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Association between hOGG1 polymorphism rs1052133 and gastric cancer

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Oncotarget. 2017; 8:34321-34329. https://doi.org/10.18632/oncotarget.16124

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Dingding Zhang, Xiaoxin Guo, Jinliang Hu, Guangqun Zeng, Maomin Huang, Dandan Qi and Bo Gong _

Abstract

Dingding Zhang1,*, Xiaoxin Guo1,*, Jinliang Hu2,3,*, Guangqun Zeng4, Maomin Huang1,5, Dandan Qi1, Bo Gong1

1Sichuan Provincial Key Laboratory for Disease Gene Study, Hospital of University of Electronic Science and Technology of China and Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610072, China

2Institute of Health Policy and Hospital Management, Sichuan Academy of Medical Science and Sichuan Provincial People’s Hospital, Chengdu, Sichuan, 610072, China

3School of Public Health, Sichuan University, Chengdu, Sichuan, 610041, China

4Department of Clinical Laboratory, People’s Hospital of Pengzhou, Pengzhou, Sichuan, 611930, China

5Department of Immunology, Zunyi Medical College, Zunyi, Guizhou, 563000, China

*These authors contributed equally to this study

Correspondence to:

Bo Gong, email: gongbo2007@hotmail.com

Keywords: gastric cancer, meta-analysis, hOGG1 rs1052133, association

Received: January 10, 2017     Accepted: March 04, 2017     Published: March 11, 2017

ABSTRACT

Purpose: To conduct a comprehensive evaluation of the association of the human8-oxoguanine glycosylase 1 (hOGG1) gene polymorphism rs1052133 with gastric cancer (GC) through a systematic review and meta-analysis of genetic association study.

Results: A total of 15 articles from published papers were included in our analysis. The meta-analyses for hOGG1 rs1052133, composed of 4024GC patients and 6022controls, showed low heterogeneity for the included populations in all the genetic models, except for the Caucasian population under allelic genetic model, the Asian population under addictive model and Caucasian population under dominant model. The analyses of all the genetic models in overall pooled populations did not identify any significant association between GC and hOGG1 rs1052133 (Allelic model: C vs. G , p = 0.746; Addictive model: CC vs. GG, p = 0.888; Recessive model: CC +GC vs. GG, p = 0.628; Dominant model: CC vs. GG+GC, p = 0.147), even though stratified analyses were conducted in different ethnicities under each genetic model.

Materials and Methods: All case-control association studies on hOGG1 and GC reported up to December 15, 2016 in PubMed, Embase, Web of Science, and the Chinese Biomedical Database were retrieved. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated for single-nucleotide polymorphism (SNP) using fixed- and random- effects models according to between-study heterogeneity. Publication bias analyses were conducted using Begg test.

Conclusions: This meta-analysis showed there was no association between hOGG1 rs1052133 and GC. Given the limited sample size, further investigations including more ethnic groups are required to validate the association.


INTRODUCTION

Gastric cancer (GC) is one of the most common cancers and the leading causes of cancer death in the world [1], however, there is a gradually decrease in incidence and mortality rates in most developed countries [2]. GC is a multifactorial disease caused by dietary, genetic and environmental factors [3, 4]. Although the pathogenetic mechanism of GC is very complicated and is still not fully understood, more and more evidence has been shown that there is a correlation between genetic polymorphisms and GC risk [57].

To date, many genetic polymorphisms in the carcinogen detoxification, antioxidant protection, DNA repair and cell proliferation processes have been reported to play a crucial role in the development of GC [8]. The human 8-oxoguanine glycosylase 1 (hOGG1) gene, specifically involved in the repair of DNA oxidative damage in the base excision response pathway, has been shown to be associated with a variety of cancers [913]. As an important component of DNA repair pathway, hOGG1 encodes a DNA glycosylase enzyme that actively removes 8-hydroy-2-deoxyguanine, which is highly mutagenic and a major form of oxidative DNA damage [14, 15]. The dysfunction of hOGG1 might result in the DNA repair deficiency and then induce gene mutation and cell canceration. Functional studies showed that the hOGG1 variant had normal enzymatic activity, but maintained greater sensitivity to oxidation than wildtype hOGG1 protein [16].

