Oncotarget

Research Papers:

XPG gene polymorphisms and cancer susceptibility: evidence from 47 studies

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Oncotarget. 2017; 8:37263-37277. https://doi.org/10.18632/oncotarget.16146

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Jiawen Huang, Xiaoqi Liu, Ling-Ling Tang, Jian-Ting Long, Jinhong Zhu, Rui-Xi Hua and Jufeng Li _

Abstract

Jiawen Huang1, Xiaoqi Liu2, Ling-Ling Tang3, Jian-Ting Long4, Jinhong Zhu5, Rui-Xi Hua4 and Jufeng Li1

1Department of Pharmacy, The First Affiliated Hospital of Jinan University, Guangzhou 510630, Guangdong, China

2Department of Pharmacy, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, Guangdong, China

3School of Public Health, Sun Yat-sen University, Guangzhou 510060, Guangdong, China

4Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, Guangdong, China

5Molecular Epidemiology Laboratory and Department of Laboratory Medicine, Harbin Medical University Cancer Hospital, Harbin 150040, Heilongjiang, China

Correspondence to:

Jufeng Li, email: [email protected]

Keywords: XPG, polymorphism, cancer, meta-analysis

Received: January 09, 2017    Accepted: February 15, 2017    Published: March 13, 2017

ABSTRACT

Xeroderma pigmentosum group G (XPG) is a single-strand-specific DNA endonuclease that functions in the nucleotide excision repair pathway. Genetic variations in XPG gene can alter the DNA repair capacity of this enzyme. We evaluated the associations between six single nucleotide polymorphisms (SNPs) in XPG (rs1047768 T>C, rs2296147 T>C, rs2227869 G>C, rs2094258 C>T, rs751402 C>T, and rs873601 G>A) and cancer risk. Forty-seven studies were identified in searches of the PubMed, Scopus, Web of Science, China National Knowledge Infrastructure, and WanFang databases. Crude odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using a fixed or random effects model. We found that rs873601 G>A was associated with an increased overall cancer risk (AA vs. GG: OR = 1.14, 95% CI = 1.06–1.24; GA/AA vs. GG: OR = 1.08, 95% CI = 1.02–1.15; A vs. G: OR = 1.06, 95% CI = 1.02–1.10). In a stratified analysis, rs1047768 T>C was associated with an increased risk of lung cancer, rs2227869 G>C was associated with a decreased risk of cancer in population-based studies, and rs751402 C>T and rs873601 G>A were associated with the risk of gastric cancer. Our data indicate that rs873601 G>A is associated with cancer susceptibility.


INTRODUCTION

There were an estimated 14.1 million new cancer cases and 8.2 million cancer-related deaths in 2012 worldwide [1, 2]. Although recent advances in the diagnosis and treatment of various cancers have improved patient prognosis, most malignancies still impose a heavy burden on society. Cancer is a multifactorial, chronic disease caused by both endogenous (genetic, immune, and endocrine disorders) and exogenous factors (environmental carcinogens and unhealthy behaviors) [1]. Among these etiological factors, gene-environment interactions have been shown to play key roles in cancer development.

The maintenance of genomic integrity is essential for human health. However, DNA damage can occur due to exposure to various chemicals, environmental agents, and ultraviolet radiation. DNA damage can also occur naturally. For example, metabolic processes can generate compounds that damage DNA, which include reactive oxygen and reactive nitrogen species. There are five major DNA damage repair pathways in humans: nucleotide excision repair (NER), base excision repair, double-strand break repair, mismatch repair, and homologous recombination [3]. Failure to properly repair DNA damage can lead to tumorigenesis. The versatile NER pathway is responsible for excising DNA lesions including cross-links, bulky adducts, thymidine dimers, alkylating damage, and oxidative DNA damage [3].

There are at least eight core functional genes in the NER pathway. These include Excision repair cross complementing group 1 (ERCC1) and Xeroderma pigmentosum group (XP) A-G. XPG, also known as ERCC5, is located on chromosome 13q22-q33 [4]. The XPG gene encodes a single-strand specific DNA endonuclease of 1,186 amino acids that cleaves the damaged DNA strand at the 3’ end [5]. Defects in the XPG gene can impair DNA repair resulting in genomic instability and carcinogenesis [6]. Single nucleotide polymorphisms (SNPs) in the XPG gene have been associated with various cancers including colorectal [7], lung [8, 9], gastric [10, 11], and laryngeal [12]. However, different studies have achieved conflicting results. For example, Duan et al. found that rs2296147 T>C in XPG was associated with an increased risk of gastric cancer [13], but this association was not replicated in other studies [10, 11]. The discordances might be attributed to the limited sample sizes of individual studies, different sources of controls, and ethnic variation. In this study, we performed a meta-analysis of the associations between six potentially functional SNPs: rs1047768 T>C, rs2296147 T>C, rs2227869 G>C, rs2094258 C>T, rs751402 C>T, and rs873601 G>A in the XPG gene and the risk of cancer.

