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Role of TNF-[alpha] -308G/A gene polymorphism in gastric cancer risk: A case control study and meta-analysis.

ABSTRACT

Background/Aims: In the Chinese population, gastric cancer (GC) is ranked as the third most common type of cancer. Although the exact etiology of GC development is unclear, several factors, including genetic and environmental, have been identified as risk factors. Variations in cytokine genes and their receptors have been related to a higher risk of GC. A single nucleotide polymorphism in the promoter region of tumor necrosis factor-[alpha] (TNF-[alpha]) (-308G>A) has been associated with a higher risk of GC and in the present study we evaluated its possible association with GC in a Chinese cohort. In addition, we performed a meta-analysis to draw a firm conclusion about the association between TNF-[alpha] gene polymorphisms and GC.

Materials and Methods: We enrolled 400 Chinese GC patients and matched healthy controls hailing from similar geographical areas. The TNF-[alpha] -308G/A polymorphism was genotyped by allele-specific polymerase chain reaction (AS-PCR). For the meta-analysis, earlier published articles were searched and eligible studies were included.

Results: Prevalence of the heterozygous mutant (GA) and minor allele (A) were significantly higher in GC cases compared to healthy controls (GA: p<0.0001, odds ratio (OR)=4.90; A: p<0.0001, OR=2.84). A total of 36 eligible studies including the present report, encompassing of 8353 GC patients and 12099 controls, were analyzed for the meta-analysis. A significant association of the TNF-[alpha] polymorphism (-308G>A) with susceptibility to GC was only found in the Caucasian population (A vs G: p=0.001; AA vs GG: p=0.01; AG vs GG: p<0.0001; AA vs AG+GG: p=0.01; AA+AG vs p=0.003).

Conclusion: The results of the present case control study and meta-analysis showed that associations between TNF-a variants with susceptibility to GC development is population and ethnic specific.

Keywords: Gene polymorphism, TNF-[alpha], gastric cancer, meta-analysis, association, Chinese

INTRODUCTION

In China, gastric cancer (GC) ranks third as the most common form of cancer. In the year 2005, 400,000 new cases and 300,000 deaths due to GC in China have been reported (1). The age standardized incidence for men and women was 37.1 and 17.4, respectively, and the mortality rate was 32.7 for men and 15 for women in 2005 (1). The incidence of GC varies among different populations and ethnicity. Although the exact etiology of GC development is unclear, several factors such as host genetics and the environment are believed to play a major role in pathogenesis. The importance of diet including consuming fruit and vegetables, smoking habits, and alcohol consumption have been shown to modulate disease severity (2,3). Infection with Heli-cobacter pylori is also a major cause of non-cardia and chronic GC. Although a limited number of infected humans (<1%) develops GC, the contribution of H. pylori infection to GC cannot be ruled out because it enhances the development of chronic gastritis to GC through various clinical phenotypes (atrophic gastritis, intestinal metaplasia, and dysplasia) in a sequential manner.

Immune responses to H. pylori has been well investigated. Various cytokines produced in response to H. pylori infection are intended to clear the microbes. However, a dual function of certain pro-inflammatory molecules, including tumor necrosis factor-[alpha] (TNF-[alpha]) have been demonstrated; optimum levels help in clearance of microbes and in contrast an excessive level is associated with chronic inflammation. A role of TNF-[alpha] in GC has been well characterized. Elevated TNF-[alpha] induces inflammation in gastric mucosa, one of the important steps toward GC development (4). Several in vitro studies have demonstrated the induction of TNF-[alpha] production by H. pylori and inhibition of gastric acid secretion reveling the importance of TNF-[alpha] in GC pathogenesis (5,6).

TNF-[alpha] is located on the small arm of chromosome 6. So far, 106 single nucleotide polymorphisms (SNPs) in TNF-[alpha] have been reported (https://www.ncbi.nlm.nih.gov/SNP/snp_ref cgi?locusId=7124). However, certain gene polymorphisms in the regulatory region of TNF-[alpha], which correlate with the plasma level of TNF-[alpha], is point of interest for most researchers. Although several polymorphisms in the promoter region of TNF-[alpha] such as -238G>A, -308G>A, -857C>T, -863C>A, and -1031T>C have been shown to regulate TNF-[alpha] levels, the results are not consistent. An association of TNF-[alpha] -308G>A (rs1800629) with susceptibility/resistance to GC development has been widely investigated and yielded conflicting results. Studies including GC patients and controls from China (7-14), Brazil (15,16), Portugal (17,18), United States (19), Poland (20), South Korea (21-23), Honduras (24), Italy (25), Colombia (26), and Japan (27) have been associated with susceptibility to GC development. However, other studies including those from India (28), Brazilian (29,30), Romania (31), Spain (32,33), South Korea (34,35), China (36), Mexico (37), Germany (38) and Finland (39) failed to show a possible link between the TNF-[alpha] (-308G>A) polymorphism and GC. These observations highlight the necessity for a population-based investigation into the possible link between the TNF-[alpha] polymorphism (-308G>A) and GC. In the present study, we conducted a case control study followed by a meta-analysis to draw a firm conclusion on the role of this TNF-[alpha] polymorphism in GC.

