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Association between the ERCC2 Asp312Asn polymorphism and risk of cancer

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Oncotarget. 2017; 8:48488-48506. https://doi.org/10.18632/oncotarget.17290

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Feifan Xiao, Jian Pu, Qiongxian Wen, Qin Huang, Qinle Zhang, Birong Huang, Shanshan Huang, Aihua Lan, Yuening Zhang, Jiatong Li, Dong Zhao, Jing Shen, Huayu Wu, Yan He, Hongtao Li and Xiaoli Yang _

Abstract

Feifan Xiao1,2,*, Jian Pu3,*, Qiongxian Wen4,*, Qin Huang5,*, Qinle Zhang6,*, Birong Huang1,2, Shanshan Huang1,2, Aihua Lan1,2, Yuening Zhang1, Jiatong Li1, Dong Zhao1, Jing Shen1, Huayu Wu7, Yan He8, Hongtao Li1 and Xiaoli Yang1

1Medical Scientific Research Center, Guangxi Medical University, Nanning, Guangxi, P.R. China

2First Clinical Academy, Guangxi Medical University, Nanning, Guangxi, P.R. China

3Liver and Gall Surgical Department, The Affiliated Hospital of Youjiang Medical College for Nationalities, Baise, Guangxi, P.R. China

4School of Nursing, The Second Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, P.R. China

5Guangxi Key Laboratory of Chemistry and Engineering of Forest Products, Guangxi University for Nationalities, Nanning, Guangxi, P.R. China

6Genetic and Metabolic Central Laboratory, The Maternal and Children Health Hospital of Guangxi, Nanning, Guangxi, P.R. China

7Department of Cell Biology and Genetics, School of Premedical Sciences, Guangxi Medical University, Nanning, Guangxi, P.R. China

8Geriatrics Cardiology Division, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, P.R. China

*These authors have contributed equally to this work and should be considered as co-first authors

Correspondence to:

Xiaoli Yang, email: cncsyxl@126.com

Keywords: ERCC2 Asp312Asn, polymorphism, cancer, meta-analysis, trial sequence analysis

Received: September 23, 2016     Accepted: April 04, 2017     Published: April 20, 2017

ABSTRACT

Cancer is the leading cause of death in economically developed countries and the second leading cause of death in developing countries. The relationship between genetic polymorphisms and the risk of cancers has been widely researched. Excision repair cross-complementing group 2 (ERCC2) gene plays important roles in the nucleotide excision repair pathway. There is contrasting evidence on the association between the ERCC2 Asp312Asn polymorphism and the risk of cancer. We conducted a comprehensive meta-analysis in order to assess the correlation between these factors. We searched the PubMed, EMBASE, Science Direct, Web of Science, and CNKI databases for studies published from January 1, 2005 to January 1, 2016. Finally, 86 articles with 38,848 cases and 48,928 controls were included in the analysis. The overall analysis suggested a significant association between the ERCC2 Asp312Asn polymorphism and cancer risk. Furthermore, control source, ethnicity, genotyping method, and cancer type were used for subgroup analysis. The result of a trial sequential analysis indicated that the cumulative evidence is adequate; hence, further trials were unnecessary in the overall analysis for homozygote comparison. In summary, our results suggested that ERCC2 Asp312Asn polymorphism is associated with increased cancer risk. A significantly increased cancer risk was observed in Asian populations, but not in Caucasian populations. Furthermore, the ERCC2 Asp312Asn polymorphism is associated with bladder, esophageal, and gastric cancers, but not with breast, head and neck, lung, prostate, and skin cancers, and non-Hodgkin lymphoma. Further multi-center, well-designed studies are required to validate our results.


Association between the <i>ERCC2</i> Asp312Asn polymorphism and risk of cancer | Xiao | Oncotarget

INTRODUCTION

Cancer describes a group of diseases characterized by the uncontrolled growth and spread of abnormal cells [1]. It is the leading cause of death in economically developed countries and the second leading cause of death in developing countries [2]. According to statistics, a total of 1,658,370 new cancer cases and 589,430 cancer deaths were projected to occur in the United States in 2015 [3]. In general, cancer is the result of multiple environmental and genetic risk factors, as well as gene-environment interactions [4]. Among genetic factors, genetic and epigenetic mutations, such as aberrant DNA methylation, can lead to carcinogenesis [1].

Recently, the relationship between genetic polymorphisms and the risk of cancer has been widely researched. Among the polymorphic genes, excision repair cross-complementing group 2 (ERCC2), also called xeroderma pigmentosum group D (XPD), plays important roles in the nucleotide excision repair (NER) pathway [5]. The ERCC2 gene is located on chromosome 19q13.3, comprises 23 exons, and spans approximately 54,000 base pairs [6]. It encodes an evolutionarily conserved helicase, which has ATP-dependent helicase activity within its multi subunit core transcription factor IIH (TFIIH). The helicase participates in DNA unwinding as part of the NER pathway, and plays an important role in the recognition and repair of structurally unrelated DNA lesions containing bulky adducts and thymidine dimers [7, 8]. Some studies have shown that ERCC2 polymorphisms may be related to reduced DNA repair due to a possible reduction in its helicase activity [9, 10].

