Oncotarget

Meta-Analysis:

Human 8-oxoguanine DNA glycosylase gene polymorphism (Ser326Cys) and cancer risk: updated meta-analysis

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Oncotarget. 2017; 8:44761-44775. https://doi.org/10.18632/oncotarget.16226

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Sang Wook Kang, Su Kang Kim, Hae Jeong Park, Joo-Ho Chung and Ju Yeon Ban _

Abstract

Sang Wook Kang2,*, Su Kang Kim1,*, Hae Jeong Park1, Joo-Ho Chung1 and Ju Yeon Ban2

1 Kohwang Medical Institute, School of Medicine, Kyung Hee University, Seoul, Republic of Korea

2 Department of Dental Pharmacology, School of Dentistry, Dankook University, Cheonan, Republic of Korea

* First two authors equally contributed

Correspondence to:

Ju Yeon Ban, email:

Keywords: hOGG1, polymorphism, Ser326Cys, cancer, meta-analysis

Received: September 27, 2016 Accepted: March 03, 2017 Published: March 15, 2017

Abstract

Genetic polymorphism of human 8-oxoguanine glycosylase 1 (hOGG1) has been reported to have a relationship with the risk of the development of various cancers. Many studies have described the influence of Ser326Cys polymorphism of the hOGG1 gene on cancer susceptibility. However, the results have remained inconclusive and controversial. Therefore, we performed a meta-analysis to more precisely determine the relationship between the hOGG1 polymorphism and the development of cancer.

Electronic databases including PubMed, Embase, Google Scholar, and the Korean Studies Information Service System (KISS) were searched. The odds ratio (OR), 95% confidence interval (CI), and p value were calculated to assess the strength of the association with the risk of cancer using Comprehensive Meta-analysis software (Corporation, NJ, USA). The 127 studies including 38,757 cancer patients and 50,177 control subjects were analyzed for the meta-analysis.

Our meta-analysis revealed that G allele of Ser326Cys polymorphism of the hOGG1 gene statistically increased the susceptibility of cancer (all population, OR = 1.092, 95% CI = 1.051-1.134, p < 0.001; in Asian, OR = 1.095, 95% CI = 1.048-1.145, p < 0.001; in Caucasian, OR = 1.097, 95% CI = 1.033-1.179, p = 0.002). Also, other genotype models showed significant association with cancer (p < 0.05, respectively).

The present meta-analysis concluded that the G allele was associated with an increased risk of cancer. It suggested that the hOGG1 polymorphism may be a candidate marker of cancer.


INTRODUCTION

Cancers are serious problem around the world and complex, multistep, multifactorial, and highly fatal diseases. The environment and genetic inheritance have been known as risk factor in development of cancer [1]. Several recent studies focused on the genetic background and how the single nucleotide polymorphism (SNP) of specific genes, including DNA damage, can enhance cancer susceptibility [2].

DNA damage plays an important role in tumor development. Reactive oxygen species (ROS) increases damage to DNA and causes miscoding by DNA polymerase [3]. The level of ROS in tissue DNA reflects a balance between the rate of damage and repair. Abnormal balance results in DNA mutations that can activate oncogenes or inactivate tumor suppressor genes, which leads to cancer [4]. The base excision repair (BER) pathway is one of the DNA repair process. An important role of BER is to remove DNA damage caused by various carcinogens, such as ionizing radiation or reactive oxidative species [5]. BER has also evolved to cope with mutagenic and cytotoxic hydrolytic, oxidative, and alkylation damages. A relationship of BER to cancer progression has been drawn from the observation that mutations or altered expression in BER genes [6].

The hOGG1 is a DNA repair enzyme that excises 7,8-dihydro-8-oxoguanine (8oxoG) from DNA. The hOGG1 is located on chromosome 3p26, a vital member of the BER pathway, and encodes 8-oxoguanine glycosylase that is a key enzyme in the repair of 8-oxoguanine [7]. ROS can lead to mutagenic base 8oxoG formation in DNA and carcinogenesis [8]. Since 8oxoG is a highly mispairing lesion, it was suggested that decreased hOGG1expression level could lead to a higher background mutation frequency and could possibly increase the cancer risk of an individual under oxidative stress [7].

