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. 2015 Dec 15;8(12):22393–22402.

Association between Interleukin-8-251A/T polymorphism and gastric cancer susceptibility: a meta-analysis based on 5286 cases and 8000 controls

Yafei Zhang 1, Xianling Zeng 2, Hongwei Lu 1, Yiming Li 1, Hong Ji 1
PMCID: PMC4730005  PMID: 26885219

Abstract

Objectives: Published data on the association between Interleukin-8-251A/T polymorphism and gastric cancer (GC) risk are inconclusive. Thus, we conducted a meta-analysis to evaluate the relationship between cyclin D1 G870A polymorphism and GC risk. Methods: We searched PubMed, EMBASE, Web of science and the Cochrane Library up to July 12, 2015 for relevant studies. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to estimate the strength of associations. Results: Twenty-six studies published from 2004 to 2015, with a total of 5286 cases and 8000 controls, were included in this meta-analysis. The pooled results showed that there was significant association between Interleukin-8-251A/T polymorphism and GC risk in any genetic model. In the subgroup analysis by ethnicity, the effects remained in Asians. However, no genetic models reached statistical association in Europeans. The subgroup analysis stratified by Source of controls showed an increased breast cancer risk in hospital-based (HB) studies in any genetic model except recessive model. However, there was no association in any genetic model in population based (PB) studies. When stratifying by Genotyping method, we found statistical association in Non-RFLP (restriction fragment length polymorphism) in any genetic model except heterozygote comparison, the effect was remain in PCR-RFLP in dominant model and heterozygote comparison. Conclusions: This meta-analysis suggests that Interleukin-8-251A/T polymorphism is a risk factor for susceptibility to GC in overall population, especially in Asians, in hospital populations and in Non-RFLP. While, there was no association in Europeans and in general population. Further large scale multicenter epidemiological studies are warranted to confirm this finding.

Keywords: Interleukin-8-251A/T, polymorphism, gastric cancer, susceptibility, meta-analysis

Introduction

Gastric cancer (GC) is one of the most common malignant tumors and the third leading cause of cancer-related death in the word, the 5-year survival rate is low, especially for advanced GC [1,2]. In the majority of developing countries, the incidence of GC is constantly increasing, as well as mortality [3,4]. For most GCs are diagnosed to be advanced stages, early detection seems particularly important [5]. While, the determination of the association between Interleukin-8-251A/T polymorphism and GC risk provides us a promising approach to achieve this goal.

Interleukin-8 is an important member of the chemokine superfamily, which belongs to the CXC chemokine family. Under the stimulation of a variety of factors (such as lipopolysaccharide, IL.1, etc.), many cells can produce IL-8, such as monocytes, endothelial cells and tumor cells [6,7]. In recent years, the expression of IL-8 in tumor cells was significantly increased. It is found that IL-8 can induce the migration and proliferation of endothelial cells to mediate tumor angiogenesis, which can promote tumor [8-10]. Human IL-8 gene is located on fourth chromosome, composed of four exons, three introns, and a proximal promoter region. In the promoter region of IL-8, there is a genetic polymorphism in -251T, which has been reported to be closely associated with altered expression levels of IL-8 through gene transcription regulation [11].

Previous functional studies have reported the association between Interleukin-8-251A/T polymorphism and GC risk, but the results remain inconclusive [12-37]. To clarify the role of Interleukin-8-251A/T polymorphism in GC risk, four meta-analysis on the associations between Interleukin-8-251A/T polymorphism and cancers [38-41]. However, number of their studies included in their meta-analysis about GC is small, and GC is just a small part of their study. In the subgroup of their analyses the sample size is extremely small, and some just no subgroup. Therefore, we decided to carry out a meta-analysis on all eligible case-control studies to make a more precise estimation of the association. Furthermore, we conducted the subgroup analysis by stratification according to the ethnicity, source of controls and genotyping method.

Materials and methods

Literature searching strategy

We searched PubMed, EMBASE, Web of science, the Cochrane Library for relevant studies published before July 12, 2015. The following keywords were used: interleukin*/IL*, variant/genotype/polymorphism/SNP, Gastric/stomach/cardia, cancer/carcinom*/neoplasm*/tumor and the combined phrases for all genetic studies on the association between the Interleukin-8-251A/T polymorphism and GC risk. The reference lists of all articles were also manually screened for potential studies. Abstracts and citations were screened independently by two authors, all the agreed articles need a second screen for full-text reports. The searching was done without restriction on language.

