Oncotarget

Research Papers:

Association between FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and breast cancer susceptibility: a meta-analysis

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Oncotarget. 2017; 8:3454-3470. https://doi.org/10.18632/oncotarget.13839

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Yafei Zhang, Xianling Zeng, Pengdi Liu, Ruofeng Hong, Hongwei Lu, Hong Ji, Le Lu and Yiming Li _

Abstract

Yafei Zhang1, Xianling Zeng2, Pengdi Liu1, Ruofeng Hong1, Hongwei Lu1, Hong Ji1, Le Lu1, Yiming Li1

1Department of General Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710004, China

2Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi’an, Shaanxi 710061, China

Correspondence to:

Yiming Li, email: [email protected]

Keywords: breast cancer, FGFR2, polymorphism

Received: August 08, 2016     Accepted: November 22, 2016     Published: December 09, 2016

ABSTRACT

The association between fibroblast growth factor receptor 2 (FGFR2) polymorphism and breast cancer (BC) susceptibility remains inconclusive. The purpose of this systematic review was to evaluate the relationship between FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and BC risk. PubMed, Web of science and the Cochrane Library databases were searched before October 11, 2015 to identify relevant studies. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to estimate the strength of associations. Sensitivity and subgroup analyses were conducted. Thirty-five studies published from 2007 to 2015 were included in this meta-analysis. The pooled results showed that there was significant association between all the 3 variants and BC risk in any genetic model. Subgroup analysis was performed on rs2981582 and rs2420946 by ethnicity and Source of controls, the effects remained in Asians, Caucasians, population-based and hospital-based groups. We did not carryout subgroup analysis on rs2981578 for the variant included only 3 articles. This meta-analysis of case-control studies provides strong evidence that FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphisms were significantly associated with the BC risk. For rs2981582 and rs2420946, the association remained significant in Asians, Caucasians, general populations and hospital populations. However, further large scale multicenter epidemiological studies are warranted to confirm this finding and the molecular mechanism for the association need to be elucidated further.


INTRODUCTION

Breast cancer (BC), one of the most common malignant tumors among women worldwide, has the highest mortality rate in female cancer. Its incidence rate is increasing year by year and the patients are becoming younger and younger in the world [1, 2]. BC is the result of the interaction of environmental and genetic factors. Under the same carcinogenic factors, only a small fraction of people develop BC, which suggests that the genetic background differences lead to individual differences in BC susceptibility [3].

In recent years, genome-wide association study (GWAS) provides a good technical support for the study on the susceptibility loci with high variation frequency and low penetrance [4]. Large numbers of BC related susceptibility genes and single nucleotide polymorphism sites have been found through GWAS, such as LSP1, MAP3K1, FGFR2, TGFB1, TOX3, etc [5]. The discovery of these genes will have an important impact on the prevention and treatment of BC, especially FGFR2 (rs2981582, rs2420946 and rs2981578). FGFR2 gene is located in 10q26, and contains at least 22 exons [6]. FGFR2 is a member of the tyrosine kinase receptor family. It is a transmembrane protein, and is mainly composed of three parts: extracellular region, transmembrane region and intracellular region. The extracellular segment has three immunoglobulin like protein functional areas. Through the combination with FGFs, the functional areas could activate the tyrosine kinase activity and induce receptor tyrosine phosphorylation. It also starts series of cascade reaction through the RAS-MAPK, JAK-STATs and PLC-Y signal system, and then regulate the transcription of downstream genes involve in the body's physiological and pathological activities, such as cell proliferation, differentiation, migration and apoptosis, angiogenesis, skeletal development. So FGFR2 plays an important role in the processes of human growth and development [7].

Lots of researches have reported the association between FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and BC risk. However, due to differences in ethnic and regional and other factors, the conclusions of related reports are still inconclusive. Raskin et al [8] found FGFR2 rs2420946 was significantly associated with BC risk in Ashkenazi and Sephardi Jews, with a similar but not significant trend in Arabs. Liang et al’s [9] study indicated that each of thesingle nucleotide polymorphisms (SNPs) (rs2981582and rs2420946) was significantly associated with increased BC risk, and the risk was the highest for those carrying the 2 mutation sites at the same time. While, there are also some different reports. Liu et al [10] found that FGFR2 rs2420946was not significantly correlated with the occurrence of BC in Chinese population. These different conclusions may result from the diversity of genetic background and carcinogenic factors, therefore, further studies in different populations should be implemented to assess the correlation between SNPs and BC risk. Although five meta-analysis [1115] on the associations between FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and BC risk had been implemented, yet the results remained inconclusive and some just no subgroup. Therefore, we carried out this meta-analysis on all the included case-control researches to make a more accurate assessment of the relationship.