The hOGG1 gene has been regarded as a candidate for involvement in the underlying cause of GC and the hOGG1 polymorphism rs1052133 has been widely evaluated in association with GC across different ethnicities [11, 1731]. hOGG1 polymorphism rs1052133 has been reported to be associated with an altered risk for GC in Chinese, Japanese and Caucasian populations [11, 18, 19, 32]. However, other reported studies on the association between hOGG1 rs1052133 and GC risk are inconclusive and conflicting [2031]. Such inconsistence and heterogeneity could be caused by different sample sizes and diversities in multiple ethnic cohorts. Meta-analysis, which combined all studies with the same criteria, could be helpful to comprehensively explain the association of hOGG1 rs1052133 with GC and provide some new clues for the research on GC. Therefore, in this study we conduct a systematic review and meta-analysis of all association studies on hOGG1 rs1052133 with GC, to summarize and evaluate the association between hOGG1 rs1052133 and GC.

RESULTS

Literature search and characteristics

A flow diagram (Figure 1) shows the selection process of studies included in our analysis. The initial search strategy yielded 31 articles. Among them, two review articles were excluded because of the publication type and six articles were excluded because of unrelated topics. The full text of the remaining 23 studies was retrieved and reviewed. Eight articles were excluded after full-text review, five studies were not case-control studies and three articles were experimental studies. Finally, 15 articles were identified that met the inclusion criteria and included for the meta-analysis. The analyzed SNP was successfully genotyped and was within HWE (except the genotype in control subjects from the Takezaki’s study) across all the included studies. All of the articles were case-control studies and all the cases were histopathologically confirmed as GC. The characteristics of these included articles are listed in Table 1 and Table 2. The NOS results showed that the score ranged from 7 to 8 with an average of 7.50, which indicated that the methodological quality of these selected articles was generally reliable.

Flow diagram of literature search and study selection for meta-analysis.

Figure 1: Flow diagram of literature search and study selection for meta-analysis.

Table 1: Characteristics of the fifteen studies included in this meta-analysis

Study

Country

Study population

Ethnicity

Study design

Genotyping method

Mean Age

NOS

Case

Control

Case

Control

Lu, et al. 2016

China

Chinese

Asian

CB

HB

SNaPshot

63.1 ± 10.7

63.3 ± 11.0

8

Hu, et al. 2015

China

Chinese

Asian

HB

HB

PCR-LDR

8

Engin, et al. 2011

Turkey

Turkish

Others

HB

HB

PCR-RFLP

60.4 ± 1.3

55.5 ±1.3

7

Liu, et al. 2011

China

Chinese

Asian

HB

CB

PCR-HMR

60.2 ± 10.4

59.3 ± 11.8

8

Canbay, et al. 2010

Turkey

Turkish

Others

CB

PB

PCR-RFLP

60.1 ± 20.9

52.8 ± 27.2

8

Sun, et al. 2010

China

Chinese

Asian

HB

PB

PCR-RFLP

59.6 ± 11.2

43.6 ± 10.3

7

Malik, et al. 2010

India

Indian

Asian

CB

PB

PCR-SSCP

55.9 ± 9.7

58.0 ±12.7

7

Palli, et al. 2010

Italy

Italian

Caucasian

CB

PB

Taqman

68.8 ± 9.9

55.5 ± 7.0

7

Farinati, et al. 2008

Italy

Italian

Caucasian

CB

PB

PCR-RFLP

68

46

8

Capella, et al. 2008

Spain

Spanish

Caucasian

CB

PB

Probe

50.5 ± 20.5

50.5 ± 20.5

8

Poplawskiet al. 2006

Poland

Polish

Caucasian

HB

PB

PCR-SSCP

62.4 ± 27.4

62.4 ± 27.4

8

Tsukino, et al. 2004

Japan

Japanese

Asian

HB

HB

PCR-SSCP

57.5 ± 9.5

57.1 ± 9.5

7

Takezaki, et al. 2002

China

Chinese

Asian

CB

PB

PCR- SSCP

65 ± 15

65 ± 14

7

Hanaoka, et al. 2001

Japan

Japanese Brazilian

Others

HB

HB

PCR-SSCP

65 ± 13

65 ± 12

7

Hanaoka, et al. 2001

Japan

non-Japanese Brazilian

Others

HB

HB

PCR-SSCP

59 ± 8

58 ± 8

8

Shinmura, et al. 1998

Japan

Japanese

Asian

HB

PB

PCR-SSCP

7

PB, population based; CB, clinic or institute based; HB, hospital based; NOS, Newcastle-Ottawa Scale; -Data unavailable.

Table 2: Genotype frequencies of hOGG1 rs1052133 among gastric cancer cases and controls in the included studies

MAF, minor allele frequency; OR, odds ratio; CI, confidence intervals; P, p value for Z test.