Many previous studies showed the relationship between the Ser326Cys polymorphism of hOGG1 gene and cancer susceptibility. Meta-analysis on the hOGG1 polymorphism and the risk of bladder cancer shows no statistically significant association [9]. The meta-analysis on breast cancer suggested that the allele of hOGG1 326Cys plays a protective effect in European women but not in different menopausal status (premenopausal and postmenopausal) or the other ethnicities (Asians and Americans) [10]. The hOGG1 polymorphism may be also contributed to the susceptibility of digestive cancers [11], colorectal cancer [12], esophageal squamous cell carcinoma [13] but shows a lack of association in gastric cancer [14]. In addition, the hOGG1 polymorphism is associated with hepatocellular carcinoma [15], head and neck cancer [16], and prostate cancer [17], not with lung cancer [18].

Meta-analysis study in 2011 year reported that the evidence of the association between the hOGG1 polymorphism and cancer risk [19]. Since 2011, many studies reported the relation between the hOGG1 polymorphism and various cancer risks. However, the results have not been updated yet. Therefore, the purpose of this meta-analysis is to update previous meta-analysis with the aim of elucidating the association of the hOGG1 polymorphism and risk of cancer.

RESULTS

In present study, we performed the meta-analysis to assess relationship between the hOGG1 polymorphism and risk of cancer. We collected the genetic data from electronic databases. The search strategy used for this meta-analysis is shown in Figure 1. We examined the 577 articles and 442 articles were excluded as they were unrelated articles or duplicated studies. Among them, 19 studies were excluded because they were not consistent with Hardy-Weinberg equilibrium (HWE). After 116 articles were selected, 11 studies about the hOGG1 polymorphism since 2012 were added. Finally, a total of 127 genetic studies about the hOGG1 polymorphism and cancer were analyzed for meta-analysis (Supplementary Table 1) [5, 10-143]. Supplementary Table 1 shows basic characteristics of the analyzed studies. The total 88,934 individuals comprised of 38,757 cancer patients and 50,177 control subjects. The types of cancers were including colorectal (18 articles), lung (28 articles), breast (16 articles), bladder (4 articles), gallbladder (2 articles), prostate (7 articles), gastric (13 articles), esophageal (10 articles), head and neck (8 articles), hepatocellular cancers (7 articles), and etc.

Flow chart illustrating the search strategy used for this meta-analysis to identify studies that examined the association between

Figure 1: Flow chart illustrating the search strategy used for this meta-analysis to identify studies that examined the association between hOGG1 Ser326Cys polymorphism and risk of cancer.

Table 1 presents the results of meta-analysis of association between the hOGG1 polymorphism and risk of cancer in allele (C vs. G), dominant (C/C genotype vs. C/G+G/G genotypes), and recessive (C/C+C/G genotypes vs. G/G genotype) models. The frequencies of major allele C/minor allele G in the total cancer and control were 63.33%/36.67%and 65.34%/34.66%. The minor G allele frequency in the total cancer was higher more than that of control (36.67% vs. 34.66%). The difference showed the significantly strong association with risk of cancer (OR=1.088, 95% CI=1.048-1.130, p<0.001 in Table 1). In the subgroup according to type cancer, as shown in Table 1, colorectal cancer, lung cancer, prostate cancer and head and neck cancer presented the association with risk of cancer (colorectal cancer, OR=1.121, 95% CI=1.005-1.251, p=0.040; lung cancer, OR=1.094, 95% CI=1.020-1.172, p=0.012; prostate cancer OR=1.459, 95% CI=1.068-1.992, p=0.018; head and neck cancer, OR=1.335, 95% CI=1.079-1.651, p=0.008). The frequencies of CC genotype/CG+GG genotypes in the total cancer and control were 43.17%/56.83% and 45.02%/54.98%. The CG+GG genotypes frequency in the total cancer was higher more than that of control (56.83% vs. 54.98%). The difference showed the significant association with risk of cancer (OR=1.075, 95% CI=1.023-1.130, p=0.004 in Table 1). In the subgroup according to type cancer, head and neck cancer only showed the association with risk of cancer (head and neck cancer, OR=1.424, 95% CI=1.099-1.845, p=0.007). The frequencies of CC+CG genotypes/GG genotype in the total cancer and control were 83.56%/16.44% and 85.69%/14.31%. The CG+GG genotypes frequency in the total cancer was higher more than that of control (16.44% vs. 14.31%). The difference showed the significant association with risk of cancer (OR=1.174, 95% CI=1.094-1.259, p<0.001 in Table 1). In the subgroup according to type cancer, lung cancer and head and neck cancer presented the association with risk of cancer (lung cancer, OR=1.188, 95% CI=1.055-1.337, p=0.004; head and neck cancer, OR=1.551, 95% CI=1.045-2.301, p=0.029).

