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Research Papers:

Association between CYP17 T-34C rs743572 and breast cancer risk

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Oncotarget. 2018; 9:4200-4213. https://doi.org/10.18632/oncotarget.23688

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Jing Sun, Hong Zhang, Meiyan Gao, Zhishu Tang, Dongyan Guo, Xiaofei Zhang, Zhu Wang, Ruiping Li, Yan Liu, Wansen Sun _ and Xi Sun

Abstract

Jing Sun1,*, Hong Zhang2,*, Meiyan Gao3,*, Zhishu Tang1, Dongyan Guo4, Xiaofei Zhang4, Zhu Wang5, Ruiping Li5, Yan Liu5, Wansen Sun5 and Xi Sun6

1Department of Shaanxi Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China

2Department of Neurology, Second Affiliated Hospital, School of Medicine, Xi'an Jiaotong University, Xi'an, Shaanxi, China

3Clinical Laboratory, Shaanxi Provincial Hospital of traditional Chinese medicine, Xi'an, Shaanxi, China

4College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China

5Department of Integrated Traditional Chinese and Western Medicine, Second Affiliated Hospital, School of Medicine, Xi’an Jiaotong University, Xi’an, Shaanxi, China

6Department of General Medicine, Second Affiliated Hospital, School of Medicine, Xi’an Jiaotong University, Xi'an, Shaanxi, China

*These authors contributed equally to this work

Correspondence to:

Wansen Sun, email: sunwansen41@163.com

Xi Sun, email: sunxi132@163.com

Keywords: breast cancer; rs743572; polymorphism

Received: September 04, 2017     Accepted: December 18, 2017     Published: December 26, 2017

ABSTRACT

Association between CYP17 T-34C (rs743572) polymorphism and breast cancer (BC) risk was controversial. In order to derive a more definitive conclusion, we performed this meta-analysis. We searched in the databases of PubMed, EMBASE and Cochrane for eligible publications. Pooled odds ratios (ORs) with 95% confidence intervals (95% CIs) were used to assess the strength of association between CYP17 T-34C polymorphism and breast cancer risk. Forty-nine studies involving 2,7104 cases and 3,4218 control subjects were included in this meta-analysis. In overall, no significant association between CYP17 T-34C polymorphism and breast cancer susceptibility was found among general populations. In the stratified analysis by ethnicity and source, significant associations were still not detected in all genetic models; besides, limiting the analysis to studies with controls in agreement with HWE, we also observed no association between CYP17 T-34C polymorphism and breast cancer risk. For premenopausal women, we didn't detect an association between rs743572 and breast cancer risk; however, among postmenopausal women, we observed that the association was statistically significant under the allele contrast genetic model (OR = 1.10, 95% CI = 1.03–1.17, P = 0.003), but not in other four models. In conclusion, rs743572 may increase breast cancer risk in postmenopausal individuals, but not in premenopausal folks and general populations.


Association between CYP17 T-34C rs743572 and breast cancer risk | Sun | Oncotarget

INTRODUCTION

Breast cancer (BC), the most frequent malignant neoplasm among female worldwide, accounts for approximate 25% of women malignant tumor. It is reported that 1.67 million people were diagnosed as BC ever year, therefore it has become a serious health issue, especially in the developing countries [1]. It is well known that the lifetime presence of the estrogen in the blood is an important pathogenic factor of BC, and this is in consistence with the low incidence of the breast cancer in males that is due to the lower estrogen levels and lower breast tissue volume. By now, researches on the status of hormone receptors and/or menopause associated with genetic alterations in BC risk have attracted an increasing number of attention, and lots of genes, including BRIPI, CHEK2, MDM, TGFB, TP53, BRCA1, BRCA2, and PTEN, and also several gene polymorphisms. Among genes of this family, CYP17, CYP19 and CYP1A1 have important functions in synthesis, metabolism and maintaining the levels of the androgen and estrogen hormones [2]. Previous published reasearches have demonstrated that estrogen act as a crucial role in the formation of BC; in addition, evidences have also been found about the positive role of cell surface receptors of estrogen in tumorigenesis [3]. Nevertheless, the precise mechanism behind estrogen in the formation of BC remains unknown. Previous studies have indicated that cytochrome P450c17α, which is a key enzyme in the synthesis of estrogen, and could increase the breast neoplasm risk [4]. The cytochrome P450c17α enzyme, predominantly catalyzes the formation of the precursor dehydroepiandrosterone (DHEA). Meanwhile, precursor DHEA could further be converted into estrogen through a succession of tissue-specific pathways [5, 6]. Estrogen, plays a vital part in the etiology of BC and identified the risk between estrogen and BC could well elucidate the biosynthesis and metabolism mechanisms. So far, more and more researches have demonstrated the correlation of estrogen-related genes genetic variations with BC risk. The CYP17T-34C (rs743572) polymorphism which is located on the human chromosome 10, in the 50-untranslated region has been most commonly reported [7].