There are two important single nucleotide polymorphisms (SNPs) in the ERCC2 gene. One is the Lys751Gln polymorphism, which has been shown to be involved in genetic susceptibility to some cancer types. Another common ERCC2 polymorphism in the coding region is Asp312Asn (rs1799793) [11], which is characterized by a G to A transition at position 312 in exon 10 causing an aspartic acid (Asp) to asparagine amino acid (Asn) exchange [12]. This polymorphism has been widely studied for its association with susceptibility to cancer including brain [13], esophageal [1416], head and neck [11], bladder [1719], and breast cancers [2022]. However, the results reported by these studies were inconsistent.

To provide a comprehensive assessment of and to clarify associations between the ERCC2 Asp312Asn polymorphisms and the risk of cancer, we performed a meta-analysis of all the eligible case-control studies.

RESULTS

Eligible studies

A total of 449 articles were reviewed, and eventually 86 articles with 38,848 cases and 48,928 controls met the inclusion criteria. Among these publications, there was 1 osteosarcoma [23], 1 hepatocellular cancer (HCC) [24], 3 oral cancer [2527], 5 skin cancer [2832], 5 colorectal cancer [23, 3336], 6 head and neck cancer [3742], 6 esophageal cancer [4348], 6 non-Hodgkin lymphoma [4954], 6 prostate cancer [5560], 8 gastric cancer [6167], 12 bladder cancer [6879], 14 lung cancer [70, 8092], and 15 breast cancer [23, 32, 93105]. The detailed study selection process is shown in Figure 1. Table 1 presents the major characteristics of the 86 articles.

Flow chart showing the selection process for the included studies.

Figure 1: Flow chart showing the selection process for the included studies.

Table 1: Characteristics of the case–control studies included in the meta-analyses

