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Research Papers:

Pooling-analysis on hMLH1 polymorphisms and cancer risk: evidence based on 31,484 cancer cases and 45,494 cancer-free controls

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Oncotarget. 2017; 8:93063-93078. https://doi.org/10.18632/oncotarget.21810

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Sha Li, Yi Zheng, Tian Tian, Meng Wang, Xinghan Liu, Kang Liu, Yajing Zhai, Cong Dai, Yujiao Deng, Shanli Li, Zhijun Dai and Jun Lu _

Abstract

Sha Li1,2,*, Yi Zheng1,3,*, Tian Tian3,*, Meng Wang3, Xinghan Liu3, Kang Liu3, Yajing Zhai1, Cong Dai3, Yujiao Deng3, Shanli Li3, Zhijun Dai3 and Jun Lu1

1Clinical Research Center, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi 710061, China

2Department of Pharmacy, Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi 710004, China

3Department of Oncology, Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi 710004, China

*These authors have contributed equally to this work

Correspondence to:

Jun Lu, email: lujun2006@xjtu.edu.cn

Zhijun Dai, email: dzj0911@126.com

Keywords: hMLH1, polymorphism, cancer, meta-analysis

Received: May 23, 2017     Accepted: September 08, 2017     Published: October 10, 2017

ABSTRACT

To elucidate the veritable relationship between three hMLH1 polymorphisms (rs1800734, rs1799977, rs63750447) and cancer risk, we performed this meta-analysis based on overall published data up to May 2017, from PubMed, Web of knowledge, VIP, WanFang and CNKI database, and the references of the original studies or review articles. 57 publications including 31,484 cancer cases and 45,494 cancer-free controls were obtained. The quality assessment of six articles obtained a summarized score less than 6 in terms of the Newcastle-Ottawa Scale (NOS). All statistical analyses were calculated with the software STATA (Version 14.0; Stata Corp, College Station, TX). We found all the three polymorphisms can enhance overall cancer risk, especially in Asians, under different genetic comparisons. In the subgroup analysis by cancer type, we found a moderate association between rs1800734 and the risk of gastric cancer (allele model: OR = 1.14, P = 0.017; homozygote model: OR = 1.33, P = 0.019; dominant model: OR = 1.27, P = 0.024) and lung cancer in recessive model (OR = 1.27, P = 0.024). The G allele of rs1799977 polymorphism was proved to connect with susceptibility of colorectal cancer (allele model: OR = 1.21, P = 0.023; dominate model: OR = 1.32, P <0.0001) and prostate cancer (dominate model: OR = 1.36, P <0.0001). Rs63750447 showed an increased risk of colorectal cancer, endometrial cancer and gastric cancer under all genetic models. These findings provide evidence that hMLH1 polymorphisms may associate with cancer risk, especially in Asians.


Pooling-analysis on hMLH1 polymorphisms and cancer risk: evidence based on 31,484 cancer cases and 45,494 cancer-free controls | Li | Oncotarget

INTRODUCTION

As one of the pivotal pathways in maintaining genetic stability, MMR system is mainly in charge of repairing the replication-associated errors, including removing mistaken bases, correcting substitutions and rectifying insertion-deletion mismatches. Its defects may result in microsatellite instability (MSI), a type of genetic instability related to colorectal cancer, gastric cancer, and endometrial cancer, etc. [13] Interest in MLH1 has increased in the last few years because MLH1 was discovered as a key component in MMR for MSI, and its dysfunction is supposed to be implicated in cancer predisposition.

MLH1 not only takes part in the activities of MMR system, but also has other interesting cellular functions, such as participating in cell cycle arrest, triggering DNA damage-induced apoptosis to response to some chemical or physical agents [4], and interacting with tumor-related signaling molecules like BRCA1 [5] and p53 [6]. Moreover, various polymorphisms were found in MLH1 gene, part of them were proved to influence the expression of functional MLH1. We selected three most common loci rs1800734, rs1799977, and rs63750447 in hMLH1 which may alter the function of the hMLH1 gene according to literature. Among these, the A allele of rs1800734 polymorphism could alter the methylation level of nine CpG sites mapped on the MLH1 promoter [7], while rs1799977 and rs63750447 were situated at the exons of hMLH1 [1, 8]. Emerging inspiring evidences indicate these functional polymorphisms of hMLH1 may be potential candidates in mediating hereditary susceptibility to cancer, however, applying them in clinical application is still treated critically. Past decades witnessed numerous molecular epidemiological studies carried out worldwide to investigate the actual association between them, yet no coincident conclusion was reached so far.

For example, Nizam et al. [9] concluded that rs1800734 polymorphism had an influence on colorectal cancer (CRC) risk among Malaysians in 2013, while Zhang et al [10] found no obvious connection between rs1800734 and CRC risk in 2016. For rs1799977 polymorphism, Milanizadeh et al. [11] detected it could increase CRC risk particularly in female patients, but Peng et al. [1] hold a contrary opinion that no association existed between the two. The inconsistent conclusions also existed in the studies exploring the relationship between hMLH1 polymorphisms and other cancer types. Although rs63750447 polymorphism was accepted as a risk factor for east-Asian CRC patients [1, 1214], no reliable conclusion reported on the possible relationship between rs63750447 and overall cancer or other kinds of tumors. To solved these controversies, a comprehensive and persuasive meta-analysis was excepted to conduct depending on complete published data and proper methodological tools, thus we carried out this meta-analysis to illuminate the objective connection between hMLH1 polymorphisms (rs1800734, rs1799977 and rs63750447) and cancer risk.

