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Investigation of Cytotoxic T-lymphocyte antigen-4 polymorphisms in non-small cell lung cancer: a case-control study

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Oncotarget. 2017; 8:76634-76643. https://doi.org/10.18632/oncotarget.20638

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Shuchen Chen, Yafeng Wang, Yu Chen, Jihong Lin, Chao Liu, Mingqiang Kang and Weifeng Tang _

Abstract

Shuchen Chen1, Yafeng Wang2, Yu Chen3, Jihong Lin1, Chao Liu4, Mingqiang Kang1,5,6 and Weifeng Tang1

1Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China

2Department of Cardiology, The People's Hospital of Xishuangbanna Dai Autonomous Prefecture, Jinghong, Yunnan Province, China

3Department of Medical Oncology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, Fujian Province, China

4Department of Cardiothoracic Surgery, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, Jiangsu Province, China

5Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, Fujian Province, China

6Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, Fujian Province, China

Correspondence to:

Weifeng Tang, email: [email protected]

Mingqiang Kang, email: [email protected]

Keywords: CTLA-4, polymorphism, non-small cell lung cancer, immune

Received: May 25, 2017     Accepted: August 17, 2017     Published: September 04, 2017

ABSTRACT

The objective of this case-control study was to extensively explore the relationship of Cytotoxic T-lymphocyte antigen-4 (CTLA-4) tagging polymorphisms with susceptibility to non-small-cell lung cancer (NSCLC). We recruited 521 sporadic NSCLC cases and 1,030 non-cancer controls. The genotypes of CTLA-4 rs16840252 C>T, rs231775 G>A, rs3087243 G>A and rs733618 T>C polymorphisms were evaluated using a custom-by-design 48-Plex SNPscan Kit. Our findings revealed there was no statistically significant difference in CTLA-4 genotypes distribution among NSCLC patients and non-cancer controls. Similar findings were observed in the logistic regression analyses. However, the stratified analyses suggested CTLA-4 rs733618 vatiants were correlated with the development of NSCLC in ≥ 60 years subgroup (TC vs. TT: adjusted OR = 1.45, 95% CI = 1.04–2.02, P = 0.030) and even drinking subgroup (TC vs. TT: adjusted OR = 2.27, 95% CI = 1.11–4.60, P = 0.024 and TC/CC vs. TT: adjusted OR = 2.26, 95% CI = 1.15–4.43, P = 0.018). In conclusion, the present case-control study highlights that the CTLA-4 rs733618 T>C polymorphism was associated with the development of NSCLC in ≥ 60 years and even drinking subgroups. A fine-mapping study with functional assessment is necessary to confirm or refute our findings.


INTRODUCTION

In 2012, an estimated 1,824,700 new lung cancer (LC) cases occurred worldwide, accounting for approximately 13% of overall cancer diagnosis [1]. LC was the most common malignancy and the first leading cause of cancer-related death among males in 2012; in addition, it was the second cancer-related death among females [1]. Thus, exploration of potential heredity factors that might affect the risk of LC, especially non-small-cell LC (NSCLC), which was the most common subtype of cases, attracted our interest. We focused on the costimulatory molecules of immunoglobulin superfamily which regulate T-cell activation and proliferation.

Cytotoxic T-lymphocyte antigen-4 (CTLA-4), a member of the immunoglobulin superfamily, is also known as CD152. In generally, CTLA-4 is expressed on activated T cells and negatively regulate the proliferation and the activation of T cells [24]. CTLA-4 competes with CD28 and binds to B7.1 and B7.2 which are costimulatory molecules expressed on antigen-presenting cells. In addition, the affinity between CTLA-4 and B7 molecules is higher than that of CD28 with B7 molecules [5, 6]. The interaction of CTLA-4–B7 leads to the repression of T cells at the G1 phase and the down-regulated expression of interleukin-2 (IL-2) and IL-2 receptor [7]. This interaction can also induce activated T cells to FAS-independent apoptosis, and then further restrain T lymphocytes.