RESULTS

Characteristics of included papers

The specific search process is shown in Figure 1. A total of 563 references were preliminarily identified at first based on our selection strategy. We also identified 2 papers through other sources. 454 records left after removing repeated studies. We refer to titles or abstracts of all the included literatures, and then removed obviously irrelevant papers. In the end, the whole of the rest of the papers were checked based on the inclusion and exclusion criteria. Finally, 35 studies on FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and the occurrence of BC were eventually included in our study. Characteristics of eligible analysis are shown in Table 1. The 35 case-control papers were published between 2007 and 2015, among them, 1 study was performed in African, 17 in Asian, 14 in Caucasians and 3 in both Asian and Caucasians. All studies were case-controlled.

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

First author

Year

Country

Ethnicity

Source of controls

Genotyping medthod

Number(case/control)

HWE

rs2981582 (C>T)

Kawase [20]

2009

Japan

Asian

HB

TaqMan

455/912

0.773315

Hu [25]

2011

China

Asian

PB

PCR-RFLP

203/200

0.758366

Li [26]

2011

China

Asian

HB

MassArray

401/441

0.219207

Chen [27]

2012

China

Asian

PB

PCR-SSCP

388/424

0.048991

Butt [28]

2012

Swedish

Caucasian

PB

MassArray

713/1399

0.816442

Shan [29]

2012

Tunisian

African

PB

TaqMan

600/358

0.060883

Fu [30]

2012

China

Asian

HB

iPLEX

118/104

0.474243

Campa [31]

2011

Mixed

Mixed

PB

Taqman

8313/11594

0.607558

Slattery [32]

2011

American

Caucasian

PB

Taqman

1734/2040

0.822253

Han [33]

2011

Korean

Asian

PB

Taqman

3232/3489

0.361342

Tamimi [34]

2010

Swedish

Caucasian

PB

Taqman

680/734

0.535243

Gorodnova [35]

2010

Russian

Caucasian

NA

Taqman

140/174

0.000621

Ren [36]

2010

China

Asian

HB

Taqman

956/471

0.024883

McInerney [37]

2009

British

Caucasian

PB

KASPar

941/997

0.83057

Boyarskikh [38]

2009

Russia

Caucasian

PB

Taqman

744/628

0.659988

Garcia-Closas [39]

2008

Mixed

Mixed

PB, HB

Taqman

16882/26058

0.892667

Liang [9]

2008

China

Asian

HB

Taqman

1026/1062

0.97418

Antoniou [40]

2008

European

Mixed

NA

Taqman, MALDI-TOF

4990/4301

0.596563

Zhao [41]

2010

China

Asian

HB

PCR-RFLP

956/471

0.024883

Xi [42]

2014

China

Asian

HB

MALDI-TOF

815/849

0.959015

Campa [19]

2015

Mixed

Caucasian

PB

TaqMan

1234/12231

0.779613

Slattery [43]

2013

American

Caucasian

PB

multiplexed bead array

3560/4138

0.364662

Chan [44]

2012

China

Asian

HB

Taqman

1168/1475

0.164674

Dai [45]

2012

China

Asian

HB

TaqMan

1768/1844

0.423521

Jara [46]

2013

Chile

Caucasian

PB

TaqMan

351/802

0.138274

Liang [18]

2015

China

Asian

HB

MassARRAY

607/856

0.298476

Liu [47]

2013

China

Asian

HB

PCR-RFLP

203/200

0.758366

Murillo-Zamora [48]

2013

Mexico

Caucasian

PB

Multiplexed assays

687/907

0.351295

Ottini [49]

2013

Italy

Caucasian

PB

TaqMan

413/745

0.76716

Ozgoz [50]

2013

Turkey

Caucasian

PB

PCR-RFLP

31/30

0.281979

Siddiqui [51]

2014

India

Asian

HB

PCR-RFLP

368/484

0.526174

rs2420946 (C>T)

Raskin [8]

2008

USA

Caucasian

PB

TaqMan

1480/1474

0.224235

Kawase [20]

2009

Japan

Asian

HB

TaqMan

453/912

0.519554

Liu [10]

2009

China

Asian

PB

PCR-RFLP

106/116

0.361602

Hu [25]

2011

China

Asian

PB

PCR-RFLP

203/200

0.325727

Li [26]

2011

China

Asian

HB

MassArray

391/432

0.703117

Fu [30]

2012

China

Asian

HB

iPLEX

118/104

0.505449

Liang [9]

2008

China

Asian

HB

Taqman

1020/1050

0.413194

Hunter [52]

2007

USA

Caucasian

PB

Taqman

2912/3212

0.293864

Jara [46]

2013

Chile

Caucasian

PB

TaqMan

351/802

0.292806

Liang [18]

2015

China

Asian

HB

MassARRAY

603/847

0.063645

Liu [47]

2013

China

Asian

HB

PCR-RFLP

203/200

0.325727

rs2981578 (A>G)

Chen [27]

2012

China

Asian

PB

PCR-SSCP

378/458

0.290218

Lin [53]

2012

Taiwan

Asian

PB

PCR-RFLP

87/70

0.724138

Siddiqui [51]

2014

India

Asian

HB

PCR-RFLP

368/484

0.278456

HWE: hardy-weinberg equilibrium; PB: population based; HB: hospital-based.

