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Association between single nucleotide polymorphisms in the TSPYL6 gene and breast cancer susceptibility in the Han Chinese population

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Oncotarget. 2016; 7:54771-54781. https://doi.org/10.18632/oncotarget.10754

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Ming Liu, Bin Li, Wen Guo, Xiyang Zhang, Zhengshuai Chen, Jingjie Li, Mengdan Yan, Chao Chen and Tianbo Jin _

Abstract

Ming Liu1,2,3,*, Bin Li1,2,*, Wen Guo4, Xiyang Zhang1,2, Zhengshuai Chen1,2, Jingjie Li1,2, Mengdan Yan1,2, Chao Chen1,2, Tianbo Jin1,2

1School of Life Sciences, Northwest University, Xi’an 710069, China

2National Engineering Research Center for Miniaturized Detection Systems, Xi’an 710069, China

3Department of Obstetrics and Gynecology, Second Affiliated Hospital, Xi’an Jiaotong University, Xi’an 710004, China

4Inner Mongolia Medical University, Inner Mongolia, Hohhot 010050, China

*These authors contributed equally to this work

Correspondence to:

Tianbo Jin, email: jintianbo@gmail.com

Chao Chen, email: cchen898@nwu.edu.cn

Keywords: association study, breast cancer, TSPYL6, single nucleotide polymorphism

Received: April 22, 2016     Accepted: July 10, 2016     Published: July 21, 2016

ABSTRACT

We investigated the associations between single nucleotide polymorphisms (SNPs) in the testis-specific Y-encoded-like protein 6 (TSPYL6) gene and breast cancer (BC) susceptibility in the Han Chinese population. A total of 183 BC patients and 195 healthy women were included in the study. Six SNPs in TSPYL6 were genotyped and the association with BC risk analyzed. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated using unconditional logistic regression analysis. Multivariate logistic regression analysis was used to identify SNPs that correlated with BC susceptibility. Rs11896604 was associated with a decreased risk of BC based on dominant and genotype models. Rs843706 was associated with an increased risk of BC based on a recessive model. Rs11125529 was associated with decreased BC susceptibility based on a genotype model. Finally, rs843711 inversely correlated with clinical stage III/IV BC. Our findings reveal a significant association between SNPs in the TSPYL6 gene and BC risk in a Han Chinese population.


INTRODUCTION

Breast cancer (BC) is the most common type of cancer and the leading cause of cancer deaths among women worldwide (particularly in less developed regions including East Asian countries, which accounted for 324,000 deaths or 14.3% of the total) [1]. According to GLOBOCAN 2012, 187,213 individuals were diagnosed with BC in China in 2012, and 47,984 of these individuals died of the disease [2]. BC is a multifactorial disease that has been associated with various factors including age, gender, ethnicity, family history, personal history, lifestyle, as well as both hormonal and non-hormonal risk factors [3]. Hereditary BC clusters in families and is typically diagnosed at an earlier age [4]. Studies of twins have indicated that the risk of BC is higher for a monozygotic twin of a co-twin, suggesting that genetic factors play an important role in BC development [5]. Single nucleotide polymorphisms (SNPs) also play an important role in the genetic susceptibility to BC. Many genes have been associated with a moderate or high lifetime risk of BC including BRCA1, BRCA2, PALB2,ATM, and CHEK2. In addition, common variants at more than 70 loci have been identified through GWAS and large-scale replication studies [69].

The testis-specific Y-encoded-like protein 6 (TSPYL6) gene, located on human chromosome 2p16.2, is a member of the TSPY/TSPYL/SET/NAP-1 (TTSN) superfamily that includes TSPYL1, TSPYL2, TSPYL3, TSPYL4, and TSPYL5 [10]. Upregulation of TSPYL6 has been observed in both benign and malignant cells. The TSPYL6 protein has been associated with chromatin and nucleosome assembly [11]. However, the specific functions of TSPYL6 are not yet clear. Norling et al. [12] sequenced the TSPYL6 gene in an entire Sweden patient cohort, but no inactivating mutations were identified. Additionally, no studies have investigated correlations between the TSPYL6 gene and BC susceptibility. In this case-control study, we genotyped six SNPs in TSPYL6: rs843645, rs11125529, rs12615793, rs843711, rs11896604, and rs843706 and performed a comprehensive association analysis to identify SNPs associated with BC risk in Han Chinese women.