Many studies about the genetic mutations or SNP occurring in CYP17 gene could enhance CYP17’s transcription rate and increase the enzyme cytochrome P450c17 level, resuling in an increasing number of bioavailable estrogen, which is likely to affect the risk and aggressiveness of BC [8]. But many previous article results between rs743572 mutations and BC risk remain conflicting: Han’s research [9] revealed that no statistically meaningful correlation of rs743572 with risk of BC. However, significant correlation was found between rs743572 and BC risk in another research on the same theme [10]. Since few new high-quality investigations were published, we performed this study to take a more precise evaluation of rs743572 with the risk of BC.

RESULTS

The main feature of included studies

As showed in Figure 1, 331 references were retrieved at first based on our selection strategy. 186 papers were remained after removing the duplicate reports. After reading titles and abstracts, we excluded 104 studies which were clearly unrelated. In the end, the whole of the rest of the papers were checked based on the inclusion and exclusion criteria. Finally, forty-nine studies on rs743572 and the risk of BC were eventually included in our study. Thirteen articles showed the number of three genotypes (TT, TC, and CC) among premenopausal women, and thirteen studies report TT, TC, and CC number in postmenopausal women. Main information of included studies were shown in Table 1. Among these qualified researches, seventeen were performed in Asians, twenty-five in Caucasians, one in Africans, one in both Asians and Caucasians, one in both Africans and Caucasians, and four in mixed ethnicity. Moreover, twenty-two studies were considered as moderate-quality studies (NOS scores of these researches were 4–6), and other twenty-seven studies were considered as high-quality studies (NOS scores of these studies were seven or above). Except for four included researches were not in agreement with Hardy–Weinberg equilibrium (HWE), genotype distributions in the control groups of other 45 researches were all satisfied with HWE.

Flow diagram of the selection of the studies in this meta-analysis.

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

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

First author

Year

Country

Ethnicity

Source of control

Number (case/control)

HWE (P value)

NOS

Dunning [17]

1998

UK

Caucasian

PB

835/591

0.261

7

Weston [21]

1998

USA

Caucasian

HB

103/205

0.449

6

Weston [21]

1998

USA

African

HB

20/35

0.253

6

Helzlsouer [22]

1998

USA

Caucasian

PB

109/113

0.549

6

Bergman [23]

1999

Sweden

Caucasian

PB

109/117

0.304

6

Haiman [24]

1999

USA

Caucasian

PB

436/618

0.391

7

Huang [25]

1999

China

Asian

PB

123/126

0.972

6

Young [26]

1999

UK

Caucasian

PB

39/58

0.732

5

Kristensen [27]

1999

Norway

Caucasian

PB

510/201

0.351

7

Hamajima [28]

2000

Japan

Asian

HB

144/166

0.044

6

Kuligina [29]

2000

Russia

Caucasian

HB

240/182

0.017

6

Mitrunen [30]

2000

Finland

Caucasian

PB

479/480

0.967

7

Feigelson [18]

2001

USA

Mixed

PB

850/1508

0.335

7

Gudmundsdottir [31]