Flow chart of studies selection in this meta-analysis.

Figure 1: Flow chart of studies selection in this meta-analysis.

Meta-analysis results

The FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphisms genotype distribution and allele frequencies incase groups and control groups were shown in Table 2. Main results of our study were shown in Table 3. There were 31 studies with 54,677 cases and 80,418 controls for FGFR2 rs2981582 variants. As shown in Table 3, Figure 2 and Figure 3, the pooled results indicated that the correlation between FGFR2 rs2981582 polymorphism and the occurrence of BC was significant in any genetic model: Allele model (OR: 1.23; 95% CI: 1.19- 1.26; P< 0.00001), Dominant model (OR: 1.29; 95% CI: 1.24-1.34; P< 0.00001), Recessive model (OR: 1.35; 95% CI: 1.31-1.40; P<0.00001), Homozygous genetic model (OR: 1.50; 95% CI: 1.42-1.58; P< 0.00001), Heterozygote comparison (OR: 1.22; 95% CI: 1.17-1.27; P< 0.00001). In ethnicity specific analysis, FGFR2 rs2981582 were significantly associated with BC risk both in Asians (Allele model: OR=1.19, 95% CI=1.15- 1.24, P< 0.00001; Dominant model: OR=1.23, 95% CI=1.17-1.29, P< 0.00001; Recessive model: OR=1.31, 95% CI=1.21-1.42, P< 0.00001; Homozygous genetic model: OR=1.42, 95% CI=1.31-1.54, P< 0.00001; Heterozygote comparison: OR=1.18, 95% CI=1.12-1.25, P< 0.00001) and Caucasians (Allele model: OR=1.25, 95% CI=1.21-1.30, P< 0.00001; Dominant model: OR=1.33, 95% CI=1.26-1.40, P< 0.00001; Recessive model: OR=1.37, 95% CI=1.28-1.46, P< 0.00001; Homozygous genetic model: OR=1.56, 95% CI=1.45-1.68, P< 0.00001; Heterozygote comparison: OR=1.26, 95% CI=1.19-1.33, P< 0.00001). We didn’t discuss the African subgroup for just one study from African. The analysis in different source of controls showed the same association between FGFR2 rs2981582 polymorphism and BC susceptibility both in HB(Allele model: OR=1.22, 95% CI=1.16-1.27, P< 0.00001; Dominant model: OR=1.27, 95% CI=1.20-1.35, P< 0.00001; Recessive model: OR=1.31, 95% CI=1.20-1.44, P< 0.00001; Homozygous genetic model: OR=1.45, 95% CI=1.31-1.60, P< 0.00001; Heterozygote comparison: OR=1.23, 95% CI=1.15-1.31, P< 0.00001) and PB(Allele model: OR=1.24, 95% CI=1.19-1.29, P< 0.00001; Dominant model: OR=1.31, 95% CI=1.23-1.40, P< 0.00001; Recessive model: OR=1.35, 95% CI=1.29-1.42, P< 0.00001; Homozygous genetic model: OR=1.50, 95% CI=1.43-1.58, P< 0.00001; Heterozygote comparison: OR=1.23, 95% CI=1.15-1.31, P< 0.00001).

Table 2: Polymorphisms genotype distribution and allele frequency in cases and controls

First author

Genotype (N)

Allele frequency (N)

Case

Control

Case

Control

rs2981582 (C>T)

Total

TT

TC

CC

Total

TT

TC

CC

T

C

T

C

Kawase [20]

455

42

192

221

912

63

347

502

276

634

473

1351

Hu [25]

203

47

78

78

200

26

95

79

172

234

147

253

Li [26]

401

54

180

167

441

60

189

192

288

514

309

573

Chen [27]

388

48

208

132

424

60

224

140

304

472

344

504

Butt [28]

713

124

356

233

1399

185

653

561

604

822

1023

1775

Shan [29]

600

147

315

138

358

64

154

140

609

591

282

434

Fu [30]

118

21

55

42

104

8

47

49

97

139

63

145

Campa [31]

8313

1568

3951

2794

11594

1718

5456

4420

7087

9539

8892

14296

Slattery [32]

1734

315

884

535

2040

318

981

741

1514

1954

1617

2463

Han [33]