MATERIALS AND METHODS

Patients and Controls

We enrolled 400 GC patients that were reported/referred to the Department Of Gastroenterology, Beijing Chaoyang Hospital, from 2012 to 2016. This study was approved by the ethics committee of Beijing Chaoyang Hospital, China (Approval No: TCX13461). All patients' diagnosis was confirmed endoscopically and histopathologically. A total of 400 healthy individuals from similar geographical areas without any history of gastric or any other form of cancer, gastritis, or gastric ulcers, were included as controls. Information about age, sex, smoking habits, and drinking habits were also collected from each participant.

Approximately 5 mL of intravenous blood was collected from patients and controls. H. pylori infection status was screened by Enzyme Linked Immunosorbent Assay (ELISA) as instructed by the manufacturer. The protocol was approved by the Institutional Ethical Committee and written informed consent was obtained from all participants.

DNA Extraction and Genotyping of TNF-[alpha] (-308G>A) Polymorphism

DNA was extracted by using a QIAamp DNA Blood Mini Kit (QIAGEN, USA) according to the manufacturer's protocol. Extracted DNA was stored at -70 degree Celsius until used for genotyping. TNF-[alpha] -308G>A polymorphism was typed by AS-PCR as described previously (28,40). Around 20% of samples were chosen randomly and subjected to direct sequencing and those were found to be absolute concordant with the AS-PCR method.

Literature Search for Meta-Analysis

Two authors, LD and RG, independently searched various databases such as PubMed (Medline), EMBASE and Google Scholar with the following key words: 'Tumor Necrosis Factor' or TNF-[alpha] or TNF gene polymorphism and Gastric Cancer or GC (last updated on October 2016). Any discrepancy or disagreement about inclusion was resolved by group discussion. All extracted studies were investigated by their titles, abstracts, and we screened appropriate publications based on predetermined inclusion and exclusion criteria.

Inclusion and Exclusion Criteria

The following inclusion-exclusion criteria were selected for the present study: a) all studies must be case-controls that investigated the relationship between TNF-[alpha] -308G>A polymorphism and GC; b) should include confirmed GC patients and appropriate controls; and d) must have reported genotype and allele frequency. Reports were excluded based upon the following criteria: a) duplicated or overlapping publication, b) study involving only GC cases and devoid of controls, c) without genotype or allele frequency data, d) a review, abstract, or case report.

Data Extraction

Data from each eligible study were extracted such as publication year, first author name, sampling area, ethnicity, source of samples, number of cases and controls, genotype frequencies, and reported associations. Disagreements or discrepancies were resolved by discussion.

Statistical Analysis

The genotype and allele frequency was calculated by direct counting and their distributions among cases and controls were analyzed by Fisher's exact test. P<0.05 was defined as statistically significant. For meta-analysis, Comprehensive meta-analysis (CMA) V.2 was employed for calculation of pooled odds ratios (ORs) and 95% confidence interval (CIs). Heterogeneity among included studies for meta-analysis was analyzed by the Q-test and [I.sup.2] statistics. [I.sup.2] values range from 0% to 100%, where a value of 0% indicates no significant observed heterogeneity and larger values indicate an increasing degree of heterogeneity. Based on the heterogeneity results, a random or fixed-effects model was employed for derivation of pooled odds ratio, p value, and 95% CI. Publication bias was investigated by Egger's regression analysis and construction of funnel plots.

RESULTS

Baseline Characteristics of Patients and Controls

Out of 400 GC patients, 70% (n=284) were men and 30% (n=116) were women. Since the present study was a matched case-control study, we enrolled a similar number of health men and women as controls. The mean age of GC patients and healthy controls were 56.3 and 54.5 years, respectively. Smoking and drinking alcohol habits of both patients and controls were comparable (data not shown). H. pylori infection was more prevalent in GC cases compared to controls.

