298, 95% CI: 1.208–1.395, Ph = 0.974), allele model (G vs A: OR = 0.782, 95% CI = 0.745–0.822, Ph = 0.203), recessive model (GG vs AA + AG: OR = 1.330, 95% CI: 1.103–1.603, Ph = 0.015), and dominant model (GG + GA vs AA: OR = 1.360, 95% CI: 1.271–1.456, Ph = 0.773). We also observed the significantly increased risk between rs8034191 (A > G) polymorphism and Caucasians (homozygote: OR = 1.764, 95% CI: 1.568–1.983, Ph = 0.982; heterozygote: OR = 1.304, 95% CI: 1.203–1.413, Ph = 0.982; allele: OR = 0.760, 95% CI: 0.719–0.802, Ph = 0.591; recessive: OR = 1.524, 95% CI: 1.367–1.699, Ph = 0.332; and dominant: OR = 1.397, 95% CI: 1.295–1.580, Ph = 0.969) as well as African Americans (heterozygote: OR = 1.275, 95% CI: 1.089–1.494, Ph = 0.332 and dominant: OR = 1.226, 95%: CI: 1.056–1.425, Ph = 0.363), as summarized in [Table 2] and [Table 3].
Test of heterogeneity
There was a significant heterogeneity among the overall studies for the rs1051730 (G > A) polymorphism and cancer risk (dominant: Ph = 0.198) (allele: Ph = 0.045) as well as rs8034191 (A > G) polymorphism and cancer risk (recessive: Ph = 0.015). Therefore, randomeffect model was applied to generate 95%CI for genetic model comparison (Ph < 0.05), if not, fixed effect model was utilized.
To assess the stability of all results and sources of heterogeneity, sensitivity analysis was performed through the sequential delete of each eligibleindividual study. For the rs8034191 (A > G) polymorphism, the sensitivity analysis indicated that studies by Amos et al., and Schwartz et al., were responsible for heterogeneity and the heterogeneity was decreased when the studies was removed (GG vs AA: Ph= 0.075, I 2 = 43.9; Ph= 0.08, I 2 = 43.2). For the rs1051730(G > A) polymorphism, sensitivity analysis indicated that the two studies by Chen et al., and Shiraishi et al., were the main origin of heterogeneity. The heterogeneity was decreased when studies were removed (AA + AG/GG: Ph = 0.213, I 2 = 22.8; Ph = 0.144, I 2 = 30.1). However, for the rs16969968(G > A) polymorphism, statistical result showed after sequential removal of each study in homozygous and dominant models and the summary ORs in other genetic models werenot altered, which consider the results were credible and stabile.
Funnel plot and Egger's test were performed to assess the publication bias. The shape of the funnel plot showed the obviously asymmetry in rs16969968 dominant model comparison and Egger's test was used to provide statistical evidence of funnel plot asymmetry (t = 2.38, P = 0.044) [Figure 3]a, which suggested the existence of publication bias in this meta-analysis. To adjust the bias, a trim-and-fill method illustrated by Duval and Tweedie  was utilized [Figure 3]b. The conclusion with or without the trim-and-fill method wasnot changed, which indicated that the results were robust. However, the models ofrs1051730and rs8034191 did not show the publication bias (P > 0.05) [Figure 3]c and [Figure 3]d.
|Figure 3: Begg's funnel plot of Egger's test for publication bias for three polymorphisms. Each circle represents as an independent study for the indicated association. Log (OR), natural logarithm of OR. Horizontal lines mean effect size. (a) Begg's funnel plot of publication bias test for rs16969968 polymorphism. (b) Begg's funnel plot of publication bias test after trim-and-fill method. (c) Begg's funnel plot of publication bias test for rs8034191 (A>G) polymorphism. (d) Begg's funnel plot of publication bias test forrs1051730 (G>A) polymorphism|
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| > Discussion|| |
As we all know, the associations between single nucleotide polymorphisms (SNPs) in the protein-coding genes and cancer risk have been deciphered thoroughly. Cancer susceptibility study concerning the three SNPs hardly has been reported. In the present studies, associations betweenthe polymorphisms (rs1051730 (G > A), rs16969968 (G > A), and rs8034191 (A > G)) and the cancer susceptibility were also estimated. The three polymorphisms may play a critical role in tumorigenesis; development; and prognosis ofvarious kinds of cancers, such as lung cancer, upper aerodigestive, and bladder cancer.,, So we estimate the associations between three polymorphisms and cancer risk by this meta-analysis.