Selection and exclusion criteria

Inclusion criteria: A study was included in this meta-analysis if it meets the following criteria: i) independent case-control studies for humans; ii) the study evaluated the association between Interleukin-8-251A/T polymorphism and GC risk; iii) has available genotype frequencies in cancer cases and control subjects for risk estimate. We excluded comments, editorials, systematic reviews or studies lacking sufficient data. If the publications were duplicated or shared in more than one study, the most recent publications were included. All identified studies were screened by two investigators independently. What’s more, there was no limitation for publication language.

Data extraction and synthesis

We used endnote bibliographic software to construct an electronic library of citations identified in the literature search. All the PubMed, EMBASE, Web of science and the Cochrane Library searches were performed using Endnote; duplicates were found automatically by endnote and deleted manually. All data extraction were checked and calculated twice according to the inclusion criteria listed above by two independent investigators. Data extracted from the included studies were as follows: First author, year of publication, country, ethnicity, Source of controls, Genotyping method, number of cases and controls and evidence of HWE in controls. A third reviewer would participate if some disagreements were emerged, and a final decision was made by the majority of the votes.

Statistical analysis

All statistical analyses were performed using STATA version 11.0 software (StataCorp LP, College Station, TX) and Review Manage version 5.2.0 (The Cochrane Collaboration, 2012). Hardy-Weinberg equilibrium (HWE) was assessed by χ2 test in the control group of each study [42]. The strength of associations between the Interleukin-8-251A/T polymorphism and GC risk were measured by odds ratio (ORs) with 95% confidence interval (CIs). Z test was used to assess the significance of the ORs, I2 and Q statistics was used to determine the statistical heterogeneity among studies. A random-effect model was used if P value of heterogeneity tests was no more than 0.1 (P≤0.1), otherwise, a fixed-effect model was selected [42,43]. Sensitivity analyses were performed to assess the stability of the results. We used Begg’s funnel plot and Egger’s test to evaluate the publication bias [44,45]. The strength of the association was estimated in the allele model, the dominant model, the recessive model, the homozygous genetic model, and the heterozygous genetic model, respectively. P<0.05 was considered statistically significant. We performed subgroup according to Ethnicity, Source of controls, Genotyping method.

Results

Characteristics of included studies

Detailed search procedures are summarized in Figure 1. A total of 2499 references were preliminarily identified at first based on our selection strategy. We also identified 1 paper through other source. After excluding duplicate articles, we reviewed titles and abstracts of all identified studies to exclude those that were clearly irrelevant. Next, the full texts of the remaining articles were examined according to the inclusion and exclusion criteria. Finally, 26 studies [12-37] on Interleukin-8-251A/T polymorphism and GC risk were finally identified in this meta-analysis, including 5286 cases and 8000 controls. The characteristics of the included studies are listed in Table 1. The 26 case-control studies were published between 2004 and 2015, among them, 9 studies were performed in European and 17 in Asians. All studies were case-controlled. 13 studies were hospital-based and 13 were population-based studies.

Figure 1.

Figure 1

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Flow chart of studies selection in this meta-analysis.

Table 1.

