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  • Open Access

Subtype-specific associations between breast cancer risk polymorphisms and the survival of early-stage breast cancer

Contributed equally
Journal of Translational Medicine201816:270

https://doi.org/10.1186/s12967-018-1634-0

  • Received: 14 April 2018
  • Accepted: 16 September 2018
  • Published:

Abstract

Background

Limited evidence suggests that inherited predisposing risk variants might affect the disease outcome. In this study, we analyzed the effect of genome-wide association studies—identified breast cancer-risk single nucleotide polymorphisms on survival of early-stage breast cancer patients in a Chinese population.

Methods

This retrospective study investigated the relationship between 21 GWAS-identified breast cancer-risk single nucleotide polymorphisms and the outcome of 1177 early stage breast cancer patients with a long median follow-up time of 174 months. Cox proportional hazards regression models were used to estimate the hazard ratios and their 95% confidence intervals. Primary endpoints were breast cancer special survival and overall survival while secondary endpoints were invasive disease free survival and distant disease free survival.

Results

Multivariate survival analysis showed only the rs2046210 GA genotype significantly decreased the risk of recurrence and death for early stage breast cancer. After grouping breast cancer subtypes, significantly reduced survival was associated with the variant alleles of rs9485372 for luminal A and rs4415084 for triple negative breast cancer. Importantly, all three single-nucleotide polymorphisms, rs889312, rs4951011 and rs9485372 had remarkable effects on survival of luminal B EBC, either individually or synergistically. Furthermore, statistically significant multiplicative interactions were found between rs4415084 and age at diagnosis and between rs3803662 and tumor grade.

Conclusions

Our results demonstrate that breast cancer risk susceptibility loci identified by GWAS may influence the outcome of early stage breast cancer patients’ depending on intrinsic tumor subtypes in Chinese women.

Keywords

  • Breast cancer
  • Single nucleotide polymorphism
  • Genome-wide association study
  • Prognosis

Background

Breast cancer (BC) is the most common diagnosed cancer and the fifth leading cause of cancer death among women in China [1]. The 5-year survival of early stage breast cancer (EBC) patients in China is about 58–78%, which is low compared to that in American and varies in different geographic areas of China [2]. Traditionally, there are some prognostic factors for EBC survival including tumor size, lymph node involvement, tumor grade, hormone receptor (HR) status. However it has been proven that inherited host characteristics, such as single nucleotide polymorphisms (SNPs), play an important role [3].

Recently, genome-wide association studies (GWAS) have been widely applied to search genetic variations and disease association. It is worth noting that some susceptibility genes or polymorphisms identified by GWAS have been proven to not only be associated with predisposition to malignant tumors, but also influence their clinical outcome [46]. Only one study and one meta-analysis examined the relationship between GWAS-identified BC risk polymorphisms and the outcome for BC, both of which focused on Caucasian populations [6, 7]. However, rs6504950 and rs3803662 had different effects on the survival of BC patients in those two studies. Differences might be due to the different sample sizes and the different enrolled BC cases. Still, those studies already demonstrated the possible associations between BC risk loci and BC survival.

Similarly, there had been some BC-risk GWAS focusing on East Asian women and that found several BC risk variants, most of which were different from those identified in other ethnic populations [8, 9]. However, the relation between these polymorphisms and survival of EBC Asian patients has never been established. In the present study, we analyzed the association between 21 GWAS-identified SNPs and the survival of patients in Southeastern China with EBC.

Methods

Study populations

This is a hospital-based study including 1177 early breast cancer cases from Fujian Medical University Union Hospital from July 2000 and October 2014. All the participants were histopathologically confirmed with invasive breast cancer and subsequently treated with curative surgical resection and systemic therapy. Clinicopathological and demographic data were collected from the hospital records and survival data were obtained from the followed-up database which was renewed annually. The patients were staged according to the 7th version of American Joint Commission on Cancer (AJCC) tumor-node-metastasis (TNM) staging system [10]. Estrogen receptor (ER)/progesterone receptor (PR) positivity was determined by IHC analysis of the number of positively stained nuclei (≥ 10%) and hormone receptor (HR) positivity was defined as being either ER+ and/or PR+. Tumors were considered human epidermal growth factor-2 (HER2) positive when cells exhibited strong membrane staining (3+). Expressions of 2+ would require further in situ hybridization testing for HER2 gene amplification while expressions of 0 or 1+ were regarded as negative. The subtypes were categorized as follows [11]: luminal A (ER+, PR+ > 20%, HER2−, Ki67 < 14% or grade I when Ki67 was unavailable), luminal B (HR+, HER2−, Ki67 > 14% or grade II/III when Ki67 was unavailable or HR+, HER2+); HER2 enriched (HR−, HER2+) and triple negative (HR− and HER2−). The study was approved by the Institutional Ethics Committee and all participants consented to genetic testing at the time of their participation and contributed data.

SNPs selection

We selected the polymorphisms associated with breast cancer susceptibility from the US National Human Genome Research Institute (NHGRI) Catalog of Published Genome-Wide Association Studies. We used the following inclusion criteria: (i) the significance level for genome-wide association was considered to be P ≤ 1 × 10−9; (ii) the minor allele frequency (MAF) was at least 10% in the HapMap CHB data of the public SNP database (http://www.ncbi.nlm.nih.gov/SNP); (iii) pair wise linkage disequilibrium (LD) between the eligible SNPs calculated by Haploview 4.1 software must be less than 0.8 (r2 < 0.8). At last, 21 polymorphisms were applied in this study which can be found in Additional file 1: Table S1.

DNA extraction and SNPs genotyping

Blood samples were collected in EDTA anticoagulant tubes and stored at − 80 °C until DNA extraction. Genomic DNA was extracted using the Whole-Blood DNA Extraction Kit (Bioteke, Beijing, China), according to the manufacturer’s protocol. The genotype analysis was performed by SNPscan, which is a high-throughput SNPs genotyping technology (Genesky Biotechnologies Inc., Shanghai, China). Finally, the raw data were analyzed by the GeneMapper 4.0 Software (Applied Biosystems, Foster City, CA). 5% of samples were randomly selected as blinded duplicates for quality assessment purposes and 100% concordance was obtained.