Association of -308G>A Polymorphism with Gastric Cancer

TNF-[alpha] -308G>A polymorphism was genotyped by AS-PCR. To explore any relationship between the promoter -308G>A polymorphism and GC, allele and genotype distributions were compared among patients and healthy controls. As shown in Table 1, heterozygous mutant (GA) and minor alleles (A) were more prevalent in GC cases compared to healthy subjects (GA: p<0.0001, OR= 4.90, 95% CI= 3.48 to 6.88; A: p<0.0001, OR=2.84, 95% CI=2.16 to 3.73) indicating a possible association between the TNF-[alpha] -308G>A variant and GC susceptibility.

Studies Included in the Meta-Analysis

A meta-analysis is a powerful tool that pools similar studies to draw a firm conclusion. In the primary search with various online tools, we obtained 238 articles and after detailed evaluation of the titles, abstracts, removal of duplicates, and careful reading of the full text of the articles, 35 eligible articles were screened for the meta-analysis. Furthermore, data from the present study was also included leading to a total of 36 research publications, including 8353 confirmed GC patients and 12099 control subjects. The major characteristics of the selected studies are shown in Table 2. Other relevant extracted data such as genotype and minor allele frequency and Hardy-Weinberg equilibrium (HWE) probability values of control genotypes are shown Table 3. Out of 36 studies included for the present meta-analysis, the genotype distribution in eight studies deviated from HWE.

Sensitivity Analysis

Sensitivity analysis was performed by eliminating each individual study to investigate their effect on the pooled result. The results of the sensitivity analysis revealed that none of the included studies disproportionately influenced the results of the meta-analysis (data not shown).

Results of the Meta-Analysis

A total of 36 case-control studies including 12099 controls and 8353 confirmed GC cases were included in this meta-analysis. The Q test and [I.sup.2] statistics revealed heterogeneity among the included studies and thus a random-effects model was employed for allele and genotype comparison (Table 4). As shown in Figure 1, the allele (A vs G) and genotype comparison (AA vs GG) model showed a statistical significant role of the TNF-[alpha] -308G>A polymorphism in susceptibility to GC (A vs. G: p=0.04; AA vs. GG: p=0.04). However, other genetic comparison models failed to show a possible association (AG vs. GG: p=0.24; AA+AG vs. GG: p=0.09; AA vs. GG+AG: p=0.08).

Analysis based on ethnic group has been advised for metaanalysis; thus, in the present study we grouped all published literature including the present report into two broad groups based on ethnicity. a) Asian and b) Caucasian. A total of 18 case-control studies were from Asian ethnic backgrounds comprising 5957 controls and 4987 GC cases. Egger's regression analysis showed an absence of publication bias and all comparison models revealed heterogeneity among the included studies; thus, we employed a random-effects model for construction of a forest plot (Table 5). As shown in Figure 2, meta-analysis failed to show any possible association of the TNF-[alpha] -308G>A polymorphism with GC development in Asian ethnic groups (A vs. G: p=0.30; AA vs. GG: p=0.32; AG vs. GG: p=0.32; AA+AG vs. GG: p=0.27; AA vs. GG+AG: p=0.55).

Sixteen studies comprising 5862 controls and 3138 GC patients were from a Caucasian background. Egger's regression analysis revealed no publication bias in the studies considered for meta-analysis; however, two genetic models showed (A vs. G and AA+AG vs. GG) heterogeneity among the included studies (Table 6). Based on the Q statistics and [I.sup.2] value, we used a random-effects model for calculation of an OR and 95% CI. As shown in Figure 3, the TNF-[alpha] -308G>A polymorphism was significantly associated with GC in all genetic comparison models (A vs. G: p=0.001; AA vs. GG: p=0.01; AG vs. GG: p=0.00; AA+AG vs. GG: p=0.003; AA vs. GG+AG: p=0.01).

In the present investigation, a common polymorphism (-308G>A) in TNF-[alpha] was genotyped in a Chinese cohort and its association with development of GC was investigated. In addition, we searched the previous published literature on for the association of this polymorphism with GC susceptibility and performed a meta-analysis, including data from the present study. The results of the hospital-based case-control study revealed an association between heterozygous mutants and minor allele with a susceptibility to GC. Furthermore, the metaanalysis showed a link between TNF-[alpha] (-308G>A) variants with GC predisposition.

