Oncotarget

Meta-Analysis:

Genetic association between TNF-α promoter polymorphism and susceptibility to squamous cell carcinoma, basal cell carcinoma, and melanoma: A meta-analysis

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Oncotarget. 2017; 8:53873-53885. https://doi.org/10.18632/oncotarget.17179

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Ning Liu _, Guang-Jing Liu and Juan Liu

Abstract

Ning Liu1, Guang-Jing Liu1 and Juan Liu2

1Department of Plastic and Burns Surgery, The First Center Hospital of Tianjin, Tianjin 300192, PR China

2Department of General Surgery, Bailou Hospital, Tianjin 300040, PR China

Correspondence to:

Ning Liu, email: nick_joan_tj@163.com

Keywords: TNF-α, single nucleotide polymorphism, SCC, BCC, melanoma

Received: December 19, 2016     Accepted: March 16, 2017     Published: April 18, 2017

ABSTRACT

Tumor necrosis factor-alpha (TNF-α) is a multifunctional pro-inflammatory cytokine that plays an important role in cancer development. We performed a meta-analysis to assess the relationship between single nucleotide polymorphisms in the TNF-α promoter region (rs1800629 and rs361525) and susceptibility to squamous cell carcinoma (SCC), basal cell carcinoma (BCC) and melanoma. After database retrieval, article selection, data extraction, and quality assessment, 20 articles comprising 4865 cases and 6329 controls were included in this study. rs1800629 was associated with an increased overall risk of SCC, lung SCC, and oral SCC in the AA vs G and AA vs GG+GA genetic models (all OR>1, Passociation<0.05). No increased risk of skin SCC, skin BCC or melanoma was observed (all Passociation>0.05). Rs361525 was not associated with overall SCC risk in the allele, heterozygote, dominant, recessive, or carrier model (all Passociation>0.05). Begg’s and Egger’s tests (PBegg>0.05; PEgger>0.05) demonstrated there was no significant publication bias. These data indicate that the AA genotype of TNF-α rs1800629, but not rs361525, is associated with an increased risk of SCC, suggesting it could potentially serve as a prognostic marker for predicting SCC risk.


INTRODUCTION

Tumor necrosis factor-alpha (TNF-α) gene, located on chromosome 6p21.231, consists of four exons [1, 2]. TNF-α protein, encoded by TNF-α gene, is associated with cellular differentiation, proliferation, apoptosis, inflammatory responses, insulin resistance, and tumorigenesis [24]. Several single nucleotide polymorphisms (SNPs), including rs1800629 (-308 G/A) and rs361525 (-238 G/A), have been identified in the promoter region of TNF-α gene [2].

The role of TNF-α gene mutations in the risk of squamous cell carcinoma (SCC) remains inconclusive. For instance, the rs1800629 polymorphism of TNF-α gene has been linked to the risk of esophageal SCC in northern Indian patients [5], but not in Kazakh patients [6]. TNF-α rs1800629 polymorphism has been associated with the risks of oral SCC in Taiwan [7], but not in northern Indian population, which has been linked with rs361525 polymorphism [8]. There was also no association between the rs1800629 polymorphism and lung SCC risk in the German population [9].

Skin cancer comprises cutaneous melanoma, skin SCC (SSCC), and skin basal cell carcinoma (SBCC) [10]. Allelic variants of TNF-α gene have been reported to contribute to the risk of skin cancer in certain populations. For example, the study by Rizzato et al. has indicated that TNF-α rs1800629 might affect the SBCC risk in Caucasian population [11]. The A allele or GA genotype of TNF-α gene rs1800629 polymorphism was also reported to influence the course of BCC in Polish population [12]. However, the role of TNF-α polymorphisms in skin cancer is still inconclusive. For example, Skov et al. reported that TNF-α release, but not rs1800629 polymorphism, was linked to the SBCC risk in Caucasian population [13]. To our knowledge, no meta-analysis has been previously performed to assess the link between TNF-α polymorphisms and the risk of skin cancer.

Therefore, in this study, we carried out a comprehensive systematic review and meta-analysis to determine the association of TNF-α polymorphisms and the risk of skin cancer and different SCC diseases.