Characteristics of the studies included in the meta-analysis

First author Year Country Ethnicity Source of controls Genotyping method Number (case/control) HWE Quality assessment score Published language
Bo [37] 2010 China Asian HB PCR-RFLP 208/190 0.389403209 7 English
Canedo [36] 2008 Portugal European PB Taq Man-PCR 333/693 0.459719109 8 English
Crusius [35] 2008 Caucasia European PB Real-time 236/1139 0.705567725 8 English
de Oliveira [34] 2015 Brazil European HB PCR-RFLP 207/240 0.059480986 7 English
Felipe [33] 2012 Brazil European PB PCR-RFLP 104/196 0.065528611 8 English
Garza-Gonzalez [32] 2007 Mexico European HB ARMS-PCR 78/189 0.538816094 7 English
Kamali-Sarvestani [31] 2006 Iran Asian HB ASO-PCR 19/153 0.797575578 7 English
Kamangar [30] 2006 Finland European PB TaqMan-PCR 112/207 0.054934096 8 English
Kang [29] 2009 Korea Asian PB PCR-RFLP 334/322 0.22569954 8 English
Ko [28] 2009 Korea Asian PB Snapshot 81/308 0.155354832 8 English
Lee [27] 2005 Taiwan Asian HB PCR-RFLP 470/308 0.14303682 7 English
Liu [12] 2009 China Asian HB Taq Man-PCR 138/137 0.145093518 7 Chinese
Lu [26] 2005 China Asian PB PCR-DHPLC 250/300 0.515848398 8 English
Ohyauchi [25] 2005 Japan Asian HB Direct 212/346 0.549317592 7 English
Pan [24] 2014 China Asian HB SBE 308/308 0.715713139 7 English
Qadri [23] 2014 India Asian PB PCR-CTPP 130/200 0.066109789 8 English
Ramis [22] 2015 Brazil European PB PCR-RFLP 9/38 0.691258974 8 English
Savage [21] 2004 China Asian PB SBE 88/429 0.884813104 8 English
Savage [20] 2006 Poland European PB Taqman or MGB Eclipse 287/428 0.391465014 8 English
Shirai [19] 2006 Japan Asian HB PCR-RFLP 181/468 0.830460367 7 English
Song [18] 2009 China Asian HB PCR-RFLP 125/140 0.720157181 7 English
Taguchi [17] 2005 Japan Asian HB PCR-RFLP 396/252 0.994013656 7 English
Vinagre [16] 2011 Brazil European HB PCR-RFLP 102/103 0.150502334 7 English
Ye [15] 2009 Korea Asian HB PCR-RFLP 153/206 0.552934095 7 English
Zeng [14] 2005 China Asian PB PCR-RDB 206/196 0.02187681 8 Chinese
Zhang [13] 2010 China Asian PB PCR-RFLP 519/504 0.75413968 8 English
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HWE: Hardy-Weinberg equilibrium; PB: population based; HB: hospital-based; PCR: polymerase chain reaction; RFLP: restriction fragment length polymorphism; SBE, single base extension; PCR-RDB, polymerase chain reaction-reverse dot blot. DHPLC: PCR-based denaturing high-performance liquid chromatography; Direct: Direct sequence analysis of polymerase chain reaction; ASO: oligonucleotide allele specific polymerase chain reaction; MGB Eclipse: MGB Eclipse Assay polymerase chain reaction method; ARMS: Amplification refractory mutation system polymerase chain reaction; Snapshot: the Snapshot assay which provides detection of certain SNPs. CTPP: confronting two-pair primers. The quality of studies included in this meta-analysis was assessed using Newcastle-Ottawa scale, which graded the quality of a study from 0 to 10 points. Articles exceeding 6 points were considered as high quality.

Meta-analysis results

Table 2 shows the interleukin-8-251A/T polymorphisms genotype distribution and allele frequency in cases and controls. The main results of this meta-analysis were listed in Table 3. There were 26 studies with 5286 cases and 8000 controls for Interleukin-8-251A/T polymorphism. As shown in Table 3, The pooled results showed that there was significant association between Interleukin-8-251A/T polymorphism and GC risk in any genetic model: Allele model (A vs. T: OR=1.16, 95% CI=1.05-1.27, P=0.002), dominant model (AA + AT vs. TT: OR=1.22, 95% CI=1.07-1.39, P=0.003) recessive model (AA vs. AT + TT: OR=1.20, 95% CI=1.01-1.41, P=0.03) homozygous genetic model (AA vs. TT: OR=1.31, 95% CI=1.08-1.59, P=0.006) heterozygote comparison (AT vs. TT: OR=1.19, 95% CI=1.04-1.35, P=0.01). In the subgroup analysis by ethnicity, the effects remained in Asians (A vs. T: OR=1.23, 95% CI=1.10-1.37, P=0.0002; AA + AT vs. TT: OR=1.30, 95% CI=1.13-1.49, P=0.0002; AA vs. AT + TT: OR=1.33, 95% CI=1.07-1.64, P=0.009; AA vs. TT: OR=1.49, 95% CI=1.18-1.87, P=0.0009; AT vs. TT: OR=1.25, 95% CI=1.09-1.43, P=0.001). However, no genetic models reached statistical association in Europeans (Table 3).

Table 2.