Table 1: Overall analysis between hOGG1 Ser326Cys polymorphism and risk of cancer

Cancers

No. of studies

Heterogeneity

Model

OR (95% CI)

p

p

I-squared

C vs. G

All cancers

125

<0.001

61. 138

Random

1.092 (1.051-1.134)

<0.001

Colorectal cancer

17

<0.001

65.928

Random

1.121 (1.005-1.251)

0.040

Lung cancer

28

<0.001

51.835

Random

1.094 (1.020-1.172)

0.012

Breast cancer

16

0.077

35.7

Fixed

1.031 (0.985-1.079)

0.185

Bladder cancer

4

0.004

77.187

Random

1.058 (0.812-1.379)

0.676

Gallbladder cancer

2

0.015

83.061

Random

1.044 (0.877-1.242)

0.627

Prostate cancer

6

<0.001

79.676

Random

1.459 (1.068-1.992)

0.018

Gastric cancer

13

0.034

46.33

Random

1.011 (0.883-1.157)

0.874

Esophageal cancer

10

0.112

37.044

Fixed

1.050 (0.957-1.152)

0.299

Head and neck cancer

8

<0.001

77.552

Random

1.335 (1.079-1.651)

0.008

Hepatocellular cancer

7

<0.001

74.985

Random

1.089 (0.883-1.344)

0.424

Acute lymphoblastic leukemia

2

0.002

89.539

Random

1.579 (0.775-3.217)

0.208

Pancreatic adenocarcinoma

2

0.467

<0.001

Fixed

1.007 (0.885-1.146)

0.917

C/C vs. C/G+G/G

All cancers

127

<0.001

56.428

Random

1.079 (1.027-1.134)

0.002

Colorectal cancer

18

<0.001

60.869

Random

1.140 (0.993-1.308)

0.063

Lung cancer

28

0.004

47.036

Random

1.080 (0.984-1.187)

0.106

Breast cancer

16

0.202

22.109

Fixed

1.011 (0.948-1.078)

0.742

Bladder cancer

4

0.39

0.312

Fixed

1.000 (0.847-1.181)

0.996

Gallbladder cancer

2

0.029

78.918

Random

1.080 (0.644-1.812)

0.771

Prostate cancer

7

<0.001

75.606

Random

1.401 (0.976-2.011)

0.067

Gastric cancer

13

0.346

9.913

Fixed

0.928 (0.821-1.048)

0.229

Esophageal cancer

10

0.199

26.588

Fixed

0.971 (0.854-1.104)

0.652

Head and neck cancer

8

<0.001

74.243

Random

1.424 (1.099-1.845)

0.007

Hepatocellular cancer

7

<0.001

88.181

Random

1.113 (0.673-1.841)

0.677

Acute lymphoblastic leukemia

2

0.006

86.606

Random

1.401 (0.583-3.364)

0.451

Pancreatic adenocarcinoma

2

0.451

<0.001

Fixed

1.051 (0.898-1.230)

0.539

C/C+C/G vs. G/G

All cancers

125

<0.001

54.586

Random

1.178 (1.098-1.263)

<0.001

Colorectal cancer

17

0.04

41.096

Random

1.154 (0.959-1.388)

0.129

Lung cancer

28

0.03

36.264

Random

1.188 (1.055-1.337)