RESULTS

Characteristics of eligible studies

Finally, we obtained a total of 57 publications including 31,484 cancer cases and 45,494 cancer-free controls (all were from the databases and no study was identified by manual search of the references of the original studies or review articles). The detail selection process was shown in the flow diagram (Figure 1). What needed illustration is that we abandon three studies contained in previous meta-analyses after comprehensive reading full text. The first one was the study performed by Chen et al [15], contained in the meta-analyses conducted in 2011 [16] and 2015 [17], which was excluded on account of both its cases group and controls group are women with cancers (cases with MLH1 methylation while controls not). Another study finished by van Roon et al. [18], also included in previous meta-analyses [17, 19], has two controls groups collected from literature [20, 21]. We excluded it after discussing with a senior author within us. And the third study we abandoned was due to deficiency of cancer-free control group [16].

The flow diagram of the meta-analysis, according to the PRISMA 2009.

Figure 1: The flow diagram of the meta-analysis, according to the PRISMA 2009. CNKI = China National Knowledge Infrastructure.

Among the 57 eligible literatures, 26 were based on Caucasian background from, Poland, Spain, the United States, Denmark, the United Kingdom, Sweden, Portugal, Czech Republic and Canada. 27 were carried out in Asians from China, Kazakhstan, India, Iran, Malaysia, Japan and Korea, and four were based on mixed ethnic groups. All the publications involving rs63750447 polymorphism were carried out among the Chinese population. Three case-cohort designed studies [2224] and 54 case-controlled studies were involved in this meta-analysis. All cancer cases were confirmed by pathology or histology, involved cancer types covering colorectal, gastric, ovarian, head and neck, endometrium, lung, bladder, prostate, thyroid, breast, prostate, Non-Hodgkin lymphoma, acute myeloid leukaemia, and acute lymphoblastic leukaemia. The quality assessment of six studies obtained a summarized score less than 6 in terms of the Newcastle-Ottawa Scale (NOS), four of them are studying on rs1800734 [2528] while one of them is for rs63750447 [29], and the other one focused on rs1800734 and rs1799977 polymorphisms [30]. Specially, two publications by Zhang et al. [8] and Wang et al. [29] contained four and three independent studies respectively. One study focused on rs1799977 polymorphism by Joshi et al. [31] did not provide complete genotype frequencies. Hence only the dominant model was evaluated. Detail characteristics of eligible publications are displayed in Table 1.

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

First author

Year

Country

Ethnic

Method

Control

Disease

SNP

NOS

Peng [1]

2016

China

Asian

PCR-HRM

Population

CRC

2, 3

7

Zhang [10]

2016

China

Asian

TaqMan

Hospital

CRC

1

6

Zhu [2]

2016

China

Asian

TaqMan

Population

GC

1

7

Djansugurova [46]

2015

Kazakhstan

Asian

PCR-RFLP

Hospital

CRC

1

8

Niu [47]

2015

China

Asian

PCR-RFLP

Hospital

OC

1, 2

6

Nogueira [48]

2015

Brazil

Mixed

TaqMan

Hospital

HNSCC

1

6

Poplawski [3]

2015

Poland

Caucasian

PCR-RFLP

Hospital

EC

1

6

Slovakova [49]

2015

Slovak

Caucasian

PCR-RFLP

Population

LC

1

8

Rodriguez [50]

2014

Spain

Caucasian

PCR-RFLP

Hospital

BT

1

6

Jha [51]

2013

India

Asian

PCR-RFLP

Population

HNSCC

1

7

Martinez-Uruena [25]

2013

Spain

Caucasian

PCR-RFLP

Hosptal

CRC

1

4

Milanizadeh [11]

2013

Iran

Asian

PCR-RFLP

Hospital

CRC

2

7

Nizam [9]

2013

Malaysia

Asian

PCR-RFLP

Hospital

CRC

1

6

Muniz-Mendoza [30]

2012

Mexico

Mixed

PCR-RFLP

Hospital

CRC

1, 2

4

Savio [32]

2012

Canada

Caucasian

PCR-RFLP

Population

CRC

1

7

Xiao [52]

2012

China

Asian

PCR

Population

GC

1, 2

8

Zhi [53]

2012

China

Asian

PCR-RFLP

Population

BLC

1

7

Lacey [54]

2011

Poland

Caucasian

iSelect bead chip

Population

EC

1, 2

8

Lo [55]

2011

China

Asian

PCR

Hospital

LC

1

7

Soni [56]

2011

India

Asian

TaqMan

Hospital

PC

1

6

Whiffin [57]

2011

UK

Asian

KASPae

Population

CRC

1

8

Zhi [58]