CTLA-4, a immunoregulatory molecule, is encoded by a gene on chromosome 2q33. A number of single-nucleotide polymorphisms (SNPs) in CTLA-4 gene have been established. Song et al. reported that CTLA-4 +49A>G polymorphism was a prognostic predictor for advanced NSCLC [8]. In addition, Antczak et al. found that CTLA-4 expression was significantly correlated with CTLA-4 TT genotype (-318C/T). Recently, several case-control studies focused on the relationship of CTLA-4 SNPs with the risk of NSCLC [812]. However, due to the limited sample size and the number of study, the association between CTLA-4 SNPs and NSCLC susceptibility was not well understood. The objective of this case-control study was to extensively explore the relationship of CTLA-4 rs16840252 C>T, rs231775 G>A, rs3087243 G>A and rs733618 T>C polymorphisms with susceptibility to NSCLC.

RESULTS

Demographic characteristics

This case-control study comprised 521 NSCLC cases and 1,030 control subjects. The NSCLC patients comprised 287 males and 234 females, while the non-cancer control subjects were 588 males and 442 females. The mean age and SD in the NSCLC patient was 59.76 ± 10.71 years and that was 60.34 ± 9.11 years in controls. Gender and age were well-matched between the groups (P = 0.453 and P = 0.843, respectively, Table 1). All 521 confirmed cases of NSCLC were sporadic. The genotyping successful rates were shown in Table 2 and they ranged from 99.81% to 99.94%. The values of MAF in control subjects were very similar to the data for Chinese (Table 2). The genotype frequencies of CTLA-4 rs16840252 C>T, rs231775 G>A, rs3087243 G>A and rs733618 T>C polymorphisms in controls reached Hardy-Weinberg equilibrium (HWE).

Table 1: Distribution of selected demographic variables and risk factors in NSCLC cases and controls

Variable

Overall Cases (n = 521)

Overall Controls (n = 1,030)

Pa

n (%)

n (%)

Age (years)

59.76 ±10.71

60.34 ± 9.11

0.268

Age (years)

0.843

 < 60

238 (45.68)

476 (46.21)

 ≥ 60

283 (54.32)

554 (53.79)

Sex

0.453

 Male

287 (55.09)

588 (57.09)

 Female

234 (44.91)

442 (42.91)

Smoking status

< 0.001

 Never

317 (60.84)

828 (80.39)

 Ever

204 (39.16)

202 (19.61)

Alcohol use

< 0.001

 Never

444 (85.22)

949 (92.14)

 Ever

77 (14.78)

81 (7.86)

BMI (kg/m2)

23.00 (±3.03)

23.84 (±3.06)

< 0.001

BMI (kg/m2)

 < 24

337 (64.68)

547 (53.11)

< 0.001

 ≥ 24

184 (35.32)

483 (46.89)

Lymph node status

 Positive

200 (38.39)

 Negative

314 (60.27)

 Unknown

7 (1.34)

TMN stage

 I + II

315 (60.46)

 III + IV

206 (39.54)

Type of NSCLC

 Adenocarcinoma

415 (79.65)

 Squamous cell carcinoma

85 (16.31)

 Others

21 (4.03)

a Two-sided χ2 test and student t test;

Bold values are statistically significant (P < 0.05);

BMI, body mass index.

NSCLC: non-small-cell lung cancer

Table 2: Primary information for CTLA-4polymorphisms (rs3087243 G>A, rs16840252 C>T, rs733618 T>C and rs231775 G>A)

Genotyped SNPs

CTLA-4rs3087243 G>A

CTLA-4rs16840252 C>T

CTLA-4rs733618 T>C

CTLA-4rs231775 G>A

Chromosome

2

2

2

2

Function

nearGene-3

nearGene-5

nearGene-5

missense

Chr Pos (NCBI Build 38)

203874196

203866796

203866221

203867991

MAFa for Chinese in database

0.183

0.122

0.390

0.314

MAF in our controls (n =1,040)

0.182

0.121

0.407

0.300

P value for HWEb test in our controls

0.532

0.146

0.314

0.950

Genotyping method

SNPscan

SNPscan

SNPscan

SNPscan

% Genotyping value

99.94%

99.94%

99.94%

99.81%

aMAF: minor allele frequency;

bHWE: Hardy–Weinberg equilibrium.

Association of CTLA-4 rs16840252 C>T, rs231775 G>A, rs3087243 G>A and rs733618 T>C Polymorphisms with NSCLC

Table 3 demonstrated the detailed frequencies of CTLA-4 rs16840252 C>T, rs231775 G>A, rs3087243 G>A and rs733618 T>C genotypes. Results of the single locus analyses were summarized in Table 4. We found no statistically significant difference in CTLA-4 genotype distribution among NSCLC patients and non-cancer controls. The similar findings were observed in the logistic regression analyses.