Several reports on the distribution of the TNF-[alpha] (-308G>A) genotype in healthy controls have been reported for Chinese populations. In the present study, controls were recruited from Beijing and genotyping data found 15% had heterozygous mutations and 3.5% had homozygous mutations. This observation is comparable with previous reports including healthy controls from Hubei province (9,36), Henan province (13), Taiwan (11,12) and Nanjing (8). However, other reports from Beijing (10) and Hubei province (7) showed a lower prevalence of heterozygous mutations (8-9%). Interestingly, the distribution of TNF-[alpha] (-308G>A) genotypes deviated from HWE in all earlier reports including the present study in a Chinese population (7-13,36). Deviation of a genotype distribution from HWE has been attributed to genotyping error, population stratification, or selection pressure. The present study and earlier reports enrolled controls from an ethnic group and employed robust genotyping methods (7-13,36). Deviation from HWE is possibly due to prevalence of various infectious diseases in Chinese populations, which applies selection pressure on the human genome (41).

Various studies have been conducted in different populations of China to identify a possible association of TNF-[alpha] -308G>A with the development of GC (7-13,36). Most of the studies showed an association of variants with susceptibility to GC. However, a study including patients and controls from Hubei province failed to demonstrate such an association. Consistent with earlier observations, we observed a significant association of heterozygotes and minor allele with GC susceptibility.

The exact mechanism whereby TNF-[alpha] -308G>A variants are predisposed to GC is unclear. The minor allele for TNF-[alpha] (-308G>A) polymorphism increases binding of transcription factors and elevated mRNA production compared to the major allele (G) (42,43). In vitro stimulation of peripheral blood mononuclear cells (PBMC) derived from heterozygous subjects (GA) with li-popolysaccharide displayed higher levels of TNF-[alpha] than those of wild type individuals (GG) (44). This minor allele is possibly linked with a higher transcription rate and that may induce a higher rate of inflammation in subjects harboring the minor allele (45). Furthermore, higher levels of TNF-[alpha] inhibit secretion of gastric acid and that may lead to spreading of H. pylori to the corpus and subsequently development of GC (6).

Meta-analysis is a powerful investigative method that combines similar studies to draw a firm conclusion. Since the association of the TNF-[alpha] (-308G>A) polymorphism with a predisposition to GC is unclear, a meta-analysis was performed combining earlier reports with the data of the present study. Results of the meta-analysis revealed a significant association of the TNF-[alpha] (-308G>A) polymorphism with GC susceptibility. Overall, the analysis showed subjects with the minor allele (A) or homozygous mutation (AA) had a 1.17 and 1.36 fold higher chance of development of GC, respectively. These observations are consistent with earlier meta-analyses (46,47). In addition, we observed an association between TNF-[alpha] polymorphisms with GC susceptibility in Caucasians but not in Asian ethnic groups, indicating a race-specific link between TNF-[alpha] -308G>A and GC susceptibility. Several reports have shown ethnic-specific genetic associations of variants with diseases and it has been advised to investigate genetic associations being aware of ethnicity.

Consistent with this, a recent study showed an association for a RAD51 135G>C substitution with susceptibility to breast cancer in Caucasians but not in East-Asians (48). Earlier meta-analyses by two different group also reported similar observations in the year 2014 (46,49). In addition, several reports including only Caucasian ethnic group for analysis demonstrated a possible association with GC susceptibility, further strengthening our observations (50-52). However, the very first meta-analysis including only 15 studies had shown an opposite association, i.e., TNF-[alpha] -308G>A variants were linked with GC susceptibility in Asians but not in Caucasians (47). Our present meta-analysis has several advantages over previous reports. The most recent meta-analyses were reported in the year 2014 (46,49,51). In the present study, five recent case-control studies investigating the role of TNF-[alpha] -308G>A in GC susceptibility were included, leading to an analysis including a much larger number of cases (n=8353) and controls (n=12099) (13,14,16,28,29).

There are several discrepancies between the results of the present case-control study and the results of the meta-analysis. First, the present case-control study revealed a significant association of heterozygous mutants (GA) with susceptibility to GC but the combined meta-analysis in the Caucasian population showed susceptibility of homozygous mutants to GC development. One possible explanation could be a lower prevalence of homozygous mutants in the studied population (3-3.5%). Because the meta-analysis combined several similar studies from different populations, the total number of studied subjects increased and possibly attained significant power to show an association, if any. Second, heterozygous mutants were associated with susceptibility to GC in the Chinese population but the meta-analysis failed to show any such link between the TNF-[alpha] genotype and GC in the Asian population. Because the Asian population included in the meta-analysis consist of various studies reported from China (n=11), India (n=1), North Korea (n=3), South Korea (n=1), and Japan (n=1), these diverse sample origins may be the reason for the discordant observations between the case-control study and the meta-analysis. Interestingly, out of 11 Chinese studies enrolled in the present meta-analysis, only one report failed to demonstrate an association between the TNF-[alpha] -308G>A polymorphism and GC.