Statistical analyses

Overall survival (OS) and breast cancer specific survival (BCSS) were our primary endpoints and defined as the time from the date of cancer diagnosis to the date of mortality for all cause and breast cancer, respectively. Disease free survival (DFS) and distant disease free survival (DDFS) were our secondary endpoints and calculated separately as the time from the date of diagnosis to the date of any recurrence and distant recurrence to the last patient contact [12]. Survival data were analyzed using the Kaplan–Meier method with the log-rank test and multivariate Cox stepwise regression analysis to the end of follow-up (2016.12.31). Adjustment for age at diagnosis, tumor size, lymph node involvement, histological grade, ER status, and HER-2/neu expression were applied. The hazard ratios (HRs) and 95% confidence interval (CI) for each factor in multivariate analyses were calculated from the Cox-regression model. The Chi square-based Q test was used to examine the heterogeneity between subgroups. The possible gene-environment interactions were also evaluated by the Cox proportional hazard regression models. All tests were 2-sided, and P values of < 0.05 were considered statistically significant. SAS 9.4 (SAS Institute Inc., Cary, NC) was used for all statistical analyses.

Results

Patient characteristics and clinical features

Patients’ clinical characteristics and survival are summarized in Table 1. All the 1177 early breast cancer cohort, were female and their mean age was 47.0 ± 10.3 years old at breast cancer diagnosis. During a median follow-up time of 174 months, 446 cases experienced recurrence (142 locoregional and 410 distant) and 343 died (333 died of BC and 10 died of other disease).
Table 1

Patients’ clinicopathological characteristics and clinical outcome

Variables

Patients

N = 1177

iDFS

DDFS

BCSS

OS

Events

LogRank P

Events

LogRank P

Events

LogRank P

Events

LogRank P

Age at diagnosis

  

0.021

 

0.087

 

0.420

 

0.402

 ≤ 35

184

85

 

76

 

59

 

61

 

 > 35

993

361

 

334

 

274

 

282

 

Tumor size (cm)

  

< 0.001

 

< 0.001

 

< 0.001

 

< 0.001

 ≤ 2

403

88

 

80

 

67

 

70

 

 > 2

774

358

 

330

 

266

 

273

 

Nodal status

  

< 0.001

 

< 0.001

 

< 0.001

 

< 0.001

 Negative

510

116

 

101

 

69

 

75

 

 Positive

667

330

 

309

 

264

 

268

 

Clinical stage

  

< 0.001

 

< 0.001

 

< 0.001

 

< 0.001

 I

257

40

 

35

 

29

 

31

 

 II + III

920

406

 

375

 

304

 

312

 

Gradea

  

< 0.001

 

< 0.001

 

< 0.001

 

< 0.001

 I + II

904

310

 

286

 

228

 

236

 

 III

271

134

 

122

 

103

 

105

 

ER

  

< 0.001

 

< 0.001

 

< 0.001

 

< 0.001

 Negative

378

177

 

165

 

149

 

150

 

 Positive

799

269

 

245

 

184

 

193

 

Variables

Patients

N = 1177

iDFS

DDFS

BCSS

OS

iDFS

DDFS

BCSS

OS

Events

LogRank P

Events

LogRank P

Events

LogRank P

Events

LogRank P

PR

  

< 0.001

 

< 0.001

 

< 0.001

 

< 0.001

 Negative

367

171

 

159

 

144

 

145

 

 Positive

810

275

 

251

 

189

 

198

 

HER2

  

< 0.001

 

< 0.001

 

< 0.001

 

< 0.001

 Negative

860

292

 

268

 

214

 

222

 

 Positive

317

154

 

142

 

119

 

121

 

Subtype

  

< 0.001

 

< 0.001

 

< 0.001

 

< 0.001

 Luminal A

236

35

 

33

 

26

 

26

 

 Luminal B

574

240

 

218

 

163

 

172

 

 HER2+

160

80

 

76

 

67

 

67

 

 Triple negative

207

91

 

83

 

77

 

78

 

aVariable including missing data

No significant difference in BC-DDFS, BCSS, and OS was shown in the subgroup of age at diagnosis (P = 0.087, 0.420, and 0.402). But patients with a tumor size > 2 cm, lymph node positive, grade III, clinical stage II + III, or HER2 positive had significantly shorter survival times, whereas being ER or HR positivity remarkably improved the survival of EBC patients (log-rank P < 0.05, Table 1). Furthermore, our intrinsic molecular subtypes (luminal A, luminal B, HER2-enriched, and triple negative) were also associated with significantly different survival (log-rank P < 0.05, Table 1).

Effects of each polymorphism on survival of EBC

Among the 21 SNPs, 6 SNPs (rs13281615, rs4415084, rs4784227, rs889312, rs10474352 and rs10816625) had a log-rank P under 0.05 in some genetic models and in some outcome indicators (log-rank P < 0.05, Table 2). But after adjusting for age at breast cancer diagnosis, tumor size, lymph node involvement, grade, hormone receptor status, and HER2 status, only rs889312 and rs2046210 had significant effect on improving survival of EBC patients. In a recessive model, rs889312 was significantly associated with better iDFS and DDFS (iDFS: adjusted HR (aHR): 0.761, 95% CI 0.583–0.994, and DDFS: aHR: 0.631, 95% CI 0.470–0.848; Table 3). Similarly, in contrast to the GG + AA genotypes, the GA genotype of rs2046210 also improve the survival of EBC patients (iDFS: aHR: 0.812, 95% CI 0.673–0.980; DDFS: aHR: 0.771, 95% CI 0.635–0.938; BCSS: aHR: 0.790, 95% CI 0.636–0.981 and OS aHR: 0.786, 95% CI 0.635–0.934, Table 3).
Table 2

Genotyping results with EBC’s survival

SNPs

Cases

WH/H/VH

iDFS (LogRank P)

DDFS (LogRank P)

BCSS (logRank P)

OS (Log Rank P)