Table 3: The frequencies of CTLA-4 rs3087243 G>A, rs16840252 C>T, rs733618 T>C and rs231775 G>A polymorphisms in different NSCLC subgroups

Genotype

NSCLC Cases
(n = 521)

Adenocarcinoma
(n = 415)

Non-adenocarcinoma
(n = 106)

Controls (n = 1,030)

n

%

n

%

n

%

n

%

rs3087243 G>A

GG

344

66.03

273

65.78

71

66.98

686

66.67

GA

160

30.71

131

31.57

29

27.36

312

30.32

AA

17

3.26

11

2.65

6

5.66

31

3.01

A allele

194

18.62

153

18.43

41

19.34

374

18.17

rs16840252 C>T

CC

400

76.78

323

77.83

77

72.64

791

76.87

CT

111

21.31

86

20.72

25

23.58

228

22.16

TT

10

1.92

6

1.45

4

3.77

10

0.97

T allele

131

12.57

98

11.81

33

15.57

248

12.05

rs733618 T>C

TT

172

33.01

138

33.25

34

32.08

370

35.96

TC

267

51.25

214

51.57

53

50.00

481

46.74

CC

82

15.74

63

15.18

19

17.92

178

17.30

C allele

431

41.36

340

40.96

91

42.92

837

40.67

rs231775 G>A

GG

254

48.85

206

49.76

48

45.28

504

49.03

GA

219

42.12

175

42.27

44

41.51

431

41.93

AA

47

9.04

33

7.97

14

13.21

93

9.05

A allele

313

30.10

241

29.11

72

33.96

617

30.01

Table 4: Logistic regression analyses of association between CTLA-4 polymorphisms and risk of NSCLC

src="https://www.oncotarget.com/index.php?journal=oncotarget&page=article&op=viewFile&path%5B%5D=20638&path%5B%5D=65762&path%5B%5D=295827"

a Adjusted for age, sex, smoking status, alcohol use and BMI status

In a stratified analysis by cancer type of NSCLC, logistic regression analyses indicated that there was no difference in genotype distribution of CTLA-4 rs231775 G>A, rs16840252 C>T, rs3087243 G>A and rs733618 T>C polymorphisms among different NSCLC types and controls (Table 4).

Association of CTLA-4 rs16840252 C>T, rs231775 G>A, rs3087243 G>A and rs733618 T>C Polymorphisms with NSCLC in a stratification analysis

Tables 57 summarized the genotype frequencies of CTLA-4 rs3087243 G>A, rs16840252 C>T and rs231775 G>A polymorphisms in the stratified analyses by gender, age, BMI, drinking and smoking status. We found no difference in genotype distribution of CTLA-4 rs16840252C>T, rs231775 G>A and rs3087243 G>A polymorphisms among NSCLC cases and the control subjects in any subgroup.

Table 5: Stratified analyses between CTLA-4 rs3087243 G>A polymorphism and NSCLC risk by sex, age, BMI, smoking status and alcohol consumption