First author

Year

Ethnicity

Countrya

Source of controls

Cancer site

Genotyping method

cases

controls

Asp/Asp

Asp/Asn

Asn/Asn

Asp/Asp

Asp/Asn

Asn/Asn

Liu G

2007

Caucasian

USA

HB

esophageal cancer

PCR-RFLP

75

92

16

144

160

32

An

2007

Caucasian

USA

HB

head and neck cancera

PCR-RFLP

330

395

104

370

386

98

Harth

2008

Caucasian

Germany

HB

head and neck cancera

Real-time PCR

113

158

40

101

145

52

Abbasi

2009

Caucasian

Germany

PB

head and neck cancera

Real-time PCR

93

119

34

258

304

82

Ji

2010

Asian

Korea

HB

head and neck cancera

PCR

235

29

0

309

30

3

Gugatschka

2011

Caucasian

Austria

PB

head and neck cancera

TaqMan

116

133

42

171

208

83

Smedby

2006

Caucasian

Sweden

PB

non- Hodgkin lymphoma

PCR

167

211

50

262

255

85

Shen

2006

Caucasian

USA

PB

non- Hodgkin lymphoma

Real-time PCR

199

189

57

226

238

70

Song

2008

Asian

China

HB

non- Hodgkin lymphoma

PCR-RFLP

256

47

4

265

35

3

Baris

2009

Caucasian

Turkey

HB

non- Hodgkin lymphoma

PCR-RFLP

13

16

4

15

27

10

Worrillow

2009

Caucasian

England

PB

non- Hodgkin lymphoma

TaqMan

270

265

79

316

335

79

EI-Din

2013

Caucasian

Egypt

HB

non- Hodgkin lymphoma

PCR-RFLP

30

37

14

38

44

18

Capella G

2008

Mixed

Spain

PB

gastric cancer

PCR-RFLP

110

96

38

444

532

159

Zhou RM

2007

Asians

China

PB

gastric cancer

PCR-RFLP

221

32

0

528

82

2

Lou Y

2006

Asians

China

HB

gastric cancer

PCR-RFLP

189

39

10

176

21

3

Agalliu

2010

Caucasian

USA

PB

prostate cancer

PCR-RFLP

545

575

120

527

528

166

Agalliu

2010

African

USA

PB

prostate cancer

PCR-RFLP

106

31

7

65

15

2

Moreno V

2006

Caucasian

Spain

HB

colorectal cancer

PCR

95

91

100

77

72

63

Hansen RD

2007

Caucasian

Denmark

PB

colorectal cancer

TaqMan

159

191

46

333

354

108

De Ruyck

2007

Caucasian

Belgium

HB

Lung Cancer

PCR-RFLP

44

53

13

49

46

14

Zienolddiny

2006

Caucasian

Norway

PB

Lung Cancer

PCR

119

102

54

120

121

49

Matullo

2006

Caucasian

Europe

PB

Lung Cancer

PCR-RFLP

49

48

19

418

506

170

Hu

2006

Asian

China

HB

Lung Cancer

TaqMan

850

116

4

874

111

1

Shen

2005

Asian

China

PB

Lung Cancer

PCR

109

9

0

99

14

0

Huang

2006

Mixed

USA

NA

Lung Cancer

PCR

301

300

82

301

304

93

Broberg

2005

Caucasian

Sweden

PB

bladder cancer

PCR

16

29

12

61

71

13

Matullo

2005

Caucasian

Italy

HB

bladder cancer

PCR-RFLP and TaqMan

92

153

47

103

155

47

Matullo

2006

Caucasian

European

PB

bladder cancer

TaqMan

48

60

16

418

506

170

Schabath

2005

Mixed

USA

HB

bladder cancer

PCR-RFLP

225

215

57

248

179

50

Andrew

2006

Mixed

USA

PB

bladder cancer

PCR-RFLP

113

145

38

205

251

51

Garcia-Closas

2006

Caucasian

Spain

HB

bladder cancer

PCR

517

474

138

538

467

117

Wu

2006

Caucasian

USA

HB

bladder cancer

PCR-RFLP

264

283

78

283

243

65

Fontana

2008

Caucasian

French

HB

bladder cancer

TaqMan

25

19

7

21

18

6

Chang

2009

Asian

China

HB

bladder cancer

PCR-RFLP

153

98

57

199

67

42

Gangwar

2009

Asian

India

HB

bladder cancer

PCR-RFLP

72

100

34

128

104

18

Mittal

2012

Asian

India

PB

bladder cancer

PCR

78

100

34

128

104

18

Ye

2006

Caucasian

Sweden

PB

esophageal cancer

PCR-RFLP

61

92

24

176

237

57

Tse

2008

Mixed

USA

HB

esophageal cancer

TaqMan

117

150

43

199

206

49

Pan

2009

Caucasian

USA

HB

esophageal cancer

TaqMan

16

20

1

201

185

48

Pan

2009

Caucasian

USA

HB

esophageal cancer

TaqMan

137

163

43

201

185

48

Huang

2012

Asian

China

HB

esophageal cancer

PCR-RFLP

171

42

0

298

60

0

Li

2013

Asian

China

HB

esophageal cancer

PCR-RFLP

342

56

2

351

47

2

Han

2005

Mixed

USA

PB

Skin Cancer

TaqMan

88

99

19

342

373

121

Wang LL

2009

Asian

China

HB

colorectal cancer

PCR-RFLP

132

29

9

176

21

3

Mahimkar MB

2010

Asian

India

NA

oral cancer

PCR-RFLP

23

13

4

23

21

1

Wang Y

2007

Caucasian

USA

HB

oral cancer

PCR and Taqman

50

59

16

140

109

29

Majumder M

2007

Asian

India

HB

oral cancer

PCR

269

208

52

205

146

36

Crew

2007

NA

USA

PB

breast cancer

Taqman

415

478

138

490

454

139

Jorgensen

2007

Caucasian

USA

PB

breast cancer

Taqman

110

128

22

102

142

29

Kuschel

2005

Australian

UK

PB

breast cancer

TaqMan

1529

1530

497

1401

1437

430

Lee

2005

Asian

Korea

HB

breast cancer

PCR

475

50

3

401

41

3

Bernard-Gallon

2008

NA

France

HB

breast cancer

Taqman

403

383

118

458

418

118

Debniak

2006

Polish

Poland

PB

breast cancer

PCR-RFLP

672

785

269

180

252

79

Jakubowska

2010

Polish

Poland

HB

breast cancer

PCR

118

152

44

106

135

49

Mechanic

2006

Caucasian

USA

PB

breast cancer

PCR-RFLP

543

589

130

489

516

128

Mechanic

2006

African-American

USA

PB

breast cancer

PCR-RFLP

564

181

15

517

145

13

Shen

2006

American

USA

PB

breast cancer

Taqman

60

80

16

59

64

30

Smith

2008

Caucasian

USA

HB

breast cancer

PCR

126

137

41

161

188

42

Smith

2008

African-American

USA

HB

breast cancer

PCR

33

14

2

57

16

1

Zhang

2005

Asian

China

PB

breast cancer

PCR-RFLP