RESULTS

Study characteristics

A total of 215 articles were identified using the Web of Science, Scopus, and PubMed. An additional 26 potential relevant articles were identified in the CNKI and WanFang databases. After screening the titles and abstracts, 135 studies remained for further full-text review. We excluded 17 meta-analyses and reviews as well as 69 studies that did not assess the SNPs of interest. A detailed assessment was then performed of 49 studies. Two of these studies were removed, one because there was a lack of detailed genotype data and the other because of study population overlap. The final meta-analysis included 47 articles. There were 22 articles with 12,833 cases and 151,86 controls for rs1047768 T>C [7-9, 12, 14-31], 14 studies with 11,327 cases and 12,684 controls for rs2296147 T>C [9-11, 13, 18, 24, 26-28, 32-37], 11 studies with 5,898 cases and 7,448 controls for rs2227869 G>C [8, 9, 14, 17, 18, 20, 22, 25, 38-40], 17 studies with 9,826 cases and 10,552 controls for rs2094258 C>T [10, 11, 18, 24, 26-28, 34-37, 41-46], 21 studies with 10,369 cases and 11,207 controls for rs751402 C>T [10, 13, 24, 26-29, 31, 32, 36, 37, 42-45, 47-52], and 14 studies with 10,873 cases and 12,535 controls for rs873601 G>A [9-11, 18, 24, 26-28, 32, 34, 36, 52-54]. A flow chart summarizing the process of relevant study identification is shown in Figure 1, and the study characteristics are shown in Table 1.

Flow diagram showing the process used to identify eligible studies.

Figure 1: Flow diagram showing the process used to identify eligible studies.