RESULTS

Characteristics of studies included in meta-analysis

Six databases, including PUBMED, Web of Science (WOS), EMBASE, WANFANG, CNKI, and SCOPUS, were electronically searched on January 17th, 2017 to identify the eligible studies. The search details are shown in Supplementary Table 1. Flowchart of the search strategy and article selection for meta-analysis is shown in Figure 1. Briefly, 985 related articles were obtained from the above databases. After 241 duplicated articles were removed, 699 articles were excluded by screening the title and abstract. The eligibility of 45 full-text articles was then assessed, and 25 articles were excluded. The results are shown in Supplementary Table 2. Finally, 20 eligible articles with 4865 cases and 6329 controls were included for quantitative synthesis [1, 59, 1124]. All selected articles met the inclusion and exclusion criteria. We used the Newcastle-Ottawa Scale (NOS) to assess the quality of the studies. As shown in Supplementary Table 3, the NOS scores of all studies were equal to or greater than 7, indicating a high quality. After covariate adjustment in logistic regression, the characteristics and genotype distributions of included studies are shown in Tables 1 and 2.

The selection process of the meta-analysis.

Figure 1: The selection process of the meta-analysis.

Table 1: Characteristics of studies included in meta-analysis

First author
[Ref]

Year

Country

Ethnicity

Number

Source of controls

Age (mean value)

Genotyping assay

Gender (male %)

Case

Control

Case

Control

Case

Control

Cui [6]

2015

China

Asian

212

200

Population

52.5

54.4

PCR-RFLP

59.7%

51.8%

Flego [14]

2009

Croatia

Caucasian

113

230

Population

NA

NA

PCR-RFLP

NA

NA

Gu [15]

2009

USA

Caucasian

212

211

Population

NA

NA

ARMS-PCR

NA

NA

Gupta [1]

2008

India

Asian

94

133

Population

50.7

NA

PCR-RFLP

80.9%

NA

Huang [19]

2005

China

Asian

65

65

Population

65.0

55.0

PCR-RFLP

81.5%

77.0%

Kietthubthew [24]

2010

Thailand

Asian

97

152

Population

67.5

69.7

Taqman PCR

73.8%

55.4%

Kostic [17]

2013

Serbia

Caucasian

50#

60

Population

69.0#

NA

PCR-RFLP

70.0%#

NA

 

 

 

 

50&

60

Population

73.0&

NA

PCR-RFLP

40.0%&

NA

Liu [7]

2005

China

Asian

192

146

Population

54.1

52.0

PCR-RFLP

90.1%

89.0%

Oh [23]

2010

USA

Caucasian

75

839

Population

NA

NA

SNPlex assay

NA

NA

Rizzato [11]

2011

Mixed

Caucasian

506

515

Hospital

67.0

61.0

KASPar SNP genotyping

44.8%

51.4%

Seifart [9]

2005

Germany

Caucasian

40

242

Population

65.4

37.9

PCR-RFLP

NA

55.1%

Shih [20]

2006

China

Asian

83

205

Population

NA

62.8

PCR-RFLP+ sequencing

NA

66.3%

Singh [8]

2015

India

Asian

272

185

Population

47.7

43.1

PCR-RFLP

80.5%

76.8%

Skov [13]

2003

Denmark

Caucasian

191

107

Population

65.9

64.6

PCR-RFLP

59.2%

53.3%

Sobjanek [12]

2015

Poland

Caucasian

176

261

Population

68.9

NA

ARMS-PCR

46.6%

NA

Umar [5]

2013

India

Asian

290

311

Population

57.0

55.0

ARMS-PCR

72.8%

71.1%

Welsh [16]

2011

USA

Caucasian

894&

816

Population

58.7&

61.3

Taqman PCR

56.0%&

59.9%

 

 

 

 

681*

816

Population

64.1*

61.3

Taqman PCR

63.5%*

59.9%

Whiteman [18]

2010

Australia

Caucasian

207

1293

Population

NA

NA

Sequenom iPLEX

58.0%

66.0%

Yang [21]

2011

China

Asian

205

198

Population

49.3

48.9

Taqman PCR

100.0%

100.0%

Zhang [22]

2011

China

Asian

160

160

NA

NA

NA

PCR-SSP

NA

NA

Ref: reference; #: OSCC group; &: SBCC group; *: SSCC group; NA: not available; PCR-RFLP: polymerase chain reaction-restriction fragment length polymorphism; ARMS-PCR: amplification refractory mutation system-polymerase chain reaction; Taqman PCR: Taqman polymerase chain reaction; PCR-SSP: Polymerase chain reaction-sequence specific primer.