2003

Iceland

Caucasian

PB

500/395

0.131

7

Wu [32]

2003

Singapore

Asian

PB

188/671

0.512

6

Ambrosone [33]

2003

USA

Caucasian

PB

207/188

0.130

7

Tan [34]

2003

China

Asian

PB

250/250

0.117

7

Hefler [35]

2004

Austria

Asian

PB

388/1698

0.455

7

Ahsan [36]

2004

USA

Mixed

HB

313/271

0.457

6

Chacko [37]

2005

India

Asian

HB

140/140

0.133

6

Einarsdo´ttir [38]

2005

Sweden

Caucasian

PB

1499/1338

0.885

7

Shin [39]

2005

Korean

Asian

HB

462/337

0.134

7

Verla-Tebit [40]

2005

Germany

Caucasian

PB

527/904

0.380

7

Hopper [41]

2005

Australia

Caucasian

PB

1404/788

0.697

7

Onland-More [42]

2005

Netherlands

Caucasian

PB

335/373

0.189

7

Han [9]

2005

China

Asian

PB

210/427

0.037

6

Piller [43]

2006

Germany

Caucasian

PB

608/1298

0.062

7

Chakraborty [44]

2007

India

Asian

PB

186/212

0.550

6

Setiawan [45]

2007

USA

Mixed

PB

5147/6882

0.312

7

Chen [46]

2008

USA

Caucasian

PB

1037/1096

0.884

7

Sakoda [47]

2008

China

Asian

PB

615/877

0.232

7

Zhang [48]

2008

China

Asian

PB

299/342

0.454

7

Samson [49]

2009

India

Asian

PB

250/500

0.720

7

Sangrajrang [50]

2009

Thailand

Asian

HB

564/489

0.418

7

Sobczuk [51]

2009

Poland

Caucasian

PB

100/106

0.503

6

Antognelli [52]

2009

Italy

Caucasian

PB

547/544

0.982

7

Hosseini [53]

2009

Iran

Caucasian

HB

53/53

0.057

5

Jakubowska [54]

2009

Poland

Caucasian

HB

319/290

0.519

6

MARIE-GENICA [55]

2009

Germany

Caucasian

PB

3145/5487

0.254

7

Kato [56]

2009

USA

African

PB

184/189

0.152

6

Tuzuner [57]

2010

Turkey

Caucasian

PB

55/91

0.466

5

Syamala [58]

2010

India

Asian

HB

359/367

0.464

7

Surekha [59]

2010

India

Asian

PB

249/249

0.949

7

Iwasaki [60]

2010

Japan

Asian

HB

388/388

0.299

6

Iwasaki [60]

2010

Brazil

Asian

HB

78/79

0.144

6

Iwasaki [60]

2010

Brazil

Caucasian

HB

379/379

0.039

6

Kaufman [61]

2011

Mixed

Mixed

HB

1175/829

0.944

7

Cribb [10]

2011

Canada

Caucasian

HB

207/621

0.033

6

Ghisari [62]

2014

Inuit

Asian

PB

30/113

0.882

5

Chattopadhyay [63]

2014

India

Asian

PB

360/360

0.692

7

Karakus [64]

2015

Turkey

Caucasian

PB

199/197

0.934

6

Farzaneh [65]

2016

Iranian

Caucasian

PB

124/100

0.189

6

HWE: Hardy-Weinberg equilibrium for controls. PB: population-based study. HB: hospital-based study.

Meta-analysis results

Meta-analysis results among overall populations, distribution of this polymorphism in case groups and control groups are presented in Table 2. For premenopausal women and postmenopausal women, distribution of this polymorphism in case groups and control groups are presented in Table 3, and the main outcome of our study are shown in Tables 4 and 5.