2011

China

Asian

PCR-RHM

Hospital

GC

1

6

Langeberg [59]

2010

USA

Caucasian

ABI

Population

PC

2

7

Picelli [22]

2010

Sweden

Caucasian

Direct sequencing

Population

CRC

2

7

Shi [12]

2010

China

Asian

PCR

Hospital

TC

1, 2, 3

6

Campbell [41]

2009

USA

Caucasian

PCR-RFLP

Population

CRC

1, 2

8

Conde [37]

2009

Portugal

Caucasian

QIAamp

Hospital

BC

2

6

Joshi [31]

2009

USA

Caucasian

TaqMan

Population

CRC

2

7

Nejda [38]

2009

Spain

Caucasian

PCR-RFLP

Hospital

CRC

2

7

Ohsawa [13]

2009

Japan

Asian

PCR-RFLP

Unknown

CRC

3

6

Shih [33]

2009

China

Asian

PCR-RFLP

Population

LC

1

7

Tanaka [60]

2009

Japan

Asian

Direct sequencing

Population

PC

2

7

An [61]

2008

China

Asian

PCR-RFLP

Population

LC

1, 2

8

Christensen [23]

2008

Denmark

Caucasian

SBE-tags

Population

CRC

2

8

Harlay [26]

2008

Canada

Mixed

MassARRAY

Hospital

OC

1

5

Koessler [62]

2008

UK

Caucasian

TaqMan

Population

CRC

1

7

Samowitz [20]

2008

USA

Caucasian

Direct sequencing

Population

CRC

1

7

Scott [34]

2008

UK

Caucasian

TaqMan

Population

NHL

1

6

Tulupova [63]

2008

Czech

Caucasian

TaqMan

Hospital

CRC

1

7

Worrillow [64]

2008

UK

Caucasian

PCR-RFLP

Population

AML

1

6

Berndt [24]

2007

USA

Caucasian

TaqMan

Population

CRC

2

8

Raptis [21]

2007

Canada

Caucasian

TaqMan

Population

CRC

1, 2

7

Beiner [35]

2006

Canada

Mixed

MassARRAY

Hospital

EC

1

6

Landi [65]

2006

Mixed

Caucasian

PCR

Hospital

LC

2

7

Mei [14]

2006

China

Asian

PCR

Hospital

CRC

2, 3

6

Song [39]

2006

Mixed

Caucasian

TaqMan

Population

OC

1, 2

6

Chen [66]

2005

China

Asian

PCR-RFLP

Hospital

HCC

1

7

Lee [67]

2005

Korea

Caucasian

MassARRAY

Hospital

BC

1

6

Kim [68]

2004

Korea

Asian

TaqMan

Population

CRC

2

6

Listgarten [40]

2004

Canada

Caucasian

QIAmp

Hospital

BC

2

6

Park [36]

2004

Korea

Caucasian

PCR

Population

LC

1

8

Zhang [8]

2004

China

Asian

DHPLC

Population

Mixed

3

7

Deng [69]

2003

China

Asian

DHPLC

Hospital

GC

1

7

Mathonnet [70]

2003

Canada

Caucasian

PCR-ASO

Population

ALL

2

6

Shin [27]

2002

Korea

Asian

PCR-SSCP

Hospital

CRC

1

4

Wang [29]

2000

China

Asian

PCR-SSCP

Hospital

Mixed

3

5

Ito [28]

1999

Japan

Asian

PCR-SSCP

Hospital

CRC

1

4

Quantitative synthesis

The distributions of genotypes frequencies of hMLH1 polymorphisms (rs1800734; rs1799977; rs63750447) for every single study are exhibited in Table 2. The minor allele frequencies (MAF) among cancer cases varied widely according to the included studies, ranging from 0.205 to 0.656 for rs1800734 polymorphism, 0.016 to 0.744 for rs1799977 polymorphism, and 0.032 to 0.069 for rs63750447 polymorphism. The average MAF of case-group for the three polymorphisms is 0.396, 0.233, 0.053, respectively. The meta-analysis results of these three polymorphisms were shown in Supplementary Table 1.

Table 2: Genotype distribution and allele frequency of hMLH1 polymorphisms

First author

Genotype (N)

Allele frequency (N)

MAF

HWE

Case (n)

Control (n)

Case (n)

Control (n)

total

AA

AB

BB

total

AA

AB

BB

A

B

A

B

-93G>A (rs1800734)

Zhang 2016 [10]

312

66

139

107

300

52

154

94

271

353

258

342

0.566

0.414

Zhu 2016 [2]

406

49

213

144

444

79

235

130

311

501

393

495

0.617

0.125

Niu 2015 [47]

421

51

188

182

689

150

356

183

290

552

656

722

0.656

0.348

Djansugurova 2015 [46]

249

126

94

29

244

101

115

28

346

152

317

171

0.305

0.581

Nogueira 2015 [48]

450

248

171

31

450

269

159

22

667

233

697

203

0.259

0.809

Poplawski 2015 [3]

100

18

81

1

100

9

50

41

117

83

68

132

0.415

0.254

Slovakova 2015 [49]