89

111

20

119

140

51

Hussien

2012

Caucasian

Egypt

HB

breast cancer

PCR

12

45

43

25

50

25

Jelonek

2010

Mixed

Poland

PB

breast cancer

PCR-RFLP

41

59

21

85

123

23

Wang

2010

Asian

China

PB

breast cancer

PCR-RFLP

624

388

220

925

315

193

Zhou

2012

Asian

Asia

PB

Lung Cancer

PCR-RFLP

85

18

0

85

17

1

Sakoda

2012

Caucasian

USA

PB

Lung Cancer

TaqMan

326

329

89

610

685

182

Qian

2011

Asian

China

PB

Lung Cancer

PCR

464

82

4

497

79

3

Yin

2009

Asian

China

HB

Lung Cancer

PCR-RFLP

246

38

1

255

30

0

Raaschou-Nielsen

2008

Caucasian

Denmark

PB

Lung Cancer

PCR

177

188

59

329

351

107

Chang

2008

Latino-American

USA

PB

Lung Cancer

WGA

60

40

8

192

93

12

Chang

2008

African-American

USA

PB

Lung Cancer

WGA

186

58

3

212

60

5

Yin

2007

Asian

China

HB

Lung Cancer

PCR-RFLP

200

1

0

170

0

1

Lopez-Cima

2007

Caucasian

Spain

HB

Lung Cancer

PCR-RFLP

240

221

55

260

230

43

Han

2005

Mixed

USA

PB

Skin Cancer

TaqMan

104

149

32

342

373

121

Han

2005

Mixed

USA

PB

Skin Cancer

TaqMan

128

115

37

342

373

121

Lovatt

2005

Caucasian

UK

PB

Skin Cancer

PCR-RFLP

224

219

66

151

163

65

Li

2006

Mixed

USA

HB

Skin Cancer

PCR

242

290

70

273

259

71

Millikan

2006

Caucasian

USA

PB

Skin Cancer

PCR

1039

1098

162

1039

1098

260

Debniak

2006

Polish

Poland

mixed

Skin Cancer

PCR

168

188

69

492

597

173

Bau

2007

Asian

Taiwan

HB

prostate cancer

PCR

62

39

22

310

106

63

Mandal

2010

Asian

India

PB

prostate cancer

PCR

76

56

39

99

81

20

Lavende

2010

African

America

HB

prostate cancer

PCR and Taqman

146

39

5

510

116

5

Dhillon

2011

Caucasian

Australia

NA

prostate cancer

PCR-RFLP

71

37

8

80

42

10

Yuan T

2011

Asian

China

HB

gastric Cancer

PCR

156

18

16

133

35

12

Chen Z

2011

Asian

China

HB

gastric Cancer

PCR-RFLP

75

118

15

220

111

8

Zhang CZ

2009

Asian

China

HB

gastric Cancer

PCR-RFLP

75

117

15

132

72

8

Ruzzo A

2007

Caucasian

Italy

HB

gastric Cancer

PCR-RFLP

23

26

20

41

67

13

Deng Sl

2010

Asian

China

HB

gastric Cancer

PCR

132

15

13

118

31

11

Wu JS

2014

Asian

China

HB

HCC

PCR

138

58

22

181

70

26

Sambuddha

2015

Asian

Northeast India

NA

head and neck cancer

PCR

32

40

8

57

31

4

Benjamin

2015

Mexican

Mexica

HB

osteosarcoma

PCR

21

3

4

68

8

21

Benjamin

2015

Mexican

Mexica

HB

colorectal cancer

PCR

74

26

8

81

23

15

Benjamin

2015

Mexican

Mexica

HB

breast cancer

PCR

54

9

8

54

1

19

Min Ni

2014

Asian

China

HB

colorectal cancer

Real-time PCR

182

26

5

210

27

3

Volha P. Ramaniuk

2014

Belarusians

Belarus

HB

bladder cancer

PCR-RFLP

99

178

56

128

169

71

Aneta Mirecka

2014

Polish

Poland

PB

prostate cancer

real-time PCR

199

249

124

377

218

32

a Country of first author.

Meta-analysis

Overall analysis

In the dominant model, increased cancer risk was found with an odds ratio (OR) of 1.110 (95% confidence interval [CI] 1.078-1.143, P<0.01). In the recessive model, significantly increased risk was determined with an OR of 1.059 (95% CI 1.013-1.108, P<0.01). Furthermore, when the homozygote and heterozygote comparisons were performed, increased risk was identified, with an OR of 1.103 (95% CI 1.052-1.157, P<0.01), and an OR of 1.106 (95% CI 1.072-1.141, P<0.01), respectively. Overall, the results of our meta-analysis showed a significant association between the ERCC2 polymorphism and cancer risk (Table 2).

Table 2: Results of overall and stratified meta-analyses

Model (Comparison)

Subgroup

No. of trials

I2(%)

Pa

Fixed

Random

P for bias

homozygote comparison (Asn/Asn vs. Asp/Asp)

Total

95

68.3

0

1.103(1.052,1.157)

1.170(1.060,1.293)

0.079

PB

41

79.8

0

1.037(0.977,1.101)

1.074(0.922,1.250)

0.53

HB

49

39

0.004

1.249(1.149,1.358)

1.283(1.135,1.450)

0.462

Asia

30

48.3

0.003

1.664(1.461,1.894)

1.734(1.371,2.192)

0.961

Caucasian

37

50.8

0

0.964(0.899,1.034)

1.019(0.913,1.137)

0.041

PCR

29

65

0

1.041(0.951,1.140)

1.175(0.983,1.404)

0.054

PCR-RFLP

38

62.5

0

1.160(1.068,1.260)

1.238(1.053,1.455)

0.054

Taqman

18

24.8

0.163

1.003(0.921,1.093)

0.983(0.878,1.100)

0.16

Bladder cancer

12

56.4

0.008

1.370(1.198,1.566)

1.446(1.160,1.803)

0.191

Breast cancer

18

66.6

0

1.098(1.009,1.194)

1.042(0.871,1.246)

0.543

Esophageal cancer

7

0

0.62

1.219(0.945,1.571)

1.243(0.962,1.608)

0.074

Gastric cancer

8

65.3

0.005

1.517(1.167,1.972)

1.876(1.105,3.186)

0.258

Head and neck cancer

6

52.4

0.062

0.993(0.814,1.212)

0.989(0.707,1.384)

0.909

Lung Cancer

16

0

0.533

1.043(0.901,1.207)

1.042(0.899,1.207)

0.386

Prostate cancer

7

93.5

0

1.570(1.314,1.874)

2.038(0.848,4.894)

0.419

Skin Cancer

7

59.9

0.021

0.784(0.689,0.893)

0.818(0.657,1.020)

0.448

Non- Hodgkin lymphoma

6

0

0.782

0.998(0.811,1.229)

1.000(0.812,1.231)

0.505

heterozygote comparison (Asp/Asn vs. Asp/Asp)

Total

95

61.1

0

1.106(1.072,1.141)

1.133(1.072,1.198)

0.111

PB

41

64.7

0

1.061(1.020,1.104)

1.064(0.988,1.146)

0.889

HB

49

53.9

0

1.205(1.143,1.270)

1.229(1.128,1.339)

0.329

Asia

30

71.8

0

1.373(1.275,1.480)

1.287(1.105,1.499)