Interleukin-8-251A/T polymorphisms genotype distribution and allele frequency in cases and controls

First author Genotype (N) Allele frequency (N)
Case Control Case Control
Total AA AT TT Total AA AT TT A T A T
Bo 208 36 108 64 190 26 96 68 180 236 148 232
Canedo 333 53 169 111 693 137 353 203 275 391 627 759
Crusius 236 48 113 75 1139 250 574 315 209 263 1074 1204
de Oliveira 207 47 98 62 240 45 134 61 192 222 224 256
Felipe 104 15 58 31 196 52 85 59 88 120 189 203
Garza-Gonzalez 78 16 47 15 189 33 87 69 79 77 153 225
Kamali-Sarvestani 19 9 6 4 153 22 74 57 24 14 118 188
Kamangar 112 14 56 42 207 24 111 72 84 140 159 255
Kang 334 49 159 126 322 27 148 147 257 411 202 442
Ko 81 12 35 34 308 27 146 135 59 103 200 416
Lee 470 59 213 198 308 62 138 108 331 609 262 354
Liu 138 23 89 26 137 15 72 50 135 141 102 172
Lu 250 54 102 94 300 37 144 119 210 290 218 382
Ohyauchi 212 13 106 93 346 20 118 208 132 292 158 534
Pan 308 48 168 92 308 59 148 101 264 352 266 350
Qadri 130 12 68 50 200 12 94 94 92 168 118 282
Ramis 9 4 1 4 38 7 20 11 9 9 34 42
Savage 88 23 39 26 429 75 207 147 85 91 357 501
Savage 287 76 140 71 428 117 205 106 292 282 439 417
Shirai 181 20 78 83 468 49 208 211 118 244 306 630
Song 125 20 72 33 140 23 70 47 112 138 116 164
Taguchi 396 44 191 161 252 22 105 125 279 513 149 355
Vinagre 102 25 56 21 103 19 42 42 106 98 80 126
Ye 153 17 82 54 206 23 86 97 116 190 132 280
Zeng 206 59 110 37 196 39 114 43 228 184 192 200
Zhang 519 128 261 130 504 93 251 160 517 521 437 571
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Table 3.

Meta-analysis results

Subgroup OR 95% CI P value Heterogeneity Effects model
I2 P value
Allele model A vs. T
    Overall 1.16 1.05-1.27 0.002 66% P<0.00001 R
    Ethnicity European 1.01 0.87-1.17 0.90 54% 0.03 R
Asian 1.23 1.10-1.37 0.0002 61% 0.0005 R
    Source of controls PB 1.10 0.98-1.24 0.09 58% 0.004 R
HB 1.23 1.05-1.43 0.01 72% P<0.0001 R
    Genotyping method PCR-RFLP 1.13 0.98-1.30 0.10 68% 0.0003 R
Non-RFLP 1.18 1.04-1.35 0.01 66% 0.0002 R
Dominant model AA + AT vs. TT
    Overall 1.22 1.07-1.39 0.003 61% P<0.0001 R
    Ethnicity European 1.05 0.81-1.35 0.71 64% 0.005 R
Asian 1.30 1.13-1.49 0.0002 49% 0.01 R
    Source of controls PB 1.09 0.98-1.21 0.13 27% 0.17 F
HB 1.40 1.12-1.76 0.004 73% P<0.0001 R
    Genotyping method PCR-RFLP 1.20 1.00-1.46 0.06 62% 0.002 R
Non-RFLP 1.23 1.02-1.49 0.03 63% 0.0007 R
Recessive model AA vs. AT + TT
    Overall 1.20 1.01-1.41 0.03 60% P<0.0001 R
    Ethnicity European 0.94 0.80-1.10 0.43 38% 0.12 F
Asian 1.33 1.07-1.64 0.009 61% 0.0005 R
    Source of controls PB 1.25 0.99-1.58 0.06 65% 0.0005 R
HB 1.14 0.90-1.44 0.29 54% 0.01 R
    Genotyping method PCR-RFLP 1.12 0.87-1.45 0.39 63% 0.002 R
Non-RFLP 1.27 1.01-1.58 0.04 60% 0.002 R
Homozygous genetic model AA vs. TT
    Overall 1.31 1.08-1.59 0.006 63% P<0.00001 R
    Ethnicity European 1.01 0.76-1.34 0.97 50% 0.04 R
Asian 1.49 1.18-1.87 0.0009 60% 0.0008 R
    Source of controls PB 1.27 0.98-1.64 0.07 63% 0.001 R
HB 1.38 1.02-1.88 0.04 66% 0.0005 R
    Genotyping method PCR-RFLP 1.24 0.91-1.67 0.17 67% 0.0005 R
Non-RFLP 1.38 1.06-1.80 0.02 62% 0.001 R
Heterozygote comparison AT vs. TT
    Overall 1.19 1.04-1.35 0.01 57% 0.0002 R
    Ethnicity European 1.07 0.81-1.42 0.63 67% 0.002 R
Asian 1.25 1.09-1.43 0.001 41% 0.04 R
    Source of controls PB 1.04 0.93-1.16 0.54 5% 0.39 F
HB 1.39 1.11-1.74 0.004 68% 0.0002 R
    Genotyping method PCR-RFLP 1.20 1.00-1.45 0.05 55% 0.01 R
Non-RFLP 1.18 0.97-1.42 0.09 60% 0.002 R
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F-fixed effects model; R-random effects model.