3232

342

1393

1497

3489

281

1457

1751

2077

4387

2019

4959

Tamimi [34]

680

136

304

240

734

91

324

319

576

784

506

962

Gorodnova [35]

140

23

67

50

174

25

54

95

113

167

104

244

Ren [36]

956

130

400

426

471

56

181

234

660

1252

293

649

McInerney [37]

941

214

458

269

997

179

483

335

886

996

841

1153

Boyarskikh [38]

744

126

371

247

628

71

273

284

623

865

415

841

Garcia-Closas [39]

16882

3243

8218

5421

26058

3747

12255

10056

14704

19060

19749

32367

Liang [9]

1026

119

460

447

1062

91

439

532

698

1354

621

1503

Antoniou [40]

4990

936

2407

1647

4301

703

2051

1547

4279

5701

3457

5145

Zhao [41]

956

130

400

426

471

56

181

234

660

1252

293

649

Xi [42]

815

100

423

292

849

94

376

379

623

1007

564

1134

Campa [19]

1234

241

608

385

12231

1847

5793

4591

1090

1378

9487

14975

Slattery [43]

3560

708

1749

1103

4138

638

2009

1491

3165

3955

3285

4991

Chan [44]

1168

155

527

486

1475

162

618

695

837

1499

942

2008

Dai [45]

1768

216

820

732

1844

164

796

884

1252

2284

1124

2564

Jara [46]

351

80

178

93

802

141

366

295

338

364

648

956

Liang [18]

607

103

266

238

856

111

375

370

472

742

597

1115

Liu [47]

203

47

78

78

200

26

95

79

172

234

147

253

Murillo-Zamora [48]

687

145

309

233

907

139

415

353

599

775

693

1121

Ottini [49]

413

98

205

110

745

139

361

245

401

425

639

851

Ozgoz [50]

31

9

16

6

30

10

12

8

34

28

32

28

Siddiqui [51]

368

56

168

144

484

53

205

226

280

456

311

657

rs2420946 (C>T)

Total

TT

TC

CC

Total

TT

TC

CC

T

C

T

C

Raskin [8]

1480

356

715

409

1474

285

700

489

1427

1533

1270

1678

Kawase [20]

453

60

226

167

912

99

416

397

346

560

614

1210

Liu [10]

106

16

51

39

116

21

51

44

83

129

93

139

Hu [25]

203

50

92

61

200

34

105

61

192

214

173

227

Li [26]

391

74

186

131

432

68

202

162

334

448

338

526

Fu [30]

118

25

55

38

104

9

48

47

105

131

66

142

Liang [9]

1020

163

519

338

1050

142

505

403

845

1195

789

1311

Hunter [52]

2912

603

1409

900

3212

484

1562

1166

2615

3209

2530

3894

Jara [46]

351

85

175

91

802

143

374

285

345

357

660

944

Liang [18]

603

116

297

190

847

145

379

323

529

677

669

1025

Liu [47]

203

50

92

61

200

34

105

61

192

214

173

227

rs2981578 (A>G)

Total

GG

GA

AA

Total

GG

GA

AA

G

A

G

A

Chen [27]

378

150

188

40

458

160

212

86

488

268

532

384

Lin [53]

87

35

39

13

70

21

36

13

109

65

78

62

Siddiqui [51]

368

129

185

54

484

151

228

105

443

293

530

438

Table 3: Meta-analysis results

Outcome or Subgroup

Studies

Participants

Statistical Method

Effect Estimate

P value

Heterogeneity

I2

P value

Allele model

rs2981582 (C>T)

31

270190

OR (M-H, Random, 95% CI)

1.23 [1.19, 1.26]

< 0.00001

41%

0.01

Asian

15

51892

OR (M-H, Fixed, 95% CI)

1.19 [1.15, 1.24]

< 0.00001

0%

0.54

Caucasian

12

72106

OR (M-H, Fixed, 95% CI)

1.25 [1.21, 1.30]

< 0.00001

4%

0.4

HB

12

36020

OR (M-H, Fixed, 95% CI)

1.22 [1.16, 1.27]

< 0.00001

0%

0.87

PB

16

129080

OR (M-H, Random, 95% CI)

1.24 [1.19, 1.29]

< 0.00001

46%

0.02

rs2420946 (C>T)

11

34378

OR (M-H, Fixed, 95% CI)

1.23 [1.18, 1.29]

< 0.00001

0%

0.67

Asian

8

13916

OR (M-H, Fixed, 95% CI)

1.19 [1.11, 1.28]

< 0.00001

0%

0.67

Caucasian

3

20462

OR (M-H, Fixed, 95% CI)

1.26 [1.19, 1.33]