0.004

Breast cancer

16

0.378

6.626

Fixed

1.092 (1.004-1.189)

0.041

Bladder cancer

4

<0.001

86.372

Random

1.118 (0.557-2.246)

0.753

Gallbladder cancer

2

0.074

68.567

Fixed

1.099 (0.761-1.585)

0.615

Prostate cancer

6

0.003

72.07

Random

1.691 (0.965-2.965)

0.066

Gastric cancer

13

0.037

46.74

Random

1.088 (0.823-1.438)

0.553

Esophageal cancer

10

0.02

54.195

Random

1.252 (0.929-1.686)

0.139

Head and neck cancer

8

0.009

62.394

Random

1.551 (1.045-2.301)

0.029

Hepatocellular cancer

7

<0.001

85.472

Random

1.126 (0.723-1.754)

0.600

Acute lymphoblastic leukemia

2

0.011

84.554

Random

2.435 (0.632-9.376)

0.196

Pancreatic adenocarcinoma

2

0.809

<0.001

Fixed

0.823 (0.578-1.172)

0.280

OR odds ratio, vs versus. Bold numbers indicate significant association with risk of cancer.

Table 2 and Table 3 present the results of meta-analysis of association between the hOGG1 polymorphism and risk of cancer according to ethnic difference. In Asian population, analysis of allele, dominant, and recessive models showed the association with risk of cancer (C vs. G, OR=1.095, 95% CI=1.048-1.145, p<0.001: CC vs. CG+GG, OR=1.096, 95% CI=1.015-1.183, p=0.019; CC+CG vs. GG, OR=1.171, 95% CI=1.070-1.282, p=0.001 in Table 2). According to the type of cancer, risk of lung, breast, and head and neck cancers was associated with the hOGG1 polymorphism (p<0.05, Table 2). In Caucasian population, analysis of allele and recessive models showed the association with risk of cancer (C vs. G, OR=1.097, 95% CI=1.021-1.179, p=0.012: CC+CG vs. GG, OR=1.158, 95% CI=1.005-1.334, p=0.043 in Table 3). According to type of cancer, risk of colorectal, esophageal, and head and neck cancer was associated with the hOGG1 polymorphism (p<0.05, Table 3). Begg’s funnel plot and Egger’s test were used to evaluate publication bias. The results of funnel plots and the Egger’s test showed no publication bias in this meta-analysis except for allele model of all cancers (Figure 2). We found a weak publication bias in allele model of all cancers (p = 0.03425). In addition, 3 more subgroup analysis showed publication bias (recessive model of head and neck cancer and colorectal cancer in all population; recessive model of all cancer in Caucasian population, data not shown). In sensitivity analysis for our meta-analysis, some results were influenced by some studies. In all population analysis, allele model of lung, prostate, and head and neck cancer, dominant model of all cancers and head and neck cancer, and recessive model of all cancers, lung, and head and neck cancer were not influenced according to sensitivity analysis. In Asian population analysis, allele model of all cancers and lung cancer, dominant model of all cancers and head and neck cancer, and recessive model of all cancers, lung and breast cancer were not influenced. In Caucasian population analysis, allele model of all cancers and dominant model of esophageal cancer were not influenced by studies. These results indicate that individual with minor G allele of the hOGG1 polymorphism may be increased risk of cancer.

Table 2: Overall analysis between hOGG1 Ser326Cys polymorphism and risk of cancer in Asian

Cancers

Comparison

Heterogeneity

Model

OR (95% CI)

p

p

I-squared

All cancers

C vs. G

<0.001

44.278

Random

1.095 (1.048-1.145)

<0.001

CC vs. CG+GG

<0.001

48.900

Random

1.096 (1.015-1.183)

0.019

CC+CG vs. GG

<0.001

63.487

Random

1.171 (1.070-1.282)

0.001

Colorectal cancer

C vs. G

0.149

47.481

Fixed

0.987 (0.879-1.108)

0.822

CC vs. CG+GG

0.123

<0.001

Fixed

1.068 (0.868-1.315)