0.096

Caucasian

37

0

0.801

1.034(0.988,1.083)

1.034(0.987,1.082)

0.526

PCR

29

44.2

0.006

1.057(0.996,1.121)

1.076(0.982,1.180)

0.281

PCR-RFLP

38

70

0

1.187(1.126,1.251)

1.203(1.081,1.338)

0.745

Taqman

18

14.5

0.28

1.030(0.974,1.090)

1.039(0.973,1.109)

0.348

Bladder cancer

12

31.2

0.142

1.235(1.128,1.353)

1.265(1.125,1.423)

0.231

Breast cancer

18

70.7

0

1.086(1.025,1.149)

1.101(0.972,1.248)

0.42

Esophageal cancer

7

0

0.994

1.213(1.051,1.401)

1.213(1.051,1.401)

0.932

Gastric cancer

8

91.1

0

1.209(1.038,1.409)

1.066(0.614,1.848)

0.491

Head and neck cancer

6

27.4

0.229

1.114(0.977,1.271)

1.121(0.950,1.323)

0.334

Lung Cancer

16

0

0.808

1.000(0.918,1.090)

1.001(0.918,1.091)

0.294

Prostate cancer

7

78.4

0

1.281(1.140,1.440)

1.297(0.965,1.743)

0.879

Skin Cancer

7

36.5

0.15

1.018(0.938,1.105)

1.023(0.913,1.146)

0.868

Non- Hodgkin lymphoma

6

27.7

0.227

1.038(0.907,1.187)

1.047(0.881,1.244)

0.938

dominant model((Asn/Asn+Asp/Asn) vs. Asp/Asp)

Total

95

69.3

0

1.110(1.078,1.143)

1.143(1.078,1.212)

0.126

PB

41

75.9

0

1.060(1.021,1.101)

1.067(0.981,1.160)

0.754

HB

49

56.6

0

1.217(1.158,1.278)

1.237(1.139,1.343)

0.587

Asia

30

73.4

0

1.416(1.321,1.518)

1.336(1.153,1.547)

0.13

Caucasian

37

3.2

0.414

1.020(0.976,1.065)

1.021(0.976,1.068)

0.102

PCR

29

47.4

0.003

1.053(0.996,1.113)

1.091(0.999,1.191)

0.137

PCR-RFLP

38

74.5

0

1.191(1.133,1.251)

1.216(1.091,1.356)

0.647

Taqman

18

11.5

0.317

1.026(0.972,1.082)

1.028(0.968,1.093)

0.908

Bladder cancer

12

50.2

0.024

1.266(1.162,1.379)

1.309(1.148,1.494)

0.242

Breast cancer

17

73.4

0

1.091(1.034,1.151)

1.083(0.958,1.223)

0.962

Esophageal cancer

7

0

0.989

1.214(1.057,1.394)

1.214(1.057,1.394)

0.236

Gastric cancer

8

90.7

0

1.277(1.106,1.474)

1.229(0.745,2.027)

0.88

Head and neck cancer

6

50.7

0.071

1.091(0.963,1.236)

1.104(0.908,1.343)

0.493

Lung Cancer

15

0

0.763

1.010(0.931,1.097)

1.010(0.931,1.097)

0.474

Prostate cancer

7

89.8

0

1.353(1.213,1.509)

1.407(0.951,2.081)

0.71

Skin Cancer

7

37.6

0.142

0.968(0.895,1.046)

0.978(0.877,1.090)

0.682

Non- Hodgkin lymphoma

6

9.4

0.356

1.033(0.909,1.173)

1.035(0.901,1.189)

0.932

recessive model (Asn/Asn vs. (Asp/Asp+Asp/Asn))

Total

95

62.7

0

1.059(1.013,1.108)

1.108(1.016,1.208)

0.098

PB

41

76.4

0

1.010(0.954,1.069)

1.044(0.914,1.192)

0.501

HB

49

30.6

0.025

1.157(1.070,1.252)

1.178(1.059,1.310)

0.481

Asia

30

35.8

0.032

1.445(1.275,1.637)

1.515(1.240,1.852)

0.668

Caucasian

37

52.2

0

0.954(0.894,1.019)

1.006(0.906,1.115)

0.055

PCR

29

64.2

0

1.022(0.939,1.113)

1.131(0.959,1.335)

0.107

PCR-RFLP

38

53

0

1.087(1.006,1.175)

1.147(1.002,1.314)

0.152

Taqman

18

28.8

0.123

0.987(0.911,1.609)

0.958(0.859,1.069)

0.082

Bladder cancer

12

48.6

0.029

1.225(1.080,1.389)

1.271(1.052,1.536)

0.189

Breast cancer

17

60.1

0.001

1.062(0.981,1.149)

1.018(0.874,1.186)

0.421

Esophageal cancer

7

0

0.615

1.102(0.869,1.398)

1.130(0.888,1.437)

0.086

Gastric cancer

8

39

0.119

1.563(1.215,2.011)

1.739(1.190,2.541)

0.341

Head and neck cancer

6

35.4

0.171

0.951(0.790,1.144)

0.944(0.729,1.223)

0.815

Lung Cancer

15

0

0.806

1.046(0.910,1.203)

1.046(0.910,1.203)

0.495

Prostate cancer

7

92.4

0

1.406(1.186,1.667)

1.851(0.846,4.050)

0.357

Skin Cancer

7

63.4

0.012

0.781(0.691,0.883)

0.810(0.653,1.006)

0.557

Non- Hodgkin lymphoma

6

0

0.619

0.987(0.813,1.200)

0.989(0.814,1.203)

0.646

a P for heterogeneity.