The subgroup analysis stratified by Source of controls showed an increased breast cancer risk in hospital-based (HB) studies in any genetic model except recessive model (A vs. T: OR=1.23, 95% CI=1.05-1.43, P=0.01; AA + AT vs. TT: OR=1.40, 95% CI=1.12-1.76, P=0.004; AA vs. TT: OR=1.38, 95% CI=1.02-1.88, P=0.04; AT vs. TT: OR=1.39, 95% CI=1.11-1.74, P=0.004). However, there was no association in any genetic model in population based (PB) studies (Table 3).

When stratifying by Genotyping method, we found statistical association in Non-RFLP (restriction fragment length polymorphism) in any genetic model except heterozygote comparison (A vs. T: OR=1.18, 95% CI=1.04-1.35, P=0.01; AA + AT vs. TT: OR=1.23, 95% CI=1.02-1.49, P=0.03; AA vs. AT + TT: OR=1.27, 95% CI=1.01-1.58, P=0.04; AA vs. TT: OR=1.38, 95% CI=1.06-1.80, P=0.02), the effect was remain in PCR-RFLP in dominant model and heterozygote comparison (AA + AT vs. TT: OR=1.20, 95% CI=1.00-1.46, P=0.06; AT vs. TT: OR=1.20, 95% CI=1.00-1.45, P=0.05).

Sensitivity analyses

As shown in Table 1, all the studies conformed to he balance of Hardy-Weinberg equilibrium (HWE) in controls except Zeng’s (P<0.05), however, after performing the sensitivity analyses, The overall results did not show quantitative changes when excluding any study, suggesting the stability and reliability of this meta-analysis.

Detection for heterogeneity

Statistically significant heterogeneity was observed between trials of the following analyses using Q statistic: allele model (A vs. T: P<0.00001, I2=66%), the dominant model (AA + AT vs. TT: P<0.0001, I2=61%), the recessive model (AA vs. AT + TT: P<0.0001, I2=60%), the homozygous genetic model (AA vs. TT: P<0.00001, I2=63%), and the heterozygous genetic model (AT vs. TT: P=0.0002, I2=57%), and the random-effects model was performed in these studies.

Publication bias

Begg’s funnel plot and Egger’s test were employed to assess the publication bias. As shown in Figure 2, the funnel plots failed to reveal any obvious asymmetry in all genotypes in overall population. Neither Begg’s test nor Egger’s test showed statistical evidence for publication bias in our meta-analysis (P>0.05).

Figure 2.

Figure 2

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Funnel plot assessing evidence of publication bias from 26 studies (A vs. T). Abbreviations: SE, standard error; OR, odds ratio; A vs. G, Allele model.

Discussion

A large amount of evidence suggests that genetics is important in determining the risk of cancer. Related research is to search for the susceptibility genes associated with cancer [46]. It is believed that single nucleotide polymorphism is the most common source of human genetic variation, which may contribute to the susceptibility of individuals to cancer [38-41,47]. In recent years, genetic susceptibility to cancer has caused people’s great interest, and the study on the genetic polymorphism of the tumor is increasing.