Table 2: The genotype data of studies included in meta-analysis

Group

SNP

First author [Ref]

Year

Case

Disease

Control

HWE

Total

G/G

G/A

A/A

Total

G/G

G/A

A/A

χ2

P

SSC

rs1800629

Flego [14]

2009

113

79

30

4

LSCC

230

171

53

6

0.59

0.44

 

 

Huang [19]

2005

65

64

1

0

LSCC

65

55

10

0

0.45

0.50

 

 

Oh [23]

2010

75

56

18

1

LSCC

839

632

194

13

0.19

0.67

 

 

Seifart [9]

2005

40

24

15

1

LSCC

242

171

67

4

0.79

0.37

 

 

Shih [20]

2006

83

50

25

8

LSCC

205

169

34

2

0.04

0.84

 

 

Gupta [1]

2008

94

61

23

10

OSCC

133

114

19

0

0.79

0.38

 

 

Kietthubthew [24]

2010

97

83

14&

-

OSCC

152

133

19&

-

NA

>0.05

 

 

Kostic [17]

2013

50

35

14

1

OSCC

60

39

21

0

2.70

0.10

 

 

Liu [7]

2005

192

175

16

1

OSCC

146

120

24

2

0.39

0.53

 

 

Singh [8]

2015

272

235

35

2

OSCC

185

164

20

1

0.21

0.65

 

 

Cui [6]

2015

212

150

57

5

ESCC

200

140

58

2

2.29

0.13

 

 

Umar [5]

2013

290

227

62

1

ESCC

311

268

42

1

0.23

0.63

 

 

Whiteman [18]

2010

207

128

71

8

ESCC

1293

842

403

48

0.00

0.98

 

 

Zhang [22]

2011

160

135

23

2

ESCC

160

140

18

2

2.36

0.12

 

 

Yang [21]

2011

205

180

23

2

OPSCC

198

155

43

0

2.94

0.09

 

 

Welsh [16]

2011

681

476

188

17

SSCC

816

571

223

22

0.00

0.97

SSC

rs361525

Kietthubthew [24]

2010

97

92

5&

-

OSCC

152

141

11&

-

NA

>0.05

 

 

Liu [7]

2005

192

188

4

0

OSCC

146

136

10

0

0.18

0.67

 

 

Singh [8]

2015

272

252

20

0

OSCC

185

180

5

0

0.03

0.85

 

 

Flego [14]

2009

113

108

5

0

LSCC

230

214

16

0

0.30

0.58

 

 

Shih [20]

2006

83

75

8

0

LSCC

205

161

44

0

2.96

0.09

 

 

Yang [21]

2011

205

200

5

0

OPSCC

198

187

11

0

0.16

0.69

skin cancer

rs1800629

Gu [15]

2009

212

156

46

10

melanoma

211

140

61

10

0.98

0.32

 

 

Kostic [17]

2013

50

29

21

0

SBCC

60

39

21

0

2.70

0.10

 

 

Rizzato [11]

2011

506

358

128

20

SBCC

515

390

117

8

0.05

0.82

 

 

Skov [13]

2003

191

133

49

9

SBCC

107

68

37

2

1.45

0.23

 

 

Sobjanek [12]

2015

176

134

41

1

SBCC

261

178

80

3

3.38

0.07

 

 

Welsh [16]

2011

894

612

265

17

SBCC

816

571

223

22

0.00

0.97

 

 

 

 

681

476

188

17

SSCC

816

571

223

22

0.00

0.97

Ref: reference; SCC: squamous cell carcinoma; SNP: single nucleotide polymorphisms; &: the number of GA+AA; NA: not available; LSCC: lung squamous cell carcinoma; OSCC: oral squamous cell carcinoma; ESCC: esophageal squamous cell carcinoma; OPSCC: oral and pharyngeal squamous cell carcinoma; SSCC: skin squamous cell carcinoma; SBCC: skin basal cell carcinomas; HWE: Hardy-Weinberg equilibrium.