Variable

CTLA-4 rs3087243 G>A (case/control)a

Adjusted ORb (95% CI); P

GG

GA

AA

additive model

homozygote model

Dominant model

Recessive model

Sex

Male

184/387

94/178

9/22

1.16 (0.84–1.61); P: 0.381

0.76 (0.33–1.75); P: 0.513

1.11 (0.81–1.52); P: 0.531

0.72 (0.31–1.66); P: 0.442

Female

160/299

66/134

8/9

0.89 (0.62–1.28); P: 0.541

1.66 (0.62–4.46); P: 0.312

0.94 (0.67–1.33); P: 0.734

1.72 (0.65–4.59); P: 0.277

Age

< 60

162/320

72/142

4/13

1.09 (0.76–1.55); P: 0.653

0.67 (0.21–2.20); P: 0.514

1.05 (0.74–1.49); P: 0.785

0.66 (0.20–2.13); P: 0.483

≥ 60

182/366

88/170

13/18

1.00 (0.72–1.39); P: 0.987

1.33 (0.62–2.86); P: 0.459

1.04 (0.76–1.42); P: 0.825

1.33 (0.63–2.84); P: 0.456

Smoking status

Never

207/556

100/249

10/22

1.06 (0.79–1.41); P: 0.708

1.21 (0.55–2.65); P: 0.632

1.07 (0.81–1.42); P: 0.647

1.19 (0.55–2.58); P: 0.665

Ever

137/130

60/63

7/9

0.90 (0.58–1.39); P: 0.630

0.77 (0.27–2.14); P: 0.610

0.88 (0.58–1.34); P: 0.556

0.79 (0.29–2.19); P: 0.653

Alcohol consumption

Never

292/636

138/285

14/27

1.01 (0.78–1.31); P: 0.927

1.04 (0.52–2.06); P: 0.916

1.01 (0.79–1.30); P: 0.918

1.03 (0.52–2.04); P: 0.926

Ever

52/50

22/27

3/4

0.96 (0.46–1.99); P: 0.911

0.76 (0.15–3.91); P: 0.747

0.93 (0.46–1.87); P: 0.842

0.77 (0.15–3.90); P: 0.756

BMI (kg/m2)

 < 24

223/350

104/178

10/18

0.91 (0.67–1.24); P: 0.546

0.95 (0.42–2.18); P: 0.911

0.91 (0.68–1.23); P: 0.546

0.98 (0.43–2.23); P: 0.967

 ≥ 24

121/336

56/134

7/13

1.21 (0.82–1.78); P: 0.343

1.13 (0.42–3.06); P: 0.806

1.20 (0.83–1.74); P: 0.342

1.07 (0.40–2.86); P: 0.985

a The genotyping was successful in 521 (100.00%) NSCLC cases, and 1029 (99.90%) controls for CTLA-4 rs3087243 G>A; b Adjusted for age, sex, BMI, smoking status and alcohol consumption (besides stratified factors accordingly) in a logistic regression model.

Table 6: Stratified analyses between CTLA-4 rs16840252 C>T polymorphism and NSCLC risk by sex, age, BMI, smoking status and alcohol consumption

Variable

CTLA-4 rs16840252 C>T (case/control)a

Adjusted ORb (95% CI); P

CC

CT

TT

Additive model

Homozygote model

Dominant model

Recessive model

Sex

Male

214/462

65/121

8/4

1.23 (0.85–1.78); P: 0.269

3.22 (0.90–11.54); P: 0.073

1.31 (0.92–1.87); P: 0.136

3.08 (0.86–10.99); P: 0.084

Female

186/329

46/107

2/6

0.70 (0.47–1.04); P: 0.074

0.39 (0.07–2.04); P: 0.262

0.68 (0.46–1.00); P: 0.052

0.43 (0.08–2.24); P: 0.315

Age

< 60

177/367

56/102

5/6

1.08 (0.73–1.60); P: 0.701

1.27 (0.37–4.37); P: 0.702

1.09 (0.75–1.60); P: 0.652

1.25 (0.37–4.28); P: 0.722

≥ 60

223/424

55/126

5/4

0.84 (0.58–1.22); P: 0.366

1.49 (0.38–5.92); P: 0.570

0.87 (0.61–1.25); P: 0.448

1.55 (0.39–6.13); P: 0.534

Smoking status

Never

245/631

70/188

2/8

0.94 (0.68–1.29); P: 0.704

0.46 (0.10–2.22); P: 0.333

0.91 (0.67–1.25); P: 0.579

0.47 (0.10–2.25); P: 0.340

Ever

155/160

41/40

8/2

1.04 (0.64–1.71); P: 0.874

4.10 (0.84–19.97); P: 0.081

1.18 (0.74–1.90); P: 0.485

4.07 (0.84–19.72); P: 0.082

Alcohol consumption

Never

343/731

91/210

10/7

0.96 (0.72–1.27); P: 0.759

2.04 (0.74–5.62); P: 0.167

1.00 (0.76–1.32); P: 0.996

2.06 (0.75–5.66); P: 0.162

Ever

57/60

20/18

0/3

1.05 (0.50–2.23); P: 0.890

-

0.90 (0.44–1.87); P: 0.785

-

BMI(kg/m2)

 < 24

256/410

74/127

7/9

0.98 (0.70–1.38); P: 0.901

1.02 (0.37–2.84); P: 0.974

0.98 (0.71–1.36); P: 0.906

1.02 (0.37–2.84); P: 0.967

 ≥ 24

144/381

37/101

3/1

0.94 (0.61–1.45); P: 0.773

5.70 (0.53–61.80); P: 0.152

0.99 (0.65–1.53); P: 0.977

5.77 (0.53–62.46); P: 0.149

aThe genotyping was successful in 521 (100.00%) NSCLC cases, and 1029 (99.90%) controls for CTLA-4 rs16840252 C>T; bAdjusted for age, sex, BMI, smoking status and alcohol consumption (besides stratified factors accordingly) in a logistic regression model.