0.532

CC+CG vs. GG

0.451

<0.001

Fixed

0.945 (0.795-1.122)

0.517

Lung cancer

C vs. G

0.487

<0.001

Fixed

1.110 (1.048-1.176)

<0.001

CC vs. CG+GG

0.163

27.193

Fixed

1.116 (1.013-1.229)

0.027

CC+CG vs. GG

0.220

21.505

Fixed

1.176 (1.074-1.289)

0.000

Breast cancer

C vs. G

0.376

6.340

Fixed

1.085 (1.013-1.162)

0.019

CC vs. CG+GG

0.942

<0.001

Fixed

1.098 (0.971-1.241)

0.135

CC+CG vs. GG

0.108

44.685

Fixed

1.122 (1.014-1.242)

0.026

Bladder cancer

C vs. G

0.018

75.198

Random

1.135 (0.821-1.571)

0.444

CC vs. CG+GG

0.848

<0.001

Fixed

1.157 (0.910-1.472)

0.235

CC+CG vs. GG

<0.001

89.184

Random

1.264 (0.503-3.174)

0.619

Gallbladder cancer

C vs. G

0.015

83.061

Random

1.108 (0.710-1.728)

0.652

CC vs. CG+GG

0.029

78.918

Random

1.080 (0.644-1.812)

0.771

CC+CG vs. GG

0.074

68.567

Fixed

1.099 (0.761-1.585)

0.615

Gastric cancer

C vs. G

0.075

49.976

Fixed

1.010 (0.904-1.129)

0.855

CC vs. CG+GG

0.561

<0.001

Fixed

0.977 (0.811-1.176)

0.802

CC+CG vs. GG

0.012

65.907

Random

1.232 (0.823-1.843)

0.311

Esophageal cancer

C vs. G

0.837

<0.001

Fixed

1.087 (0.980-1.205)

0.113

CC vs. CG+GG

0.733

<0.001

Fixed

1.017 (0.875-1.182)

0.821

CC+CG vs. GG

0.031

56.757

Random

1.277 (0.939-1.735)

0.119

Head and neck cancer

C vs. G

0.020

81.661

Random

1.499 (0.729-3.084)

0.271

CC vs. CG+GG

0.274

16.259

Fixed

1.856 (1.262-2.731)

0.002

CC+CG vs. GG

0.124

57.636

Fixed

0.964 (0.696-1.335)

0.825

Hepatocellular cancer

C vs. G

0.001

74.985

Random

1.089 (0.883-1.344)

0.424

CC vs. CG+GG

<0.001

89.658

Random

1.113 (0.673-1.841)

0.677

CC+CG vs. GG

<0.001

85.472

Random

1.126 (0.723-1.754)

0.600

OR odds ratio, vs versus. Bold numbers indicate significant association with risk of cancer.

Table 3: Overall analysis between hOGG1 Ser326Cys polymorphism and risk of cancer in Caucasian

Cancers

Comparison

Heterogeneity

Model

OR (95% CI)

p

p

I-squared

All cancers

C vs. G

<0.001

68.987

Random

1.097 (1.033-1.166)

0.002

CC vs. CG+GG

<0.001

62.672

Random

1.078 (1.007-1.154

0.031

CC+CG vs. GG

<0.001

45.629

Random

1.183 (1.053-1.331)

0.004

Colorectal cancer

C vs. G

<0.001

74.844

Random

1.255 (1.053-1.497)

0.011

CC vs. CG+GG

<0.001

73.239

Random

1.234 (1.004-1.516)

0.045

CC+CG vs. GG

0.036

49.696

Random

1.509 (1.031-2.211)

0.034

Lung cancer

C vs. G

<0.001

74.319

Random

1.097 (0.920-1.309)

0.303

CC vs. CG+GG

0.001

67.238

Random

1.081 (0.895-1.306)

0.420

CC+CG vs. GG

0.034

50.306

Random

1.175 (0.814-1.696)

0.389

Breast cancer

C vs. G

0.117

36.459

Fixed

0.990 (0.932-1.053)

0.756

CC vs. CG+GG

0.075

42.399

Fixed

0.980 (0.909-1.056)