Events

WH/H/VH

DOM

REC

COD

Events

WH/H/VH

DOM

REC

COD

Events

WH/H/VH

DOM

REC

COD

Events

WH/H/VH

DOM

REC

COD

rs10069690

789/353/34

298/139/8

0.938

0.065

0.152

273/129/8

0.689

0.128

0.221

218/107/7

0.510

0.230

0.291

225/110/7

0.533

0.191

0.257

rs13281615

293/575/308

126/196/124

0.043

0.397

0.035

112/186/112

0.178

0.619

0.241

86/154/93

0.592

0.402

0.482

89/157/97

0.531

0.320

0.362

rs13387042

932/234/11

351/91/4

0.803

1.000

0.968

322/84/4

0.767

0.830

0.944

264/66/3

0.891

0.891

0.977

274/66/3

0.664

0.934

0.898

rs1562430

801/344/32

297/136/13

0.419

0.787

0.720

272/125/13

0.363

0.516

0.600

228/97/8

0.840

0.738

0.938

234/100/9

0.940

0.955

0.996

rs2046210

361/602/214

142/220/84

0.327

0.873

0.611

134/198/78

0.180

0.964

0.361

107/162/64

0.359

0.970

0.633

112/166/65

0.231

0.829

0.481

rs2180341

715/394/68

270/147/29

0.858

0.381

0.679

245/136/29

0.556

0.136

0.326

198/115/20

0.554

0.783

0.836

204/118/21

0.547

0.676

0.809

rs2981582

493/545/139

187/204/55

0.891

0.459

0.708

173/189/48

0.843

0.843

0.945

143/149/41

0.491

0.554

0.556

146/154/43

0.581

0.459

0.547

rs3112612

776/354/46

290/140/15

0.541

0.563

0.610

263/132/14

0.263

0.660

0.391

210/110/12

0.213

0.818

0.393

218/111/13

0.290

1.000

0.545

rs3803662

532/512/133

214/185/47

0.102

0.472

0.258

193/172/45

0.309

0.795

0.594

157/138/38

0.284

0.957

0.537

165/139/39

0.141

0.946

0.309

rs4415084

392/558/226

144/204/98

0.332

0.043

0.124

130/189/91

0.256

0.038

0.106

105/152/76

0.245

0.039

0.107

110/156/77

0.345

0.059

0.160

rs4784227

550/513/113

191/211/44

0.035

0.714

0.104

177/195/38

0.077

0.905

0.164

146/155/32

0.173

0.793

0.389

148/162/33

0.091

0.773

0.235

rs889312

346/631/200

130/252/64

0.770

0.059

0.111

124/235/51

0.823

0.003

0.010

98/189/46

0.840

0.070

0.142

101/196/46

0.841

0.038

0.080

rs9485372

388/588/200

136/227/82

0.122

0.177

0.200

127/208/74

0.230

0.360

0.415

104/169/59

0.334

0.529

0.592

107/173/62

0.320

0.382

0.513

rs10474352

374/572/230

158/214/74

0.052

0.029

0.041

143/199/68

0.142

0.049

0.101

115/161/57

0.285

0.156

0.301

119/165/59

0.241

0.160

0.284

rs10816625

350/595/231

145/213/88

0.047

0.825

0.127

136/196/78

0.022

0.468

0.073

114/156/63

0.017

0.559

0.056

118/160/65

0.012

0.567

0.041

rs12922061

539/529/108

199/206/41

0.590

0.905

0.865

185/188/37

0.799

0.926

0.953

156/147/30

0.613

0.943

0.877

158/154/31

0.847

0.970

0.981

rs2290203

270/587/319

96/229/121

0.464

0.891

0.760

89/211/110

0.519

0.962

0.800

67/174/92

0.218

0.689

0.468

69/179/95

0.206

0.647

0.449

rs2296067

418/567/191

160/215/71

0.869

0.814

0.968

144/200/66

0.774

0.923

0.940

116/166/51

0.649

0.751

0.798

119/172/52

0.590

0.668

0.704

rs2981578

416/548/212

150/219/77

0.465

0.619

0.556

132/208/70

0.148

0.512

0.650

105/172/56

0.158

0.488

0.166

110/176/57

0.232

0.421

0.210

rs4951011

522/528/126

204/191/51

0.350

0.421

0.340

186/178/46

0.516

0.475

0.516

150/142/41

0.597

0.163

0.233

157/145/41

0.388

0.246

0.230

rs9693444

572/486/118

215/179/52

0.762

0.154

0.357

196/164/50

0.619

0.068

0.188

156/141/36

0.379

0.483

0.616

160/144/39

0.329

0.259

0.428

WH/H/VH wide homozygous type/heterozygote/variant homozygous type, DOM dominant model, REC recessive model, COD codominant model

Table 3

Association between the SNPs’ genotype with EBC’ survival (multivariate cox proportional hazard model)

SNPs

Cases

iDFS

DDFS

BCSS

OS

Events

Adjusted HR (95% CI)a

P value

Events

Adjusted HR (95% CI)a

P value

Events

Adjusted HR (95% CI)a

P value

Events

Adjusted HR (95% CI)a

P value

All cases

 rs889312

  CC

346

130

1 (reference)

 

124

1 (reference)

 

98

1 (reference)

 

101

1 (reference)

 

  CA

631

252

1.089 (0.880–1.347)

0.433

235

1.065 (0.856–1.326)

0.569

189

1.087 (0.850–1.389)

0.507

196

1.094 (0.859–1.393)

0.465

  AA

200

64

0.804 (0.595–1.087)

0.157

51

0.658 (0.474–0.913)

0.012

46

0.814 (0.573–1.158)

0.253

46

0.782 (0.510–1.111)

0.170

  DOM

  

1.017 (0.828–1.248)

0.876

 

0.960 (0.777–1.187)

0.706

 

1.020 (0.804–1.293)

0.872

 

1.017 (0.805–1.285)

0.887

  REC

  

0.761 (0.583–0.994)

0.045

 

0.631 (0.470–0.848)

0.002

 

0.772 (0.564–1.055)

0.105

 

0.738 (0.540–1.009)

0.057

 rs2046210

  GG

361

142

1 (reference)

 

134

1 (reference)

 

107

1 (reference)

 

112

1 (reference)

 

  GA

602

220

0.796 (0.644–0.985)

0.035

198

0.761 (0.610–0.949)

0.015

162

0.775 (0.606–0.991)

0.042

166

0.762 (0.598–0.970)

0.027

  AA

214

84

0.948 (0.722–1.244)

0.700

78

0.963 (0.727–1.275)

0.792

64

0.951 (0.696–1.299)

0.752

65

0.919 (0.675–1.250)

0.589

  DOM

  

0.833 (0.682–1.018)

0.074

 

0.809 (0.658–0.996)

0.045

 

0.818 (0.649–1.031)

0.090

 

0.800 (0.638–1.005)

0.055

  REC

  

1.094 (0.861–1.391)

0.462

 

1.142 (0.890–1.464)

0.296

 

1.116 (0.847–1.469)

0.436

 

1.089 (0.829–1.430)

0.541

  OVE

  

0.812 (0.673–0.980)

0.030

 

0.771 (0.635–0.938)

0.009

 

0.790 (0.636–0.981)

0.033

 

0.786 (0.635–0.934)

0.028

Luminal A

 rs9485372

  GG

72

10

1 (reference)

 

10

1 (reference)

 

7

1 (reference)

 

7

1 (reference)

 

  GA

124

16

0.833 (0.372–1.863)

0.656

14

0.717 (0.313–1.644)

0.432

11

0.890 (0.332–2.385)

0.817

11

0.890 (0.332–2.385)

0.817

  AA

40

9

2.201 (0.883–5.486)

0.090

9

2.192 (0.880–5.459)

0.092

8

3.280 (1.152–9.378)

0.026

8

3.280 (1.152–9.378)