RESULTS

Participant characteristics

A total of 183 patients with BC and 195 healthy individuals were enrolled in the study. The participant characteristics are shown in Table 1. No significant differences in age, body mass index (BMI), or the menopause age were observed between patients in the case and control groups (p > 0.05). The mean age of the participants was 45.35 years in the control group and 46.40 years in the case group. The mean BMI was 22.53 in the control group and 23.08 in the case group.

Table 1: Basic characteristics of the control individuals and patients with breast cancer

Characteristic

Cases (N = 183)

Controls (N = 195)

P -value

 Mean age ± SD

46.40 ± 9.383 (N= 183)

45.35 ± 6.899 (N= 195)

0.218a

 Mean BMI ± SD

23.08 ± 3.00 (N= 183)

22.53 ± 2.55 (N = 195)

0.056a

Menopause

Premenopausal

115 (62.8%)

119 (61.0%)

0.716b

Postmenopausal

68 (37.2%)

76 (39.0%)

Age of Menarche

≤ 12

25 (13.7%)

> 12

158 (86.3%)

Breastfeeding Duration

≤ 6

12 (6.5%)

> 6

158 (93.5%)

Clinical Stages

I/II

135 (73.8%)

III/IV

48 (26.2%)

Estrogen Receptor

negative

60 (32.8%)

positive

123 (67.2%)

Family Tumor History

no

156 (85.2%)

yes

27 (14.8%)

Incipientw or Recurrence

Incipient

109 (59.9%)

Recurrence

73 (40.1%)

Lymph node metastasis

no

105 (58.3%)

yes

75 (41.7%)

Menopause

no

115 (62.8%)

yes

68 (37.2%)

Primiparous Age

< 30

170 (96.6%)

≥ 30

6 (3.4%)

Procreative Times

< 1

142 (81.1%)

≥ 1

33 (18.9%)

Progestrone Receptor

negative

75 (41.0%)

positive

108 (59.0%)

Tumor Location

left

84 (45.9%)

right

97 (53.0%)

both

2 (1.1%)

Tumor Size (cm)

≤ 3

94 (51.4%)

> 3

89 (48.6%)

Tumor Type

carcinoma

165 (90.2%)

others

18 (9.8%)

Whether fertility

no

7 (3.8%)

yes

176 (96.2%)

SD: Standard deviation. BMI: Body mass index (weight [kg]/height[m]2).

a P value was calculated by Welch’s t test. b P value was calculated by Pearson’s χ2 test.

Association between TSPYL6 polymorphisms and BC risk

Detailed SNP data and the associations between various SNPs and BC risk are shown in Table 2. Our data indicated that all 6 SNPs investigated were in Hardy-Weinberg equilibrium in the control subjects (p > 0.05). No associations were observed between the alleles and BC risk in an allele model. We also performed a Bonferroni correction and determined that none of the SNPs showed statistical significant associations with BC risk.

Table 2: Basic information of candidate SNPs in this study

SNPs

Position

Band

AllelesA/B

MAF-control

MAF-case

HWE-p

OR

95% CI

p-x2

rs843645

54474664

2p16.2

G/T

0.297

0.279

0.4968

0.913

0.666–1.251

0.57

rs11125529

54475866

2p16.2

A/C

0.195

0.150

0.1695

0.731

0.499–1.069

0.105

rs12615793

54475914

2p16.2

A/G

0.201

0.161

0.1166

0.764

0.525–1.109

0.156

rs843711

54479117

2p16.2

C/T

0.487

0.544

0.1531

1.254

0.942–1.669

0.12

rs11896604

54479199

2p16.2

G/C

0.221

0.167

0.0929

0.707

0.491–1.018

0.062

rs843706

54480369

2p16.2

C/A

0.482

0.544

0.06103

1.281

0.962–1.705

0.090

SNPs: Single nucleotide polymorphisms; MAF: Minor allele frequency; HWE: Hardy-Weinberg equilibrium; OR: Odds ratio; CI: Confidence interval. AMinor alleles. BMajor alleles.