In conclusion, TNF-[alpha] heterozygous and minor alleles are associated with susceptibility to GC in the studied Chinese population. However, a combined meta-analysis and studies on Asian subjects failed to demonstrate a possible association of the TNF-[alpha] allele with GC development. Interestingly, meta-analysis of the TNF-[alpha] (-08G>A) polymorphism in Caucasians revealed a significant association with GC. We conclude that the TNF-[alpha] (-308G>A) polymorphism is linked to a GC predisposition in a population-specific manner. In future, studies from different populations including larger samples size are essential to establish a role of TNF-[alpha] in GC.

Ethics Committee Approval: Ethics committee approval was received for this study from the ethics committee of Beijing Chaoyang Hospital, China (Decision No: TCX13461).

Informed Consent: Written informed consent was obtained from patients who participated in this study.

Peer-review: Externally peer-reviewed.

Author contributions: Concept - L.D., R.G.; Design - L.D., R.G.; Supervision - R.G.; Resource - L.D., R.G.; Materials - L.D., R.D.; Data Collection and/or Processing - L.D., R.G.; Literature Search - L.D., R.G.; Writing - L.D., R.G.; Critical Reviews - L.D., R.G.

Acknowledgements: Authors would like to thank patients and controls participated in the present study.

Conflict of Interest: No conflict of interest was declared by the authors.

Financial Disclosure: This study was supported by Beijing Chaoyang Hospital (Project No: YX132267).

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Li-Chuan Du, Ru Gao

Department of Gastroenterology, Beijing Chaoyang Hospital, Beijing, China

Cite this article as: Du LC, Gao R. Role of TNF-[alpha] -308G/A gene polymorphism in gastric cancer risk: a case control study and metaanalysis. Turk J Gastroenterol 2017; 28: 272-82.

Address for Correspondence: Ru Gao E-mail: rugao145@hotmail.com

Received: January 15, 2017

Accepted: March 24, 2017

[c] Copyright 2017 by The Turkish Society of Gastroenterology * Available online at www.turkjgastroenterol.org
Table 1. Distribution of TNF-[alpha] (-308G>A) polymorphism in gastric
cancer patients and healthy controls

TNF-[alpha] -308G>
A genotype          HC          GC                 OR
and allele          (n=400)     (n=400)   p        (95% CI)

Genotype                                  ref      1
GG                  326 (81.5)  204 (51)
GA                   60 (15)    184 (46)  <0.0001  4.90 (3.48 to 6.88)
AA                   14 (3.5)    12 (3)    0.53    1.37 (0.62 to 3.02)
Allele
G                   712 (89)    592 (74)  ref      1
A                    88 (11)    208 (26)  <0.0001  2.84 (2.16 to 3.73)

HC: healthy controls; GC: gastric cancer patients; OR: odds ratio; CI:
confidence interval; TNF-[alpha]: tumor necrosis factor-[alpha]

Table 2. Main characteristics of all studies included in the
meta-analysis

First authors and year             Country        Ethnicity   Control

Present study                      China          Asian        400
Bhayal et al. 2013 (28)            India          Asian        229
Xu et al. 2016 (13)                China          Asian        319
de Oliveira et al. 2015 (29)       Brazil         American     240
Zabaglia et al. 2015 (16)          Brazil         American      40
Yu et al. 2014 (14)                China          Asian        300
Hong et al. (test) 2013 (8)        China          Asian        750
Hong et al. (validation) 2013 (8)  China          Asian        936
Burada et al. 2012 (31)            Romania        Caucassian   242
Canedo et al. 2008 (17)            Portugal       Caucassian   713
Crusius et al. 2008 (32)           Spain          Caucassian  1125
El-Omar et al. 2003 (19)           United States  Caucassian   210
Guo et al. 2005 (7)                China          Asian        437
Jang et al. 2001 (34)              South Korea    Asian         92
Fei et al. 2004 (36)               China          Asian        164
Garcia-Gonzalez et al. 2007 (33)   Spain          Caucassian   404
Garza-Gonzalez et al. 2005 (37)    Mexico         Caucassian   215
Glas et al. 2004 (38)              Germany        Caucassian   145
Hou et al. 2007 (20)               Poland         Caucassian   428
Kamangar et al. 2006 (39)          Finland        Caucassian   208
Kim et al. 2006 (21)               South Korea    Asian        461
Lee et al. 2004 (22)               South Korea    Asian        261
Lee et al. 2005 (35)               South Korea    Asian        120
Li et al. 2005 (9)                 China          Asian        264
Lu et al. 2005 (10)                China          Asian        300
Machado et al. 2003 (18)           Portugal       Caucassian   304
Melo et al. 2009 (15)              Brazil         Caucassian   100
Morgan et al. 2006 (24)            Honduras       Caucassian   161
Perri et al. 2005 (25)             Italy          Caucassian   362
Rocha et al. 2005 (30)             Brazil         Caucassian   535
Sugimoto et al. 2007 (27)          Japan          Asian        172
Torres et al. 2004 (26)            Colombia       Caucassian    66
Wu et al. 2002 (12)                China          Asian        220
Wu et al. 2004 (11)                China          Asian        210
Yang et al. 2009 (23)              South Korea    Asian        322
Zambon et al. 2005 (52)            Italy          Caucassian   644