422

250

144

28

511

260

228

23

644

200

748

274

0.237

0.002

Rodriguez 2014 [50]

115

61

44

10

200

115

79

6

166

64

309

91

0.278

0.080

Jha 2013 [51]

245

52

90

100

205

98

79

28

194

290

275

135

0.599

0.067

Martinez-Uruena2013 [25]

383

233

131

19

236

129

102

5

597

169

360

112

0.221

0.003

Nizam 2013 [9]

104

22

50

32

104

33

33

38

94

114

99

109

0.548

0.000

Muniz-Mendoza2012 [30]

100

47

44

9

115

39

55

21

138

62

133

97

0.310

0.835

Savio 2012 [32]

252

150

96

6

845

528

264

53

396

108

1320

370

0.214

0.012

Xiao 2012 [52]

554

104

262

188

588

124

271

193

470

638

519

657

0.576

0.113

Zhi 2012 [53]

311

43

163

105

302

41

161

100

249

373

243

361

0.600

0.059

Larcy 2011 [54]

414

251

141

22

404

241

146

17

643

185

628

180

0.223

0.381

Lo 2011 [55]

719

235

344

140

728

256

366

106

814

624

878

578

0.434

0.177

Soni 2011 [56]

105

44

40

21

106

27

61

18

128

82

115

97

0.390

0.101

Whiffin 2011 [57]

10409

6408

3504

497

6965

4395

2261

309

16320

4498

11051

2879

0.216

0.401

Zhi 2011 [58]

236

36

111

89

240

42

114

84

183

289

198

282

0.612

0.757

Shi 2010 [12]

204

40

102

62

204

34

99

71

182

226

167

241

0.554

0.959

Campbell 2009 [33]

1600

952

553

95

1963

1170

688

105

2457

743

3028

898

0.232

0.769

Shih 2009 [33]

165

41

64

60

193

36

113

44

146

184

185

201

0.558

0.016

An 2008 [61]

500

163

243

94

517

169

258

90

569

431

596

438

0.431

0.618

Harley 2008 [26]

842

483

297

62

776

532

206

38

1263

421

1270

282

0.250

0.003

Koessler 2008 [62]

2288

1407

778

103

2276

1392

777

107

3592

984

3561

991

0.215

0.914

Samowitz 2008 [20]

1006

610

344

52

1963

1170

688

105

1564

448

3028

898

0.223

0.769

Scott 2008 [34]

601

375

205

21

942

610

310

22

955

247

1530

354

0.205

0.016

Tulupova 2008 [63]

619

359

216

44

611

365

209

37

934

304

939

283

0.246

0.336

Worrillow 2008 [64]

390

246

128

16

918

585

292

41

620

160

1462

374

0.205

0.554

Raptis 2007 [21]

929

554

331

44

1098

687

352

59

1439

419

1726

470

0.226

0.118

Beiner 2006 [35]

654

377

220

57

764

524

202

38

974

334

1250

278

0.255

0.002

Song 2006 [39]

1306

825

414

67

1951

1224

638

89

2064

548

3086

816

0.210

0.615

Chen 2005 [66]

545

86

261

198

374

85

178

111

433

657

348

400

0.603

0.400

Lee 2005 [67]

783

201

348

234

594

117

292

185

750

816

526

662

0.521

0.927

Park 2004 [36]

372

66

176

130

371

71

206

94

308

436

348

394

0.586

0.027

Deng 2003 [69]

54

8

27

19

56

9

29

18

43

65

47

65

0.602

0.636

Shin 2002 [27]

139

33

61

45

157

42

74

41

127

151

158

156

0.543

0.473

Ito 1999 [28]

27

8

10

9

84

22

46

16

26

28

90

78

0.519

0.355

655A>G(rs1799977)

Peng2016 [1]

156

151

5

0

311

307

4

0

307

5

618

4

0.016

0.909

Niu 2015 [47]

418

383

33

2

689

613

75

1

799

37

1301

77

0.044

0.406

Milanizadeh 2013 [11]

219

25

62

132

248

54

119

75

112

326

227

269

0.744

0.599

Muniz-Mendoza 2012 [30]

102

71

26

5

100

81

19

0

168

36

181

19

0.176

0.294

Xiao 2012 [52]

554

522

31

1

592

568

23

1

1075

33

1159

25

0.030

0.143

Larcy 2011 [54]

417

210

160

47

406

196

165

45

580

254

557

255

0.305

0.253

Langeberg 2010 [59]

1251

578

555

118

1236

607

514

115

1711

791

1728

744

0.316

0.681

Picelli 2010 [22]

1781

819

781

181

1701

832

708

161

2419

1143

2372

1030

0.321

0.560

Shi 2010 [12]

204

185

17

2

204

192

11

1

387

21

395

13

0.051

0.072

Campbell 2009 [41]

1601

764

678

159

1944

937

848

159

2206

996

2722

1166

0.311

0.087

Conden 2009 [37]

287

129

129

29

546

255

251

40

387

187

761

331

0.326

0.039

Joshi 2009 [31]