We further assessed the association between each SNP and BC risk in an unconditional logistic regression analysis, which was performed using four models: additive, dominant, recessive, and genotype model (Tables 3 and 4). Rs11896604 was associated with a decreased risk of BC in a dominant model (odds ratio [OR] = 0.623, 95% confidence interval [95% CI] = 0.405–0.958, p = 0.031). Rs843706 was associated with an increased risk of BC under the recessive model (OR = 1.709, 95% CI = 1.055–2.770, p = 0.030) (Table 3). Rs11125529 was associated with a decreased risk of BC under the genotype model (OR = 0.612, 95% CI = 0.391–0.959, p = 0.032) (Table 4). Rs11896604 was associated with a decreased risk of BC in a genotype model (OR = 0.574, 95% CI = 0.370–0.891, p = 0.013). No statistical associations were detected under the other models. In addition, no positive results were observed after Bonferroni correction.

Table 3: Single loci association with breast cancer risk (adjusted by age, BMI and menopause)

SNP

Model

Genotype

Cases

Controls

OR (95% CI)

P

rs843645

Dominant model

T/T

99

94

1

0.246

G/G-G/T

84

101

0.785 (0.521–1.182)

Recessive model

T/T-T/G

165

180

1

0.535

G/G

18

15

1.259 (0.608–2.606)

Additive model

-

-

-

0.904 (0.658–1.241)

0.532

rs11125529

Dominant model

C/C

133

123

1

0.057

A/A-A/C

50

72

0.652 (0.419–1.013)

Recessive model

C/C-C/A

178

191

1

0.618

A/A

5

4

1.413 (0.364–5.488)

Additive model

-

-

-

0.730 (0.491–1.085)

0.120

rs12615793

Dominant model

G/G

129

120

1

0.085

A/A-A/G

54

74

0.682 (0.441–1.054)

Recessive model

G/G-G/A

178

190

1

0.625

A/A

5

4

1.402 (0.361–5.445)

Additive model

-

-

-

0.755 (0.510–1.117)

0.159

rs843711

Dominant model

T/T

39

46

1

0.532

C/C-C/T

144

149

1.169 (0.405–0.958)

Recessive model

T/T-T/C

128

154

1

0.066

C/C

55

41

1.563 (0.972–2.515)

Additive model

-

-

-

1.261 (0.937–1.700)

0.126

rs11896604

Dominant model

C/C

128

114

1

0.031

G/G-G/C

55

81

0.623 (0.405–0.958)

Recessive model

C/C-C/G

177

190

1

0.644

G/G

6

5

1.336 (0.391–4.564)

Additive model

-

-

-

0.709 (0.484–1.039)

0.078

rs843706

Dominant model

A/A

39

45

1

0.603

C/C-C/A

144

149

1.140 (0.696–1.866)

Recessive model

A/A-A/C

128

156

1

0.030

C/C

55

38

1.709 (1.055–2.770)

Additive model

-

-

-

1.294 (0.958–1.750)

0.093

SNPs: Single nucleotide polymorphisms; OR: Odds ratio. CI: Confidence interval.

P value was calculated by Wald test. *p < 0.05 indicates statistical significant.

Table 4: The association between the single-nucleotide polymorphisms and BC risk in Genotype model (adjusted by age, BMI and menopause)

Genotype

Cases

Controls

OR (95% CI)

P

rs843645

 TT

99

94

1.00 [Ref]

 GT

66

86

0.729 (0.475–1.117)

0.147

 GG

18

15

1.139 (0.543–2.391)

0.730

rs11125529

 CC

133

123

1.00 [Ref]

 AC

45

68

0.612 (0.391–0.959)

0.032

 AA

5

4

1.156 (0.304–4.404)

0.832

rs12615793

 GG

129

120

1.00 [Ref]

 AG

49

70

0.651 (0.419–1.013)

0.057

 AA

5

4

1.163 (0.305–4.432)

0.825

rs843711

 TT

39

46

1.00 [Ref]

 CT

89

108

0.972 (0.583–1.620)

0.913

 CC

55

41

1.582 (0.879–2.848)

0.126

rs11896604

 CC

128

114

1.00 [Ref]

 GC

49

76

0.574 (0.37–0.891)

0.013

 GG

6

5

1.069 (0.318–3.596)

0.915

rs843706

 AA

39

45

1.00 [Ref]

 CA

89

111

0.925 (0.555–1.543)

0.766

 CC

55

38

1.670 (0.921–3.030)

0.092

OR: odd ratio; CI: confidence interval;

p value was calculated by Wald test. *p < 0.05 indicates statistical significance.