Meta-analysis results

Fifteen studies provided results of the association of hOGG1 rs1052133 with GC and a total of 10046 subjects (4024GC patients and 6022 controls) were tested for hOGG1 rs1052133 in this meta analysis. Stratified analyses were conducted based on three groups, including Asian [11, 18, 24, 26, 27, 2931], Caucasian [19, 21, 22, 25] and other ethnicities [20, 23, 28], and the heterogeneity of hOGG1 rs1052133 in Asian, Caucasian, other and pooled populations were evaluated firstly. The results showed low heterogeneity for the included populations under all the genetic models by fixed-effect analysis, except for the Caucasian population under allelic genetic model (P = 0.052, I2 = 61.2%), the Asian population under recessive model (P = 0.021, I2 = 57.6%) and Caucasian population under dominant model (P = 0.054, I2 = 60.7%), the random-effect analysis was thus adopted for them under each genetic model (Table 3).

Table 3: Pooled measure for the association of hOGG1 rs1052133 and gastric cancer different allelic models

Genetic model

Ethnicity

Pooled OR( 95% CI)

Heterogeneity

Test for overall effect

Begg test

P

I2 (%)

Z

P

P

Allelic model
( C vs. G)

Asian

0.979 (0.879–1.091)

0.121

38.8

0.38

0.705

0.344

Caucasian’

1.085 (0.789–1.492)

0.052

61.2

0.50

0.616

others

1.072 (0.841–1.366)

0.265

24.4

0.56

0.574

Overall

1.016 (0.924–1.117)

0.046

40.6

0.32

0.746

Addictive model
(CC vs. GG)

Asian

0.973 (0.839–1.130)

0.127

38.0

0.35

0.723

0.235

Caucasian

1.171 (0.778–1.764)

0.351

4.5

0.76

0.450

others

1.210 (0.736–1.989)

0.261

25.0

0.75

0.452

Overall

1.010 (0.883–1.155)

0.186

24.2

0.14

0.888

Recessive model
(CC + GC vs. GG)

Asian’

0.939 (0.830–1.064)

0.021

57.6

1.01

0.313

0.322

Caucasian

1.128 (0.752–1.692)

0.405

0.0

0.61

0.543

others

1.223 (0.765–1.956)

0.300

18.1

0.53

0.594

Overall

0.969 (0.864–1.087)

0.052

40.5

0.48

0.628

Dominant model
(CC vs. GG +GC)

Asian

1.039 (0.930–1.160)

0.692

0.0

0.68

0.498

0.260

Caucasian’

1.136 (0.954–1.353)

0.054

60.7

1.43

0.152

others

1.079 (0.828–1.404)

0.470

0.0

0.56

0.575

Overall

1.067 (0.977–1.165)

0.408

4.0

1.45

0.147

*OR, odds ratio; CI, confidence intervals; P, p value for Z test; I2 (%), the value to identify heterogeneity.

’Pooling model is random effect (Inverse Variance heterogeneity).

▲Continuity corrected.

Our meta-analysis showed that there was no significant association of hOGG1 rs1052133 with GC in the overall pooled populations under all the genetic models (Allelic model: C vs. G, OR = 1.016, 95% CI 0.924 to 1.117, p = 0.746; I2 = 40.6;Addictive model: CC vs. GG, OR = 1.010, 95% CI 0.883 to 1.155, p = 0.888; I2 = 24.2; Recessive model: CC +GC vs. GG, OR = 0.969, 95% CI 0.864 to 1.087, p = 0.628; I2 = 40.5; Dominant model: CC vs. GG+GC, OR = 1.067, 95% CI 0.977 to 1.165, p = 0.147; I2 = 4; Table 3). Figure 2 shows the forest plot of estimates of odds ratios of the association of hOGG1 rs1052133 with GC. When stratified analyses were conducted to explore further association of hOGG1 rs1052133 with GC in different ethnicities under each genetic model, no significant association was detected between hOGG1 rs1052133 and GC, either (Table 3 and Figure 2B2D).

Forest plots for meta-analysis of hOGG1 rs1052133 and the risk for GC.

Figure 2: Forest plots for meta-analysis of hOGG1 rs1052133 and the risk for GC. (A) allelic model (C vs. G); (B) addictive genetic model (CC vs. GG); (C) recessive genetic model (CC + GC vs. GG); (D) dominant genetic model (CC vs. GG+GC).

Publication bias and sensitivity analysis

Publication biases were assessed by the Egger’s test quantitatively and the result for hOGG1 rs1052133 based on the 15 included studies did not observe any obvious evidence of publication bias in the overall analyses under all genetic models (Table 3 and Figure 3). The effect of each study on the pooled OR was also assessed by sequential omission of individual studies. We did not find that the exclusion of any single study alter the significance of the final pooled OR.

Funnel plot analysis for publication bias between hOGG1 rs1052133 and GC risk.