301

161

/

/

354

194

/

/

/

/

/

/

/

/

Nejda 2009 [38]

140

41

72

27

125

64

44

17

154

126

172

78

0.450

0.044

Tanaka 2009 [60]

177

159

16

2

131

120

11

0

334

20

251

11

0.056

0.616

An 2008 [61]

500

479

20

1

504

493

11

0

978

22

997

11

0.022

0.804

Christensen 2008 [23]

380

172

170

38

770

364

327

79

514

246

1055

485

0.324

0.661

Berndt 2007 [24]

211

100

94

17

2090

968

896

226

294

128

2832

1348

0.303

0.387

Raptis 2007 [21]

929

451

391

87

1098

514

485

99

1293

565

1513

683

0.304

0.310

Landi 2006 [65]

291

145

123

23

309

129

151

29

413

169

409

209

0.290

0.107

Mei 2006 [14]

160

144

14

2

150

141

9

0

302

18

291

9

0.056

0.705

Song 2006 [39]

1022

507

418

97

1224

624

477

123

1432

612

1725

723

0.299

0.026

Kim 2004 [68]

107

100

7

0

330

311

18

1

207

7

640

20

0.033

0.192

Listgarten 2004 [40]

170

89

64

17

156

76

75

5

242

98

227

85

0.288

0.008

Mathonnet 2003 [70]

287

149

112

26

320

154

132

34

410

164

440

200

0.286

0.474

1151T>A(rs63750447)

Peng2016 [1]

156

142

13

1

311

310

1

0

297

15

621

1

0.048

0.977

Shi 2010 [12]

204

178

24

2

204

191

12

1

380

28

394

14

0.069

0.108

Ohsawa 2009 [13]

670

630

39

1

332

327

5

0

1299

41

659

5

0.031

0.890

Mei 2006 [14]

160

142

18

0

150

141

9

0

302

18

291

9

0.056

0.705

Zhang 2004 (EC) [8]

233

206

27

0

268

251

17

0

439

27

519

17

0.058

0.592

Zhang 2005 (CRC) [8]

90

82

8

0

268

251

17

0

172

8

519

17

0.044

0.592

Zhang 2004 (BC) [8]

111

104

7

0

268

251

17

0

215

7

519

17

0.032

0.592

Zhang 2004 (GC) [8]

273

240

33

0

268

251

17

0

513

33

519

17

0.060

0.592

Wang 2000 (CRC) [29]

101

88

13

0

100

94

6

0

189

13

194

6

0.064

0.757

Wang 2000 (EC) [29]

76

69

7

0

100

94

6

0

145

7

194

6

0.046

0.757

Wang 2000 (GC) [29]

79

68

11

0

100

94

6

0

147

11

194

6

0.070

0.757

A: the major allele, B: the minor allele, MAF: minor allele frequencies; HWE: Hardy–Weinberg equilibrium.

Rs1800734 polymorphism

Overall, there are 39 studies including 29,331 cases and 29,588 controls for rs1800734 polymorphism. Statistically significance was found between rs1800734 polymorphism and overall cancer risk under five genetic models (recessive comparison: OR = 1.22, 95%CI = 1.09-1.37, P = 0.001; homozygote comparison: OR = 1.23, 95%CI = 1.06-1.42, P = 0.006; allele comparison: OR = 1.08, 95%CI = 1.01-1.16, P = 0.023). After excluding nine studies that were not in accordance with HWE [3, 9, 25, 26, 32-36], we observed increased risks of all kinds of cancers under two genetic models (recessive comparison: OR = 1.18, 95%CI = 1.04-1.34, P = 0.012; homozygote comparison: OR = 1.18, 95%CI = 1.00-1.39, P = 0.048, Figure 2A).

Figure 2:

Figure 2: Forest plot of OR with 95%CI for the hMLH1 polymorphisms with cancer risk under dominate model according to HWE ((A) rs1800734; (B) rs1799977; (C) rs63750447). CI: confidence interval, OR: odds ratio, HWE: Hardy-Weinberg equilibrium.

In the stratification analysis based on ethnicity (Figure 3A), we found no association between cancer risk and Caucasian population, while the mutation allele A contributed to an increasing cancer risk in Asian population under three comparison models (recessive comparison: OR = 1.30, 95%CI = 1.11-1.53, P = 0.001; homozygote comparison: OR = 1.37, 95%CI = 1.09-1.72, P = 0.006; allele comparison: OR = 1.16, 95%CI = 1.03-1.31, P = 0.014). In the cancer-specific analysis, rs1800734 polymorphism showed a potential tendency to enhance gastric and lung cancer susceptibility in different genetic comparisons (gastric cancer: dominate comparison: OR = 1.27, 95%CI = 1.03-1.56, P = 0.024; homozygote comparison: OR = 1.33, 95%CI = 1.06-1.68, P = 0.019, allele comparison: OR = 1.14, 95%CI = 1.02-1.28, P = 0.017; lung cancer: recessive comparison: OR = 1.27, 95%CI = 1.03-1.57, P = 0.024). Besides, the subgroup analysis depended on the source of controls suggested us that rs1800734 polymorphism had an influence on cancer risk under four genetic models among population-based controls (dominate comparison: OR = 1.05, 95%CI = 1.01-1.10, P = 0.016, recessive comparison: OR = 1.12, 95%CI = 1.04-1.22, P = 0.004; homozygote comparison: OR = 1.22, 95%CI = 1.00-1.49, P = 0.050; heterozygous comparison: OR = 1.05, 95%CI = 1.01-1.10, P = 0.031; allele comparison: OR = 1.10, 95% = 1.00-1.20, P = 0.041) and recessive comparison among hospital-based controls (OR = 1.27, 95%CI = 1.03-1.57, P = 0.024). And, when the subgroup analysis was conducted based on a quality score, rs1800734 polymorphism displayed an increased cancer risk among high-quality studies, but no association was found among low-quality studies (Supplementary Table 1).