Table 2: Genotype distribution of the CYP17 (rs743572) polymorphism in cases and controls among overall populations

First author

Genotype (N)

Case

Control

Total

CC

CT

TT

Total

CC

CT

TT

Dunning

835

130

402

303

591

85

277

229

Weston

103

18

47

38

205

35

93

77

Weston

20

3

10

7

35

2

18

15

Helzlsouer

109

21

47

41

113

18

58

37

Bergman

109

15

62

32

117

9

55

53

Haiman

463

73

212

178

618

94

307

217

Huang

123

44

54

25

126

35

63

28

Young

39

5

13

21

58

7

28

23

Kristensen

510

67

241

202

201

26

101

74

Hamajima

144

20

83

41

166

27

95

44

Kuligina

240

47

111

82

182

44

77

61

Mitrunen

479

53

227

199

480

60

220

200

Feigelson

850

149

409

292

1508

227

739

542

Gudmundsdottir

500

60

247

193

395

66

173

156

Wu

188

69

82

37

671

229

333

109

Ambrosone

207

15

83

109

188

22

71

95

Tan

250

89

115

46

250

89

110

51

Hefler

388

75

186

127

1698

287

804

607

Ahsan

313

49

155

109

271

51

140

80

Chacko

140

6

40

94

140

3

22

115

Einarsdo´ttir

1499

238

711

550

1338

212

638

488

Shin

462

127

223

112

337

115

152

70

Verla-Tebit

527

103

244

180

904

157

424

323

Hopper

1404

230

621

553

788

113

364

311

Onland-More

335

44

140

151

373

50

157

166

Han

210

52

105

53

427

92

235

100

Piller

608

119

289

200

1298

236

596

466

Chakraborty

186

59

98

29

212

45

110

57

Setiawan

5147

833

2445

1869

6882

1070

3338

2474

Chen

1037

168

506

363

1096

175

523

398

Sakoda

615

216

297

102

877

298

441

138

Zhang

299

84

168

47

242

73

125

44

Samson

250

32

91

127

500

54

226

220

Sangrajrang

564

96

281

187

489

92

230

167

Sobczuk

100

46

44

10

106

34

55

17

Antognelli

547

60

258

229

544

68

249

227

Hosseini

53

6

29

18

53

13

33

7

Jakubowska

319

45

166

108

290

54

136

100

MARIE-GENICA

3145

529

1573

1043

5487

941

2712

1834

Kato

184

32

82

70

189

29

78

82

Tuzuner

55

10

27

18

91

9

44

38

Syamala

359

44

152

163

367

41

154

172

Surekha

249

9

69

171

249

16

95

138

Iwasaki

388

88

189

111

388

84

182

122

Iwasaki

78

13

48

17

79

23

33

23

Iwasaki

379

59

185

135

379

49

200

130

Kaufman

1175

171

581

423

829

124

392

313

Cribb

207

23

85

99

621

89

259

273

Ghisari

30

6

12

12

113

32

57

24

Chattopadhyay

360

14

116

230

360

7

93

260

Karakus

199

18

79

102

197

15

78

104

Farzaneh

124

22

70

32

100

17

56

27

Table 3: Genotype distribution of the CYP17 (rs743572) polymorphism in cases and controls among premenopausal women and postmenopausal women

First author

Genotype (N)