Association between TNF-α rs1800629 polymorphism and the risk of SCC

Meta-analysis of 16 studies [1, 59, 14, 1624] comprising 2836 cases and 5235 controls was performed to analyze the association between TNF-α rs1800629 polymorphism and the risk of SCC under allele model (A vs G), homozygote model (AA vs GG), heterozygote model (GA vs GG), dominant model (GA+AA vs GG), recessive model (AA vs GG+GA), and carrier model (carrier A vs G). Pooled analysis data are shown in Table 3. Compared with the control group, no significant overall SCC risk was observed in the case group under A vs G model (OR=1.18, 95% CI=0.92~1.51, Passociation=0.192), GA vs GG model (OR=1.10, 95% CI=0.87~1.39, Passociation=0.439), GA+AA vs GG (OR=1.15, 95% CI=0.90~1.47, Passociation=0.255), or carrier A vs G model (OR=1.12, 95% CI=0.91~1.39, Passociation=0.287). However, an increased overall SCC risk was observed in AA vs GG model (OR=1.62, 95% CI=1.15~2.29, Passociation=0.006) and AA vs GG+GA model (OR=1.56, 95% CI=1.10~2.20, Passociation=0.012).

Table 3: Pooled analysis for the association between TNF-α rs1800629 polymorphism and the risk of SCC

Comparison

Subgroup

Test of association

Number

ORs

95% CIs

z

Passociation

Studies

Case

Control

A vs G

overall

1.18

0.92~1.51

1.30

0.192

15

2739

5083

 

Asian

1.18

0.73~1.92

0.67

0.501

9

1573

1603

 

Caucasian

1.07

0.93~1.22

0.94

0.349

6

1166

3480

 

ESCC

1.19

0.99~1.44

1.84

0.066

4

869

1964

 

LSCC

1.39

0.72~2.36

0.87

0.385

5

376

1581

 

OSCC

1.19

0.49~2.88

0.39

0.697

4

608

524

AA vs GG

overall

1.62

1.15~2.29

2.73

0.006

14

2674

5018

 

Asian

3.67

1.89~7.16

3.82

<0.001

8

1508

1538

 

Caucasian

1.08

0.70~1.66

0.33

0.743

6

1166

3480

 

ESCC

1.24

0.66~2.32

0.68

0.497

4

869

1964

 

LSCC

2.72

1.32~5.61

2.72

0.007

4

311

1516

 

OSCC

3.91

1.38~11.05

2.57

0.010

4

608

524

GA vs GG

overall

1.10

0.87~1.39

0.77

0.439

15

2739

5083

 

Asian

1.05

0.67~1.64

0.20

0.839

9

1573

1603

 

Caucasian

1.08

0.92~1.27

0.97

0.334

6

1166

3480

 

ESCC

1.23

0.95~1.61

1.55

0.120

4

869

1964

 

LSCC

1.27

0.74~2.18

0.85

0.393

5

376

1581

 

OSCC

0.99

0.51~1.95

0.02

0.986

4

608

524

GA+AA vs GG

overall

1.15

0.90~1.47

1.14

0.255

16

2836

5235

 

Asian

1.14

0.73~1.77

0.56

0.577

10

1670

1755

 

Caucasian

1.08

0.93~1.26

0.99

0.324

6

1166

3480

 

ESCC

1.24

0.97~1.57

1.73

0.084

4

869

1964

 

LSCC

1.31

0.71~2.40

0.86

0.389

5

376

1581

 

OSCC

1.12

0.58~2.15

0.33

0.738

5

705

676

AA vs GG+GA

overall

1.56

1.10~2.20

2.53

0.012

14

2674

5018

 

Asian

3.52

1.80~6.88

3.68

<0.001

8

1508

1538

 