< 0.00001

0%

0.53

HB

6

12666

OR (M-H, Fixed, 95% CI)

1.20 [1.12, 1.29]

< 0.00001

0%

0.61

PB

5

21712

OR (M-H, Fixed, 95% CI)

1.25 [1.18, 1.32]

< 0.00001

0%

0.5

rs2981578 (A>G)

3

3690

OR (M-H, Fixed, 95% CI)

1.29 [1.13, 1.47]

0.0002

0%

0.93

Dominant model

rs2981582 (C>T)

31

135095

OR (M-H, Random, 95% CI)

1.29 [1.24, 1.34]

< 0.00001

46%

0.003

Asian

15

25946

OR (M-H, Fixed, 95% CI)

1.23 [1.17, 1.29]

< 0.00001

0%

0.63

Caucasian

12

36053

OR (M-H, Fixed, 95% CI)

1.33 [1.26, 1.40]

< 0.00001

16%

0.28

HB

12

18010

OR (M-H, Fixed, 95% CI)

1.27 [1.20, 1.35]

< 0.00001

0%

0.89

PB

16

64540

OR (M-H, Random, 95% CI)

1.31 [1.23, 1.40]

< 0.00001

55%

0.004

rs2420946 (C>T)

11

17189

OR (M-H, Fixed, 95% CI)

1.28 [1.20, 1.37]

< 0.00001

0%

0.77

Asian

8

6958

OR (M-H, Fixed, 95% CI)

1.25 [1.13, 1.39]

< 0.00001

0%

0.75

Caucasian

3

10231

OR (M-H, Fixed, 95% CI)

1.31 [1.20, 1.42]

< 0.00001

0%

0.38

HB

6

6333

OR (M-H, Fixed, 95% CI)

1.28 [1.15, 1.42]

< 0.00001

0%

0.73

PB

5

10856

OR (M-H, Fixed, 95% CI)

1.29 [1.19, 1.40]

< 0.00001

0%

0.44

rs2981578 (A>G)

3

1845

OR (M-H, Fixed, 95% CI)

1.71 [1.32, 2.21]

< 0.0001

0%

0.63

Recessive model

rs2981582 (C>T)

31

135095

OR (M-H, Fixed, 95% CI)

1.35 [1.31, 1.40]

< 0.00001

15%

0.24

Asian

15

25946

OR (M-H, Fixed, 95% CI)

1.31 [1.21, 1.42]

< 0.00001

19%

0.24

Caucasian

12

36053

OR (M-H, Fixed, 95% CI)

1.37 [1.28, 1.46]

< 0.00001

0%

0.74

HB

12

18010

OR (M-H, Fixed, 95% CI)

1.31 [1.20, 1.44]

< 0.00001

0%

0.5

PB

16

64540

OR (M-H, Fixed, 95% CI)

1.35 [1.29, 1.42]

< 0.00001

0%

0.45

rs2420946 (C>T)

11

17189

OR (M-H, Fixed, 95% CI)

1.36 [1.26, 1.48]

< 0.00001

4%

0.4

Asian

8

6958

OR (M-H, Fixed, 95% CI)

1.27 [1.12, 1.45]

0.0003

8%

0.37

Caucasian

3

10231

OR (M-H, Fixed, 95% CI)

1.42 [1.29, 1.57]

< 0.00001

0%

0.61

HB

6

6333

OR (M-H, Fixed, 95% CI)

1.27 [1.11, 1.46]

0.0006

4%

0.39

PB

5

10856

OR (M-H, Fixed, 95% CI)

1.41 [1.28, 1.56]

< 0.00001

0%

0.46

rs2981578 (A>G)

3

1845

OR (M-H, Fixed, 95% CI)

1.24 [1.02, 1.50]

0.03

0%

0.75

Homozygous genetic model

rs2981582 (C>T)

31

71786

OR (M-H, Random, 95% CI)

1.50 [1.42, 1.58]

< 0.00001

33%

0.04

Asian

15

14673

OR (M-H, Fixed, 95% CI)

1.42 [1.31, 1.54]

< 0.00001

2%

0.43

Caucasian

12

18824

OR (M-H, Fixed, 95% CI)

1.56 [1.45, 1.68]

< 0.00001

0%

0.73

HB

12

10192

OR (M-H, Fixed, 95% CI)

1.45 [1.31, 1.60]

< 0.00001

0%

0.69

PB

16

34101

OR (M-H, Fixed, 95% CI)

1.50 [1.43, 1.58]

< 0.00001

32%

0.11

rs2420946 (C>T)

11

8925

OR (M-H, Fixed, 95% CI)

1.52 [1.39, 1.66]

< 0.00001

0%

0.54

Asian

8

3629

OR (M-H, Fixed, 95% CI)

1.40 [1.21, 1.62]