Subgroup analysis

In order to evaluate the effects of specific study characteristics on the association between the ERCC2 polymorphism and cancer risk, we performed subgroup analysis if there were 6 or more studies. The ORs and 95% CIs were obtained from the subgroups of control source, ethnicity, genotyping method, and type of cancer. For control source subgroup, we found a significant association between the ERCC2 polymorphism and cancer risk when the source of the controls was hospital-based (HB). Meanwhile, when the studies recruited population-based (PB) control, no association was found. For ethnicity, no significant association was detected in Caucasians, but significant associations were observed in Asians. When stratified according to the genotyping method, significant associations were observed when the method was polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP). By comparison, no relationship was found when the methods used were PCR and TaqMan assay. According to the type of cancer, the ERCC2 polymorphism was associated with a significantly higher risk of bladder cancer. In contrast, we observed no association between this polymorphism and breast cancer. Similarly, the results of subgroups of other cancers indicated no association with the ERCC2 polymorphism, including head and neck, lung, prostate, and skin cancers and non-Hodgkin lymphoma. For the esophageal cancer group, a significant association was obtained in the heterozygote comparison, but not in the homozygote comparison and the recessive model. In the group with gastric cancer, the ERCC2 polymorphism was confirmed to increase the risk of cancer in the homozygote comparison and the recessive model, but not in the heterozygote comparison and the dominant model. The detailed results are shown in Table 2.

Test of heterogeneity

High heterogeneity was observed after the data were pooled (homozygote comparison: P for heterogeneity = 0, I2 = 68.3%). As shown in Table 2, when the subjects were stratified on the basis of the control source, high heterogeneity remained with PB controls (homozygote comparison: P for heterogeneity = 0, I2 = 79.8%). Additionally, in analyses of ethnicity, moderate heterogeneity was found in Asian studies (homozygote comparison: P for heterogeneity = 0.003, I2 = 48.3%), and high heterogeneity was found in Caucasian studies (homozygote comparison: P for heterogeneity = 0, I2 = 50.8%). Moreover, in analyses of genotyping methods, low heterogeneity was detected in the TaqMan group (homozygote comparison: P for heterogeneity = 0.163, I2 = 24.8%), but high heterogeneity was found in the PCR (homozygote comparison: P for heterogeneity = 0, I2 = 65%) and PCR-RFLP groups (homozygote comparison: P for heterogeneity = 0, I2 = 62.5%). Furthermore, heterogeneity was not detected in esophageal cancer studies (homozygote comparison: P for heterogeneity = 0.62, I2 = 0.0%), lung cancer studies (homozygote comparison: P for heterogeneity = 0.533, I2 = 0.0%), and non-Hodgkin lymphoma studies (homozygote comparison: P for heterogeneity = 0.782, I2 = 0.0%). Nonetheless, high heterogeneity was still present in studies of prostate cancer (homozygote comparison: P for heterogeneity = 0, I2 = 93.5%), bladder cancer (homozygote comparison: P for heterogeneity = 0.008, I2 = 56.4%), breast cancer (homozygote comparison: P for heterogeneity = 0, I2 = 66.6%), gastric cancer (homozygote comparison: P for heterogeneity = 0.005, I2 = 65.3%), head and neck cancer (homozygote comparison: P for heterogeneity = 0.062, I2 = 52.4%), and skin cancer (homozygote comparison: P for heterogeneity = 0.021, I2 = 59.9%).

Publication bias and sensitivity analysis

We used the Begg's funnel plot to estimate publication bias. There was no statistical evidence of publication bias in the overall analysis under each model (Figure 2). Table 2 shows the P details for bias. We also removed studies one by one to determine their effect on the test of heterogeneity, and evaluated the stability of the overall results; the results did not change in the overall analysis (Supplementary Table 1) neither in other analysis.

Figure 2:

Figure 2: (A) Begg’s funnel plot for the publication bias test in the overall analysis under homozygote comparison. (B) Begg’s funnel plot for the publication bias test in the overall analysis under heterozygote comparison. (C) Begg’s funnel plot for the publication bias test in the overall analysis under dominant model. (D) Begg’s funnel plot for the publication bias test in the overall analysis under recessive model.

Trial sequential analysis (TSA)

In the overall analysis for homozygote comparison, the required information size was 72,622 patients to demonstrate the issue (Figure 3), and the result showed that the Z-curve had crossed the trial monitoring boundary before reaching the required information size, indicating that the cumulative evidence is adequate and further trials are unnecessary.

TSA for overall analysis under homozygote comparison.

Figure 3: TSA for overall analysis under homozygote comparison.

DISCUSSION

Nowadays, cancer is one of the most important global public health problems [106]. Personalized analysis and improved methods of cancer diagnoses can be provided, based on an understanding of the association between genetic polymorphisms and cancer risk [107]. In the relationship between gene polymorphisms and cancer risk, the ERCC2 Asp312Asn polymorphism is an important risk factor. Impaired DNA repair capacity is a risk factor for the development of cancer. The ERCC2 Asp312Asn polymorphism influences DNA repair through the NER pathway. To date, many publications have shown an association between the ERCC2 Asp312Asn polymorphism and risk of cancer. However, the results remain controversial. In order to resolve this conflict, we performed a meta-analysis that evaluates the relationship between the ERCC2 Asp312Asn polymorphism and risk of cancer.