First authors and year             Cases  Type

Present study                       400   ASP
Bhayal et al. 2013 (28)             114   ARMS-PCR
Xu et al. 2016 (13)                 296   RFLP-PCR
de Oliveira et al. 2015 (29)        204   RFLP-PCR
Zabaglia et al. 2015 (16)            24   RFLP-PCR
Yu et al. 2014 (14)                 360   PCR
Hong et al. (test) 2013 (8)         834   TaqMan
Hong et al. (validation) 2013 (8)  1060   TaqMan
Burada et al. 2012 (31)             105   TaqMan
Canedo et al. 2008 (17)             508   TaqMan
Crusius et al. 2008 (32)            236   Real-time PCR
El-Omar et al. 2003 (19)            314   TaqMan
Guo et al. 2005 (7)                 264   RFLP-PCR
Jang et al. 2001 (34)                52   RFLP-PCR
Fei et al. 2004 (36)                 56   PCR
Garcia-Gonzalez et al. 2007 (33)    404   TaqMan
Garza-Gonzalez et al. 2005 (37)      63   RFLP-PCR
Glas et al. 2004 (38)                88   RFLP-PCR
Hou et al. 2007 (20)                305   TaqMan
Kamangar et al. 2006 (39)           112   TaqMan
Kim et al. 2006 (21)                237   RFLP-PCR
Lee et al. 2004 (22)                341   PCR
Lee et al. 2005 (35)                122   RFLP-PCR
Li et al. 2005 (9)                   59   RFLP-PCR
Lu et al. 2005 (10)                 250   DHPLC-PCR
Machado et al. 2003 (18)            287   SSCP-PCR
Melo et al. 2009 (15)                30   RFLP-PCR
Morgan et al. 2006 (24)             168   TaqMan
Perri et al. 2005 (25)              184   RFLP-PCR
Rocha et al. 2005 (30)              161   RFLP-PCR
Sugimoto et al. 2007 (27)           105   RFLP-PCR
Torres et al. 2004 (26)              44   PCR
Wu et al. 2002 (12)                 150   Direct Sequencing
Wu et al. 2004 (11)                 204   Direct Sequencing
Yang et al. 2009 (23)                83   SNaPshot
Zambon et al. 2005 (52)             129   TaqMan

First authors and year             Association

Present study                      Yes
Bhayal et al. 2013 (28)            No
Xu et al. 2016 (13)                Yes
de Oliveira et al. 2015 (29)       No
Zabaglia et al. 2015 (16)          Yes
Yu et al. 2014 (14)                Yes
Hong et al. (test) 2013 (8)        Yes
Hong et al. (validation) 2013 (8)  Yes
Burada et al. 2012 (31)            No
Canedo et al. 2008 (17)            Yes
Crusius et al. 2008 (32)           No
El-Omar et al. 2003 (19)           Yes
Guo et al. 2005 (7)                Yes
Jang et al. 2001 (34)              No
Fei et al. 2004 (36)               No
Garcia-Gonzalez et al. 2007 (33)   No
Garza-Gonzalez et al. 2005 (37)    No
Glas et al. 2004 (38)              No
Hou et al. 2007 (20)               Yes
Kamangar et al. 2006 (39)          No
Kim et al. 2006 (21)               Yes
Lee et al. 2004 (22)               Yes
Lee et al. 2005 (35)               No
Li et al. 2005 (9)                 Yes
Lu et al. 2005 (10)                Yes
Machado et al. 2003 (18)           Yes
Melo et al. 2009 (15)              Yes
Morgan et al. 2006 (24)            Yes
Perri et al. 2005 (25)             Yes
Rocha et al. 2005 (30)             No
Sugimoto et al. 2007 (27)          Yes
Torres et al. 2004 (26)            Yes
Wu et al. 2002 (12)                Yes
Wu et al. 2004 (11)                Yes
Yang et al. 2009 (23)              Yes
Zambon et al. 2005 (52)            Yes