In order to assess the associations between SNP haplotypes and BC risk, a Wald test was performed using an unconditional multivariate regression analysis. However, no positive results were observed (Table 5, Figure 1).

Table 5: Haplotype frequency and their association with BC risk in case and control subjects (adjusted by age, BMI and menopause)

SNPs

Haplotype

Freq %

P1

OR

95% CI

P2

case

control

rs843645|rs11125529|rs12615793|rs8437
11|rs11896604|rs843706

TAATGA

0.150

0.192

0.126

0.745

0.501

1.108

0.146

TCGTGA

0.016

0.023

0.510

0.741

0.256

2.149

0.581

GCGTCA

0.276

0.292

0.619

0.912

0.662

1.257

0.574

TCGCCC

0.530

0.474

0.126

1.266

0.939

1.707

0.122

*P-value < 0.05 indicates statistical significance.

P1- values were calculated from two-sided Chi-squared test.

P2 -values were calculated by unconditional logistic regression.

The reference standard for each haplotype is the other haplotype.

Haplotype block map for all the SNPs of the TSPYL6 gene.

Figure 1: Haplotype block map for all the SNPs of the TSPYL6 gene.

Association between TSPYL6 polymorphisms and BC patient clinicopathological features

We next analyzed the association between TSPYL6 polymorphisms and BC patient clinicopathological features, which included age, age of menarche, BMI, breastfeeding duration, clinical stage, estrogen receptor status, family history of cancer, procreative time, progesterone receptor status, tumor location, tumor size (cm), tumor type, incipient recurrence, presence of lymph node metastasis, age of menopause, and prim parous age. Positive results are shown in (Table 6A, 6B). For rs11125529, we found that more recurrent BC patients had the AA + CA genotype than the CC genotype (OR = 2.321, 95% CI = 1.192–4.521, p = 0.012) (Table 6A). For rs843711, the CT + CC genotype was observed less frequently in patients with clinical stage III/IV disease (OR = 0.411, 95% CI = 0.194–0.869, p = 0.018) and in patients with recurrent BC (OR = 0.458, 95% CI = 0.222–0.944, p = 0.032) than the TT genotype (Table 6A). Our results suggested that the frequency of recurrent BC patients with the CC genotype of rs11896604 was higher than the frequency of patients with the GG + CG genotype (OR = 2.471, 95% CI = 1.290–4.734, p = 0.006) (Table 6B). Finally, the CA + CC genotype of rs843706 was more frequently observed in patients with clinical stage III/IV disease (OR = 0.411, 95% CI = 0.194–0.869, p = 0.018) and in patients with recurrent BC (OR = 0.458, 95% CI = 0.222–0.944, p = 0.032) than the AA genotype (Table 6B). No statistical associations were detected between the other loci and the clinical parameters that were investigated.

Table 6A: The Associations between TSPYL6 polymorphisms and clinical characteristics of breast cancer patients

Variables

rs11125529

rs843711

AA + CA

CC

ORa

95% CI

Pb

CT+CC

TT

ORa

95% CI

Pb

Age

50

133

144

39

 ≤ 40

14

43

1

(reference)

41

16

1

(reference)

 > 40

36

90

1.229

(0.600–2.515)

0.573

103

23

1.748

(0.839–3.639)

0.133

Age of Menarche

50

133

144

39

 ≤ 12

5

20

1

(reference)

22

3

1

(reference)

 > 12

45

113

0.628

(0.222–1.775)

0.377

122

36

2.164

(0.612–7.646)

0.221

BMI

50

133

144

39

 ≤ 24

34

87

1

(reference)

98

23

1

(reference)

 > 24

16

46

0.89

(0.445–1.780)

0.742

46

16

0.675

(0.326–1.397)

0.288

Breastfeeding Duration

46

124

133

37

 ≤ 6

3

9

1

(reference)

10

2

1

(reference)

 > 6

43

115

0.891

(0.230–3.448)