Caucasian

1.05

0.68~1.62

0.22

0.829

6

1166

3480

 

ESCC

1.19

0.64~2.22

0.56

0.577

4

869

1964

 

LSCC

2.48

1.20~5.12

2.45

0.014

4

311

1516

 

OSCC

3.84

1.34~11.01

2.50

0.012

4

608

524

carrier A vs G

overall

1.12

0.91~1.39

1.06

0.287

15

2739

5083

 

Asian

1.12

0.74~1.69

0.52

0.604

9

1573

1603

 

Caucasian

1.06

0.91~1.22

0.73

0.468

6

1166

3480

 

ESCC

1.17

0.96~1.42

1.59

0.111

4

869

1964

 

LSCC

1.24

0.75~2.07

0.84

0.402

5

376

1581

 

OSCC

1.10

0.54~2.24

0.27

0.789

4

608

524

ESCC: esophageal squamous cell carcinoma; LSCC: lung squamous cell carcinoma; OSCC: oral squamous cell carcinoma; Ors: odd ratios; CIs: confidence intervals.

There are several types of SCC, including skin SCC (SSCC), esophageal SCC (ESCC), oral SCC (OSCC), and lung SCC (LSCC). We performed subgroup analyses of the above SCC types and different ethnicities under all models. As shown in Table 3 and Figure 2A, an increased overall SCC risk was observed in the Asian population under AA vs GG model (OR=3.67, 95% CI=1.89~7.16, Passociation<0.001). The increased risk of LSCC (OR=2.72, 95% CI=1.32~5.61, Passociation=0.007) and OSCC (OR=3.91, 95% CI=1.38~11.05, Passociation=0.010) was also observed (Table 3 and Figure 2B). Similar results were observed for the AA vs GG+GA model (Table 3 and Figure 3). No significant difference was found under other genetic models (all Passociation>0.05). These data indicate that the AA genotype of TNF-α rs1800629 polymorphism correlates with the higher susceptibility towards SCC.

Forest plot for the association between TNF-&#x03B1; rs1800629 polymorphism and the risk of SCC under AA vs GG model.

Figure 2: Forest plot for the association between TNF-α rs1800629 polymorphism and the risk of SCC under AA vs GG model. (A) Subgroup analyses based on ethnicity; (B) Subgroup analyses based on disease type.

Forest plot for the association between TNF-&#x03B1; rs1800629 polymorphism and the risk of SCC under AA vs GG+GA model.

Figure 3: Forest plot for the association between TNF-α rs1800629 polymorphism and the risk of SCC under AA vs GG+GA model. (A) Subgroup analyses based on ethnicity; (B) Subgroup analyses based on disease type.

Association between TNF-α rs361525 polymorphism and the risk of SCC

Meta-analysis of the relationship between TNF-α rs361525 polymorphism and susceptibility to SCC was also performed. Six studies comprising 962 cases and 1116 controls were analyzed [7, 8, 14, 20, 21, 24]. Data of pooled analysis indicated that there was no significant difference for overall SCC risk under all genetic models (Table 4 and Supplementary Figure 1; all Passociation>0.05). Subgroup analysis (Table 4) indicated a decreased LSCC risk under A vs G model (OR=0.49, 95% CI=0.26~0.90, Passociation=0.023), GA vs GG model (OR=0.46, 95% CI=0.25~0.87, Passociation=0.018); GA+AA vs GG (OR=0.46, 95% CI=0.25~0.87, Passociation=0.018), and carrier A vs G (OR=0.51, 95% CI=0.27~0.96, Passociation=0.037). However, only two case-control studies comprising 196 cases and 435 controls were included in the LSCC subgroup [14, 20]. No significant difference was found for other comparisons (all Passociation>0.05). These data indicate that TNF-α rs361525 polymorphism does not contribute to the risk of SCC.