Recently, a growing number of epidemiological studies have been performed to assess the association of Interleukin-8-251A/T polymorphisms with GC risk [12-37]. However, the results are conflicting. Thus, we conducted a comprehensive meta-analysis involving published data, to assess the strength of association between the polymorphisms and GC risk.

In this present meta-analysis, 26 studies with 5286 cases and 8000 controls were included. And we explored the association between the potentially functional polymorphisms of Interleukin-8-251A/T and GC risk. In the overall population, the pooled results showed that there was significant association between Interleukin-8-251A/T polymorphism and GC risk in any genetic model: Allele model (A vs. T: OR=1.16, 95% CI=1.05-1.27, P=0.002), dominant model (AA + AT vs. TT: OR=1.22, 95% CI=1.07-1.39, P=0.003) recessive model (AA vs. AT + TT: OR=1.20, 95% CI=1.01-1.41, P=0.03) homozygous genetic model (AA vs. TT: OR=1.31, 95% CI=1.08-1.59, P=0.006) heterozygote comparison (AT vs. TT: OR=1.19, 95% CI=1.04-1.35, P=0.01). In a previous meta-analysis by Wang et al. [39], they failed to find association between Interleukin-8-251A/T polymorphism and gastric cancer susceptibility. This contradiction may result from different sample size and ethnic groups.

In the subgroup analysis by ethnicity, the effects remained in Asians (A vs. T: OR=1.23, 95% CI=1.10-1.37, P=0.0002; AA + AT vs. TT: OR=1.30, 95% CI=1.13-1.49, P=0.0002; AA vs. AT + TT: OR=1.33, 95% CI=1.07-1.64, P=0.009; AA vs. TT: OR=1.49, 95% CI=1.18-1.87, P=0.0009; AT vs. TT: OR=1.25, 95% CI=1.09-1.43, P=0.001). However, no genetic models reached statistical association in Europeans (Table 3). It was partially in line with the results of Cheng (2013)’s [41], Xue (2012)’s [38] and Wang (2012)’s [40] finding. However, there were only 18 studies respectively in their meta-analysis, while 26 studies were involved in our meta-analysis.

The subgroup analysis stratified by Source of controls showed an increased breast cancer risk in hospital-based (HB) studies in any genetic model except recessive model (A vs. T: OR=1.23, 95% CI=1.05-1.43, P=0.01; AA + AT vs. TT: OR=1.40, 95% CI=1.12-1.76, P=0.004; AA vs. TT: OR=1.38, 95% CI=1.02-1.88, P=0.04; AT vs. TT: OR=1.39, 95% CI=1.11-1.74, P=0.004). However, there was no association in any genetic model in population based (PB) studies (Table 3). When stratifying by Genotyping method, we found statistical association in Non-RFLP (restriction fragment length polymorphism) in any genetic model except heterozygote comparison (A vs. T: OR=1.18, 95% CI=1.04-1.35, P=0.01; AA + AT vs. TT: OR=1.23, 95% CI=1.02-1.49, P=0.03; AA vs. AT + TT: OR=1.27, 95% CI=1.01-1.58, P=0.04; AA vs. TT: OR=1.38, 95% CI=1.06-1.80, P=0.02), the effect was remain in PCR-RFLP in dominant model and heterozygote comparison (AA + AT vs. TT: OR=1.20, 95% CI=1.00-1.46, P=0.06; AT vs. TT: OR=1.20, 95% CI=1.00-1.45, P=0.05). Above findings was reported first by the paper.

There are some limitations in this meta-analysis. Firstly, this meta-analysis was based on pooled data. We could not assess the risk of cancer according to stratification of age, Sex and H. pylori infection, smoking, alcohol consumption, environment factors, and other risk factors. Secondly, we just included the published studies in the meta-analysis. It is possible that we missed some related unpublished studies that might meet the inclusion criteria. Moreover, small study effect, in which effects reported in small studies are larger, could not be avoided in that some studies were of a relative small size. Further large scale multicenter studies are warranted to further validate on Interleukin-8-251A/T polymorphisms and GC risk.

Conclusions

In conclusion, this meta-analysis suggests that Interleukin-8-251A/T polymorphism is a risk factor for susceptibility to GC in overall population, especially in Asians, in hospital populations and in Non-RFLP. While, there was no association in Europeans and in general population. Further large scale multicenter epidemiological studies are warranted to confirm this finding.

Disclosure of conflict of interest

None.

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