0.868

123

35

1.423

(0.298–6.797)

0.657

Clinical Stages

50

133

144

39

 I/II

38

97

1

(reference)

112

23

1

(reference)

 III/IV

12

36

0.851

(0.401–1.807)

0.674

32

16

0.411

(0.194–0.869)

0.018*

Estrogen Receptor

50

133

144

39

 negative

17

43

1

(reference)

49

11

1

(reference)

 positive

33

90

0.927

(0.466–1.847)

0.83

95

28

0.762

(0.350–1.658)

0.492

Family Tumor History

50

133

144

39

 no

8

114

1

(reference)

121

35

1

(reference)

 yes

42

19

1.143

(0.465–2.807)

0.771

23

4

1.663

(0.539–5.031)

0.372

Procreative Times

47

128

138

37

 < 1

37

105

1

(reference)

112

30

1

(reference)

 ≥ 1

10

23

0.81

(0.353–1.862)

0.62

26

7

1.005

(0.398–2.539)

0.991

Progestrone Receptor

50

133

144

39

 negative

22

53

1

(reference)

59

16

1

(reference)

 positive

28

80

0.843

(0.437–1.627)

0.611

85

23

1.002

(0.488–2.058)

0.995

Tumor Location

50

133

144

39

 left

22

62

1

(reference)

66

18

1

(reference)

 right

28

69

1

(reference)

77

20

1

(reference)

 both

0

2

---

---

0.631

1

1

---

---

0.603

Tumor Size (cm)

50

133

144

39

 ≤ 3

24

70

1

(reference)

76

18

1

(reference)

 > 3

26

63

1.204

(0.628–2.308)

0.576

68

21

0.767

(0.377–1.559)

0.463

Tumor Type

50

133

144

39

 Infiltrating ductal carcinoma

47

118

1

(reference)

128

37

1

(reference)

 others

3

15

1.992

(0.551–7.198)

0.285

16

2

0.432

(0.095–1.967)

0.266

Incipience/Recurrence

49

133

144

38

 Incipience

22

87

1

(reference)

92

17

1

(reference)

 Recurrence

27

46

2.321

(1.192–4.521)

0.012*

52

21

0.458

(0.222–0.944)

0.032*

Lymph node metastasis

49

131

141

39

 no

29

76

1

(reference)

83

17

1

(reference)

 yes

20

55

0.953

(0.489–1.857)

0.887

58

22

0.904

(0.442–1.851)

0.783

Menopause

50

133

144

39

 no

27

88

1

(reference)

87

28

1

(reference)

 yes

23

45

1.666

(0.859–3.230)

0.129

57

11

1.668

(0.770–3.614)

0.192

Primiparous Age

47

129

139

37

 < 30

45

125

1

(reference)

136

34

1

(reference)

 ≥ 30

2

4

0.72

(0.127–4.006)

0.709

3

3

4

(0.773–20.70)

0.076

Table 6B: The Associations between TSPYL6 polymorphisms and clinical characteristics of breast cancer patients

Variables

rs11896604

rs843706

GG + CG

CC

ORa

95% CI

Pb

CA + CC

AA

ORa

95% CI

Pb

Age

55

128

144

39

 ≤ 40

16

41

1

(reference)

41

16

1

(reference)

 > 40

39

87

1.149

(0.576–2.291)

0.694

103

23

1.748

(0.839–3.639)

0.133

Age of Menarche

55

128

144

39

 ≤ 12

6

19

1

(reference)

22

3

1

(reference)

 > 12

49

109

0.702

(0.264–1.868)

0.477

122

36

2.164

(0.612–7.646)

0.221

BMI

55

128

144

39

 ≤ 24

38

83

1

(reference)

98

23

1

(reference)

 > 24

17

45

0.825

(0.419–1.624)

0.578

46

16

0.675

90.326–1.397

0.288

Breastfeeding Duration

51

119

133

37

 ≤ 6

3

9

1

(reference)

10

2

1

(reference)

 > 6

48

110

0.764

(0.198–2.946)

0.695

123

35

1.423

(0.298–6.797)

0.657

Clinical Stages

55

128

144

39

 I/II

43

92

1

(reference)

112

23

1

(reference)

 III/IV

12

36

0.713

(0.338–1.505)