0.026

  DOM

  

1.087 (0.518–2.283)

0.825

 

0.995 (0.469–2.109)

0.989

 

1.328 (0.546–3.229)

0.532

 

1.328 (0.546–3.229)

0.532

  REC

  

2.465 (1.133–5.360)

0.023

 

2.671 (1.214–5.875)

0.015

 

3.522 (1.464–8.473)

0.005

 

3.522 (1.464–8.473)

0.005

Triple negative

 rs4415084

  TT

59

24

1 (reference)

 

20

1 (reference)

 

20

1 (reference)

 

20

1 (reference)

 

  CT

83

44

1.622 (0.979–2.688)

0.061

42

1.799 (1.048–3.087)

0.033

39

1.686 (0.975–2.917)

0.062

40

1.736 (1.006–2.996)

0.047

  CC

65

23

1.785 (0.996–3.201)

0.052

21

1.813 (0.971–3.385)

0.062

18

1.549 (0.809–2.969)

0.187

18

1.551 (0.810–2.972)

0.186

  DOM

  

1.674 (1.043–2.687)

0.033

 

1.804 (1.084–3.002)

0.023

 

1.640 (0.979–2.750)

0.060

 

1.674 (1.000–2.803)

0.049

  REC

  

1.345 (0.827–2.187)

0.232

 

1.274 (0.765–2.120)

0.352

 

1.139 (0.661–1.962)

0.639

 

1.119 (0.650–1.926)

0.685

Luminal B

 rs4951011

  AA

265

120

1 (reference)

 

109

1 (reference)

 

82

1 (reference)

 

88

1 (reference)

 

  GA

253

92

0.682 (0.526–0.896)

0.006

84

0.698 (0.524–0.929)

0.014

59

0.652 (0.466–0.914)

0.013

62

0.630 (0.454–0.874)

0.006

  GG

55

28

0.883 (0.579–1.346)

0.562

25

0.888 (0.568–1.386)

0.645

22

1.025 (0.631–1.664)

0.921

22

0.965 (0.597–1.560)

0.885

  DOM

  

0.719 (0.557–0.928)

0.011

 

0.734 (0.561–0.960)

0.024

 

0.721 (0.528–0.984)

0.039

 

0.690 (0.510–0.934)

0.016

  REC

  

1.068 (0.714–1.598)

0.749

 

1.075 (0.703–1.645)

0.738

 

1.259 (0.794–1.998)

0.328

 

1.205 (0.762–1.908)

0.425

 rs889312

  CC

162

74

1 (reference)

 

70

1 (reference)

 

51

1 (reference)

 

54

1 (reference)

 

  CA

308

135

1.304 (0.778–1.374)

0.819

126

1.048 (0.782–1.406)

0.753

94

1.113 (0.790–1.568)

0.542

100

1.108 (0.794–1.546)

0.545

  AA

104

31

0.570 (0.373–0.870)

0.009

22

0.432 (0.266–0.701)

0.001

18

0.534 (0.310–0.918)

0.023

18

0.498 (0.290–0.853)

0.011

  DOM

  

0.901 (0.684–1.187)

0.459

 

0.871 (0.654–1.160)

0.344

 

0.954 (0.682–1.333)

0.781

 

0.940 (0.679–1.301)

0.708

  REC

  

0.558 (0.381–0.817)

0.003

 

0.419 (0.269–0.653)

< 0.000

 

0.498 (0.304–0.815)

0.006

 

0.465 (0.285–0.761)

0.002

Luminal B

 rs9485372

  GG

204

72

1 (reference)

 

63

1 (reference)

 

47

1 (reference)

 

49

1 (reference)

 

  GA

275

125

1.439 (1.076–1.924)

0.014

115

1.524 (1.121–2.073)

0.007

89

1.517 (1.065–2.162)

0.021

93

1.520 (1.075–2.149)

0.018

  AA

95

43

1.622 (1.111–2.370)

0.122

38

1.665 (1.116–2.485)

0.013

27

1.463 (0.910–2.350)

0.116

30

1.596 (1.012–2.516)

0.044

  DOM

  

1.482 (1.124–1.954)

0.005

 

1.557 (1.161–2.088)

0.003

 

1.504 (1.071–2.112)

0.018

 

1.538 (1.104–2.142)

0.011

  REC

  

1.307 (0.939–1.820)

0.112

 

1.294 (0.914–1.831)

0.146

 

1.137 (0.752–1.720)

0.544

 

1.239 (0.835–1.839)

0.288

DOM dominant model, REC recessive model, OVE overdominant model

aHR hazard risk, CI confidence interval; For all patients: Adjusted for age at diagnosis, tumor size, lymph node involvement, grade, hormone receptor status and Her2 status; For subtypes: Adjusted for age at diagnosis, tumor size, lymph node involvement, grade

Prognostic implication of risk variants in molecular subtypes

For a large number of patients enrolled in this study, we analyzed the association between enrolled SNPs and survival associated with different molecular subtypes of EBC. As showed in Table 3, rs9485372 and rs4415084 were still associated with a worse outcome in luminal A and triple negative EBC patients, respectively, after adjustment (for rs9485372 under the recessive model: iDFS: aHR: 2.465, 95% CI 1.133–5.360; DDFS: aHR: 2.671, 95% CI 1.214–5.875; BCSS and OS: aHR: 3.522, 95% CI 1.464–8.473; for rs4415084 under the dominant model: iDFS: aHR: 1.674, 95% CI 1.043–2.687; DDFS: aHR: 1.804, 95% CI 1.084–3.002 and OS: aHR: 1.674, 95% CI 1.000–2.803). Furthermore, in the luminal B subtype we found that rs4951011 (under the dominant model) and rs889312 (under the recessive model) could significantly improve the iDFS, DDFS, BCSS and OS of the breast cancer, while rs9485372 (under dominant model) worsens outcome (iDFS: aHR = 0.719, 95% CI 0.557–0.928, DDFS: aHR = 0.734, 95% CI 0.561–0.960, BCSS: aHR = 0.721, 95% CI 0.528–0.984 and OS: aHR = 0.690, 95% CI 0.510–0.934 for rs4951011; iDFS: aHR = 0.558, 95% CI 0.381–0.817, DDFS: aHR = 0.419, 95% CI 0.269–0.653, BCSS: aHR = 0.498, 95% CI 0.304–0.815 and OS: aHR = 0.465, 95% CI 0.285–0.761 for rs889312 and iDFS: aHR = 1.482, 95% CI 0.124–1.954, DDFS: aHR = 1.557, 95% CI 0.161–2.088, BCSS: aHR = 1.504, 95% CI 1.071–2.112 and OS: aHR = 1.538, 95% CI 1.104–2.142 for 9485872, Table 3). However, no significant effect was observed in the HER2-enriched subtype in any model of the 21 polymorphisms.