In our meta-analysis, the association of the ERCC2 Asp312Asn polymorphism with the risk of cancer was evaluated in 38,848 cases and 48,928 controls. A significant association was observed between the ERCC2 Asp312Asn polymorphism and overall cancer risk in all genetic models. To the best of our knowledge, this is the most comprehensive meta-analysis on this topic until now. Moreover, the result of the TSA indicated that the cumulative evidence is adequate and further trials are unnecessary in the overall analysis for homozygote comparison.

In the subgroup analysis based on ethnicity, a significantly increased cancer risk was observed in Asian populations, but not in Caucasian populations. One possible reason for these discrepancies is that different ethnicities may have distinct genetic backgrounds, and therefore, tumor susceptibility can be influenced by ethnicity [108]. Moreover, this may indicate that these groups have distinct environmental or genetic cancer co-etiologies [109]. In subgroup analysis based on the control source, we found that a significantly increased cancer risk was observed in HB studies, but not in PB studies. The former may have certain biases for such controls and may only represent a sample of an ill-defined reference population. Furthermore, HB controls may not be representative of the general population or it may be that numerous subjects in the PB controls were individuals susceptible to cancer [110]. In the subgroup analysis based on the genotyping method, a significantly increased cancer risk was found in the PCR-RFLP studies, but not in the PCR or TaqMan studies. A possible reason for this may be that the different genotyping methods are specialized for different aspects, and the results would be more accurate and reliable if the same genotyping method was applied in different studies [111].

In the subgroup analysis according to the cancer site, a significant association with the ERCC2 Asp312Asn polymorphism was observed for bladder, esophageal, and gastric cancers; however, no significant association was observed for breast, head and neck, lung, prostate, and skin cancers, and non- Hodgkin lymphoma. Some previous meta-analyses assessed the effect of the ERCC2 Asp312Asn polymorphism on the risk of these cancers and reached conclusions consistent with those of our study. For example, Li et al. [19] and Wen et al. [14] suggested that the ERCC2 Asp312Asn polymorphism might be associated with an increased risk of bladder cancer and esophageal cancer, respectively. Yin et al. [48] showed that this polymorphism might be a potential biomarker of gastric cancer susceptibility in the overall population. In contrast, Yan et al. [21], Hu et al. [11], and Zhu et al. [112] suggested that the ERCC2 Asp312Asn polymorphism was not associated with breast cancer, head and neck cancer, and skin cancer, respectively. Moreover, Chen et al. [113], Feng et al. [12], and Ma et al. [114] suggested that the ERCC2 Asp312Asn polymorphism contributed to the risk of non-Hodgkin lymphoma, lung cancer, and prostate cancer, respectively. Because we only included studies published from 2005 to 2016, we drew different conclusions in lung cancer and prostate cancer studies. Therefore, more research should be undertaken in the future. Moreover, the exact mechanism for the associations between different cancer sites and the ERCC2 Asp312Asn polymorphism is not clear; the mechanism of carcinogenesis may differ between different cancer sites and the ERCC2 genetic variants may exert varying effects in different cancers [115].

Notably, HCC, osteosarcoma, oral cancer, and colorectal cancer were not included for further analysis as there were fewer than 6 studies available for analysis for such cancers. Wu et al. indicated that the ERCC2 Asp312Asn polymorphism was not associated with the development of HCC [24]. Gomez-Diaz et al. demonstrated no relationship between ERCC2 Asp312Asn polymorphism and osteosarcoma [23]. Interestingly, based on a study by Mahimkar et al. this polymorphism was associated with an overall increase in chromosomal damage in oral cancer [25]. Wang et al. [35] observed a slightly lower statistical significance between the ERCC2 Asp312Asn polymorphism and colorectal cancer. In fact, this polymorphism has also been shown to be related to other diseases; previous studies have indicated that it may have a role in the development of ultraviolet-related diseases, such as maturity onset cataract. [116]. However, no significant association of this polymorphism was found with either idiopathic azoospermia [117] or arsenic-related skin lesions [118]. Therefore, the equivocal association between the ERCC2 Asp312Asn polymorphism and some diseases remains to be confirmed.

Heterogeneity is a major concern for meta-analysis [119]. In our overall analysis, high heterogeneity was observed for all genetic models. However, when data were pooled in to subgroups according the control source, ethnicity, genotyping method, and cancer type, the heterogeneity decreased. Sensitivity analysis showed that the results have sufficient statistical power. There are some limitations of our meta-analysis that should be addressed. First, subgroup analysis cannot be conducted based on sex, age, lifestyle, and other factors owing to insufficient data. Second, some cancers, such as oral cancer and colorectal cancer, were not suitable for further analysis because of the small sample sizes. Thus, more studies on these cancers should be conducted in the future. Third, a single gene has only a moderate effect on cancer development; hence, the ERCC2 gene may influence susceptibility of cancer along with other genes. However, enough data for further analysis is not available. Finally, only published articles were included in the analysis; therefore, unpublished data may modify our conclusions.