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

Author

Year

Country

Ethnicity

Source

Cancer

Case

Control

MAF

HWE

Score

BB

Bb

bb

All

BB

Bb

bb

All

rs1047768 T>C

Shen M

2005

China

Asian

PB

Lung

55

49

14

118

63

36

13

112

0.28

0.037

10

Zienolddiny S

2006

Norway

Caucasian

PB

Lung

60

119

137

316

109

126

138

373

0.54

<0.001

11

Moreno V

2006

Spain

Caucasian

HB

Colorectal

114

184

53

351

105

164

51

320

0.42

0.325

11

Garcia-Closas M

2006

Spain

Caucasian

HB

Bladder

188

530

385

1103

222

506

366

1094

0.57

0.052

12

Xie WM

2007

China

Asian

PB

HCC

194

195

38

427

235

196

48

479

0.30

0.451

11

Abbasi R

2009

Germany

Caucasian

PB

Laryngeal

43

127

78

248

115

320

212

647

0.57

0.762

13

Hussain SK

2009

China

Asian

PB

Gastric

97

61

12

170

189

168

29

386

0.29

0.173

13

Ma H

2012

USA

Caucasian

HB

SCCHN

184

506

369

1059

179

507

379

1065

0.59

0.669

11

Sakoda LC

2012

USA

Caucasian

PB

Lung

108

378

256

742

245

722

507

1474

0.59

0.656

15

He J

2013

China

Asian

HB

Gastric

571

469

85

1125

610

474

112

1196

0.29

0.155

13

Paszkowska-Szczur K

2013

Poland

Caucasian

PB

Melanoma

128

291

214

633

242

623

465

1330

0.58

0.189

13

Li X

2014

China

Asian

HB

Laryngeal

49

101

60

210

46

97

67

210

0.55

0.333

9

Mirecka A

2014

Poland

Caucasian

HB

Prostate

128

272

221

621

154

368

259

781

0.57

0.260

9

Li XC

2014

China

Asian

HB

Gastric

37

95

85

217

29

93

95

217

0.65

0.414

8

Na N

2015

China

Asian

HB

Breast

161

140

24

325

171

134

20

325

0.27

0.352

10

Paszkowska-Szczur K

2015

Poland

Caucasian

HB

Colorectal

104

221

138

463

242

623

465

1330

0.58

0.189

9

He J

2016

China

Asian

HB

Neuroblastoma

135

93

20

248

307

198

26

531

0.24

0.409

10

Hua RX

2016

China

Asian

HB

Colorectal

970

758

173

1901

1023

812

142

1977

0.28

0.266

10

Hua RX

2016

China

Asian

HB

Gastric

607

445

90

1142

625

461

87

1173

0.27

0.875

11

Li RJ

2016

China

Asian

HB

Gastric

57

92

67

216

68

87

61

216

0.48

0.004

7

Wang MY

2016

China

Asian

HB

Prostate

491

433

80

1004

534

440

81

1055

0.29

0.461

10

Bai Y

2016

China

Asian

HB

Gastric

41

98

55

194

32

106

87

225

0.62

0.975

6

rs2296147 T>C

Shao MH

2007

China

Asian

HB

Lung

570

304

52

926

590

358

31

979

0.21

0.008

10

Doherty JA

2011

USA

Mixed

PB

Endometrial

194

356

165

715

199

364

157

720

0.47

0.696

11

Duan Z

2012

China

Asian

HB

Gastric

257

122

24

403

260

132

11

403

0.19

0.232

11

He J

2012

China

Asian

HB

Gastric

700

371

54

1125

742

398

56

1196

0.21

0.779

13

Ma H

2012

USA

Caucasian

HB

SCCHN

280

532

244

1056

294

543

228

1065

0.47

0.440

11

Sakoda LC

2012

USA

Caucasian

PB

Lung

182

385

174

741

407

723

341

1471

0.48

0.565

15

Zhu ML

2012

China

Asian

HB

ESCC

757

305

53

1115

699

368

50

1117

0.21

0.860

13

Yang WG

2012

China

Asian

HB

Gastric

208

105

24

337

196

110

41

347

0.28

<0.001

9

Yang B

2013

China

Asian

HB

Prostate

37

49

143

229

25

46

167

238

0.80

<0.001

8

Na N

2015

China

Asian

HB

Breast

188

104

33

325

199

98

28

325

0.24

0.003

9

Sun Z

2015

China

Asian

HB

NPC

119

177

76

372

111

180

80

371

0.46

0.660

11

Chen YZ

2016

China

Asian

HB

Gastric

442

217

33

692

475

264

32

771

0.21

0.535

11

He J

2016

China

Asian

HB

Neuroblastoma

160

79

9

248

343

170

18

531

0.19

0.583

10

Hua RX

2016

China

Asian

HB

Colorectal

1169

644

88

1901

1213

692

72

1977

0.21

0.027

9

Hua RX

2016

China

Asian

HB

Gastric

725

364

53

1142

746

388

39

1173

0.20

0.182

11

rs2227869 G>C

Shen M

2005

China

Asian

PB

Lung

103

14

1

118

100

11

0

111

0.05

0.583

11

Garcia-Closas M

2006

Spain

Caucasian

HB

Bladder

1050

91

2

1143

1046

90

0

1136

0.04

0.164

12

Huang WY

2006

USA

Caucasian

PB

Colorectal

598

52

1

651

601

60

1

662

0.05

0.694

14

Hooker S

2008

USA

African

HB

Prostate

234

20

0

254

274

27

0

301

0.05

0.415

7

Hussain SK

2009

China

Asian

PB

Gastric

174

13

0

187

314

56

3

372

0.08

0.773

13

Ma H

2012

USA

Caucasian

HB

SCCHN

987

70

2

1059

974

90

2

1066

0.04

0.958

11

Sakoda LC

2012

USA

Caucasian

PB

Lung

1

63

680

744

2

110

1362

1474

0.96

0.886

15

Santos LS

2013

Portugal

Caucasian

HB

Thyroid

99

6

1

106

184

27

1

212

0.02

0.993

8

Paszkowska-Szczur K

2013

Poland

Caucasian

PB

Melanoma

567

67

2

636

1168

162

2

1332

0.06

0.137

13

Mirecka A

2014

Poland

Caucasian

HB

Prostate

485

83

3

571

682

99

1

782

0.06

0.181

9

Paszkowska-Szczur K

2015

Poland

Caucasian

HB

Colorectal

372

55

2

429

1168

162

2

1332

0.06

0.137

9

rs2094258 C>T

He J

2012

China

Asian

HB

Gastric

457

518

150

1125

457

560

179

1196

0.62

0.728

13

Ma H

2012

USA

Caucasian

HB

SCCHN

706

295

37

1038

721

291

41

1053

0.82

0.092

11

Yang WG

2012

China

Asian

HB

Gastric

131

149

57

337

145

166

36

347

0.66

0.252

10

Zhu ML

2012

China

Asian

HB

ESCC

414

524

177

1115

424

525

168

1117

0.61

0.793

13

Yang B

2013

China

Asian

HB

Prostate

61

75