Combined analysis of three risk SNPs on survival of luminal B EBC

To assess the combined effects on risk of recurrence and death from luminal B EBC, we combined the risk genotypes of rs4951011, rs889312 and 9485372. According to the number of combined risk genotypes, the univariate survival analysis show that all of iDFS, DDFS, BCSS and OS were significantly different among different groups with different combined risk genotypes (P Log-rank < 0.01) (Fig. 1). As shown in Table 4, compared to subjects with one or no unfavorable genotype, subjects carrying more unfavorable loci had shorter survival time and had a 1.534–1.645 fold increased risk of recurrence and/of death even after adjustment (iDFS: aHR = 1.534, 95% CI 1.288–1.827, DDFS: aHR = 1.632, 95% CI 1.356–1.964, BCSS: aHR = 1.570, 95% CI 1.267–1.944 and OS: aHR = 1.645, 95% CI 1.334–2.029, respectively for trend).
Fig. 1
Fig. 1

Kaplan–Meier plots of survival for combined effect of the three SNPs on luminal B EBC survival

Table 4

Cumulative effect of unfavorable genotypes in luminal B subtype breast cancer

Number of risk genotypesa

Cases

iDFS

DDFS

BCSS

OS

Events

Adjusted HR (95% CI)b

P value

Events

Adjusted HR (95% CI)b

P value

Events

Adjusted HR (95% CI)b

P value

Events

Adjusted HR (95% CI)b

P value

0–1

165

49

1 (reference)

 

42

1 (reference)

 

32

1 (reference)

 

33

1 (reference)

 

2

272

123

1.912 (1.369–2.670)

1.44 × E−4

109

1.894 (1.324–2.711)

4.74 × E−4

81

1.787 (1.184–2.697)

5.70 × E−3

84

1.786 (1.192–6.678)

4.97 × E−3

3

137

68

2.431 (1.679–3.519)

2.52 × E−6

67

2.744 (1.862–4.043)

3.53 × E−7

50

2.525 (1.617–3.943)

4.61 × E−5

55

2.755 (1.786–4.251)

4.59 × E−6

Trend P

  

1.534 (1.288–1.827)

1.63 × E−6

 

1.632 (1.356–1.964)

2.18 × E−7

 

1.570 (1.267–1.944)

3.66 × E−5

 

1.645 (1.334–2.029)

3.25 × E−6

ars4951011 AA, rs889312 CC + CA and rs9485372 GA + AA were presumed as unfavorable genotypes

bHR hazard risk, CI confidence interval; Adjusted for age at diagnosis, tumor size, lymph node involvement, grade

Stratification and interaction analysis

The associations between breast cancer risk loci genotypes and EBC survival were then evaluated by stratified analysis of age at diagnosis, tumor size, lymph node involvement, grade, hormone-receptor status and HER2 status. As shown in Table 5, we found that rs4415084 and rs2981582 were associated with shorter survival of the patients who were younger (rs4415084 for age at diagnosis ≤ 35 years: iDFS: aHR = 1.792, 95% CI 1.161–2.915, DDFS: aHR = 2.172, 95% CI 1.310–3.602, BCSS: aHR = 2.250, 95% CI 1.278–3.959 and OS: aHR = 1.871, 95% CI 0.988–3.544) and with higher grade tumors (rs2981582 for grade III: iDFS: aHR = 1.666, 95% CI 1.051–2.639, DDFS: aHR = 1.682, 95% CI 1.049–2.698, BCSS: aHR = 1.783, 95% CI 1.080–2.944 and OS: aHR = 1.732, 95% CI 1.050–2.855). But rs2046210 and rs3803662 had beneficial effects on survival of the patients with larger tumor (rs2046210 for tumor size > 2 cm: iDFS: aHR = 0.757, 95% CI 0.606–0.944, DDFS: aHR = 0.732, 95% CI 0.582–0.919, BCSS: aHR = 0.713, 95% CI 0.533–0.920 and OS: aHR = 0.694, 95% CI 0.540–0.992) and with higher grade tumors (rs3803662 for grade III: iDFS: aHR = 0.588, 95% CI 0.414–0.834, DDFS: aHR = 0.586, 95% CI 0.407–0.845, BCSS: aHR = 0.479, 95% CI 0.319–0.717 and OS: aHR = 0.484, 95% CI 0.324–0.722) respectively. However, we did not find that the other SNPs affected survival in the subgroups of patients with different tumor characteristics.
Table 5

Stratification analysis of polymorphism genotypes associated with EBC survival

SNPs

Variables

iDFS

DDFS

BCSS

OS

Adjusted HR (95% CI)

P valuea

Adjusted HR (95% CI)

P valuea

Adjusted HR (95% CI)

P valuea

Adjusted HR (95% CI)

P valuea

rs4415084

Age at diagnosis

        

 ≤ 35

1.792 (1.161–2.915)

0.068

2.172 (1.310–3.602)

0.014

2.250 (1.278–3.959)

0.018

1.871 (0.988–3.544)

0.009

 > 35

1.073 (0.830–1.386)

 

1.056 (0.809–1.379)

 

1.067 (0.796–1.431)

 

0.743 (0.584–0.946)

 

rs2046210

Tumor size (cm)

        

 ≤ 2

1.277 (0.791–2.061)

0.052

1.277 (0.773–2.109)

0.048

1.558 (0.874–2.780)

0.015

1.522 (0.867–2.670)

0.012

 > 2

0.757 (0.606–0.944)

 

0.732 (0.582–0.919)

 

0.713 (0.553–0.920)

 

0.694 (0.540–0.992)

 

rs2981582

Grade

        

 I + II

0.922 (0.642–1.323)

0.048

0.791 (0.532–1.177)

0.017

0.822 (0.529–1.278)

0.023

0.872 (0.571–1.331)

0.040

 III

1.666 (1.051–2.639)

 

1.682 (1.049–2.698)

 

1.783 (1.080–2.944)

 

1.732 (1.050–2.855)

 

rs3803662

Grade

        

 I + II

1.017 (0.812–1.273)

0.010

1.096 (0.866–1.387)

0.005

1.151 (0.884–1.500)

0.000

1.075 (0.830–1.392)

0.001

 III

0.588 (0.414–0.834)

 

0.586 (0.407–0.845)

 

0.479 (0.319–0.717)

 

0.484 (0.324–0.722)

 

Adjusted for age at diagnosis, tumor size, lymph node involvement, grade, hormone receptor, HER2 status, exception for stratification factor