Case

Control

Total

CC

CT

TT

Total

CC

CT

TT

Helzlsouer

24

4

9

11

25

4

13

8

Bergman

109

15

62

32

117

9

55

53

Mitrunen

163

15

71

77

203

27

88

88

Wu

57

24

20

13

203

66

100

37

Ambrosone

96

7

31

58

86

10

28

48

Verla-Tebit

527

103

244

180

904

157

424

323

Chen

334

55

153

126

373

69

174

130

Samson

115

16

40

59

303

31

145

127

Antognelli

187

18

81

88

230

31

99

100

Kato

75

12

27

36

74

13

30

31

Zhang

150

38

87

25

124

37

67

20

Tan

95

32

45

18

97

30

40

27

Han

117

25

61

31

163

36

85

42

Helzlsouer

85

17

38

30

88

14

45

29

Mitrunen

316

38

156

122

277

33

132

112

Wu

131

45

62

24

468

163

233

72

Ambrosone

111

8

52

51

102

12

43

47

Einarsdo´ttir

1499

238

711

550

1338

212

638

488

Onland-More

335

44

140

151

373

50

157

166

Chen

680

111

339

230

677

96

333

248

Samson

134

16

50

68

197

23

99

75

Antognelli

360

42

177

141

314

37

150

127

Kato

109

20

55

34

115

16

48

51

Zhang

146

44

80

22

118

36

58

24

Tan

155

57

70

28

153

59

70

24

Han

93

27

44

22

264

56

150

58

Table 4: Meta-analysis results among overall populations

Comparisons

OR

95% CI

P (OR)

Heterogeneity

Effects model

P (Begg)

P (Egger)

I2

P

Total

T VS C

0.99

0.96–1.01

0.281

37.1%

0.005

R

0.856

0.766

TT VS CC

0.99

0.98–1.01

0.309

18.6%

0.127

F

0.987

0.408

TC VS CC

0.98

0.93–1.03

0.365

0.80%

0.457

F

0.825

0.563

TT+TC VS CC

0.98

0.93–1.02

0.287

15.0%

0.182

F

0.975

0.574

TT VS TC+CC

0.99

0.95–1.02

0.463

30.5%

0.022

R

1.000

0.902

Stratification by ethnicity

Caucasian

T VS C

0.99

0.96–1.03

0.673

11.5%

0.294

F

-

-

TT VS CC

0.99

0.93–1.06

0.804

10.4%

0.233

F

-

-

TC VS CC

1.00

0.94–1.07

0.936

0.00%

0.467

F

-

-

TT+TC VS CC

1.00

0.94–1.06

0.604

11.1%

0.301

F

-

-

TT VS TC+CC

0.99

0.94–1.04

0.907

0.00%

0.596

F

-

-

Asian

T VS C

0.97

0.89–1.06

0.574

60.8%

0.023

R

-

-

TT VS CC

0.99

0.95–1.02

0.483

33.9%

0.075

R

-

-

TC VS CC

0.97

0.88–1.07

0.525

11.4%

0.282

F

-

-

TT+TC VS CC

0.97

0.88–1.06

0.479

29.1%

0.114

F

-

-

TT VS TC+CC

0.97

0.84–1.11

0.652

60.0%

0.000

R

-

-

African

T VS C

0.83

0.63–1.10

0.198

0.0%

0.621

F

-

-

TT VS CC

0.72

0.41–1.27

0.255

0.0%

0.393

F

-

-

TC VS CC

0.88

0.50–1.54

0.654

0.0%

0.363

F

-

-

TT+TC VS CC

0.80

0.47–1.36

0.408

0.0%

0.358

F

-

-

TT VS TC+CC

0.79

0.54–1.17

0.237

0.0%

0.859

F

-

-

Stratification by Source

PB

T VS C

0.98

0.95–1.00

0.102

35.2%

0.021

R

-

-

TT VS CC

0.95

0.90–1.01

0.073

18.7%

0.165

F

-

-

TC VS CC

0.95

0.90–1.00

0.047

0.0%

0.726

F

-

-

TT+TC VS CC

0.95

0.91–1.00

0.034

1.8%

0.439

F

-

-

TT VS TC+CC

0.99

0.95–1.02

0.474

32.4%

0.033

F

-

-

HB

T VS C

1.03

0.97–1.09

0.299

38.9%

0.057

R

-

-

TT VS CC

1.10

0.97–1.24

0.128

18.2%

0.245

F

-

-

TC VS CC

1.14

1.02–1.28

0.024

0.0%

0.548

F

-

-

TT+TC VS CC

1.13

1.01–1.26

0.031

8.6%

0.355

F

-

-

TT VS TC+CC

0.99

0.91–1.08

0.847

30.6%

0.118

F

-

-

Stratification by HWE

Yes

T VS C

0.99

0.96–1.01

0.250

41.2%

0.002

R

-

-

TT VS CC

0.97

0.93–1.02

0.288

26.6%

0.050

R

-

-

TC VS CC

0.99

0.98–1.01

0.421

0.0%

0.501

F

-

-

TT+TC VS CC

0.99

0.99–1.02

0.264

15.6%

0.180

F

-

-

TT VS TC+CC

0.98

0.95–1.02

0.381

35.3%

0.010

R

-

-

F: fixed effects model; R: random effects model.