< 0.00001

0%

0.57

Caucasian

3

5296

OR (M-H, Fixed, 95% CI)

1.60 [1.43, 1.79]

< 0.00001

0%

0.56

HB

6

3303

OR (M-H, Fixed, 95% CI)

1.43 [1.22, 1.66]

< 0.00001

0%

0.53

PB

5

5622

OR (M-H, Fixed, 95% CI)

1.57 [1.41, 1.76]

< 0.00001

0%

0.47

rs2981578 (A>G)

3

957

OR (M-H, Fixed, 95% CI)

1.80 [1.36, 2.39]

< 0.0001

0%

0.8

Heterozygote genetic model

rs2981582 (C>T)

31

114046

OR (M-H, Random, 95% CI)

1.22 [1.17, 1.27]

< 0.00001

42%

0.007

Asian

15

23025

OR (M-H, Fixed, 95% CI)

1.18 [1.12, 1.25]

< 0.00001

1%

0.44

Caucasian

12

30051

OR (M-H, Fixed, 95% CI)

1.26 [1.19, 1.33]

< 0.00001

26%

0.19

HB

12

15893

OR (M-H, Fixed, 95% CI)

1.23 [1.15, 1.31]

< 0.00001

0%

0.75

PB

16

54285

OR (M-H, Random, 95% CI)

1.23 [1.15, 1.31]

< 0.00001

52%

0.009

rs2420946 (C>T)

11

14127

OR (M-H, Fixed, 95% CI)

1.21 [1.13, 1.29]

< 0.00001

0%

0.69

Asian

8

5852

OR (M-H, Fixed, 95% CI)

1.21 [1.08, 1.34]

0.0005

0%

0.62

Caucasian

3

8275

OR (M-H, Fixed, 95% CI)

1.21 [1.11, 1.32]

< 0.0001

0%

0.37

HB

6

5348

OR (M-H, Fixed, 95% CI)

1.23 [1.10, 1.38]

0.0002

0%

0.66

PB

5

8779

OR (M-H, Fixed, 95% CI)

1.19 [1.09, 1.30]

< 0.0001

0%

0.42

rs2981578 (A>G)

3

1199

OR (M-H, Fixed, 95% CI)

1.65 [1.26, 2.16]

0.0003

0%

0.51

CI: Confidence interval.

Forest plots of rs2981582 (C&#x003E;T) polymorphism and breast cancer risk stratified by ethnicity (Recessive model TT vs. CC &#x002B; TC).

Figure 2: Forest plots of rs2981582 (C>T) polymorphism and breast cancer risk stratified by ethnicity (Recessive model TT vs. CC + TC).

Forest plots of rs2981582 (C&#x003E;T) polymorphism and breast cancer risk stratified by Source of controls (Recessive model TT vs. CC &#x002B; TC).

Figure 3: Forest plots of rs2981582 (C>T) polymorphism and breast cancer risk stratified by Source of controls (Recessive model TT vs. CC + TC).

For rs2420946, 11 studies with 7,840 cases and 9,349 controls were included to assess the association. As shown in Table 3, Figure 4 and Figure 5, the pooled ORs suggested that rs2420946 was significantly associated with BC susceptibility in all the five genetic models: Allele model 1.23 (95% CI: 1.18-1.29; P< 0.00001), Dominant model 1.28 (95% CI: 1.20-1.37; P< 0.00001), Recessive model 1.36 (95% CI: 1.26-1.48; P< 0.00001), Homozygous genetic model 1.52 (95% CI: 1.39-1.66; P< 0.00001), Heterozygote comparison 1.21 (95% CI: 1.13-1.29; P< 0.00001). When stratified by Ethnicity and Source of controls, the results showed that FGFR2 rs2420946 was significantly associated with BC risk in Asians, Caucasians, HB and PB.

Forest plots of rs2420946 (C&#x003E;T) polymorphism and breast cancer risk stratified by ethnicity (Dominant model TC &#x002B; TT vs. CC).

Figure 4: Forest plots of rs2420946 (C>T) polymorphism and breast cancer risk stratified by ethnicity (Dominant model TC + TT vs. CC).

Forest plots of rs2420946 (C&#x003E;T) polymorphism and breast cancer risk stratified by Source of controls (Dominant model TC &#x002B; TT vs. CC).

Figure 5: Forest plots of rs2420946 (C>T) polymorphism and breast cancer risk stratified by Source of controls (Dominant model TC + TT vs. CC).