HR hazard risk, CI confidence interval

aHeterogeneity test for differences between groups

An interaction analysis was performed (Table 6) and statistically significant multiplicative interactions on EBC survival were found both between rs4415084 genotypes and age at diagnosis (adjusted Pint: iDFS 0.045, DDFS 0.013, BCSS 0.025 and OS 0.018) and between rs3803662 genotypes and tumor grade (adjusted Pint: iDFS 0.011, DDFS 0.001, BCSS 4.7 × 10−4 and OS 9.9 × 10−4).
Table 6

The interaction analysis between risk variants and clinicopathological parameters

SNPs

Variable

iDFS

DDFS

BCSS

OS

Adjusted HRa

P value

Adjusted HRa

P value

Adjusted HRa

P value

Adjusted HRa

P value

rs4415084

Age at diagnosis

        

 CC

 ≤ 35

1 (reference)

 

1 (reference)

 

1 (reference)

 

1 (reference)

 

 CC

 > 35

1.113 (0.739–1.676)

0.609

1.270 (0.814–1.983)

0.292

1.366 (0.829–2.249)

0.221

1.346 (0.827–2.189)

0.232

 CT

 ≤ 35

1.317 (0.797–2.176)

0.282

1.421 (0.829–2.438)

0.202

1.358 (0.733–2.516)

0.331

1.271 (0.692–2.336)

0.440

 CT

 > 35

1.090 (0.734–1.619)

0.669

1.246 (0.810–1.917)

0.316

1.373 (0.847–2.229)

0.198

1.340 (0.835–2.148)

0.225

 TT

 ≤ 35

2.013 (1.161–3.488)

0.013

2.427 (1.357–4.339)

0.003

2.505 (1.310–4.788)

0.005

2.497 (1.328–4.693)

0.004

 TT

 > 35

1.180 (0.767–1.815)

0.452

1.332 (0.836–2.124)

0.228

1.461 (0.868–2.460)

0.153

1.378 (0.826–2.298)

0.219

P for multiplicative interaction

 

0.045

 

0.013

 

0.025

 

0.018

 rs3803662

 Grade

        

  GG

  I + II

1 (reference)

 

1 (reference)

 

1 (reference)

 

1 (reference)

 

  GG

  III

1.858 (1.400–2.466)

1.8E−5

1.877 (1.394–2.527)

3.3E−5

2.134 (1.543–2.952)

4.6E−6

2.018 (1.469–2.773)

1.5E−5

  GA

  I + II

1.031 (0.814–1.306)

0.801

1.106 (0.864–1.416)

0.425

1.139 (0.862–1.505)

0.361

1.054 (0.801–1.385)

0.709

  GA

  III

1.043 (0.746–1.459)

0.804

1.014 (0.711–1.446)

0.939

0.979 (0.655–1.462)

0.917

0.946 (0.639–1.403)

0.784

  AA

  I + II

0.994 (0.684–1.443)

0.973

1.081 (0.735–1.592)

0.691

1.246 (0.820–1.893)

0.303

1.195 (0.793–1.800)

0.394

  AA

  III

1.085 (0.582–2.023)

0.798

1.245 (0.665–2.331)

0.493

1.043 (0.501–2.169)

0.911

0.983 (0.474–2.041)

0.964

P for multiplicative interaction

 

0.011

 

0.001

 

4.7E−4

 

9.9E−4

aHR hazard risk, CI confidence interval; adjusted for age at diagnosis, tumor size, Lymph node involvement, grade, hormone receptor status and HER2 status, except for the interaction factor

Discussion

In this study, we evaluated the possible relation between 21 GWAS-identified BC susceptibility germline variations and EBC clinical outcome in a large Chinese cohort of 1177 EBC cases. To the best of our knowledge, this is the first study that reports the association between GWAS-identified BC susceptibility loci and clinical outcomes in a Chinese population and it produced different results from two other American studies findings [6, 7]. The most significant and novel result of this study is that the influence of BC risk polymorphisms on the outcome of EBC depends on different intrinsic molecular subtypes, especially for luminal B breast cancer.

More recently, Zhang and his colleagues demonstrated some GWAS-identified SNPs are associated with molecular subtypes of EBC in Chinese women [13]. It has been accepted worldwide that breast cancer is a complex disease and consists of several intrinsic subtypes, which have different etiologies and prognosis [14]. By altering the related genes’ expression and/or function in key signaling pathways, we gradually realize putative SNPs may take effect on the basis of molecular subtypes, whether in risk or in clinical outcome of EBC [1517].

Loci rs889312, rs4951011, and rs9485372 play significant and independent roles in survival of luminal B breast cancer patients both individually or jointly by all of the four outcome indicators (iDFS, DDFS, BCSS and OS). Recently, MAP3K1 rs889312 has been identified as a low-penetrant risk factor for breast cancer, both for ER+ or ER− breast cancer [18]. It was also demonstrated to be an independent risk factor for poor survival in diffuse-type gastric cancer in an overdominant model [19]. However, two similar investigations failed to prove this variant was associated with BC clinical outcome [6, 7], although neither of them carried out survival analysis on the basis of BC intrinsic subtypes. From most recent available data, rs889312 (C/C) was found to be significantly associated with poor DFS, DDFS and OS among HR positive breast cancer patients [20], which was similar to our results. The MAP3K1 gene is the most important member in the MAPK signal pathway which activates the transcription of essential cancer genes [21]. But the exact mechanism as to how rs889312 can change MAP3K1 protein structure and/or function is still beyond our knowledge.

The rs4951011 located in intron 2 of the zinc finger CCCH domain-containing protein 11A (ZC3H11A) and 5′-UTR of ZBED6 gene, has been first identified as a BC susceptibility loci in East Asian [8]. In another study, it was only associated with triple negative breast cancer but not other BC subtypes [22]. For rs4951011 in the dominant model, we found that the GA + GG genotype was significantly associated with a better DFS, DDFS, BCSS and OS (aHR = 0.690–0.734). However, there was no evidence indicating a relation between this variant and clinical outcome of other malignant tumors. The data of ENCODE from human mammary epithelial cells (HMEC) suggests that rs4951011 may be located in a strong enhancer region marked by peaks of several active histone acetylation modifications (H3K4me1, H3K4me3, H3K9ac, and H3K27ac) [23]. Furthermore, it was found in colorectal cancer cell lines that repressing transcription of ZBED6 modulates expression of 10 genes, including PTBN1, WWC1, WWTR1, etc., linked to important signal pathway and tumor development depended on the genetic background of tumor cells and the transcription state of its target genes [24]. So rs4951011 may regulate expression of some important metastasis-related genes and then influence the course of breast cancer.