Table 5: Meta-analysis results among premenopausal women and postmenopausal women

Comparisons

OR

95% CI

P (OR)

Heterogeneity

Effects model

P (Begg)

P (Egger)

I2

P

Premenopausal

T VS C

1.02

0.93–1.10

0.717

17.6%

0.267

F

-

-

TT VS CC

1.01

0.85–1.20

0.885

6.4%

0.383

F

-

-

TC VS CC

0.97

0.83–1.14

0.709

0.0%

0.492

F

-

-

TT+TC VS CC

1.04

0.92–1.18

0.513

21.4%

0.227

F

-

-

TT VS TC+CC

0.95

0.81–1.10

0.467

29.3%

0.151

F

-

-

Postmenopausal

T VS C

1.10

1.03–1.17

0.003

10.6%

0.339

F

-

-

TT VS CC

0.96

0.84–1.10

0.539

0.0%

0.835

F

-

-

TC VS CC

0.96

0.85–1.08

0.478

0.0%

0.902

F

-

-

TT+TC VS CC

0.96

0.85–1.08

0.467

0.0%

0.930

F

-

-

TT VS TC+CC

0.99

0.90–1.08

0.796

8.9%

0.357

F

-

-

F: fixed effects model; R: random effects model.

In overall populations, the association of CYP17 T-34C polymorphism with BC susceptibility was studied in forty-nine researches including 27,104 cases and 34,218 controls. No significant correlation was found between this polymorphism and BC susceptibility among any of the five genetic models: T/C (OR = 0.99, 95% CI = 0.96–1.01, P = 0.281), TT/CC (OR = 0.99, 95% CI = 0.98–1.01, P = 0.309), TC/CC (OR = 0.98, 95% CI = 0.93–1.03, P = 0.365), TT+TC/CC (OR = 0.98, 95% CI = 0.93–1.02, P = 0.287) and TT/TC+CC (OR = 0.99, 95% CI = 0.95–1.02, P = 0.463). Analogously, further subgroup analysis by ethnicity and source found similar results that in all the ethnic groups, HB group and PB group there is no significant correlation between rs743572 and BC susceptibility. Moreover, if we only analyze the studies with controls in agreement with HWE, no correlation between rs743572 and BC risk were observed (Table 4) (Figure 2).

Forest plots of associations between rs743572 and breast cancer risk.

Figure 2: Forest plots of associations between rs743572 and breast cancer risk. (A) the overall populations in the allele contrast genetic model; (B) limiting the analysis to studies with controls in agreement with HWE under the allele contrast genetic model.

In premenopausal individuals, thirteen included researches with 2, 029 breast cancer case groups and 2, 920 control groups were eventually included. There is no statistical correlation of rs743572 with breast cancer susceptibility in T/C model, the TT/CC, the TC/CC, the TT+TC/CC, and the TT/TC+CC (OR = 1.02 with 95% CI 0.93–1.10, OR = 1.01 with 95% CI 0.85–1.20, OR = 0.97 with 95% CI 0.83–1.14, OR = 0.95 with 95% CI 0.81–1.10, and OR = 1.04 with 95% CI 0.92–1.18, respectively). In postmenopausal women, significant correlation was found in T/C model (OR = 1.10, 95% CI = 1.03–1.17, P = 0.003) (Table 5) (Figure 3). However, there were no significant associations between the rs743572 polymorphism and breast cancer risk in other genotype distributions: TT/CC (OR = 0.96, 95% CI =0.84–1.10, P = 0.539), TC/CC (OR = 0.96, 95% CI =0.85–1.08, P = 0.478), TT+TC/CC (OR = 0.96, 95% CI =0.85–1.08, P = 0.930) and TT/TC+CC (OR = 0.99, 95% CI =0.90–1.08, P = 0.357) (Table 5).