0.596

CC+CG vs. GG

0.728

<0.001

Fixed

1.026 (0.880-1.197)

0.739

Prostate cancer

C vs. G

<0.001

90.407

Random

1.397 (0.702-2.780)

0.342

CC vs. CG+GG

0.001

85.774

Random

1.503 (0.734-3.078)

0.265

CC+CG vs. GG

<0.001

87.260

Random

1.604 (0.344-7.479)

0.548

Gastric cancer

C vs. G

0.081

55.491

Fixed

0.889 (0.752-1.050)

0.166

CC vs. CG+GG

0.131

56.157

Fixed

0.880 (0.722-1.073)

0.207

CC+CG vs. GG

0.467

<0.001

Fixed

0.802 (0.498-1.292)

0.364

Esophageal cancer

C vs. G

0.149

52.096

Fixed

0.660 (0.482-0.904)

0.148

CC vs. CG+GG

0.102

51.614

Fixed

0.627 (0.434-0.905)

0.013

CC+CG vs. GG

0.943

<0.001

Fixed

0.427 (0.144-1.263)

0.124

Head and neck cancer

C vs. G

<0.001

83.256

Random

1.396 (1.001-1.946)

0.049

CC vs. CG+GG

0.001

79.552

Random

1.406 (0.975-2.027)

0.068

CC+CG vs. GG

0.014

67.859

Random

2.037 (1.047-3.960)

0.036

Pancreatic adenocarcinoma

C vs. G

0.562

<0.001

Fixed

1.007 (0.885-1.146)

0.917

CC vs. CG+GG

0.451

<0.001

Fixed

1.051 (0.898-1.230)

0.539

CC+CG vs. GG

0.809

<0.001

Fixed

0.823 (0.578-1.172)

0.280

OR odds ratio, vs versus. Bold numbers indicate significant association with risk of cancer.

Begg&#x2019;s funnel plot for publication bias in selection of studies on the

Figure 2: Begg’s funnel plot for publication bias in selection of studies on the hOGG1 Ser326Cys polymorphism (C vs. G, C/C vs. C/G+G/G, and C/C+C/G vs. G/G in all population).

Discussion

It was suggested that cancer susceptibility could result from the interaction of genetic background. Exposure and reductions in DNA repair capacity by common genetic variation affect cancer predisposition [6]. The hOGG1 is generally involved in DNA repair, and has been studied extensively on its relationship with various types of cancer. Low OGG activity in peripheral blood mononuclear cells increases risk of lung cancer [144]. Also, lower expression of hOGG1 mRNA and hOGG1 protein decreases mitochondrial DNA repair to oxidative damage in lung cancer cells [145]. Immunohistochemical expressions in diffuse-type adenocarcinoma of gastric cardia showed lower expression of OGG1, which related to higher T-stage, lymphatic invasion, and lymph node metastasis [146]. A previous study on lung cancer patients showed a close relationship between Ser326Cys polymorphism and OGG1 mRNA levels [57]. The Ser326Cys polymorphisms have been shown to be associated with delayed repair of oxidative DNA damage [147]. In a recent study, changes in the functional and structural characteristics of the hOGG1 protein by the Ser326Cys polymorphism using in silico computational biology tools have been reported. According to this study, hOGG1 326Cys variant is smaller and more hydrophobic than wild type, which can have deleterious effects on the function of the hOGG1 protein. And this variant has been found to be closely related to breast cancer [143]. Although the relationship between hOGG1 expression and cancer risk and the Ser326Cys hOGG1 polymorphism and the expression of OGG1 has been reported, the results of previous genetic studies on the relationship between hOGG1 polymorphism and various cancers risks were conflicting and contradictory. This meta-analysis was performed to provide a quantitative approach to for the different results.