Table 7: Stratified analyses between CTLA-4 rs231775 G>A polymorphism and NSCLC risk by sex, age, smoking status and alcohol consumption

Variable

CTLA-4 rs231775 G>A (case/control)a

Adjusted ORb (95% CI); P

GG

GA

AA

Additive model

Homozygote model

Dominant model

Recessive model

Sex

Male

132/292

123/241

31/54

1.16 (0.85–1.60); P: 0.355

1.20 (0.71–2.02); P: 0.500

1.18 (0.87–1.60); P: 0.286

1.12 (0.68–1.85); P: 0.655

Female

122/212

96/190

16/39

0.81 (0.58–1.14); P: 0.233

0.64 (0.34–1.20); P: 0.164

0.78 (0.56–1.08); P: 0.129

0.70 (0.38–1.29); P: 0.254

Age

< 60

114/237

103/195

21/43

1.13 (0.80–1.59); P: 0.485

1.01 (0.56–1.82); P: 0.980

1.11 (0.80–1.53); P: 0.550

0.95 (0.54–1.68); P: 0.863

≥ 60

140/267

116/236

26/50

0.89 (0.65–1.23); P: 0.487

0.88 (0.51–1.51); P: 0.640

0.90 (0.66–1.21); P: 0.469

0.93 (0.55–1.57); P: 0.781

Smoking status

Never

155/407

137/346

24/73

0.98 (0.74–1.30); P: 0.893

0.82 (0.50–1.37); P: 0.454

0.96 (0.73–1.25); P: 0.734

0.83 (0.51–1.36); P: 0.461

Ever

99/97

82/85

23/20

0.96 (0.63–1.45); P: 0.838

1.10 (0.57–2.15); P: 0.773

0.99 (0.67–1.46); P: 0.942

1.13 (0.59–2.13); P: 0.717

Alcohol consumption

Never

218/470

183/397

42/80

0.95 (0.74–1.22); P: 0.696

1.04 (0.68–1.60); P: 0.842

0.97 (0.77–1.23); P: 0.791

1.07 (0.71–1.61); P: 0.751

Ever

36/34

36/34

5/13

1.13 (0.56–2.29); P: 0.726

0.38 (0.12–1.20); P: 0.099

0.90 (0.47–1.74); P: 0.756

0.36 (0.12–1.08); P: 0.068

BMI(kg/m2)

 < 24

162/249

148/238

27/58

0.99 (0.74–1.34); P: 0.966

0.69 (0.41–1.16); P: 0.160

0.93 (0.70–1.23); P: 0.590

0.69 (0.42–1.13); P: 0.144

 ≥ 24

92/255

71/193

20/35

0.94 (0.64–1.36); P: 0.732

1.50 (0.81–2.80); P: 0.200

1.04 (0.73–1.47); P: 0.848

1.56 (0.86–2.85); P: 0.146

aThe genotyping was successful in 520 (99.81%) NSCLC cases, and 1028 (99.81%) controls for CTLA-4 rs231775 G>A; bAdjusted for age, sex, BMI, smoking status and alcohol consumption (besides stratified factors accordingly) in a logistic regression model;

As shown in Table 8, the stratified analyses suggested CTLA-4 rs733618 vatiants were correlated with the development of NSCLC in ≥ 60 years subgroup (TC vs. TT: adjusted odds ratio (OR) = 1.45, 95% confidence intervals (CI) = 1.04–2.02, P = 0.030) and even drinking subgroup (TC vs. TT: adjusted OR = 2.27, 95% CI = 1.11–4.60, P = 0.024 and TC/CC vs. TT: adjusted OR = 2.26, 95% CI = 1.15–4.43, P = 0.018).