Figure 3: Funnel plot analysis for publication bias between hOGG1 rs1052133 and GC risk. P for publication bias of this funnel plot for dominant model (CCvs.GG+GC) with all 15 studies is 0.260.

DISCUSSION

As a powerful statistical method, meta-analysis could provide a quantitative approach for pooling the variant results on the same topic to estimate and explain their diversity. This led us to conduct this meta-analysis of 15 published case-control studies, which may help us in distinguishing the truth from the false, and to explore a more robust estimate of the effect of hOGG1 rs1052133 on GC. The hOGG1 rs1052133 has been thought to constitute a candidate genetic risk factor for GC [11, 18, 19, 32]; however, several other studies have investigated the genetic effect of hOGG1 rs1052133 on GC susceptibility with conflicting results [2031]. In this study, our meta-analysis showed there was no association between hOGG1 rs1052133 and GC in all genetic models, suggesting that hOGG1 rs1052133 may not affect individual susceptibility to GC.

GC is considered to be a common, complex and multifactorial disease, and is estimated to have a significant heritable component. GC ranks as the third leading cause of cancer mortality worldwide and confers a 5-year survival of 20% [33]. Identifying causative genes has helped to understand the disease process and may aid in prevention. About 1%–3% of GC could be attributed to inherited cancer predisposition syndromes. More advances have been made in human GC genetics, but there is still to be known [8]. To date, Germline E-cadherin/CDH1 mutations have been identified in families with an autosomal dominant inherited predisposition to diffuse GC [34]. hOGG1 has been reported to be related to GC risk in several studies [11, 18, 19, 32]. It is an important glycosylase enzyme that plays a critical role in the repair of DNA oxidative damage [14, 15]. The dysfunction of hOGG1 might lead to deficiency of DNA repair and result in cell canceration. hOGG1 variant has been shown to exhibit lower DNA repair activity and greater sensitivity to oxidation than wildtype hOGG1 protein [16].

The association between hOGG1 rs1052133 and GC in previous studies were inconsistent. hOGG1 rs1052133 was reported to be significantly associated with GC in Japanese and Chinese patients with GC [11, 18, 19, 32]. However, no evidence of association between hOGG1 rs1052133 and GC was detected in Turkish, Spanish, Italian, Polish and Indian studies [2125, 28], meanwhile, the association of hOGG1 rs1052133 with GC was not replicated in other Japanese and Chinese populations [20, 26, 27]. Recently, the association between hOGG1 rs1052133 with GC risks was also accessed in Lu et al’ study with 1,275 GC patients and 1,436 controls [29] and in Hu et al’s study with 2745 GC patients and 4588 controls [30], respectively. Both the two studies demonstrated that the significant association was not present between hOGG1 rs1052133 and GC. In order to provide powerful statistical analysis, we conduct this study with more samples (a total of 4024GC patients and 6022 controls) to evaluate the association between hOGG1 rs1052133 and GC. Our results did not show any significant association of hOGG1 rs1052133 with GC, even across different ethnic populations. Therefore, our analyses failed to conclude whether hOGG1 rs1052133 is really a GC-associated SNP and more replication data is needed to validate this association. Of course, other factors, such as environment and different lifestyle, might play roles in these differences as well. Further analysis should be performed in more large-scale cohorts or case-control studies to explore the association of hOGG1 rs1052133 with GC. Future studies on gene-environment interaction should also be considered.

This systematic review and meta-analysis has sufficient power to evaluate and review all the published genetics studies in hOGG1 rs1052133, however, some potential limitations of our study on the understanding of hOGG1 in GC genetics should be considered. (1) This meta-analysis was mainly based on the studies with 2950 GC patients and 3817 controls in Japanese and Chinese populations [11, 18, 20, 26, 27, 2931]. Besides, only 1074 GC patients and 2205 controls in Turkish, Indian, Italian, Spanish and Polish populations were included in the present study. It may restrict our conclusions which indicate the need for larger sample sizes in other ethnic populations. (2) There was a high heterogeneity detected in the Caucasian population under allelic genetic model, in the Asian population under addictive model and in the Caucasian population under dominant model. It is possible that different populations with different clinical patient characteristics were included. (3) GC is a multifactorial disease that results from complex interactions between various genetic factors and other factors. Therefore, our results may be influenced by confronting factors, such as age, gender, environment, and lifestyle. If the investigation of gene-environment interactions in different ethnic subgroups could be carried out, we might get more conclusive claims about the association of hOGG1 rs1052133 with GC.