PCR: polymerase chain reaction; ASP: allele specific PCR; RFLP:
restriction fragment length polymorphism

Table 3. Genotypic distribution of the TNF-308G/A gene polymorphism
included in the meta-analysis

                                             Controls
Authors and year (ref)                  GG   GA   AA   MAF

Present Study                           326   60   14  0.22
Bhayal et al. 2013 (28)                  76  128   25  0.388
de Oliveira et al. 2015 (29)            167   69    4  0.160
Yu et al. 2014 (14)                     251   38   11  0.1
Hong et al. (test) 2013 (8)             589  154    7  0.112
Hong et al. (validation) 2013 2013 (8)  746  179   11  0.107
Xu et al. 2016 (13)                     237   50   32  0.178
Zabaglia et al. 2015 (16)                33    4    3  0.125
Burada et al. 2012 (31)                 196   44    2  0.099
Canedo et al. 2008 (17)                 544  169    0  0.118
Crusius et al. 2008 (32)                820  274   31  0.149
El-Omar et al. 2003 (19)                152   52    6  0.152
Gou et al. 2005 (7)                     391   40    6  0.059
Jang et al. 2001 (34)                    85    7    0  0.038
Fei et al. 2004 (36)                    143   20    1  0.067
Garcia-Gonzalez et al. 2007 (33)        320   77    7  0.112
Garza-Gonzalez et al. 2005 (37)           1   35  179  0.913
Glas et al. 2004 (38)                   105   36    4  0.151
Hou et al. 2007 (20)                    304  109   15  0.162
Kamangar et al. 2006 (39)               154   52    2  0.134
Kim et al. 2006 (21)                    400   59    2  0.068
Lee et al. 2004 (22)                    218   42    1  0.084
Lee et al. 2005 (35)                    103   17    0  0.070
Li et al. 2005 (9)                      228   34    2  0.071
Lu et al. 2005 (10)                     274   24    2  0.046
Machado et al. 2003 (18)                231   69    4  0.126
Melo et al. 2009 (15)                    86   13    1  0.075
Morgan et al. 2006 (24)                 149   12    0  0.037
Perri et al. 2005 (25)                  290   65    7  0.109
Rocha et al. 2005 (30)                  399  123   13  0.139
Sugimoto et al. 2007 (27)               169    3    0  0.008
Torres et al. 2004 (26)                  56   10    0  0.075
Wu et al. 2002 (12)                     180   27   13  0.120
Wu et al. 2004 (11)                     171   26   13  0.123
Yang et al. 2009 (23)                   288   34    0  0.052
Zambon et al. 2005 (52)                 496  138   10  0.122

                                             Cases           HWE
Authors and year (ref)                  GG   GA   AA  MAF    p

Present Study                           204  184  12  0.26   0.000
Bhayal et al. 2013 (28)                  32   76   6  0.385  0.007
de Oliveira et al. 2015 (29)            138   63   3  0.169  0.296
Yu et al. 2014 (14)                     325    6  29  0.088  0.000
Hong et al. (test) 2013 (8)             690  139   5  0.089  0.376
Hong et al. (validation) 2013 2013 (8)  895  156   9  0.082  0.943
Xu et al. 2016 (13)                     142   66  88  0.408  0.000
Zabaglia et al. 2015 (16)                17    4   3  0.208  0.000
Burada et al. 2012 (31)                  78   26   1  0.133  0.784
Canedo et al. 2008 (17)                 330  178   0  0.175  0.000
Crusius et al. 2008 (32)                170   64   2  0.144  0.165
El-Omar et al. 2003 (19)                201   87  26  0.221  0.548
Gou et al. 2005 (7)                     240   20   4  0.053  0.000
Jang et al. 2001 (34)                    46    4   2  0.076  0.704
Fei et al. 2004 (36)                     53    3   0  0.026  0.743
Garcia-Gonzalez et al. 2007 (33)        309   84  11  0.131  0.350
Garza-Gonzalez et al. 2005 (37)           0    8  55  0.936  0.607
Glas et al. 2004 (38)                    66   19   3  0.142  0.669
Hou et al. 2007 (20)                    186   98  21  0.229  0.186
Kamangar et al. 2006 (39)                86   23   3  0.129  0.292
Kim et al. 2006 (21)                    199   34   4  0.088  0.911
Lee et al. 2004 (22)                    297   43   1  0.065  0.493
Lee et al. 2005 (35)                    112   10   0  0.040  0.403
Li et al. 2005 (9)                       55    4   0  0.033  0.559
Lu et al. 2005 (10)                     214   36   0  0.072  0.080
Machado et al. 2003 (18)                179  105   3  0.193  0.649
Melo et al. 2009 (15)                    24    5   1  0.116  0.528
Morgan et al. 2006 (24)                 151   17   0  0.050  0.623
Perri et al. 2005 (25)                  152   30   2  0.092  0.145
Rocha et al. 2005 (30)                  120   37   4  0.139  0.343
Sugimoto et al. 2007 (27)               101    4   0  0.019  0.908
Torres et al. 2004 (26)                  41    3   0  0.034  0.505
Wu et al. 2002 (12)                     114   27   9  0.15   0.000
Wu et al. 2004 (11)                     163   29  12  0.129  0.000
Yang et al. 2009 (23)                    75    8   0  0.048  0.317
Zambon et al. 2005 (52)                  95   31   3  0.143  0.909