Table 8: Stratified analyses between CTLA-4 rs733618 T>C polymorphism and NSCLC risk by sex, age, smoking status and alcohol consumption

Variable

CTLA-4 rs733618 T>C (case/control)a

Adjusted ORb (95% CI); P

TT

TC

CC

Additive model

Homozygote model

Dominant model

Recessive model

Sex

Male

94/211

149/271

44/105

1.28 (0.91–1.79); P: 0.156

1.06 (0.67–1.67); P: 0.810

1.22 (0.88–1.67); P: 0.234

0.91 (0.61–1.38); P: 0.669

Female

78/159

118/210

38/73

1.19 (0.83–1.70); P: 0.354

1.14 (0.70–1.85); P: 0.607

1.17 (0.84–1.65); P: 0.359

1.03 (0.67–1.59); P: 0.900

Age

< 60

81/163

114/226

43/86

1.01 (0.70–1.46); P: 0.948

1.05 (0.66–1.69); P: 0.836

1.02 (0.72–1.44); P: 0.911

1.04 (0.68–1.59); P: 0.847

≥ 60

91/207

153/255

39/92

1.45 (1.04–2.02); P: 0.030

1.09 (0.68–1.74); P: 0.729

1.35 (0.99–1.86); P: 0.061

0.87 (0.57–1.33); P: 0.530

Smoking status

Never

106/295

165/380

46/152

1.24 (0.92–1.66); P: 0.155

0.91 (0.61–1.37); P: 0.654

1.15 (0.87–1.52); P: 0.340

0.80 (0.56–1.16); P: 0.242

Ever

66/75

102/101

36/26

1.19 (0.77–1.83); P: 0.440

1.61 (0.88–2.97); P: 0.125

1.27 (0.84–1.93); P: 0.253

1.46 (0.84–2.53); P: 0.183

Alcohol consumption

Never

149/331

225/449

70/168

1.12 (0.87–1.46); P: 0.387

0.99 (0.69–1.41); P: 0.944

1.08 (0.85–1.39); P: 0.520

0.92 (0.67–1.27); P: 0.615

Ever

23/39

42/32

12/10

2.27 (1.11–4.60); P: 0.024

2.23 (0.81–6.13); P: 0.121

2.26 (1.15–4.43); P: 0.018

1.40 (0.56–3.54); P: 0.472

BMI (kg/m2)

 < 24

112/212

173/250

52/84

1.29 (0.9–1.76); P: 0.113

1.18 (0.77–1.81); P: 0.459

1.26 (0.94–1.69); P: 0.131

1.02 (0.69–1.51); P: 0.932

 ≥ 24

60/158

94/231

30/94

1.11 (0.75–1.65); P: 0.610

0.95 (0.56–1.60); P: 0.847

1.07 (0.73–1.55); P: 0.744

0.89 (0.56–1.42); P: 0.631

a The genotyping was successful in 521 (100.00%) NSCLC cases, and 1029 (99.90%) controls for CTLA-4 rs733618 T>C; b Adjusted for age, sex, BMI, smoking status and alcohol consumption (besides stratified factors accordingly) in a logistic regression model.

SNP haplotypes

Using SHESIS online haplotype construction software [(http://analysis.bio-x.cn/myAnalysis.php), Bio-X Inc., Shanghai, China] [13], we constructed four haplotypes (Table 9). We found there was no difference in haplotype distribution among NSCLC cases and the control subjects (Table 9).

Table 9: CTLA4 haplotype frequencies (%) in cases and controls and risk of NSCLC

Haplotypes

case (n = 1042)

control (n = 2060)

Crude OR (95% CI)

P

n

%

n (%)

%

C G G C

430

41.35

836

40.66

Reference

C G G T

285

27.40

595

28.94

0.93 (0.78–1.12)

0.445

C A A T

189

18.17

374

18.19

0.98 (0.80–1.21)

0.869

T A G T

123

11.83

239

11.62

1.00 (0.78–1.28)

0.996

Others

13

1.25

12

0.58

2.11 (0.95–4.66)

0.060

With the order of CTLA4 rs16840252 C>T, rs231775 G>A, rs3087243 G>A and rs733618 T>C in gene position.

The power of the present study (α= 0.05)

For CTLA-4 rs733618 T>C, the power value was 0.629 in additive model among ≥ 60 years subgroup, and 0.651 in additive model and 0.692 in dominant model among even drinking subgroup.