0.374

32

16

0.411

(0.194–0.869)

0.018*

Estrogen Receptor

55

128

144

39

 negative

19

41

1

(reference)

49

11

1

(reference)

 positive

36

87

0.893

(0.458–1.742)

0.74

95

28

0.762

(0.350–1.658)

0.492

Family Tumor History

55

128

144

39

 no

45

111

1

(reference)

121

35

1

(reference)

 yes

10

17

1.151

(0.617–3.410)

0.391

23

4

1.663

(0.539–5.131)

0.372

Procreative Times

52

123

138

37

 < 1

41

101

1

(reference)

112

30

1

(reference)

 ≥ 1

11

22

0.812

(0.361–1.824)

0.614

26

7

1.005

(0.398–2.539)

0.991

Progestrone Receptor

55

128

144

39

 negative

25

50

1

(reference)

59

16

1

(reference)

 positive

30

78

0.769

(0.406–1.457)

0.42

85

23

1.002

(0.488–2.058)

0.995

Tumor Location

55

128

144

39

 left

25

59

1

(reference)

66

18

1

(reference)

 right

30

67

1

(reference)

77

20

1

(reference)

 both

0

2

---

---

0.638

1

1

---

---

0.603

Tumor Size (cm)

55

128

144

39

 ≤ 3

27

67

1

(reference)

76

18

1

(reference)

 > 3

28

61

1.139

(0.605–2.144)

0.686

68

21

0.767

(0.377–1.559)

0.463

Tumor Type

55

128

144

39

 Infiltrating ductal carcinoma

52

113

1

(reference)

128

37

 others

3

15

2.301

(0.638–8.295)

0.192

16

2

0.432

(0.095–1.967)

0.266

Incipience/Recurrence

54

128

144

38

 Incipience

24

85

1

(reference)

92

17

1

(reference)

 Recurrence

30

43

2.471

(1.290–4.734)

0.006*

52

21

0.458

(0.222–0.944)

0.032*

Lymph node metastasis

54

126

141

39

 no

33

72

1

(reference)

83

22

1

(reference)

 yes

21

54

0.848

(0.442–1.627)

0.621

58

17

0.904

(0.442–1.851)

0.783

Menopause

55

128

144

39

 no

32

83

1

(reference)

87

28

1

(reference)

 yes

23

45

1.326

(0.694–2.532)

0.393

57

11

1.668

(0.770–3.614)

0.192

Primiparous Age

52

124

139

37

 < 30

50

120

1

(reference)

136

34

1

(reference)

 ≥ 30

2

4

0.833

(0.148–4.697)

0.836

3

3

4

(0.773–20.70)

0.076

BMI: body mass index; CI: confidence interval. OR: odds ratio.

aAdjusted for Age, Age of Menarche, BMI, Breastfeeding Duration, Clinical Stages, Estrogen Receptor, Family Tumor History, Procreative Times, Progestrone Receptor, Tumor Location, Tumor Size (cm), Tumor Type, Incipient/Recurrence, Lymph node metastasis, Menopause and Primiparous Age.

bTwo-sided Chi-square test for the distributions of genotype frequencies.

*p < 0.05 indicates statistical significance.

DISCUSSION

In this study, we investigated the association between SNPs in the TSPYL6 gene and BC risk in Han Chinese women. We found that four SNPs (rs11896604, rs843706, rs11125529, and rs843711) were associated with the risk of BC in this population. Rs11896604 was associated with a decreased risk of BC in a dominant and genotype model, but the various genotypes were associated with an increased risk of recurrence in BC patients. An association between this locus and other diseases has not been previously reported. Rs843706 was associated with an increased risk of BC in a recessive model, but there was a decreased association between the SNP and the risk of recurrence as well as with clinical stage III/IV BC.

We are the first to demonstrate an association between this locus and BC susceptibility. Rs11125529 was associated with a decreased risk of BC in a genotype model, but an increased risk of recurrence. Although Ding et al. reported neither the genotype nor the allele frequencies at rs11125529 in ACYP2 differed significantly between coronary heart disease patients and normal controls [13]. The association between the telomere length-related variant rs11125529 in ACYP2 and gastric cancer risk was previously investigated in a Chinese population, but no significant association was identified [14]. We found that the rs843711 genotypes in the TSPYL6 gene were inversely correlated with clinical stage III/IV BC. Finally, rs843645 and rs12615793 were not associated with the risk of BC.