Meta-analysis on the hOGG1 polymorphism and the risk of bladder cancer, gastric cancer, and lung cancer shows no statistically significant association. But the other meta-analysis reported that the hOGG1 polymorphism may contribute to the susceptibility of digestive cancers, breast cancer, colorectal cancer, esophageal squamous cell carcinoma, hepatocellular carcinoma, head and neck cancer, and prostate cancer. Among the previous meta-analysis studies, inappropriate data was included in the analysis. Therefore, there was error. For example, Zhu et al. 2012 investigated whether the hOGG1 polymorphism was associated with prostate cancer using meta-analysis [17]. The meta-analysis included genotype data of rs3218997 SNP of OGG1 that reported by Agalliu et al. 2010 [148]. Because rs3218997 SNP is different from Ser326Cys of hOGG1, the genetic data had to be excluded for the exact meta-analysis. In addition, some previous studies included articles which were not consistent with HWE. We evaluated HWE in all the articles, some articles were excluded. So, we performed this meta-analysis to combine and update from the different results.

In present study, total of 126 genetic studies about the hOGG1 polymorphism and cancer were analyzed for meta-analysis. Significant relationship between the hOGG1 polymorphism and overall cancer risk was found. In subgroup analyses by cancer types, the significant association between the hOGG1 polymorphism and colorectal, lung, prostate, and head and neck cancer risk was detected. In addition, in subgroup analyses by ethnicities, we found that the hOGG1 polymorphism was significantly associated with overall cancer risk in both Caucasian and Asian population. But there was a little different result between Asian and Caucasian population. In Asian population, lung, breast, and head and neck cancer showed a relation with the hOGG1 polymorphism but in Caucasian population only head and neck cancer showed. On the other hands, an association with colorectal cancer and esophageal cancer was only shown in Caucasian population. Some our results were consistent with or contrary to previous meta-analysis. Overall cancer risk and the hOGG1 polymorphism was significantly associated in our results (allele, p<0.001; dominant, p=0.004; recessive, p<0.001) and previous study showed similar result [19]. Results of previous meta-analysis on lung cancer were different from our present results [18]. Our results showed the statistically significance in lung cancer (allele and recessive model in overall analysis, allele, dominant, recessive model in Asian). It is seemed that it is because the previous study included some articles which were not in HWE in their meta-analysis. Meta-analysis on colorectal cancer was consistent with ours [12]. The hOGG1 polymorphism had a connection with the colorectal cancer risk among the total population, and especially among Caucasians. One meta-analysis on breast cancer reported the association between the hOGG1 polymorphism and breast cancer risk [10] but another study suggested a lack of association [149]. Our meta-analysis showed the different results. An association between the hOGG1 polymorphism and breast cancer risk was found only in Asian population (allele, p=0.019; recessive model, p=0.026). Not only previous meta-analysis on bladder, gallbladder, and gastric cancer risk but also our present meta-analysis showed no statistically significance [9, 11, 14]. Zhang et al. reported the relation between the hOGG1 polymorphism and esophageal cancer risk [13] but meta-analysis by Wang showed no association [11]. In our study, an association between the hOGG1 polymorphism and esophageal cancer risk was found in dominant model in Caucasian population (p=0.013). Previous meta-analysis reported that the hOGG1 polymorphism had a relation to hepatocellular cancer [15]. However, we could not find an association in any model. The results of previous meta-analysis on prostate cancer consisted with ours [17] but we could not find any association in Caucasian population and some studies influenced the results according to sensitivity analysis. Similar to previous study, our study showed the relation between the hOGG1 polymorphism and head and neck cancer [16]. But recessive model in all population analysis showed publication bias and several results except for allele and dominant model in all population analysis and dominant model in Asian population analysis were influenced by some studies according to sensitivity analysis.

From dbSNP database, The C and G allele frequencies have been reported to be 0.776 and 0.224 in European, 0.500 and 0.500 in Chinese, 0.477 and 0.523 in Japanese, and 0.856 and 0.144 in Sub-Saharan African populations, respectively. And the CC, CG and GG genotype frequencies have been reported to be 0.621, 0.310, and 0.069 in European, 0.244, 0.511, and 0.244 in Chinese, 0.182, 0.591, and 0.227 in Japanese, and 0.746, 0.220, and 0.034 in Sub-Saharan African populations, respectively. In our results, the C and G allele frequencies have been reported to be 0.762 and 0.238 in Caucasian and 0.484 and 0.516 in Asian. And the CC, CG and GG genotype frequencies have been shown to be 0.584, 0.356, and 0.060 in Caucasian and 0.246, 0.477, 0.277 in Asian. We found that the genotype and allele frequencies in Caucasian and Asian showed significant difference, which might affect the roles of the hOGG1 polymorphism on cancer risk in Asians and Caucasians.