Figure 3:

Figure 3: Stratified analysis by ethnicity for the association between hMLH1 polymorphisms and cancer risk under homozygote model according to HWE ((A) rs1800734; (B) rs1799977). CI: confidence interval, OR: odds ratio, HWE: Hardy-Weinberg equilibrium.

Rs1799977 polymorphism

We finally derived 11,665 cases and 15,538 controls from 24 eligible studies for rs1799977 polymorphism. All the studies obtained high-quality scores according to the Newcastle-Ottawa Scale (NOS). In general, we found the variant G allele of rs1799977 could improve overall cancer risks under three genetic models (dominant comparison: OR = 1.28, 95%CI = 1.16-1.41, P < 0.0001; homozygote comparison: OR = 1.15, 95%CI = 1.04-1.27, P = 0.006; allele comparison: OR = 1.12, 95%CI = 1.02-1.23, P = 0.017). After excluding four studies [3740] that were not in accordance with HWE (Figure 2B), the pooled ORs and 95%CI revealed a possible increased risk of cancer (dominant comparison: OR = 1.25, 95%CI = 1.18-1.33, P < 0.0001; homozygote comparison: OR = 1.13, 95%CI = 1.01-1.26, P = 0.027).

When the subgroup carried out by ethnicity (Figure 3B), a significant association was observed between rs1799977 and cancer risk among Asians in four genetic models (dominant comparison: OR = 1.52, 95%CI = 1.04-2.24, P = 0.033; recessive comparison: OR = 3.34, 95%CI = 2.33-4.78, P < 0.0001; homozygote comparison: OR = 3.44, 95%CI = 2.12-5.59, P < 0.0001; allele comparison: OR = 1.64, 95%CI = 1.38-1.95, P < 0.0001) and Caucasians in only dominant model (OR = 1.24, 95%CI = 1.16-1.32, P < 0.0001). In the cancer-specific analysis (Figure 4A), rs1799977 polymorphism showed a correlation between colorectal cancer under two genetic models (dominant comparison: OR = 1.32, 95%CI = 1.16-1.51, P < 0.0001; allele comparison: OR = 1.21, 95%CI = 1.03-1.42, P = 0.023) and prostate cancer under dominant model (OR = 1.36, 95%CI = 1.16-1.59, P < 0.0001).

Figure 4:

Figure 4: Stratified analysis by cancer type for the association between hMLH1 polymorphisms and cancer risk under dominant model according to HWE ((A) rs1799977; (B) rs63750447). CI: confidence interval, OR: odds ratio. CRC: colorectal cancer; GC: gastric cancer; BC: breast cancer; PC: prostate cancer; EC: endometrial cancer; OC: ovarian carcinoma; GC: gastric cancer; LC: lung cancer; other: other cancer; HWE: Hardy-Weinberg equilibrium.

Besides, the results of subgroup analyses by source of control and study design exhibited in the Supplementary Table 1.

Rs63750447 polymorphism

A total of 2153 cancer cases and 1365 cancer-free controls from 11 studies were involved in our meta-analysis for rs63750447 polymorphism. Since the homozygous mutant AA of rs63750447 polymorphism was in very rare frequencies, we chose allele model, heterozygous model and dominant model to evaluate the association strength. The pooled analysis observed a significant association between cancer risk and rs63750447 polymorphism (dominant comparison: OR = 2.23, 95%CI = 1.75-2.86, P < 0.0001; heterozygote comparison: OR = 2.21, 95%CI = 1.73-2.84, P < 0.0001; allele comparison: OR = 2.19, 95%CI = 1.72-2.78, P < 0.0001), as shown in Figure 2C.

The subgroup analysis by cancer type (Figure 4B) indicated that rs63750447 polymorphism had influences on colorectal cancer (dominant comparison: OR = 2.87, 95%CI = 1.42-5.82, P = 0.003; heterozygote comparison: OR = 2.81, 95%CI = 1.42-5.57, P = 0.003; allele comparison: OR = 2.84, 95%CI = 1.38-5.81, P = 0.004), gastric cancer (dominant comparison: OR = 2.15, 95%CI = 1.27-3.64, P = 0.005; heterozygote comparison: OR = 2.2115, 95%CI = 1.27-3.64, P = 0.005; allele comparison: OR = 2.19, 95%CI = 1.24-3.47, P = 0.006), and endometrium cancer (dominant comparison: OR = 2.23, 95%CI = 1.06-3.21, P= 0.029; heterozygote comparison: OR = 1.85, 95%CI = 1.06-3.21, P = 0.029; allele comparison: OR = 1.80, 95%CI = 1.05-3.09, P = 0.033). When we conducted the subgroup analysis by quality score, there was a significantly increased cancer risk for rs63750447 polymorphism in both high-quality studies and low-quality studies (shown in Supplementary Table 1).