The function of TSPYL6 may be similar to those of other members of the TTSN superfamily. However, the molecular mechanisms underlying TSPYL6 function have not been elucidated. Mutation of TSPYL can cause sudden infant death with dysgenesis of the testes (SIDDT) in affected males, indicating that TSPYL is important for the development of the testis and other tissues such as the brain [15]. Although TSPYL is expressed in all tissues [16], the role of TSPYL in tumor cells is not clear. The TSPYL4 gene is located 25 kb from TSPYL, however no coding variants were identified in affected individuals with direct sequencing. The TSPYL1 gene does not contain any introns, but the exact composition has not been determined [17].

TSPYL2 gene and cyclin B can inhibit cell proliferation by arresting cell growth in response to DNA damage [18]. Thus, it has been suggested that TSPYL2 is a negative regulator of cell cycle progression. The TSPYL2 gene is silenced in glioma and malignant lung tissue, and in certain lung cancer cell lines [19]. Overexpression of TSPYL2 can inhibit human lung and breast cancer cell lines [20]. However, there is limited evidence for a direct function of TSPYL2 in cell cycle control. Interestingly, the TSPYL5 gene has been reported to suppress gastric cancer development [21]. Further studies are required to characterize the function of TSPYL6 and elucidate the mechanisms underlying the association between the TSPYL6 and BC susceptibility. Currently, the relationship between clinical characteristics in BC patients and TSPYL6 gene expression/function is not clear.

Our study is the first to demonstrate that polymorphisms in TSPYL6 affect the pathogenesis of BC and are associated with clinicopathological characteristics of BC patients. Collectively, the results provide insight into the pathogenesis of BC. Although this study had sufficient statistical power, there were still some intrinsic limitations. First, the sample size was relatively small (183 cases and 195 controls). Therefore, our findings must be confirmed in studies with larger sample sizes as well as in a meta-analysis. Additionally, we only analyzed Han Chinese women. Therefore, our results must be validated in studies of other populations. Finally, although we identified significant associations between four SNPs (rs11896604, rs843706, rs11125529, and rs843711) and BC susceptibility, the mechanisms responsible for the associations are still unclear. Further studies of TSPYL6 and other members of the TTSN superfamily are necessary to dissect the mechanisms by which polymorphisms in these genes contribute to BC risk. Hereditary, endocrine, environmental, and life style factors should be also considered.

We performed Bonferroni correction in our statistical analysis, but found no statistical significant associations between TSPYL6 SNPs and risk of BC. This may be due to the relatively small sample size, the selection criteria for TSPYL6 SNPs (minor allele frequency [MAF] > 5%), and the weakness of Bonferroni correction itself (the interpretation of a finding depends on the number of other tests performed). True differences may have been deemed non-significant given the likelihood of type II errors.

MATERIALS AND METHODS

Study participants

A total of 183 patients with BC and 195 healthy women were included in this study. The patients were treated at the Second Affiliated Hospital of Xi’an Jiao Tong University between January 2013 and November 2015. All demographic and related clinical data including residential region, age, ethnicity, and education status were collected through a face-to-face questionnaire and a review of medical records. The clinical and demographic characteristics of the patients are shown in Table 1. Patients who had been recently diagnosed with primary BC (confirmed by histopathological analysis) were included in the study. Patients diagnosed with other types of cancers or who underwent radiotherapy or chemotherapy were excluded. Control patients who had undergone annual health evaluations were recruited from health checkup centers affiliated with our institution. All controls were matched with cases based on age (p = 0.218) and ethnicity. All control patients had no history of cancer. Factors that could influence the mutation rate were minimized. The participants were women who were ≥ 18 years old with good mental health and no blood relatives with BC going back three generations. This study was performed in accordance with the Chinese Department of Health and Human Services regulations for the protection of human research subjects. Informed consent was obtained from all participants and the study protocols were approved by the Institutional Review Board of Xi’an Jiao Tong University.