93

229

58

75

105

238

0.40

<0.001

9

Na N

2015

China

Asian

HB

Breast

102

157

66

325

131

147

47

325

0.63

0.581

10

Sun Y

2015

China

Asian

HB

Laryngeal

140

106

25

271

152

101

18

271

0.75

0.826

11

Sun Z

2015

China

Asian

HB

NPC

209

68

95

372

211

66

94

371

0.66

<0.001

10

Chen YZ

2016

China

Asian

HB

Gastric

287

304

101

692

291

368

112

771

0.62

0.803

11

He J

2016

China

Asian

HB

Neuroblastoma

116

93

39

248

203

254

74

531

0.62

0.701

10

Hua RX

2016

China

Asian

HB

Colorectal

797

856

248

1901

899

881

197

1977

0.68

0.378

10

Feng YB

2016

China

Asian

HB

Gastric

15

75

87

177

15

96

127

238

0.26

0.577

6

Hua RX

2016

China

Asian

HB

Gastric

499

508

135

1142

527

524

122

1173

0.67

0.623

11

Lu JJ

2016

China

Asian

HB

Gastric

17

67

100

184

13

72

121

206

0.24

0.605

6

Ma SH

2016

China

Asian

HB

Breast

27

136

157

320

15

96

127

238

0.26

0.577

7

Yang LQ

2016

China

Asian

HB

Gastric

71

74

10

155

121

111

14

246

0.72

0.076

6

Ying MF

2016

China

Asian

HB

Pancreatic

87

92

16

195

117

115

22

254

0.69

0.400

7

rs751402 C>T

Shao MH

2007

China

Asian

HB

Lung

105

429

433

967

110

425

448

983

0.67

0.544

11

Yoon AJ

2011

Taiwan

Asian

HB

HCC

11

52

33

96

32

137

167

336

0.70

0.614

6

Duan Z

2012

China

Asian

HB

Gastric

47

181

172

400

29

165

206

400

0.72

0.605

11

He J

2012

China

Asian

HB

Gastric

148

491

486

1125

137

499

560

1196

0.68

0.110

13

Zavras AI

2012

Taiwan

Mixed

HB

OSCC

31

110

98

239

32

137

167

336

0.70

0.614

9

Meng X

2013

China

Asian

HB

Salivary gland

11

63

59

133

23

55

64

142

0.64

0.065

8

Na N

2015

China

Asian

HB

Breast

45

152

128

325

41

147

137

325

0.65

0.872

10

Sun Z

2015

China

Asian

HB

NPC

237

118

17

372

235

117

19

371

0.21

0.377

11

Wang H

2016

China

Asian

HB

Breast

1

10

90

101

11

39

51

101

0.70

0.398

9

Chen YZ

2016

China

Asian

HB

Gastric

93

313

286

692

89

331

351

771

0.67

0.416

11

He J

2016

China

Asian

HB

Neuroblastoma

38

114

96

248

82

241

208

531

0.62

0.380

10

Hua RX

2016

China

Asian

HB

Colorectal

248

860

792

1900

301

952

724

1977

0.61

0.680

10

Guo BW

2016

China

Asian

HB

Gastric

22

73

47

142

21

136

117

274

0.68

0.029

5

Feng YB

2016

China

Asian

HB

Gastric

24

83

70

177

28

107

101

236

0.65

0.967

6

Hua RX

2016

China

Asian

HB

Gastric

161

555

426

1142

189

551

433

1173

0.60

0.537

11

Li RJ

2016

China

Asian

HB

Gastric

22

106

88

216

18

103

95

216

0.68

0.174

8

Lu JJ

2016

China

Asian

HB

Gastric

24

91

69

184

22

97

87

206

0.66

0.510

6

Ma SH

2016

China

Asian

HB

Breast

43

150

127

320

28

101

107

236

0.67

0.580

7

Yang LQ

2016

China

Asian

HB

Gastric

33

73

49

155

32

111

103

246

0.64

0.807

6

Wang MY

2016

China

Asian

HB

Prostate

104

458

442

1004

111

467

477

1055

0.67

0.834

10

Zhou RM

2016

China

Asian

HB

Gastric

61

196

174

431

46

193

193

432

0.67

0.827

12

rs873601 G>A

Shao MH

2007

China

Asian

HB

Lung

260

493

220

973

277

494

217

988

0.47

0.907

11

He J

2012

China

Asian

HB

Gastric

274

560

291

1125

327

605

264

1196

0.47

0.616

13

Ma H

2012

USA

Caucasian

HB

SCCHN

66

427

565

1058

83

411

572

1066

0.73

0.445

11

Sakoda LC

2012

USA

Caucasian

PB

Lung

51

299

392

742

107

584

783

1474

0.73

0.894

15

Yang WG

2012

China

Asian

HB

Gastric

96

163

78

337

91

164

91

346

0.50

0.333

10

Zhu ML

2012

China

Asian

HB

ESCC

314

566

235

1115

311

565

241

1117

0.47

0.601

13

Na N

2015

China

Asian

HB

Breast

99

156

70

325

109

150

66

325

0.43

0.276

10

Zhao F

2015

China

Asian

HB

Pancreatic

105

111

30

246

118

107

21

246

0.30

0.637

8

Chen YZ

2016

China

Asian

HB

Gastric

172

333

187

692

205

396

170

771

0.48

0.415

11

He J

2016

China

Asian

HB

Neuroblastoma

70

112

66

248

137

270

124

531

0.49

0.686

10

Wang B

2016

China

Asian

HB

HCC

163

271

104

538

271

408

214

893

0.47

0.014

12

Hua RX

2016

China

Asian

HB

Colorectal

476

954

471

1901

550

1025

402

1977

0.46

0.057

10

Hua RX

2016

China

Asian

HB

Gastric

311

557

274

1142

323

598

252

1173

0.47

0.424

11

Zhou RM

2016

China

Asian

HB

Gastric

115

215

101

431

132

200

100

432

0.46

0.152

12

Abbreviations: HB, hospital-based; PB, population-based; PCR-RFLP, polymerase chain reaction-restriction fragment length polymorphism; MAF, minor allele frequency; HWE, Hardy-Weinberg equilibrium; HCC, hepatocellular carcinoma; SCCHN, squamous cell carcinoma of the head and neck; ESCC, esophageal squamous cell carcinoma; OSCC, oral squamous cell carcinoma; NPC, nasopharyngeal carcinoma.

Meta-analysis results

We observed no significant association between rs1047768 T>Cand overall cancer risk (Table 2). However, in stratified analysis, rs1047768 T>C was associated with an increased risk of lung cancer under homozygous [odds ratio (OR) = 1.32, 95% confidence interval (CI) = 1.06–1.64], heterozygous (OR = 1.35, 95% CI = 1.10–1.65), dominant (OR = 1.35, 95% CI = 1.12–1.63), and allele contrast (OR = 1.14, 95% CI = 1.02–1.27) models.