DISCUSSION

In generally, immune escape may be an important mechanism in the development of malignancies. The costimulatory signal related SNPs, common variants among individuals, may play important roles in the development of human cancers. In the present study, we explored the effect of CTLA-4 tagging SNPs in NSCLC among Eastern Chinese Han population for the first time. We found that CTLA-4 tagging polymorphisms might be not correlated with the susceptibility of overall NSCLC. The results of haplotype analysis suggested that there was no difference in haplotype distribution among NSCLC cases and the control subjects. However, in the stratified analyses by age, sex, BMI, alcohol use and smoking status, we found that CTLA-4 rs733618 T>C polymorphism was associated with the development of NSCLC in ≥ 60 years and even drinking subgroups.

NSCLC is a multifactorial disease which results from the interaction between individual’s genetic backgrounds and environmental risk factors. Previous studies have demonstrated that CTLA-4 rs733618 T>C polymorphism decreases a transcription factor binding site for nuclear factor 1 and weaken CTLA-4 expression on cell surface [14, 15]. Accumulating evidences suggested that CTLA-4 rs733618 T>C polymorphism might be associated with the increased risk of systemic lupus erythematosus [1619]. However, the relationship of CTLA-4 rs733618 T>C polymorphism with the development of cancer was conflicting. With an interest in the correlation of CTLA-4 rs733618 T>C polymorphism with cancer susceptibility, a case-control study explored the hypothesis that CTLA-4 rs733618 T>C polymorphism was associated with the etiology of NSCLC [10]; however, this study in an Iranian population established null association between CTLA-4 rs733618 T>C polymorphism and NSCLC. In the current study, we found that CTLA-4 rs733618 T>C polymorphism was associated with the development of NSCLC. In addition, a previous study in China conducted by Li et al., compared with the CTLA-4 rs733618 T allele, the C allele increased the risk of breast cancer [20]. Our findings might be supported by this study. To the best of our knowledge, the present case-control study was the first study to examine the potential relationship between CTLA-4 rs733618 T>C polymorphism and the development of NSCLC in Asians. However, due to the limited sample size, these potential association should be explained with very caution. In the future, more case-control studies with detailed lifestyle and environmental factors data should be conducted to explore these potential association.

We should acknowledge some limitations in this case-control study. Firstly, the NSCLC patients and control subjects were enrolled from local hospitals in Eastern China and might not fully represent the general Chinese Han population. Secondly, the moderate sample size of NSCLC cases might limit the statistical power to obtain a real assessment, especially in the stratified analyses. Further well-designed fine-mapping studies with large sample sizes are needed to confirm our findings. Thirdly, the clinical information on metastasis and survival of NSCLC could not be derived till now, which restricted further explores on the potential role of CTLA-4tagging polymorphisms in NSCLC progression and prognosis. Fourthly, the information about family cancer history was not collected. Finally, due to lack of the information about individual’s lifestyle, further determination for the interactions of gene-gene and gene-environment were not carried out. In consideration of the complex pathological process of NSCLC, these gene and environmental risk factors should not be ignored.

In summary, the present case-control study highlights that CTLA-4 rs733618 T>C polymorphism was associated with the development of NSCLC in ≥ 60 years and even drinking subgroups. In addition, a larger population-based fine-mapping study as well as detailed functional assessment are necessary to confirm or refute our findings.

MATERIALS AND METHODS

Study population and patient selection

A total of 521 Eastern Chinese Han population with NSCLC were included in this study. These NSCLC patients were diagnosed from January 2014 to December 2016 at the Affiliated People’s Hospital of Jiangsu University (Zhenjiang, China) and the Affiliated Union Hospital of Fujian Medical University (Fuzhou, China). Diagnosis of NSCLC was confirmed via histopathological examinations after operation or bronchoscope check. The selection criteria of NSCLC cases were: (1) sporadic NSCLC cases; (2) NSCLC patients without any treatment and (3) Eastern Chinese Han population. And the corresponding major exclusion criteria were: (1) autoimmune disease history; (2) NSCLC cases who received prior chemoradiotherapy and targeted threapy and (3) a history of another malignancy. Meanwhile, a total of 1,030 non-cancer controls were enrolled when they attended a routine physical examination in the Physical Examination Center of these hospitals. Additionally, the criteria for non-cancer controls selection was: (1) cancer-free subjects; (2) without autoimmune disease; (3) sex and gender matched to NSCLC patients; (4) unrelated subjects and (5) Eastern Chinese Han population. During recruitment, all study subjects signed the written informed consents following the Declaration of Helsinki. The information of risk factors and demographics was obtained by a pre-structured questionnaire. NSCLC cases and controls were well-matched in terms of age and sex (Table 1). Subjects who smoked at least one cigarette per day over 1 year were considered as ‘ever smokers’ [21], and those who drinked no less than three times a week for more than 6 months were defined as ‘ever drinkers’ [21]. The Ethical Committee of Fujian Medical University approved the study protocols (No. 2017KY019).