Table 4: Pooled analysis for the association between TNF-α rs361525 polymorphism and the risk of SCC

Comparison

Subgroup

Test of association

Number

ORs

95% CIs

z

Passociation

Studies

Case

Control

A vs G

overall

0.63

0.29~1.35

1.19

0.235

5

865

964

 

Asian

0.63

0.24~1.68

0.93

0.354

4

752

734

 

LSCC

0.49

0.26~0.90

2.28

0.023

2

196

435

 

OSCC

0.93

0.10~8.36

0.06

0.949

2

464

331

GA vs GG

overall

0.62

0.28~1.36

1.20

0.231

5

865

964

 

Asian

0.61

0.22~1.70

0.94

0.348

4

752

734

 

LSCC

0.46

0.25~0.87

2.38

0.018

2

196

435

 

OSCC

0.93

0.10~8.78

0.06

0.949

2

464

331

GA+AA vs GG

overall

0.93

0.10~8.78

1.39

0.165

6

962

1116

 

Asian

0.63

0.28~1.41

1.13

0.259

5

849

886

 

LSCC

0.46

0.25~0.87

2.38

0.018

2

196

435

 

OSCC

0.86

0.23~3.22

0.23

0.818

3

561

483

carrier A vs G

overall

0.64

0.31~1.35

1.17

0.243

5

865

964

 

Asian

0.64

0.25~1.67

0.91

0.364

4

752

734

 

LSCC

0.51

0.27~0.96

2.09

0.037

2

196

435

 

OSCC

0.93

0.11~7.98

0.06

0.948

2

464

331

LSCC: lung squamous cell carcinoma; OSCC: oral squamous cell carcinoma; Ors: odd ratios; CIs: confidence intervals.

Association between TNF-α rs1800629 polymorphism and the risk of skin cancer

We then performed meta-analysis of the relationship between TNF-α rs1800629 and the risk of skin cancer, including SSCC, SBCC, and melanoma. Seven studies comprising 2710 cases and 2786 controls were included [1113, 1517]. Data of pooled analysis indicated no significant difference under all genetic models (all Passociation>0.05, Table 5 and Supplementary Figure 2). Subgroup analysis (based PB and SBCC) also showed no significant difference (Table 5). However, only one case-control study was included in the subgroup analysis of melanoma [15] and SSCC [16] (Table 5). These data suggest that TNF-α rs1800629 polymorphism does not have a significant correlation with the risk of skin cancer.

Table 5: Pooled analysis for the association between TNF-α rs1800629 polymorphism and the risk of skin cancer

Comparison

Subgroup

Test of association

Number

ORs

95% CIs

z

Passociation

Studies

Case

Control

A vs G

overall

1.01

0.91~1.12

0.24

0.814

7

2710

2786

 

Caucasian

1.01

0.91~1.12

0.24

0.814

7

2710

2786

 

PB

0.95

0.85~1.07

0.82

0.413

6

2204

2271

 

SBCC

1.05

0.93~1.20

0.82

0.411

5

1817

1759

 

melanoma

0.78

0.54~1.11

1.39

0.164

1

212

211

 

SSCC

1.00

0.82~1.21

0.04

0.964

1

681

816

AA vs GG

overall

1.09

0.78~1.54

0.52

0.603

6

2660

2726

 

Caucasian

1.09

0.78~1.54

0.52

0.603

6

2660

2726

 

PB

0.88

0.60~1.29

0.65

0.513

5

2154

2211

 

SBCC

1.24

0.79~1.95

0.95

0.340

4

1767

1699

 

melanoma

0.90

0.36~2.22

0.23

0.815

1

212

211

 

SSCC

0.93

0.49~1.77

0.23

0.818

1

681

816

GA vs GG

overall

0.99

0.88~1.12

0.08

0.933

7

2710

2786

 

Caucasian

0.99

0.88~1.12

0.08

0.933

7

2710

2786

 

PB

0.96

0.84~1.09

0.64

0.524

6

2204

2271

 

SBCC

1.03

0.89~1.20

0.41

0.680

5

1817

1759

 

melanoma

0.68

0.43~1.06

1.72

0.086

1

212

211

 

SSCC

1.01

0.80~1.27

0.10

0.923

1

681

816

GA+AA vs GG

overall

1.00

0.89~1.13

0.07

0.620

7

2710

2786

 

Caucasian

1.00

0.89~1.13

0.07

0.620

6

2710

2786

 