This meta-analysis has several limitations. Our results showed the genetic difference and different cancer risks in ethnicity but included studies regarded only Caucasians and Asians, but not other races like African. Because of limited data, we simply divided the ethnicity into Asian and Caucasian. The genetic heterogeneity plays an important role in the carcinogenesis but it is an interaction between environment factors and genetic background. This analysis could not reflect environmental exposures. And there were considerable inadequate data in previous studies, especially in meta-analysis. We had examined the included articles as many as possible such as HWE or data in the article but there could be still omitted data. And several results showed significant associations but revealed publication bias.

Conclusions

In spite of some limitations, this meta-analysis could provide the evidence of the strong association between the hOGG1 polymorphism and cancer risk. In summary, G allele of Ser326Cys polymorphism might play a role in the carcinogenesis and the genotype and allele frequencies difference makes the ethnicity difference in carcinogenesis. If further study with large sample size in diverse ethnic populations were performed, it would provide more precise understanding of the association between the hOGG1 polymorphism and various cancer risks. This SNP could be a candidate of biomarker for cancer screening, diagnosis, and therapy in the future.

Material and Methods

Search strategy

In order to select eligible studies about the hOGG1 polymorphism and cancer, electronic database including Pubmed, Embase, google of scholar, and KISS were investigated up to April 2015. We searched meta-analysis study about the hOGG1 polymorphism and also searched the association study between the hOGG1 polymorphism and risk of cancer. The keywords to find these studies were following: “8-oxoguanine DNA glycosylase”, “hOGG1“, or “DNA repair gene”, AND “polymorphism”, “polymorphisms”, or “variant” AND “Ser326Cys” AND “cancer or carcinoma”, or “meta analysis”. The previous meta-analysis studies about the hOGG1 polymorphism and cancer were considered as reference.

Inclusion criteria and data extraction

Selected studies were included in the meta-analysis if they met the following criteria: (1) Investigated the association study between the hOGG1 polymorphism and cancer; (2) A comparison between cancer and control; (3) Included genotype and allele distributions of Ser326Cys polymorphism for genetic analysis. The data of first author’s name, year of publication, country of origin, ethnicity of study population, sample size of cancer and control, and genotype frequencies of the hOGG1 polymorphism in cancer and control were extracted from the final selected studies. The allele distributions were calculated from genotype distributions in the cancer group and the control group. The ethnicity was divided into Asian and Caucasian.

Statistical analysis

HWE in all include studies was tested by the Chi-square test. Meta-analysis was performed using the Comprehensive Meta-analysis software. The pooled p value, OR, and 95% CI were used to assess the strength of association between risk of cancer and the hOGG1 polymorphism. All the results were re-analyzed to see the effect of each paper on the final results by sensitivity analysis. The meta-analysis was repeated while omitting each study one at a time to examine the influence of each study on the pooled OR. For the regression analysis in this meta-analysis, the random effects model or the fixed effects model was used. OR with the corresponding 95 % CI was calculated for the dominant model (C/C + C/G genotypes vs. G/G genotype) and recessive model (C/C vs. C/G + G/G genotypes), and allele (C vs. T), respectively [150,151]. The p<0.05 was regarded as statistically significant. A χ2-test-based Q statistic test was used to assess heterogeneity among studies. We also performed the effect of heterogeneity by I2 test. The random-effects Mantel–Haenszel method was adopted if the result of the Q test was p<0.05 or I2 statistic was >50 %, which indicated the statistically significant heterogeneity between the studies. Otherwise, the fixed-effects Mantel–Haenszel method was adopted. When more than 3 studies were included, Begg’s funnel plot and Egger’s test were performed to evaluated publication bias.

Acknowledgment

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2016R1A2B4012253).

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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