Forest plots of associations between rs743572 and breast cancer risk among postmenopausal women in the allele contrast genetic model.

Figure 3: Forest plots of associations between rs743572 and breast cancer risk among postmenopausal women in the allele contrast genetic model.

Sensitivity analysis

Even though four researches included in our studies were not conformed to the HWE balance (P < 0.05), final consequences were not changed when we excluded the abovementioned four studies. Besides, after performing the sensitivity analysis, the pooled OR values were not statistically significant changed when we delete each of the researches, indicating that this study has good stability and reliability.

Heterogeneity analysis

Heterogeneity was obtained by Q statistic. When the P value more than 0.1 in the Q test, then the fixed-effect models were selected to conduct relevant statistical analysis; otherwise, random-effect models were selected.

Publication bias

No statistical evidence of publication bias was found in the Begg’s test and Egger’s test. What’s more, funnel plot also did not show any evidence of obvious asymmetry (Table 4) (Figure 4).

Funnel plots of rs743572 and breast cancer risk in the heterozygote genetic model.

Figure 4: Funnel plots of rs743572 and breast cancer risk in the heterozygote genetic model.

DISCUSSION

With the popularization and the rapid development of technology in the field of medicine, people have a deeper recognition of breast cancer. However, the specific mechanisms of the occurrence and the development of this cancer remain unclear. It is well established that estrogen involves in the development of mammary gland and plays crucial role in initiating of BC [2]. Extensive evidences have also been demonstrated that lifetime exposure to endogenous and/or exogenous estrogen, increased the risk of the morbidity of breast cancer [11]. Besides, estrogen plays a positive role of cell surface receptors of in tumorigenesis [2, 3]. Significance of genes functioning in steroid hormone synthesis is well established in breast cancer susceptibility. CYP17, a commonly known gene could code for the cytochrome P450c17α enzyme that is one of the key enzymes participated in estrogen biosynthesis [4]. CYP17 T-34C polymorphism, in the region (5′-UTR) of CYP17, has been reported up-regulate CYP17 transcription in some studies but not in others [7]. The functional impact of the T/C change is still an unresolved mystery. Moreover several studies have reported conflicting results with respect to menopausal status and CYP17 polymorphism. Hence, for the purpose of acquire a more accurate assessment of the association between rs743572 and BC risk we performed this meta-analysis whose included research studies identified in the PubMed, EMBASE and the Cochrane.

In overall populations, our results indicate no significant correlation between rs743572 and the risk of BC. Similar results could be obtained when stratified by ethnicity in all ethnic groups. In addition, confining the analysis to the researches with control groups in consistent with HWE, we also observed no correlation between rs743572 and risk of BC. Nevertheless, meaningful correlation was showed between rs743572 and breast neoplasm risk in Russian individuals [10]. There were three meta-analyses, all published in 2010, including 24–43 papers from different populations and demonstrated no association between the rs743572 and BC, which further demonstrate that our results are credible [12, 13, 14].

Estrogen is mainly produced in the ovaries and mammary glands among premenopausal women. However, in postmenopausal individuals, adipose tissue mainly acts as an important part in estrogen biosynthesis [15, 16]. Several studies have reported conflicting results of menopausal and CYP17 polymorphism: the study by Dunning et al. [17] showed the association between increased A2 genotype and premenopausal breast cancer; while Feigelson et al. [18] reported increasing frequency of A2 genotype associated with postmenopausal BC patients. We observed that rs743572 was correlated with an increasing BC risk among postmenopausal women under the allele contrast genetic model, but not in other models; however, no association was found in premenopausal women. Previous published meta- analysis reported that no association existed both in postmenopausal women and among premenopausal women [12, 13, 14]. Compared with them, our study used five genetic models to reduce the probability of class I errors, so our result was more reliable.