HWE: Hardy Weinberg Equilibrium; MAF: minor allele frequency

Table 4. Statistics to test publication bias and heterogeneity in the
meta-analysis

             Egger's regression analysis
Comparisons  Intercept  95% Confidence Interval  p

A vs G       -0.75      -2.54 to 1.04            0.40
AA vs GG     -1.17      -2.14 to -0.20           0.01
AG vs GG     -0.77      -2.47 to 0.92            0.35
AA+AG vs GG  -0.59      -2.38 to 1.19            0.50
AA vs AG+GG  -1.05      -2.06 to -0.04           0.04

             Heterogeneity analysis
Comparisons  Q       [P.sub.heterogeneity]  [I.sup.2] (%)

A vs G       176.88  0.000                  80.21
AA vs GG      51.21  0.007                  43.37
AG vs GG     176.07  0.000                  80.12
AA+AG vs GG  180.95  0.000                  80.65
AA vs AG+GG   50.21  0.009                  42.25


Comparisons  Model used for meta-analysis

A vs G       Random
AA vs GG     Random
AG vs GG     Random
AA+AG vs GG  Random
AA vs AG+GG  Random

Table 5. Statistics to test publication bias and heterogeneity in
meta-analysis (Asian)

                        Egger's regression analysis
Comparisons  Intercept  95% Confidence Interval  p

A vs G       -1.04      -4.08 to 1.98            0.47
AA vs GG     -1.24      -2.83 to 0.34            0.11
AG vs GG     -0.29      -3.20 to 2.62            0.83
AA+AG vs GG  -0.64      -3.76 to 2.46            0.66
AA vs AG+GG  -1.06      -2.66 to 0.54            0.17

             Heterogeneity analysis
Comparisons  Q       [P.sub.heterogeneity]  [I.sup.2] (%)

A vs G       131.65  0.000                  87.08
AA vs GG      35.73  0.001                  60.82
AG vs GG     111.60  0.000                  84.76
AA+AG vs GG  127.23  0.000                  86.63
AA vs AG+GG   35.16  0.001                  60.19


Comparisons  Model used for meta-analysis

A vs G       Random
AA vs GG     Random
AG vs GG     Random
AA+AG vs GG  Random
AA vs AG+GG  Random

Table 6. Statistics to test publication bias and heterogeneity in the
meta-analysis (Caucasians)

             Egger's regression analysis
Comparisons  Intercept  95% Confidence interval  p

A vs G       -1.19      -2.94 to 0.54            0.16
AA vs GG     -0.97      -2.48 to 0.53            0.18
AG vs GG     -1.43      -2.93 to 0.05            0.05
AA+AG vs GG  -1.30      -2.90 to 0.29            0.10
AA vs AG+GG  -0.89      -2.52 to 0.72            0.24

             Heterogeneity analysis
Comparisons  Q      [P.sub.heterogeneity]  [I.sup.2] (%)

A vs G       25.31  0.04                   40.74
AA vs GG     12.09  0.43                    0.78
AG vs GG     24.40  0.06                   38.74
AA+AG vs GG  25.77  0.04                   41.80
AA vs AG+GG  11.55  0.48                    0.00


Comparisons  Model used for meta-analysis

A vs G       Random
AA vs GG     Fixed
AG vs GG     Fixed
AA+AG vs GG  Random
AA vs AG+GG  Fixed
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Title Annotation:Gastrointestinal Tract; tumor necrosis factor
Author:Du, Li-Chuan; Gao, Ru
Publication:The Turkish Journal of Gastroenterology
Article Type:Report
Geographic Code:9CHIN
Date:Jul 1, 2017
Words:7349
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