In summary, our meta-analysis suggested that the ERCC2 Asp312Asn polymorphism is associated with increased cancer risk. A significantly increased cancer risk was observed in Asian populations, but not in Caucasian populations. Moreover, our results indicated that this polymorphism is associated with bladder, esophageal, and gastric cancers, but not with breast, head and neck, lung, prostate, and skin cancers, and non-Hodgkin lymphoma. In addition, stratification analyses based on the control source also indicated that this polymorphism was associated with cancer risk in the HB populations, but not in the PB populations. In subgroup analysis according to the genotyping method, a significantly increased cancer risk was found in the PCR-RFLP studies, but not in the PCR and TaqMan studies. Considering the limitations of this study, further multi-center, well-designed research should be undertaken in the future.

MATERIALS AND METHODS

Literature search

A systematic search of articles relating to the ERCC2 Asp312Asn polymorphism and cancer was conducted by 2 researchers, using the PubMed, EMBASE, Science Direct, Web of Science and the China National Knowledge Infrastructure (CNKI) databases. The search included studies published between January 1, 2005 and January 1, 2016. The search strategy was based on various combinations of the following terms: “xeroderma pigmentosum group d protein “[MeSH Terms] OR “xeroderma pigmentosum group d protein” [All Fields] OR “ercc2” [All Fields]) AND Asp312Asn [All Fields] AND (“neoplasms” [MeSH Terms] OR “neoplasms” [All Fields] OR “cancer” [All Fields]. In addition, the reference lists of the publications identified were searched for further relevant studies. The PRISMA Checklist was used for this meta-analysis (Supplementary Table 2).

Selection criteria

The following inclusion criteria were set and reviewed by two independent investigators: (I) case-control study; (II) evaluation of the ERCC2 Asp312Asn polymorphism and cancer; and (III) detailed data available for calculating ORs and the corresponding 95% CIs. Studies were excluded if they: (I) had no control population; (II) were review articles or previous meta-analyses; (III) contained insufficient or duplicate data; or (IV) had no full text available.

Data extraction

Two authors performed data extraction independently. For all publications, the following data were extracted: first author, year of publication, ethnicity of the population, country, source of cases and controls, cancer site, genotyping method, and number of cases and controls.

Trial sequential analysis

To evaluate whether our meta-analysis had sufficient sample size to reach firm conclusions about the effect of interventions [120], TSA was used in this meta-analysis. If the cumulative Z curve in results exceeds the TSA boundary, a sufficient level of evidence for the anticipated intervention effect may have been reached and no further trials are needed. However, when the Z curve does not exceed the TSA boundaries and the required information size has not been reached, evidence to draw a conclusion is insufficient [121]. We used two-sided tests, type I error set at 5%, and power set at 80%. The required information size was calculated based on a relative risk reduction of 10%. Trials ignored in interim appear to be due to too low use of information (<1.0%) by the software. TSA was performed using the TSA software (version 0.9.5.5).

Statistical analysis

The primary objective of our meta-analysis was to calculate ORs and their 95% CIs to evaluate the association between ERCC2 Asp312Asn and cancer risks. In our included studies, no clear models had been chosen; thus, the following genetic models were used: homozygote comparison (Asn/Asn vs. Asp/Asp), heterozygote comparison (Asp/Asn vs. Asp/Asp), recessive model (Asn/Asn vs. Asp/Asp+Asp/Asn), and dominant model (Asn/Asn+Asp/Asn vs. Asp/Asp). The statistical heterogeneity assumption was evaluated using I2 statistics to quantify any inconsistency arising from inter-research variability that was derived from heterogeneity instead of random chance [107]. An I2 value from 0-25% indicates low heterogeneity, 25-50% moderate heterogeneity and ≥50% high heterogeneity [122]. Two models (fixed-effect model and random-effect model) were used for analysis [123]. When I2< 50%, we used a fixed effect model and when I2 ≥50%, we performed a random effect model [124, 125]. We used sensitivity analyses by omitting each study in turn to determine the effect of heterogeneity on the test, and evaluated the stability of the overall results [107]. Potential publication bias was assessed using the Begg's linear regression test [126]. Notably, subgroup analysis was not performed when there were fewer than 6 studies available, because the small number may have resulted in insufficient power [107]. All statistical analyses were performed using the STATA statistical software package (version 12.0; StataCorp, College Station, TX).

Abbreviations

nucleotide excision repair (NER); excision repair cross-complementing group 2 (ERCC2); Xeroderma pigmentosum group D (XPD); transcription factor IIH (TFIIH); single nucleotide polymorphisms (SNPs); asparagine amino acid (Asn); hospital-based (HB); population-based (PB); hepatocellular cancer (HCC); China National Knowledge Infrastructure (CNKI); trial sequential analysis (TSA).

ACKNOWLEDGMENTS

The authors gratefully acknowledge the National Natural Science Foundation of China (Grants number: 81160097; 21463006); the Guangxi Natural Science Foundation (Grants number: 2011GXNSFA018175, 2013GXNSFGA019005, 2016GXNSFDA380010); the Guangxi scientific research and technology development project (Grant number: Guikegong1355005-5-7); Youth Science Foundation of Guangxi Medical University (Grant number: GXMUYSF2014014); Guangxi medical and healthcare technology research and development project contract (Grant number: S201303-06); Students’ platform for innovation and entrepreneurship training program (Grants Number: 201510598003, 201610598112). The authors thank Editage English service for language editing services.

CONFLICTS OF INTEREST

The authors declare no conflicts of interest.

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