PB

0.95

0.84~1.08

0.75

0.453

5

2204

2271

 

SBCC

1.05

0.91~1.21

0.63

0.527

1

1817

1759

 

melanoma

0.71

0.47~1.08

1.62

0.105

1

212

211

 

SSCC

1.00

0.80~1.25

0.03

0.974

7

681

816

AA vs GG+GA

overall

1.10

0.79~1.55

0.57

0.569

6

2660

2726

 

Caucasian

1.10

0.79~1.55

0.57

0.569

6

2660

2726

 

PB

0.90

0.62~1.31

0.54

0.587

5

2154

2211

 

SBCC

1.23

0.79~1.93

0.92

0.359

4

1767

1699

 

melanoma

1.00

0.41~2.44

0.01

0.991

1

212

211

 

SSCC

0.92

0.49~1.75

0.24

0.809

1

681

816

carrier A vs G

overall

1.01

0.90~1.12

0.10

0.920

7

2710

2786

 

Caucasian

1.01

0.90~1.12

0.10

0.920

7

2710

2786

 

PB

0.96

0.85~1.09

0.59

0.553

6

2204

2271

 

SBCC

1.01

0.90~1.19

0.53

0.595

5

1817

1759

 

melanoma

0.78

0.53~1.17

1.18

0.236

1

212

211

 

SSCC

1.00

0.81~1.24

0.01

0.996

1

681

816

SBCC: skin basal cell carcinomas; SSCC: skin squamous cell carcinoma, Ors: odd ratios; CIs: confidence intervals.

Heterogeneity, publication bias and sensitivity analysis

Regarding the rs1800629 polymorphism and SCC risk, A vs G (I2 value of 77.5 % and Pheterogeneity <0.001), GA vs GG (I2=66.3 % and Pheterogeneity <0.001), GA+AA vs GG (I2=71.4 % and Pheterogeneity <0.001) and carrier A vs G (I2 =64.0 % and Pheterogeneity <0.001) data indicated a high degree of heterogeneity among the studies (Table 6). Thus, random-effect model was applied. In addition, fixed model was used in AA vs GG (I2=30.0 % and Pheterogeneity=0.137) and AA vs GG+GA contrast (I2 =23.2 % and Pheterogeneity =0.203, Table 6).

Table 6: The analysis of heterogeneity and publication bias

Group

SNP

Comparison

Heterogeneity

Model

Begg’s test

Egger’s test

I2

Pheterogeneity

z

PBegg

t

PEgger

SSC

rs1800629

A vs G

77.5%

<0.001

Random

0.49

0.621

0.07

0.942

 

 

AA vs GG

30.0%

0.137

Fixed

1.20

0.228

1.67

0.120

 

 

GA vs GG

66.3%

<0.001

Random

0.00

1.000

-0.42

0.683

 

 

GA+AA vs GG

71.4%

<0.001

Random

-0.05

1.000

-0.12

0.903

 

 

AA vs GG+GA

23.2%

0.203

Fixed

1.20

0.228

1.75

0.105

 

 

carrier A vs G

64.0 %

<0.001

Random

0.00

1.000

-0.17

0.864

SSC

rs361525

A vs G

66.2%

0.019

Random

0.73

0.462

0.05

0.962

 

 

GA vs GG

67.7%

0.015

Random

0.73

0.462

0.05

0.962

 

 

GA+AA vs GG

59.8%

0.029

Random

0.38

0.707

0.09

0.930

 

 

carrier A vs G

63.2%

0.028

Random

0.73

0.462

-0.03

0.978

skin cancer

rs1800629

A vs G

46.1%

0.084

Fixed

0.30

0.764

-0.68

0.528

 

 

AA vs GG

38.6%

0.148

Fixed

0.38

0.707

0.33

0.756

 

 

GA vs GG

45.4%

0.089

Fixed

0.30

0.764

-1.36

0.233

 

 

GA+AA vs GG

46.6%

0.081

Fixed

0.60

0.548

-1.04

0.347

 

 

AA vs GG+GA

38.1%

0.152

Fixed

0.38

0.707

0.49

0.650

 

 

carrier A vs G

7.4%

0.372

Fixed

0.30

0.764

-0.95

0.387

SCC: squamous cell carcinoma; SNP: single nucleotide polymorphisms.