SNP selection and genotyping

Validated SNPs that had a MAF > 5% in the HapMap Asian population were selected for the association analysis [12, 20, 22, 23]. Venous blood samples (5 mL) were collected from each patient during a laboratory examination. DNA was extracted from whole blood samples using the Gold Mag-Mini Whole Blood Genomic DNA Purification Kit (version 3.0; TaKaRa, Japan) [24]. The DNA concentration was measured by spectrometry (DU530 UV/VIS spectrophotometer, Beckman Instruments, Fullerton, CA, USA). The Sequenom MassARRAY Assay Design 3.0 software (Sequenom, Inc, San Diego, CA, USA) was used to design the multiplexed SNP Mass EXTEND assay. Genotyping was performed using a Sequenom MassARRAY RS1000 (Sequenom, Inc.) according to the manufacturer’s protocol [25]. The SequenomTyper 4.0 Software™ (Sequenom, Inc.) was used to manage and analyze the data [26]. The primers corresponding to each SNP are shown in Table 7. Based on these results, the following six SNPs were selected: rs843645, rs11125529, rs12615793, rs843711, rs11896604, and rs843706. The SNP data are shown in Table 3.

Table 7: Primers used for this study

SNP_ID

1st-PCRP

2nd-PCRP

UEP_SEQ

rs843645

ACGTTGGATGGAAATCTGA ATACCACCTAC

ACGTTGGATGACAGTGCCTTTA GCAAGGTG

TCATAGGCACTACT GTATC

rs11125529

ACGTTGGATGGAGCTTAGTT GTTTACAGATG

ACGTTGGATGCCGAAGAAAAG AAGATGAC

AGAAAAGAAGATG ACTAAAACAT

rs12615793

ACGTTGGATGTTTGAGCTTAG TTGTTTAC

ACGTTGGATGATCTTGGCCCTT GAAGAA

AAATTGAGTGACAA| ATATAAACTAC

rs843711

ACGTTGGATGGACAAAGGACC TTACAACTC

ACGTTGGATGTGCCTTGTGGGA ATTAGAGC

gggaTCAGGGAACCA GTGCAAA

rs11896604

ACGTTGGATGAAGTCAGAATA GTGCTTAC

ACGTTGGATGTGTCTCTGACCT AGCATGTA

GTTAAGCTTGCAA GGAG

rs843706

ACGTTGGATGTGAAAGCCAT AAATATTTTG

ACGTTGGATGTGAATAACTTGG TCTTATC

cACTTGGTCTTATCT GATGC

Statistical analysis

Chi-squared tests (categorical variables) and Student’s t-tests (continuous variables) were used to evaluate the differences in the demographic characteristics between the cases and controls [27]. The Hardy-Weinberg equilibrium of each SNP was assessed in order to compare the expected frequencies of the genotypes in the control patients. All of the minor alleles were regarded as risk alleles for BC susceptibility. To evaluate associations between the SNPs and risk of BC in the four models (genotype, dominant, recessive, and additive), ORs and 95% CIs were calculated using unconditional logistic regression analysis [28]. In multivariate analyses, unconditional logistic regression was used to assess the association between each SNP and the risk of BC after adjusting for BMI, age, and menopause [28]. Linkage disequilibrium analysis and SNP haplotypes were analyzed using the Haploview software package (version 4.2) and the SHEsi software platform (http://www.nhgg.org/analysis/) [29]. All statistical analyses were performed using the SPSS version 17.0 statistical package (SPSS, Chicago, IL, USA) and Microsoft Excel. A p < 0.05 was considered statistically significant and all statistical tests were two-sided.

CONCLUSIONS

In summary, we have identified four novel associations between SNPs (rs11896604, rs843706, rs11125529, and rs843711) in TSPYL6 and BC. Our results suggest that these SNPs may contribute to BC development and possibly other complex genetic traits. These SNPs may function as molecular markers of BC susceptibility, and could therefore be used as diagnostic and prognostic markers in clinical studies of BC patients.

ACKNOWLEDGMENTS AND FUNDING

This work was supported by Shaanxi province science and technology research projects (No. S2015YFSF0310). The authors are also grateful to the patients and control individuals for their participation in the study. We thank the clinicians and hospital staff who contributed to sample and data collection for this study.

CONFLICTS OF INTEREST

The authors declare that there are no conflicts of interest.

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