The SNP rs9485372 was also found to play a significant role in the clinical outcome of luminal A and luminal B breast cancer patients. For luminal A BC, rs9485372 in the recessive model had a worse iDFS, DDFS, BCSS, and OS (aHR 2.465–3.522). For luminal B BC, the GA + AA genotypes had a worse iDFS, DDFS, BCSS and OS (aHR = 1.482–1.557), compared to the GG genotype. This variant is located in Table 2  (TGF-β activated kinase 1/MAP3K7 binding protein 2) which plays a pivotal role in the TGF-β pathway and contributes to development of cancer [25]. Table 2 is near the ESR1 gene and it was found to be co-expressed with ESR1 in hepatocellular carcinoma [26]. Table 2 was found to be a mediator of resistance to endocrine therapy which is a poor prognostic indicator for HR+ breast cancer patients and is a potential new target to reverse pharmacological resistance and potentiate anti-estrogen action [27]. Therefore it is possible that the association both rs9485372 and survival of luminal A and B BC patients may be mediated by regulating estrogen signaling and the TGF-β pathway.

Two GWAS-identified BC risk loci, rs1219648 and rs13387042, were found to take effect on overall survival of EBC in Tunisians [28]. On the contrary, we failed to confirm this result in our Chinese population. We attribute this difference to the following reasons. Firstly, these two studies focused on different ethnic groups with different genetics background. Secondly, we used a much bigger sample size and longer follow-up than the other study which made our result more reliable. Finally, both of these two studies are retrospective. We used the multivariate Cox proportional hazard model to evaluate the independent effect of every SNP on survival of EBC patients while the other study just used Kaplan–Meier Curve and Log-Rank Test.

Some potential limitations of our study should be taken into consideration. First, as all patients were of Chinese origin, it is unclear whether our findings are Chinese Han population—specific or common in other populations. Second, the biological mechanism of the significant SNPs in breast cancer is still unclear. Therefore, more studies with diverse ethnic backgrounds and determination of the functional characterizations of the SNPs are warranted. Nevertheless, this is the first study with integrated clinicopathological data and long enough follow-up data to investigate the association between genetic breast cancer risk polymorphisms and survival of Asian breast cancer patients depended on intrinsic molecular subtypes.

Conclusions

Our findings indicated that breast cancer risk variants are not in general strongly associated with clinical outcome. However, we illustrated that, on the basis of molecular subtypes, there are some potential BC risk polymorphisms, which are probably novel predictors for EBC outcome in Chinese patients. Large better-designed investigations with a variety of populations, as well as functional assessments are needed to verify and extend our findings.

Notes

Abbreviations

GWAS: 

genome-wide association study

SNPs: 

single nucleotide polymorphisms

BC: 

breast cancer

EBC: 

early-stage breast cancer

HRs: 

hazard ratios

CIs: 

confidence intervals

BCSS: 

breast cancer special survival

OS: 

overall survival

iDFS: 

invasive disease free survival

DDFS: 

distant disease free survival

HR: 

hormone receptor

AJCC: 

American Joint Commission on Cancer

TNM: 

tumor-node-metastasis

ER: 

estrogen receptor

PR: 

progesterone receptor

HER2: 

human epidermal growth factor-2

MAF: 

minor allele frequency

NHGRI: 

National Human Genome Research Institute

ZC3H11A: 

zinc finger CCCH domain-containing protein 11A

HMEC: 

human mammary epithelial cells

Declarations

Authors’ contributions

FMF and CW designed the study. WHG, YXL, and WQ helped in sample collection. WQ and WHG assessed the molecular genotyping and generated the data. BWZ and MH analyzed the data. FMF wrote the manuscript. All authors read and approved the final manuscript.

Acknowledgements

We would like to acknowledge all the cases joining the study and assistance of all of the research nurses of Department of General Surgery, Fujian Medical University Union Hospital.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Consent for publication

Not applicable.

Ethics approval and consent to participate

All participants signed an informed consent form. The Ethics Committee of Fujian Medical University Union Hospital (China) approved the study. We followed the ethical guidelines of the Declaration of Helsinki.

Funding

This work was supported by the National Nature Science Foundation (Grant Number 81302320), Fujian Provincial Natural Science Foundation (Grant Number 2015J01473) and Medical Elite Cultivation Program of Fujian, China (Grant Number 2015-ZQN-ZD-14).

Publisher’s Note

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Authors’ Affiliations

(1)
Breast Surgery Ward, Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, Fujian Province, China
(2)
Nosocomial Infection Control Branch, Fujian Medical University Union Hospital, Fuzhou, 350001, Fujian Province, China
(3)
Fujian Center for Disease Control and Prevention, Fuzhou, 350001, Fujian Province, China