Selection of CTLA-4 tagging SNPs

The CTLA-4 tagging SNPs were selected through the Genome Variation Server (GVS) data (http://gvs.gs.washington.edu/GVS147/). The major included criterion were: (a) P value of HWE ≥ 0.05, (b) minor allele frequency (MAF) ≥ 0.05 (c) pairwise linkage disequilibrium (LD) r2 threshold of 0.8 between SNPs (r2 > 0.8) and (d) the call rate ≥ 95 % in the CHB cohort were included [22]. Finally, CTLA-4 rs3087243 G>A, rs16840252 C>T, rs733618 T>C and rs231775 G>A polymorphisms were eligible for study. Table 2 summarizes the information of the selected SNPs.

DNA extraction and genotyping

Two milliliters blood sample was donated by each enrolled subject and stored in Ethylenediamine tetraacetic acid (EDTA)-anticoagulation tube. DNA was elaborately extracted from lymphocytes by using the Promega kit (Promega, Madison, USA). We extracted DNA according to the manufacturer’s instruction (www.promega.com/protocols/). The genotypes of CTLA-4 rs16840252 C>T, rs231775 G>A, rs3087243 G>A and rs733618 T>C polymorphisms were evaluated using a custom-by-design 48-Plex SNPscan Kit (Genesky Biotechnologies Inc., Shanghai, China) as presented in previous studies [23, 24]. Briefly, we first denatured 150ng DNA sample at 98°C for 5 min. The ligation reaction was performed in an ABI 2720 thermal cycler. A 48-plex fluorescence PCR reaction was carried out for each ligation product. Then, in an ABI 3730XL sequencer, the obtained PCR products were separated and detected by using capillary electrophoresis. The raw data were analyzed by GeneMapper 4.1 software (Applied Biosystems, USA). A 4% randomly selected DNA sample was reciprocally verified by another laboratory technician, and the reproducibility was 100%.

Statistical analysis

Statistical analysis of this case-control study was done using the SAS 9.4 Statistical Package for Windows (SAS Institute, Cary, NC). A P < 0.05 (two–tailed) was accepted as the level of significance. The results of continuous variables were presented as mean ± standard deviation (SD). The mean values of age between NSCLC patients and non-cancer controls were calculated using the Student’s t-test. The deviation of HWE in controls was analyzed using an online Pearson’s two-sided χ2 test (http://ihg.gsf.de/cgi-bin/hw/hwa1.pl) [25]. The differences in smoking, drinking, demographic variables and the frequencies of genotypes between NSCLC cases and controls were also determined by χ2 test. The crude/adjusted ORs and the corresponding 95% CIs were harnessed to assess the relationship of CTLA-4 rs16840252 C>T, rs231775 G>A, rs3087243 G>A and rs733618 T>C polymorphism genotypes with NSCLC risk. SHESIS online haplotype construction software [(http://analysis.bio-x.cn/myAnalysis.php), Bio-X Inc., Shanghai, China] was used to obtain the haplotypes [13]. The power value of this study was calculated by Power and Sample Size software (http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/PowerSampleSize) [26].

ACKNOWLEDGMENTS

We appreciate all subjects who participated in this study. We wish to thank Dr. Yan Liu (Genesky Biotechnologies Inc., Shanghai, China) for technical support.

CONFLICTS OF INTEREST

The authors have no potential financial conflicts of interest.

GRANT SUPPORT

This study was supported in part by Young and Middle-aged Talent Training Project of Health Development Planning Commission in Fujian Province (2016-ZQN-25 and 2014-ZQN-JC-11), Program for New Century Excellent Talents in Fujian Province University (NCETFJ-2017B015), Medical Innovation Project of Fujian Province (2014-CX-15 and 2014-CX-18), Nursery Garden Project of Fujian Medical University (2015MP020) and Science and Technology Project of Fujian Province (2060203).

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