Test of heterogeneity and sensitivity analysis

As shown in Supplementary Table 1, significant heterogeneities existed after pooled the data of rs1800734 and rs1799977 polymorphisms under different comparison models (P ≤ 0.10 or I2 ≥ 50%), thus further subgroup analyses base on ethnicity, cancer type, source of control, and quality scores were performed. No obvious heterogeneity was found for rs63750447 polymorphism (P > 0.10 or I2 < 50%). Subsequent sensitivity analysis proved the stability of our study, since no significant alteration was detected after removing each individual study and rechecking the pooled ORs and 95%CIs for the rs1800734 and rs1799977 polymorphisms (Figure 5A, 5B). The third study performed by Zhang et al seemingly altered the pooled ORs significantly (Figure 5), and the detailed data from Stata 14.0 also showed us it was nearly approached to the upper limit. We guess it was due to the sample size of rs63750447 polymorphism was insufficient, only 11 studies from 6 articles were included. It indicated us the overall results of rs63750447 should be treated more carefully.

Figure 5:

Figure 5: Sensitivity analysis of the associations between hMLH1 polymorphisms and cancer risk according to HWE ((A) rs1800734; (B) rs1799977; (C) rs63750447). HWE: Hardy-Weinberg equilibrium.

Publication bias

The possible publication bias in the eligible literature was evaluated by Egger’s test and funnel plots. As shown in Figure 6, the Begg’s funnel plots appear to be symmetrical. This symmetry was then confirmed by the statistical results of Egger’s test (P > 0.05, shown in Table 3). These provided evidence for the absence of publication bias.

Figure 6:

Figure 6: Funnel plots of publication bias ((A) rs1800734; (B) rs1799977; (C) rs63750447).

Table 3: Egger’s test for publication bias test of hMLH1 polymorphisms

Egger’s test

SE

Coef

Std. Err

t

P>|t|

95%CI

rs1800734

slope

0.06249

0.064308

0.97

0.337

[-0.067807, 0.192794]

bias

0.15166

0.749679

0.20

0.841

[-1.367335, 1.670654]

rs1799977

slope

0.17888

0.082661

2.16

0.042

[0.007456, 0.350311]

bias

0.48454

0.597343

0.81

0.426

[-0.754272, 1.723357]

rs63750447

slope

-0.12387

0.497384

-0.25

0.809

[-1.249034, 1.001287]

bias

2.03105

1.146982

1.77

0.110

[-0.563603, 4.625704]

SE: standard error; 95%CI: 95% confidence interval.

DISCUSSION

To elucidate the veritable relationship between three hMLH1 polymorphisms (rs1800734; rs1799977; rs63750447) and cancer risk, we performed this meta-analysis based on overall published data up to May 2017. We found all of these polymorphisms can enhance overall cancer risks, especially Asians, under different genetic comparisons (Supplementary Table 1). Further subgroup analyses were carried out according to cancer type, source of control, quality score, and study design, and results worth discussing were obtained.

Interestingly, we found a moderate association existing between rs1800734 and the risk of gastric cancer in three genetic models (OR = 1.14, P = 0.017; OR = 1.33, P = 0.019; OR = 1.27, P = 0.024) and lung cancer in recessive model (OR = 1.27, P = 0.024), while no connection was display with colorectal cancer. As far as we know now, microsatellite instability (MSI) often occurs when mismatch errors failed to be corrected or hMLH1 gene was epigenetic silencing. Campbell et al. [41] found rs1800734 polymorphism enhanced MSI-positive colorectal cancer, the association was proved by Mrkonjic et al. [42] due to the effects of rs1800734 on the MLH1 promoter methylation, immunohistochemistry (IHC) deficiency, or both. This indicated us when performing further studies focused on the relationship between rs1800734 and cancer risk, the MSI-statue of cancer patients should be evaluated fundamentally.

Rs1799977 was a nonsynonymous coding polymorphism in hMLH1, which leaded to an amino acid change from isoleucine to valine. The mutational G allele of rs1799977 polymorphism was proved to connect with susceptibility of colorectal cancer and prostate cancer. For rs63750447, the cancer-specific analysis showed an increased risk of colorectal cancer, endometrial cancer and gastric cancer. Recently, rs63750447 was observed over-expressed in patients with EGFR-TKI (epidermal growth factor receptor-tyrosine kinase inhibitor) resistance, which has a possible shorter progression-free survival [43]. Thus, it was speculated that MLH1 might be involved in EGFR signaling or other pathways (such as proliferation and survival) [1].