3 papers with 833 cases and 1012 controls were adopted to evaluate the association between the rs2981578 polymorphism and the BC risk. As shown in Table 3, Figure 6, the association between rs2981578 variant and BC susceptibility was also significant in any genetic model (Allele model: OR= 1.29, 95% CI= 1.13-1.47, P= 0.0002; Dominant model: OR= 1.71, 95% CI= 1.32-2.21, P< 0.0001; Recessive model: OR= 1.24, 95% CI= 1.02-1.50, P= 0.03; Homozygous genetic model: OR= 1.80, 95% CI= 1.36-2.39, P< 0.0001; Heterozygote comparison: OR= 1.65, 95% CI= 1.26-2.16, P= 0.0003).

Forest plots of rs2981578 (A&#x003E;G) polymorphism and breast cancer risk (Allele model G vs. A).

Figure 6: Forest plots of rs2981578 (A>G) polymorphism and breast cancer risk (Allele model G vs. A).

Sensitivity analyses

As shown in Table 1, all the studies conformed to the balance of HWE in controls except Chen’s(2012), Gorodnova’s(2012), Ren’s(2012), Zhao’s(2012) studies(P<0.05) in rs2981582 group, however, after performing the sensitivity analyses, the overall outcomes were no statistically significant change when removing any of the articles, indicating that our study has good stability and reliability.

Detection for heterogeneity

Heterogeneity among studies was obtained by Q statistic. Random-effect models were applied if p-value of heterogeneity tests were less than 0.1 (p ≤ 0.1), otherwise, fixed-effect models were selected (Table 3).

Publication bias

As Figure 7 indicated, the symmetrical funnel plot indicated that there is no significant publication bias in the total population. We use Begg's funnel plot and Egger test to evaluate the published bias, no significant publication bias was found in the Begg's test and Egger's test (P>0.05).

Funnel plot assessing evidence of publication bias.

Figure 7: Funnel plot assessing evidence of publication bias. A. rs2981582 (C>T) (Recessive model TT vs. CC + TC). B. rs2420946 (C>T) (Dominant model TC + TT vs. CC). C. rs2981578 (A>G) (Allele model G vs. A). SE: standard error; OR: odds ratio.

DISCUSSION

FGFR2 has been proved to be associated with many diseases, especially the relationship between FGFR2 and cancer, which has become a hot research topic in recent years [16]. GWAS analysis revealed that FGFR2 gene was one of the BC susceptibility genes. There are 8 SNPs(rs35054928, rs2981578, rs2912778, rs2912781, rs35393331, rsl0736303, rs7895676, rs33971856) in its second intron and the SNPs of FGFR2 have become the hotspot in BC susceptibility gene study [1719]. But the difference of SNPs allele frequency and LD structure reflects the difference of the genetic variation in the race, so the occurrence and characteristics of BC were different. Therefore, a variation in one study does not have the same risk impact on other crowds. This requires repeated studies on previously related locis in multiple populations worldwide.

Lots of researches have reported the association between FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and BC risk. However, due to differences in ethnic and regional and other factors, the conclusions of related reports are still inconclusive. Thus, we conducted the meta-analysis to evaluate the relationship between FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and BC risk.

In our study, there were 31 studies with 54,677 cases and 80,418 controls for FGFR2 rs2981582 variants. In the total population, the pooled results indicated that the correlation between FGFR2 rs2981582 polymorphism and the occurrence of BC was significant in any genetic model. Furthermore, in ethnicity-specific analysis, FGFR2 rs2981582 were also significantly associated with BC risk both in Asians and Caucasians. We didn’t discuss the African subgroup for just one study was from African. The analysis in different source of controls showed the same association between FGFR2 rs2981582 polymorphism and BC susceptibility both in HB and PB, indicating that both hospital populations and general populations followed the same relationship. For rs2420946, 11 studies with 7,840 cases and 9,349 controls were included to assess the association. In the total population, the pooled ORs suggested that rs2420946 was significantly associated with BC susceptibility in all the five genetic models. When stratified by ethnicity and source of controls, the results showed the same association in Asians, Caucasians, hospital populations and general populations, indicating that different genetic backgrounds and living environment were not strong enough to change these associations. All the results for the two variants (rs2981582, rs2420946) were partially consistent with the consequences of Wang’s [13], Peng’s [14], Zhang’s [12] and Jia’s [15] meta-analysis, while they didn’t conduct analysis in different source of controls, making our results more valuable. Furthermore, they didn’t use all the five genetic models(allele model, dominant model, recessive model, homozygous model and heterozygous model) to assess the strength of association. Wang’s [13] study also reported that the association appeared to be much stronger for estrogen receptor-positive and progesterone receptor-positive BC, which was not analyzed in our study. Peng’s [14] study was conducted on the base of present mata-ananlyses, which may missed some individual studies with larger sample sizes, and this type meta-analysis may not appropriate. In Zhang’s [12] study, the increased risk was found in the subgroup of postmenopausal women for rs2420946. However, only one study [20] reported that risk in premenopausal women. For Jia’s [15] study, in the ethnicity subgroup, using Non-Caucasians represent different ethnicities may cause some heterogeneity.