Table 2: Associations between the six SNPs in the XPG gene and cancer risk

Variables

No. of studies

No. of cases

No. of controls

Homozygous

Heterozygous

Recessive

Dominant

Allele

OR
(95% CI)

P het

OR
(95% CI)

P het

OR
(95% CI)

P het

OR
(95% CI)

P het

OR
(95% CI)

P het

rs1047768 T>C

CC vs. TT

CT vs. TT

CC vs. CT/TT

CC/CT vs. TT

C vs. T

All

22

12833

15186

1.03 (0.951.11)

0.010

1.03 (0.971.09)

0.192

1.00 (0.931.07)

0.171

1.03 (0.981.09)

0.038

1.01 (0.981.05)

0.012

Ethnicity

 Caucasian

9

5536

7084

1.03 (0.881.21)

0.012

1.04 (0.951.14)

0.061

1.00 (0.931.07)

0.344

1.04 (0.901.20)

0.011

1.01 (0.941.10)

0.011

 Asian

13

7297

8102

1.03 (0.921.16)

0.081

1.02 (0.961.10)

0.493

1.00 (0.901.11)

0.116

1.03 (0.961.10)

0.304

1.02 (0.971.07)

0.105

Cancer type

 Lung

3

1176

1959

1.32 (1.06–1.64)

0.175

1.35 (1.10–1.65)

0.278

1.08 (0.921.26)

0.360

1.35 (1.12–1.63)

0.172

1.14 (1.02–1.27)

0.059

 Colorectal

3

2715

3627

0.95 (0.631.45)

0.006

0.96 (0.861.08)

0.480

0.99 (0.701.39)

0.012

0.94 (0.781.14)

0.133

0.99 (0.911.07)

0.020

 Gastric

6

3064

3413

0.88 (0.741.05)

0.118

0.98 (0.881.09)

0.263

0.88 (0.741.05)

0.279

0.97 (0.871.07)

0.127

0.93 (0.821.04)

0.073

 Others

10

5878

7517

1.04 (0.931.15)

0.507

1.05 (0.961.14)

0.670

1.01 (0.931.10)

0.725

1.05 (0.971.14)

0.628

1.03 (0.981.08)

0.659

rs2296147 T>C

CC vs. TT

CT vs. TT

CC vs. CT/TT

CC/CT vs. TT

C vs. T

All

15

11327

12684

1.10 (1.001.12)

0.068

0.95 (0.901.01)

0.480

1.08 (0.991.18)

0.057

0.97 (0.921.03)

0.297

1.00 (0.961.04)

0.118

 Gastric

5

3699

3890

1.11 (0.761.60)

0.026

0.95 (0.861.04)

0.945

1.13 (0.781.63)

0.025

0.96 (0.881.06)

0.697

0.99 (0.911.07)

0.197

rs2227869 G>C

CC vs. GG

GC vs. GG

CC vs. GC/GG

GC/CC vs. GG

C vs. G

All

11

5898

7448

1.67 (0.823.41)

0.924

0.90 (0.801.02)

0.153

0.98 (0.731.32)

0.699

0.92 (0.811.03)

0.108

0.93 (0.831.04)

0.079

 PB

5

2336

3951

1.08 (0.373.10)

0.793

0.80 (0.65–0.99)

0.239

0.89 (0.651.21)

0.766

0.81 (0.661.00)

0.170

0.84 (0.71–0.99)

0.115

 HB

6

3562

4829

2.46 (0.916.67)

0.852

0.96 (0.821.11)

0.198

2.48 (0.916.74)

0.865

0.98 (0.841.13)

0.190

1.00 (0.871.15)

0.202

rs2094258 C>T

TT vs. CC

CT vs. CC

TT vs. CT/CC

CT/TT vs. CC

T vs. C

All

17

9826

10552

1.09 (1.001.19)

0.025

1.00 (0.941.07)

0.314

1.07 (0.991.16)

0.089

1.02 (0.971.09)

0.081

1.03 (0.991.08)

0.015

 Gastric

7

3812

4177

0.99 (0.861.15)

0.083

0.95 (0.861.05)

0.734

1.01 (0.891.14)

0.119

0.96 (0.881.06)

0.409

0.98 (0.921.05)

0.133

rs751402 C>T

TT vs. CC

CT vs. CC

TT vs. CT/CC

CT/TT vs. CC

T vs. C

All

21

10369

11207

1.18 (1.001.39)

<0.001

1.10 (0.991.23)

0.082

1.02 (0.941.10)

0.006

1.11 (0.981.25)

<0.001

1.08 (0.981.18)

<0.001

 Gastric

10

4664

5150

1.38 (1.12–1.70)

0.020

1.14 (1.05–1.24)

0.936

1.27 (1.06–1.51)

0.053

1.17 (1.08–1.26)

0.437

1.17(1.07–1.27)

0.043

rs873601 G>A

AA vs. GG

GA vs. GG

AA vs. GA/GG

GA/AA vs. GG

A vs. G

All

14

10873

12535

1.14 (1.06–1.24)

0.193

1.06 (0.991.13)

0.904

1.08 (0.991.17)

0.035

1.08 (1.02–1.15)