Compare with previous meta-analyses study on the association between hMLH1 and cancer risk, our study included a larger sample size and performed more detailed stratification analysis. Besides, our study has stricter inclusion criteria and exclude criteria, thus avoided omissive and false drop (refer to the section of Characteristics of eligible studies, paragraph one). Thus, we think our results are more reliable and convinced. Moreover, we found rs1800734 was related to gastric cancer, while rs1799977 may have an influence on colorectal and prostate cancer. It may give us some hints for the further study.

There are still some limitations existing in this meta-analysis. Firstly, insufficiency of original data limited us to proceed more accurate analyses on the potential interaction between these polymorphisms and other risk factors such as age, sex, hereditary background, lifestyle, and MSI status, etc. Secondly, the studies involved in the rs63750447 analysis was insufficient, whose statistical significance was needed to verify by further well-designed study with larger sample sizes. Thirdly, we couldn’t exclude the publication bias absolutely according to the negative results of Egger’s test and funnel plots. Fourthly, the sample size was still small for any given cancer type, although we have pooled all published literatures. Hence, all the three hMLH1 polymorphisms were associated with cancer risk, but further profoundly investigation was requisite to clarify the strength of these associations.

MATERIALS AND METHODS

PRISMA statement was used to guide the process of this meta-analysis [44].

Search strategy

A comprehensive literature search was conducted using the following search terms: (“cancer”, “carcinoma”, “tumor”, “tumour”, or “neoplasm”) and (“polymorphism”, “variation”, “variant”, or “mutation”) and (“hMLH1”). The PubMed, Web of knowledge, VIP, WanFang and Chinese National Knowledge Infrastructure (CNKI) databases were searched up to May, 2017. Additional studies were identified by manual search of the references of the original studies or review articles. This study was approved by the ethics committee of Xi’an Jiaotong University.

To be eligible for this meta-analysis, the included study was required to (1) be case-control or case-cohort studies; (2) focused on the relationship between hMLH1 polymorphisms and risk of any cancer; (3) have at least three articles for each studied hMLH1 polymorphism, and available information concerning the genotype frequency of each included SNP of hMLH1 (i.e., rs1800734; rs1799977; rs63750447); (4) be published in English or Chinese. The exclusion criteria were as follows: (1) studies were not focused on cancer risk or targeted hMLH1 SNPs (rs1800734; 2: rs1799977; 3: rs63750447); (2) studies failed to supply any data on genotype distribution, (3) studies were updated by a following study where a larger number of subjects were included, (4) studies were designed as a case-case or case-only study. If 2 or more studies contained overlapping data, we selected the paper included more samples. Studies containing two or more case-control groups were considered as two or more independent studies.

Data extraction and quality assessment

For each included study, two investigators independently extracted the raw data and demographic information, including publication year, first author, ethnicity and country or origin, the number of cases and controls, source of controls, genotyping methods, genetic distribution, and P value of Hardy-Weinberg equilibrium (HWE) among the controls. Studies not follow HWE were excluded in subgroup analysis. We applied the Newcastle-Ottawa Scale (NOS) to evaluate the methodological quality of the eligible studies according to Zeng et al [45]. Accumulated score ranges from 0 to 9 points, and a score of 0-5 and 6-9 is considered to suggest a low and high quality respectively, with higher quality representing lower risks of bias. A discussion or consultation with a senior author was conducted to settle controversy until a consensus was reached.

Statistical analysis

To evaluate the strength of association between hMLH1 polymorphisms (rs1800734; rs1799977; rs63750447) and cancer risk, we calculated the odds ratios (ORs) and 95% confidence intervals (CIs) based on the genotype and allele frequencies in cases and controls of each eligible study. We used the Z test to access the significance of all pooled ORs and it was considered statistically significant if the P value < 0.05. The Chisquare-based Q statistic test and I2 statistic were applied to examine the statistical heterogeneity among studies. When no obvious heterogeneity existed across the studies (P>0.10 or I2 <50%), we pooled the ORs using fixed-effect model (Mantel– Haenszel); otherwise, the random effects model (DerSimonian and Laird) was chosen. The potential publication bias was evaluated by funnel plot and Egger’s test. To access the stability of the results in this meta-analysis, we performed sensitivity analysis by sequentially excluding each study and rechecked whether the pooled ORs were altered significantly.

The following genetic models were evaluated: allele comparison (B vs. A), homozygote comparison (BB vs. AA), heterozygote comparison (AB vs. AA), recessive model (BB vs. AA+ AB), and dominant model (BB+ AB vs. AA). “A” represents the wild allele, while “B” represents the mutation allele. After excluded studies not according to HWE, we conducted the subgroup analysis based on ethnicity (divided into Asian and Caucasian), cancer type, and source of control. All statistical analyses were calculated with the software STATA (Version 14.0; Stata Corp, College Station, TX).

CONFLICTS OF INTEREST

We declare that we have no conflicts of interest.

FUNDING

This work was supported by the National Social Science Foundation of China (16BGL183), the Natural Science Foundation of Shaanxi Province (2015JM8415) and the Fundamental Research Funds for the Central Universities of China (2011jdhz55).

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