References

  1. Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ, He J. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66:115–32.View ArticlePubMedGoogle Scholar
  2. Fan L, Strasser-Weippl K, Li JJ, St Louis J, Finkelstein DM, Yu KD, Chen WQ, Shao ZM, Goss PE. Breast cancer in China. Lancet Oncol. 2014;15:e279–89.View ArticlePubMedGoogle Scholar
  3. Pirie A, Guo Q, Kraft P, et al. Common germline polymorphisms associated with breast cancer-specific survival. Breast Cancer Res. 2015;17:58.View ArticlePubMedGoogle Scholar
  4. Świerniak M, Wójcicka A, Czetwertyńska M, Długosińska J, Stachlewska E, Gierlikowski W, Kot A, Górnicka B, Koperski Ł, Bogdańska M, Wiechno W, Jażdżewski K. Association between GWAS-derived rs966423 genetic variant and overall mortality in patients with differentiated thyroid cancer. Clin Cancer Res. 2016;22:1111–9.View ArticlePubMedGoogle Scholar
  5. Kang BW, Jeon HS, Chae YS, Lee SJ, Park JY, Choi JE, Park JS, Choi GS, Kim JG. Association between GWAS-identified genetic variations and disease prognosis for patients with colorectal cancer. PLoS ONE. 2015;10:e0119649.View ArticlePubMedGoogle Scholar
  6. Barrdahl M, Canzian F, Lindström S, Shui I, Black A, Hoover RN, Ziegler RG, Buring JE, Chanock SJ, Diver WR, Gapstur SM, Gaudet MM, Giles GG, Haiman C, Henderson BE, Hankinson S, Hunter DJ, Joshi AD, Kraft P, Lee IM, Le Marchand L, Milne RL, Southey MC, Willett W, Gunter M, Panico S, Sund M, Weiderpass E, Sánchez MJ, Overvad K, Dossus L, Peeters PH, Khaw KT, Trichopoulos D, Kaaks R, Campa D. Association of breast cancer risk loci with breast cancer survival. Int J Cancer. 2015;137:2837–45.View ArticlePubMedGoogle Scholar
  7. Bayraktar S, Thompson PA, Yoo SY, Do KA, Sahin AA, Arun BK, Bondy ML, Brewster AM. The relationship between eight GWAS-identified single-nucleotide polymorphisms and primary breast cancer outcomes. Oncologist. 2013;18:493–500.View ArticlePubMedGoogle Scholar
  8. Cai Q, Zhang B, Sung H, et al. Genome-wide association analysis in East Asians identifies breast cancer susceptibility loci at 1q32.1, 5q14.3 and 15q26.1. Nat Genet. 2014;46:886–90.View ArticlePubMedGoogle Scholar
  9. Low SK, Takahashi A, Ashikawa K, Inazawa J, Miki Y, Kubo M, Nakamura Y, Katagiri T. Genome-wide association study of breast cancer in the Japanese population. PLoS ONE. 2013;8:e76463.View ArticlePubMedGoogle Scholar
  10. Edge SB, Compton CC. The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol. 2010;17:1471–4.View ArticleGoogle Scholar
  11. Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thürlimann B, Senn HJ, Panel members. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol. 2013;24:2206–23.View ArticlePubMedGoogle Scholar
  12. Gourgou-Bourgade S, Cameron D, Poortmans P, et al. Guidelines for time-to-event end point definitions in breast cancer trials: results of the DATECAN initiative (Definition for the Assessment of Time-to-event Endpoints in CANcer trials). Ann Oncol. 2015;26:873–9.View ArticlePubMedGoogle Scholar
  13. Xu Y, Chen M, Liu C, Zhang X, Li W, Cheng H, Zhu J, Zhang M, Chen Z, Zhang B. Association study confirmed three breast cancer-specific molecular subtype-associated susceptibility loci in Chinese Han Women. Oncologist. 2017;22:890–4.View ArticlePubMedGoogle Scholar
  14. Anderson WF, Rosenberg PS, Prat A, Perou CM, Sherman ME. How many etiological subtypes of breast cancer: two, three, four, or more? J Natl Cancer Inst. 2014. https://doi.org/10.1093/jnci/dju165.View ArticlePubMed CentralPubMedGoogle Scholar
  15. Sapkota Y. Germline DNA variations in breast cancer predisposition and prognosis: a systematic review of the literature. Cytogenet Genome Res. 2014;144:77–91.View ArticlePubMedGoogle Scholar
  16. Song N, Choi JY, Sung H, Jeon S, Chung S, Park SK, Han W, Lee JW, Kim MK, Lee JY, Yoo KY, Han BG, Ahn SH, Noh DY, Kang D. Prediction of breast cancer survival using clinical and genetic markers by tumor subtypes. PLoS ONE. 2015;10:e0122413.View ArticlePubMedGoogle Scholar
  17. Chan CHT, Munusamy P, Loke SY, Koh GL, Wong ESY, Law HY, Yoon CS, Tan MH, Yap YS, Ang P, Lee ASG. Identification of novel breast cancer risk loci. Cancer Res. 2017;77:5428–37.View ArticlePubMedGoogle Scholar
  18. Zheng Q, Ye J, Wu H, Yu Q, Cao J. Association between mitogen-activated protein kinase kinase kinase 1 polymorphisms and breast cancer susceptibility: a meta-analysis of 20 case–control studies. PLoS ONE. 2014;9:e90771.View ArticlePubMedGoogle Scholar
  19. Wei X, Zhang E, Wang C, Gu D, Shen L, Wang M, Xu Z, Gong W, Tang C, Gao J, Chen J, Zhang Z. A MAP3k1 SNP predicts survival of gastric cancer in a Chinese population. PLoS ONE. 2014;9:e96083.View ArticlePubMedGoogle Scholar
  20. Kuo SH, Yang SY, You SL, Lien HC, Lin CH, Lin PH, Huang CS. Polymorphisms of ESR1, UGT1A1, HCN1, MAP3K1 and CYP2B6 are associated with the prognosis of hormone receptor-positive early breast cancer. Oncotarget. 2017;8:20925–38.PubMed CentralPubMedGoogle Scholar
  21. Witowsky JA, Johnson GL. Ubiquitylation of MEKK1 inhibits its phosphorylation of MKK1 and MKK4 and activation of the ERK1/2 and JNK pathways. J Biol Chem. 2003;278:1403–6.View ArticleGoogle Scholar
  22. Chen Y, Fu F, Lin Y, Qiu L, Lu M, Zhang J, Qiu W, Yang P, Wu N, Huang M, Wang C. The precision relationships between eight GWAS-identified genetic variants and breast cancer in a Chinese population. Oncotarget. 2016;7:75457–67.PubMed CentralPubMedGoogle Scholar
  23. ENCODE Project Consortium, Birney E, Stamatoyannopoulos JA, Dutta A, et al. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature. 2007;447:799–816.View ArticleGoogle Scholar
  24. Ali MA, Younis S, Wallerman O, Gupta R, Andersson L, Sjöblom T. Transcriptional modulator ZBED6 affects cell cycle and growth of human colorectal cancer cells. Proc Natl Acad Sci USA. 2015;112:7743–8.View ArticleGoogle Scholar
  25. Ikushima H, Miyazono K. TGFbeta signalling: a complex web in cancer progression. Nat Rev Cancer. 2010;10:415–24.View ArticlePubMedGoogle Scholar
  26. Li J, Wang Y, Zhu Y, Gong Y, Yang Y, Tian J, Zhang Y, Zou D, Peng X, Ke J, Gong J, Zhong R, Chang J. Breast cancer risk-associated variants at 6q25.1 influence risk of hepatocellular carcinoma in a Chinese population. Carcinogenesis. 2017;38:447–54.View ArticlePubMedGoogle Scholar
  27. Cutrupi S, Reineri S, Panetto A, Grosso E, Caizzi L, Ricci L, Friard O, Agati S, Scatolini M, Chiorino G, Lykkesfeldt AE, De Bortoli M. Targeting of the adaptor protein Tab 2 as a novel approach to revert tamoxifen resistance in breast cancer cells. Oncogene. 2012;31:4353–61.View ArticlePubMedGoogle Scholar
  28. Shan J, Mahfoudh W, Dsouza SP, Hassen E, Bouaouina N, Abdelhak S, Benhadjayed A, Memmi H, Mathew RA, Aigha II, Gabbouj S, Remadi Y, Chouchane L. Genome-Wide Association Studies (GWAS) breast cancer susceptibility loci in Arabs: susceptibility and prognostic implications in Tunisians. Breast Cancer Res Treat. 2012;135:715–24.View ArticlePubMedGoogle Scholar

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