0.841

1.06 (1.02–1.10)

0.234

 Gastric

5

3727

3918

1.18 (1.04–1.34)

0.333

1.04 (0.931.16)

0.663

1.16 (1.04–1.28)

0.263

1.08 (0.981.20)

0.578

1.09 (1.02–1.16)

0.336

No significant association was observed between rs2296147 T>C and overall cancer risk. Similarly, there was no significant association between rs2227869 G>C and overall cancer risk. However, a significant association was identified in population-based studies when the data were stratified based on the source of the controls under heterozygous (OR = 0.80, 95% CI = 0.65–0.99) and allele contrast (OR = 0.84, 95% CI = 0.71–0.99) models. We observed an association between rs2094258 C>T and overall cancer risk under the homozygous model (OR = 1.09, 95% CI = 1.00–1.19), which approached borderline statistical significance. Another borderline significant association was observed between rs751402 C>T and overall cancer risk under the homozygous model (OR = 1.18, 95% CI = 1.00–1.39). In the stratified analysis, a significant association was observed for gastric cancer under homozygous (OR = 1.38, 95% CI = 1.12–1.70), heterozygous (OR = 1.14, 95% CI = 1.05–1.24), recessive (OR = 1.27, 95% CI = 1.06–1.51), dominant (OR = 1.17, 95% CI = 1.08–1.26), and allele contrast (OR = 1.17, 95% CI = 1.07–1.27) models.

A significant association was observed between rs873601 G>A and overall cancer risk under homozygous (OR = 1.14, 95% CI = 1.06–1.24), dominant (OR = 1.08, 95% CI = 1.02–1.15), and allele contrast (OR = 1.06, 95% CI = 1.02-1.10) models (Figure 2). The association with gastric cancer remained statistically significant under homozygous (OR = 1.18, 95% CI = 1.04–1.34), recessive (OR = 1.16, 95% CI = 1.04–1.28), and allele contrast (OR = 1.09, 95% CI = 1.02–1.16) models.

Forest plot of overall cancer risk associated with rs873601 G&#x003E;A in the XPG gene under an allele contrast model.

Figure 2: Forest plot of overall cancer risk associated with rs873601 G>A in the XPG gene under an allele contrast model. For each study, estimated ORs and 95% CIs are plotted with a box and horizontal line, respectively. (◇, pooled ORs and associated 95% CIs).

Heterogeneity and sensitivity analysis

Study heterogeneity was observed for the association between rs1047768 T>C and overall cancer risk under homozygous, dominant, and allele contrast models (P = 0.010, P = 0.038, and P = 0.012, respectively); rs2094258 C>T under homozygous and allele contrast models (P = 0.025 and P = 0.015, respectively); rs751402 C>T under homozygous, recessive, dominant, and allele contrast models (P < 0.001, P = 0.006, P < 0.001, P < 0.001, respectively); and rs873601 G>A under a recessive model (P = 0.035). These data indicated that the removal of any individual study from the analysis did not qualitatively change the pooled ORs (data not shown).

Publication bias

The Begg’s funnel plots of the associations between the SNPs in the XPG gene and cancer risk were basically symmetrical (Figure 3). Egger’s tests indicated there was no publication bias for rs1047768 T>C under homozygous (P = 0.107), heterozygous (P = 0.190), recessive (P = 0.325), dominant (P = 0.137), and allele contrast (P = 0.301) models; rs2296147 T>C under homozygous (P = 0.789), heterozygous (P = 0.925), recessive (P = 0.577), dominant (P = 0.464), and allele contrast (P = 0.129) models; rs2227869 G>C under homozygous (P = 0.708), heterozygous (P = 0.289), recessive (P = 0.042), dominant (P = 0.297), and allele contrast (P = 0.197) models; rs2094258 C>T under homozygous (P = 0.387), heterozygous (P = 0.350), recessive (P = 0.844), dominant (P = 0.276), and allele contrast (P = 0.351) models; rs751402 C>T under homozygous (P = 0.107), heterozygous (P = 0.336), recessive (P = 0.137), dominant (P = 0.325), and allele contrast (P = 0.301) models; and rs873601 G>A under homozygous (P = 0.395), heterozygous (P = 0.656), recessive (P = 0.645), dominant (P = 0.811), and allele contrast (P = 0.346) models (Table 3).

Funnel plot of the association between rs873601 G&#x003E;A in the XPG gene and overall cancer risk under an allele contrast model.

Figure 3: Funnel plot of the association between rs873601 G>A in the XPG gene and overall cancer risk under an allele contrast model. Each point represents an individual study that reported the indicated association.

Table 3: Publication bias among studies that evaluated the associations between the six SNPs in the XPG gene and cancer susceptibility

Polymorphism

No. of studies

Egger’s test P values

Homozygous

Heterozygous

Recessive

Dominant

Allele contrast

rs1047768

22

0.107

0.190

0.325

0.137

0.301

rs2296147

15

0.789

0.925

0.577

0.464

0.129

rs2227869

11

0.708

0.289

0.042

0.297

0.197

rs2094258

17

0.387

0.350

0.844

0.276

0.351

rs751402

21

0.107

0.336

0.137

0.325

0.301

rs873601

14

0.395

0.656

0.645

0.811

0.346

False-positive report probability (FPRP) analysis and trial sequential analysis (TSA)

All significant findings remained significant at a prior probability of 0.1, with all the FPRP values less than 0.20 with the exception of the population-designed studies of rs2227869 G>C (Table 4). TSA indicated that the cumulative z-curve crossed the trial sequential monitoring boundary, suggesting that the sample size was sufficient and that no further analysis was required to confirm the results (Figure 4).