Unavoidable, there are some limitations in meta-analysis. First, breast cancer is a multifactorial disease involving genetic and environmental interactions; however, it was still not addressed the impact of gene–environmental interactions in this meta-analysis [19]. Second, the detailed individual information in some studies was unknown; thus, we could not assess the susceptibility of breast cancer according to other risk factors including obesity, family history, radiation therapy in young age, history of pregnancy, breast-feeding, hormone therapy and so on [20]. Last, there are only two studies about Africans, more well designed studies with different population should be performed to make more persuasive conclusions. In summary, our results indicate that rs743572 could increase risk of BC in postmenopausal individuals, but not in premenopausal women and the general population. Further multicenter research with complete risk factors are required to validate the potential role of rs743572 polymorphism in BC. More multicenter studies and complete risk factors are needed to further confirm the possible role of rs743572 polymorphism in the occurrence and development of breast cancer.

MATERIALS AND METHODS

Literature and search strategy

We searched the PubMed, EMBASE and Cochrane databases for studies performed prior to March 7, 2017 that reported an association between rs743572 SNP and breast cancer risk. There were no language restrictions in our searching process. The searching strategy was as follow: (breast cancer OR breast carcinoma) AND (polymorphism OR variant OR genotype OR SNP) AND (CYP17 OR CYP17A1 OR P450c17). Besides, the references of the retrieved studies were also reviewed to identify additional eligible studies.

Inclusion criteria

The included studies must meet the following criteria: (1) case-control design; (2) investigating the association between CYP17 T-34C polymorphism and breast cancer risk; (3) sufficient genotyping data that could be used to calculate odds ratios (ORs) and 95% confidence intervals (CIs); (4) all the breast cancer subjects in case groups must be pathologically confirmed. The exclusion criteria were: (1) not case-control studies; (2) review article or commentary; (3) duplicate studies; (4) studies lacking relevant data.

Data extraction

Two reviewers independently extracted the relevant data from the included studies, and discrepancies were resolved during a discussion with a third author. The following information was extracted: the first author, year of publication, country, ethnicity, source of controls, number of cases and controls, and P value for Hardy-Weinberg equilibrium (HWE). In addition, we also evaluated the methodological quality of included studies based on Newcastle-Ottawa Scale (NOS), which scored studies according to three aspects: selection, comparability, and exposure. Therefore, all studies could be divided into three categories: “low quality” studies (score 0–3); “moderate quality” studies (score 4–6); “high quality” studies (score 7–9).

Statistical analysis

The association between CYP17 T-34C polymorphism and BC susceptibility was measured by pooled odds ratios (ORs) and 95% confidence intervals (CIs) in five genetic models, including an allele contrast genetic model, a homozygote genetic model, a heterozygote genetic model, a dominant genetic model, and a recessive genetic model. Pooled ORs were performed for homozygote comparison (TT vs. CC for rs743572), heterozygote comparison (TC vs. CC for rs743572), dominant model (TT/TC vs. CC for rs743572), recessive model (TT vs. TC/CC for rs743572) and allelic model (T vs. C for rs743572) respectively. Statistical heterogeneity was evaluated by I2 test and Q test, P < 0.05 was considered statistically significant. For I2 test, the criteria for heterogeneity were as follows: I2 < 25%, no heterogeneity; 25%–75%, moderate heterogeneity; I2 > 75%, high heterogeneity. If the P value of Q test was < 0.1, the random-effects model was used; otherwise, the fixed-effects model was applied. Sensitivity analysis was performed by excluding one study at a time to assess the influence of each study on the pooled ORs. Begg’s funnel plot and Egger’s tests were used to examine publication bias and to evaluate the stability of the results by sensitivity analysis. The P value for Hardy-Weinberg equilibrium (HWE) in controls of every included study was calculated by Chi-square test. Subgroup analysis was performed according to ethnicity. All statistical analyses were performed using STATA version 10.0 software (StataCorp LP, College Station, TX, USA). All P values were two sided, and P < 0.05 was considered statistically significant.

CONFLICTS OF INTEREST

The authors declare that no conflicts of interest exist.

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