In conclusion, this meta-analysis showed that hOGG1 rs1052133 is not associated with GC under all the genetic models, suggesting that it may not affect individual susceptibility to GC. Further investigations with larger sample sizes and more ethnic groups are required to validate the association and confirm the roles of hOGG1 in GC.

MATERIALS AND METHODS

Searching strategy

A systematic literature search using the databases PubMed, Embase, Web of Science, and the Chinese Biomedical Database was conducted, to identify all published studies on the association of hOGG1 polymorphisms with GC from their starting date to December 15, 2016. Following keywords were used: ‘‘cancer,’’ ‘‘gastric cancer,’’ ‘‘human 8-oxoguanine glycosylase,’’ ‘‘hOGG1,’’ ‘‘rs1052133,’’ ‘‘polymorphism(s),’’ ‘‘variant(s),’’ and ‘‘mutation(s).’’ The internet was searched using the Google search engine. Reference lists of the retrieved articles and reviews were manually checked for additional articles, to identify studies not yet included in the electronic searches.

Inclusion and exclusion criteria

The eligible articles were considered if they (1) evaluated associations between hOGG1 rs1052133 and GC; (2) used a case–control design to compare GC cases and normal controls in defined populations; (3) gave an OR with 95% CI or other available data from which they could be estimated; and (4) were original research articles, not reviews or comments. Excluded were abstracts from conferences, full texts without raw data available for retrieval, republished data, duplicate studies and reviews.

Data extraction

Two observers (DDZ and XNG) independently abstracted data from all eligible publications onto paper data collection forms. Two reviewers (JLH and GQZ) were blinded to the details (title, author, and academic address) of these studies during assessment. Disagreements were resolved by discussion until a consensus was achieved. Otherwise, a third investigator was consulted to resolve the dispute. The following items were collected from each study: first author’s surname, year of publication, statistical data, ethnicity of subjects, whether Hardy-Weinberg equilibrium (HWE) was examined in controls, genotyping method, total numbers of cases and controls, as well as total numbers of cases and controls for each hOGG1genotypes, respectively.

Quality assessment

The Newcastle-Ottawa Scale (NOS) was used to assess the quality of the included studies by two investigators independently. Through the rating system, the NOS was conducted to judge the study quality based on three aspects, which were selection, comparability, and exposure situation in case-control studies. Rating scores range from 0 (worst) to 9 (best). Studies with a score of 7 or greater were thought to have an adequate or good quality.

Statistical analysis

A pooled OR with its corresponding 95% CI was used as a measure of the association of hOGG1 rs1052133 with GC. For genotypic comparison, allelic, addictive, dominant and recessive models were applied in the investigation of the disease association. We conducted stratified analyses by ethnicity, including Asian populations (Japanese, Chinese and Indian), Caucasian populations (Polish, Italian and Spanish), other populations (Turkish, Japanese Brazilian and non-Japanese Brazilian) and pooled populations. Sensitivity analysis was carried out by excluding one study at a time to explore whether the results were influenced by a specific study. Heterogeneity (between-study inconsistency) was investigated and measured using I2statistic. A p value of I2 < 50% indicated an absence of heterogeneity among studies, the fixed-effect model (Mantel-Haenszel method) was thus used to calculate the pooled ORs. In contrast, if the p value for heterogeneity was I2 ≥ 50%, indicating a high degree of heterogeneity between studies, then the random-effect model (DerSimonian-Laird method) was used to evaluate the summary ORs. Begg liner regression test was used to assess the potential publication bias, where a value of p < 0.05 was considered statistically significant. The Hardy-Weinberg equilibrium (HWE) for each SNP was tested by the χ2 test. All statistical analyses were performed by the Review Manager software (RevMan, version 5.2; The Nordic Cochrane Centre, The Cochrane Collaboration, Copenhagen;2012), and the STATA software (version 12.0, STATA Corp., College Station, TX, USA), as well as the Hardy Weinberg package (version 1.3) in R language (version 2.15.0, http://cran.r-project.org/). Two-sided P values < 0.05 were considered statistically significant.

Authors’ contributions

Bo Gong conceived and designed the study; Xiaoxin Guo, Guangqun Zeng, Dingding Zhang, Maomin Huang and Dandan Qi collected the data; Jinliang Hu analyzed the data; Bo Gong and Dingding Zhang contributed to the writing of the manuscript.

ACKNOWLEDGMENTS

Supported in part by grants from the Natural Science Foundation of China (81670853(B.G.), 81371048 (B.G.), 81470067 (D.Z.) and 81271049 (D.Z.)); grants from Department of Science and Technology of Sichuan Province (2016SZ0022(D.Z.)); grants from health program of Sichuan(2016213(D.Z.)).

CONFLICTS OF INTEREST

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

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