Table 4: False-positive report probability values for significant results

Genotype

Crude OR (95% CI)

P a

Statistical power b

Prior probability

0.25

0.1

0.01

0.001

0.0001

rs1047768 T>C (lung cancer)

 CC vs. TT

1.32 (1.06–1.64)

0.012

0.998

0.035

0.097

0.542

0.923

0.992

 CT vs. TT

1.35 (1.10–1.65)

0.004

0.995

0.011

0.033

0.273

0.791

0.974

 CC/CT vs. TT

1.35 (1.12–1.63)

0.002

0.859

0.006

0.019

0.177

0.685

0.956

C vs. T

1.14 (1.02–1.27)

0.017

1.000

0.048

0.130

0.622

0.943

0.994

rs2227869 G>C (population-based studies)

 GC vs. GG

0.80 (0.65–0.99)

0.041

0.987

0.111

0.272

0.805

0.976

0.998

 C vs. G

0.84 (0.71–0.99)

0.041

1.000

0.110

0.271

0.803

0.976

0.998

rs751402 C>T (gastric cancer)

 TT vs. CC

1.38 (1.12–1.70)

0.002

1.000

0.007

0.019

0.179

0.687

0.956

 CT vs. CC

1.14 (1.05–1.24)

0.003

1.000

0.008

0.024

0.213

0.732

0.965

 TT vs. CT/CC

1.27 (1.06–1.51)

0.010

1.000

0.030

0.085

0.506

0.912

0.990

 CT/TT vs. CC

1.17 (1.08–1.26)

<0.001

1.000

0.001

0.002

0.019

0.161

0.658

 T vs. C

1.17 (1.07–1.27)

0.001

1.000

0.002

0.006

0.063

0.404

0.871

rs873601 G>A (overall)

 AA vs. GG

1.14 (1.06–1.24)

0.001

1.000

0.002

0.006

0.061

0.394

0.867

 GA/AA vs. GG

1.08 (1.02–1.15)

0.012

1.000

0.036

0.101

0.552

0.926

0.992

 A vs. G

1.06 (1.02–1.10)

0.002

1.000

0.006

0.016

0.155

0.650

0.949

rs873601 G>A (gastric cancer)

 AA vs. GG

1.18 (1.04–1.34)

0.009

1.000

0.027

0.078

0.482

0.904

0.989

 AA vs. GA/GG

1.16 (1.04–1.28)

0.008

1.000

0.022

0.064

0.431

0.884

0.987

 A vs. G

1.09 (1.02–1.16)

0.011

1.000

0.031

0.089

0.517

0.915

0.991

aChi-square tests were used to assess the genotype frequency distributions.

bStatistical power was calculated using the number of observations in the subgroup and the P values in this table.

TSA of rs873601 G&#x003E;A in the XPG gene and overall cancer risk under an allele contrast model.

Figure 4: TSA of rs873601 G>A in the XPG gene and overall cancer risk under an allele contrast model.

DISCUSSION

The NER pathway is critical for the repair of bulky DNA lesions resulting from exposure to chemical carcinogens as well as ionizing radiation in order to maintain genomic integrity and prevent carcinogenesis [55]. Because the XPG gene is an indispensable component of the NER pathway, SNPs in XPG may alter the expression or function of XPG thereby modifying the risk of cancer. Most previous meta-analyses of the association between SNPs in XPG and cancer risk have focused on rs17655 G>C [56-59]. However, recent studies have shown that other SNPs in XPG may also be associated with cancer risk. For example, Chen et al. found that rs873601 G>A was associated with an increased risk of gastric cancer in a Chinese Han population [36]. Wang et al. found that rs751402 C>T was protective against breast cancer in Chinese Han women [47]. Additionally, the T allele of rs2296147 was associated with an increased risk of prostate cancer [35]. However, the results of previous studies have been inconsistent, possibly due to variations in the study populations and limited sample sizes. We therefore performed a meta-analysis of 47 studies to comprehensively evaluate the associations between six SNPs in XPG: rs1047768 T>C, rs2296147 T>C, rs2227869 G>C, rs2094258 C>T, rs751402 C>T, and rs873601 G>A and cancer risk.

The rs873601 G>A polymorphism is located in a miRNA binding site in the XPG gene. Thus, it may alter XPG expression by modulating the miRNA-mRNA interaction, which could play a role in carcinogenesis [10]. We demonstrated that rs873601 G>A was significantly associated with overall cancer risk. Individuals with the AA genotype of rs873601 had a 1.14-fold higher risk of cancer compared to individuals with the GG genotype. Similar results were obtained for gastric cancer. The A allele of rs873601 was previously shown to result in reduced mRNA expression of XPG in both adjacent normal gastric cancer tissue and normal cell lines in a recessive manner [10]. These findings provide insight into the molecular mechanisms by which the AA genotype of rs873601 may increase the risk of gastric cancer.