We concluded that the rs1051730 A allele was significantly associated with increased cancer risk based on 12,901 cases and 13,505 controls in overall pooled results from 16 studies. The stratified analysis by the cancer types indicated that the five models of rs1051730 (homozygous, heterozygous, allele, recessive, and dominant) increased risk of lung cancer, which was consistent with the resultsofprevious studies. Meanwhile, no obvious association was shown in other cancers, which was shown that the rs1051730 polymorphism has different effects on the distinct cancers. So far, the results in other cancers have no significance, because of the limited studies., Different inclusion and exclusion criteria should be pondered, which mightinfluence the final pooled results. However, for lung cancer based on 11,936 cases and 11,962 controls, rs1051730 A allele was a protective factor to contradictory with the hypothesis that the rs1051730 A allele was significantly associated with reduced CHRNA3 transcription, which the discrepancy might result from different mechanisms of the carcinogenesis. The varied mechanisms could have different effects on rs1051730 (G > A) polymorphism resulting in different results. And then stratified analysis by race showed increased cancer risk was found in several populations, which may be controlled by environmental exposure and genetic background, but the same race in different model has different results. The five models of Caucasians have obviously increased the risk of cancer, African Americans have significance in heterozygous and dominant model and Asian in heterozygous, allele and dominant models have obviously increased the risk of cancer. Different results between African and Asian maybe on account of small sample sizes (1,417 cases and 1,720 controls in African and 3,013 cases and 2,768 controls in Asian) or different frequency of rs1051730 A allele variant in the meta-analysis.
The present meta-analysis evaluated the associations between the rs8034191 (A > G) polymorphisms of AGPHD1 and cancer risk, and in further analysis, this meta-analysis stratified by ethnicity, cancer type, and source of controls in five models. The previous study showed rs8034191 (A > G) polymorphisms were discernible association with the increased cancer risk in allele and dominant models. This study also showed that rs8034191 was closely associated with cancer risk in overall pooled ORs among all models. Cancer type by the subgroup analysis indicated that the increased cancer risk was found in lung and bladder cancer, which was new discovery. However, no significant association was observed in pancreatic cancers, which inadequate number of studies and selection bias lead to the condition (487 cases and 974 controls in pancreatic cancer). Meanwhile, Caucasian descendent showed a significantly increased cancer risk among all model (homozygous, heterozygous, allele, recessive, and dominant), so did African American. However, there was no obvious association in Asian based on the small sample sizes (123 cases and 247 controls in Asian). As the described above all, both the genetic background and frequencies of rs8043191 G allele in the diverse ethnicity contributed to the results, but only 12 studies enrolled in this meta-analysis. Large case-control studies, unbiased and well-designed should be performed to acquire a more accurately association between rs8034191 (A > G) polymorphism and the cancer risk on account of a large size of populations for the three races.
As for rs16969968 (G > A) polymorphism, there has been no meta-analysis concerning the association between rs16969968 (G > A) and cancer risk until now. It has reported that CHRNA5 gene was detected a strong upregulation in both tumor tissues  and NSCLC cell lines. But our study showed that rs16969968 polymorphism had no association with cancer risk in the overall pooled ORs among the five models, which was inconsistent with the previous study. By subgroup analysis with cancer type indicated that no significance was found in different cancers. What's more, there was not significantly associate with the three races (Caucasian, African American, and Asian), too. In addition, there were only 10 studies enrolled in this meta-analysis, which might affect the result by the small amount studied. In order to obtain a more accurate result, both more well-designed and related studies should be urged to further clarify the connection with rs16969968 (G > A) polymorphism and cancer risk.
Many limitations of our meta-analysis should be recognized. First, all the eligible studies were confined to paper in English, so some studies were lost due to not in English, but corresponded with inclusion criteria. Second, in the subgroup analysis, the number of cases and controls was quite small in the different cancers, and then not owing the insufficient statistical power to get the real association. Third, the control was not uniformly defined. Many of them may be patients, regardless of the fact that the healthy populations were the dominating source of controls. Fourth, the publication bias, which was detected in rs16969968 (G > A) polymorphism, we didnot detect might appear in the others polymorphisms owing to the small amount of the studies. Finally, our data had to interpret with the caution due to basing on the unadjusted estimates, and then further study was conducted to confirm our unadjusted estimates.
In brief, we performed our meta-analysis to evaluate the association between the three polymorphism and cancer risk. Despite of these above limitations, it was a significantly increased risk between rs1051730 (G > A) polymorphism and lung cancer risk, as well as the obviously increased cancer risk among Caucasians, African Americans, and Asian. Moreover, the rs8034191 (A > G) polymorphisms also had a significant association with lung cancer among the five models. In addition, the rs16969968 polymorphism has no association with cancer risk in the overall pooled ORs among the five models. However, it is essential to conduct larger trials by using the homogeneous cancer patients, well-matched controls, and standardized unbiased design. Above all, both gene–gene interactions and gene–environment should be taken into account in this meta-analysis to get us better and comprehensive evaluation of the three polymorphisms and cancer risk.
| > Acknowledgment|| |
This project was supported by grants from The National Nature Science Foundation of China (no. 81172141, 81200401).
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[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2], [Table 3]