Three articles with 833 cases and 1012 controls were adopted to evaluate the association between the rs2981578 polymorphism and the BC risk. As the preceding two variants, the association between rs2981578 variant and BC susceptibility was also significant in any genetic model. For just only 3 studies, no stratified study was conducted for rs2981578 polymorphism. However, in Zhou’s [11] meta-analysis, they found that rs2981578 polymorphism might decrease BC risk. This may result from the literature selection bias. While the sample size of our study for rs2981578 was so small, data from a large sample of multiple centers is still needed to assess the association.

Our meta-analysis has several limitations. First, our study is a summary of the data. For lack of all individual raw data, we could not assess the cancer risk stratified by other covariates including age, sex, environment, hormone level, menopause age and other risk factors. We also cannot analyze the potential interaction of gene-environment and gene-gene. Second, only published papers were included in our meta-analysis, there still may be some unpublished studies which are in line with the conditions. Therefore, publication bias may exist even no statistical evidence was found in the meta-analysis. Third, for just only 3 papers, no stratified study was conducted for rs2981578 polymorphism. Moreover, our study is a summary of the data. We did not verify it from the level of molecular mechanism. Data from large scale multicenter epidemiological studies is still needed to confirm the relationship between FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphisms and BC risk, and the molecular mechanism for the associations need to be elucidated further.

In conclusion, our meta-analysis based on case-control studies provides strong evidence that FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphisms are significantly associated with the BC risk. For rs2981582 and rs2420946, the association remained significant in Asians, Caucasians, general populations and hospital populations. However, further large scale multicenter epidemiological studies are warranted to confirm this finding and the molecular mechanism for the associations need to be elucidated further.

MATERIALS AND METHODS

Literature search

We searched PubMed, Web of science and the Cochrane Library for relevant studies published before October 11, 2015. The following keywords were used: (Fibroblast Growth Factor Receptor 2 or FGFR2) and (variant* or genotype or polymorphism or SNP) and (breast) and (cancer or carcinom* or neoplasm* or tumor), and the combined phrases for all genetic studies on the association between the FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and BC risk. The reference lists of all articles were also manually screened for potential studies. Abstracts and citations were screened by two researchers independently. All the eligible articles need a second screening for the full-text. The searching was done without language limitations.

Selection and exclusion criteria

Inclusion criteria: A study was included in this meta-analysis if it met the following criteria: i)independent case-control studies for humans; ii) the study evaluating the association between FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and BC risk; iii) the study presenting available genotype frequencies in cancer cases and control subjects for risk estimated; iiii) cases should have been diagnosed by a pathological examination. We excluded comments, editorials, systematic reviews and studies lacking sufficient data or studies with male cases. If the researches were duplicated or shared in more than one study, the most recent publications were included.

Data extraction and synthesis

We used endnote bibliographic software to construct an electronic library of citations identified in the literature search. All the PubMed, Web of science and the Cochrane Library searches were performed using Endnote. Duplicates were found automatically by endnote and deleted manually. All data extraction were checked and calculated twice according to the inclusion criteria listed above by two independent investigators. Data extracted from the included studies were as follows: First author, year of publication, country, Ethnicity, Source of controls, Genotyping method, number of cases and controls and evidence of Hardy-Weinberg equilibrium(HWE) in controls. A third reviewer would participate if some disagreements were emerged, and a final decision was made by the majority of the votes.

Statistical analysis

All statistical analyses were performed using STATA version 11.0 software (StataCorp LP, College Station, TX) and Review Manage version 5.2.0 (The Cochrane Collaboration, 2012). Hardy-Weinberg equilibrium (HWE) was assessed by χ2 test in the control group of each study [21]. The strength of associations between the FGFR2 (rs2981582, rs2420946 and rs2981578) polymorphism and BC risk were measured by odds ratio (ORs) with 95% confidence interval (CIs). Z test was used to assess the significance of the ORs, I2 and Q statistics was used to determine the statistical heterogeneity among studies. A random-effect model was used if p value of heterogeneity tests was no more than 0.1 (p ≤ 0.1), and otherwise, a fixed-effect model was selected [21, 22]. Sensitivity analyses were performed to assess the stability of the results. We used Begg’s funnel plot and Egger’s test to evaluate the publication bias [23, 24]. The strength of the association was estimated in the allele model, the dominant model, the recessive model, the homozygous genetic model and the heterozygous genetic model, respectively. p< 0.05 was considered statistically significant. We performed subgroup according to Ethnicity and Source of controls.

CONFLICTS OF INTEREST

The authors have declared that no conflicts of interest exists.

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