The rs751402 C>T polymorphism is located in the E2F1/YY1 binding and response site in the proximal promoter region of XPG [60]. This variant might reduce the DNA repair capacity of XPG by disrupting the DNA binding motifs and altering transcription factor affinities [47]. In our study, rs751402 C>T was significantly associated with overall cancer risk. The TT genotype of rs751402 was associated with an 18% increase in cancer risk compared to the CC genotype. Moreover, a significant association was observed between rs751402 C>T and gastric cancer risk under all genetic models. The rs751402 C>T polymorphism is likely to influence cancer risk by regulating XPG expression, but its effect on XPG function is not yet clear [47].

The rs2094258 C>T polymorphism is located in a transcription factor binding site in the 5’ region of the XPG gene. We found that the association between rs2094258 C>T and overall cancer risk was borderline significant. Individuals with the TT genotype of rs2094258 had a 9% higher risk of cancer compared to those with the CC genotype. However, the association was not significant in gastric cancer, indicating that it may not impact gastric cancer risk. Significant associations were observed among some subgroups for all other selected SNPs. We found that the C allele of rs1047768 may increase the risk of lung cancer. Moreover, the C allele of rs2227869 significantly reduced cancer risk in population-based studies. No statistically significant association was observed between rs2296147 T>C and overall cancer risk.

Although we found significant associations between SNPs in the XPG gene and cancer risk, our study had several limitations. First, although Egger’s tests showed no obvious publication bias, some bias was unavoidable since only studies published in English and Chinese were included in our meta-analysis. Second, we observed significant heterogeneity in some of our analyses, which is a common drawback of a meta-analysis. Third, due to a lack of sufficient individual data, we were unable to perform multivariate analysis with adjustment for potential confounding factors such as tobacco use, alcohol consumption, and other carcinogenic factors.

Our study is the first meta-analysis of the association between the six selected SNPs in XPG gene and cancer risk. The results indicate that the AA genotype of rs873601 increases overall cancer risk. Additionally, rs751402 C>T and rs873601 G>A were associated with gastric cancer risk. Finally, rs1047768 T>C was found to confer susceptibility to lung cancer. Further epidemiological investigations with larger sample sizes are warranted to validate our findings. Functional studies are also required to elucidate the mechanisms by which these SNPs modify cancer risk.

MATERIALS AND METHODS

Study identification

We searched multiple databases including PubMed, Scopus, Web of Science, CNKI, and the WanFang database using combinations of keywords such as “XPG”, “polymorphism”, and “cancer” as well as synonyms “Xeroderma pigmentosum group G, ERCC5 or Excision repair cross complementing group 5”, “variant or variation”, and “tumor, neoplasm, or carcinoma”. Human studies published before December 20, 2016 in either English or Chinese were included. The reference lists in eligible studies and review articles were examined in order to identify additional relevant studies. In cases of study population overlap, the study with the largest sample size was selected.

Inclusion and exclusion criteria

All studies included in this analysis were required to meet the following criteria: (1) study of the associations between any of the six potentially functional SNPs: rs1047768 T>C, rs2296147 T>C, rs2227869 G>C, rs2094258 C>T, rs751402 C>T, and rs873601 G>A in the XPG gene and cancer risk; (2) case-control study; and (3) sufficient genotype data available to calculate ORs and 95% CIs. The exclusion criteria were: (1) studies conducted in the same or overlapping population and (2) review article or conference report.

Data extraction

Key information was independently extracted from eligible studies by two investigators and included the following items: the first author, year of publication, type of cancer, country, ethnicity, control source, number of cases and controls, the quantity of each genotype in cases and controls, minor allele frequency (MAF), and the Hardy-Weinberg equilibrium (HWE) test P value for the control subjects. Disagreements regarding these items were resolved through discussion.

Statistical analysis

Chi-square tests were used to test deviation from HWE in the study control groups. Genetic associations between the six selected SNPs in the XPG gene and cancer risk were assessed using the crude ORs and corresponding 95% CIs under homozygous, heterozygous, recessive, dominant, and allele contrast models. Heterogeneity between studies was assessed using the Q and I2 values. A random effects model was adopted to calculate the pooled OR and 95% CI in the case of Phet < 0.1 or I2 > 50%. Otherwise, a fixed effects model was applied. Stratified analyses were conducted by ethnicity (Asians and Caucasians), source of control [population-based (PB) or hospital-based (HB)], and cancer type.

Sensitivity analyses were performed to assess the influence of the individual studies on the pooled OR by sequentially removing one study at a time and recalculating the pooled OR. Egger’s tests were used to evaluate publication bias. FPRP analysis [61, 62] and TSA were performed as described previously [63]. All statistical analyses were performed using the STATA 12.0 software (Stata Corporation, College Station, TX, USA). All statistics were two-sided. P values < 0.05 were considered statistically significant.

CONFLICTS OF INTEREST

The authors declare that there are no conflicts of interest.

FUNDING

This study was supported by a grant from the Natural Science Foundation of Guangdong Province (No. 2015A030310324).

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