For the rs361525 polymorphism and SCC risk, random-effect model was used for the overall SCC, due to the presence of overall significant heterogeneity (Table 6, all I2>50 %, Pheterogeneity<0.05). For the rs1800629 polymorphism and the risk of skin cancer, fixed-effect model was used for all models (Table 6, all I2 < 50 %, Pheterogeneity >0.05).

We also performed Begg’s and Egger’s tests to evaluate the potential publication bias among the included articles. The results indicate that publication bias can be ruled out for all comparisons (Table 6 and supplementary Figures 3-4; all PBegg>0.05, PEgger>0.05). Moreover, we conducted a sensitivity analysis and confirmed the stability of our results (Supplementary Figure 5 for rs1800629 and SCC risk; data no shown for others).

DISCUSSION

In the present study, 16 case-control studies of TNF-α rs1800629 polymorphism [1, 59, 14, 1624] and 6 case-control studies of rs361525 polymorphism [7, 8, 14, 20, 21, 24] were included in the meta-analysis of TNF-α polymorphism and the risk of SCC disease. We found that an increased overall SCC risk was associated with the rs1800629 polymorphisms in the Asian population under AA vs GG, and AA vs GG+GA models, but not A vs G, GA vs GG, GA+AA vs GG, or carrier A vs G models. A significant difference between LSCC/OSCC risks and the rs1800629 polymorphism was found under the AA vs GG, and AA vs GG+GA models; this corresponds with previous data on the link of rs1800629 and the risk of upper aero-digestive tract or head/neck SCC [25, 26]. However, in 2013, Chen et al performed a meta-analysis to analyze the association between rs1800629 and oral cancer, and observed a negative association between rs1800629 and OSCC [27]. Different selection criteria may contribute to this discrepancy. In our meta-analysis, two studies were excluded due to the requirement of Hardy-Weinberg equilibrium (HWE) or genotype data [28, 29]. Regarding the ESCC risk and rs1800629 polymorphism, the negative result was found under all genetic models, which was in line with the data of Luo et al [30]. The rs361525 allele was reported to be significantly increased in healthy controls compared with cancer patients, indicating a protective function [31]. Here, no significant difference was detected for rs361525 and overall SCC risks under all genetic models, which was partly in accordance with the data of Gao et al regarding head and neck SCC [26] and Zhou et al for overall cancer [32]. In addition, seven case-control studies in Caucasian population were included for the analysis of skin cancer [1113, 1517]. We failed to observe a significant association between TNF-α rs1800629 and skin cancer. In 2011, Nan et al also did not find any association between TNF-α gene variants and skin BCC or SCC in the Genome-Wide Association Studies (GWAS) from 2045 cases and 6013 controls of European population [33].

Although our results were validated by Begg’s and Egger’s tests, and by sensitivity analysis, the limitations in our meta-analysis should also be addressed. (1) Due to the limited number of studies published to date, only the common genetic polymorphisms of TNF-α, including rs1800629 and rs361525, were chosen. In addition, small sample size and/or limited genotype data in eligible articles affected our analysis. For example, there are two case-control studies of the association between TNF-α rs1800629 and melanoma risk [15, 34]. However, one study was excluded due to the departure of HWE [34]. The frequency data of GA+AA combined genotype and GG genotype were extracted in one OSCC study [24]. (2) A considerable heterogeneity was observed in the meta-analysis of rs1800629/rs361525 and the SSC risks. SCC has many different etiologies, and stratified analyses by every SCC disease type were not performed. The variations of clinical characteristics, ethnicity, geographical location, habits, gender, age and population feature were not fully considered. In spite of the use of random-effect model, a limited number of studies was included in the subgroup analysis. For example, only one case-control study was included for the rs1800629 and the susceptibility to a specific SCC disease, including SSCC [16] and OPSCC [21]. The subgroup analysis of LSCC and rs361525 was based on only 2 case-control studies [14, 20], and showed a positive correlation under A vs G model, GA vs GG model; GA+AA vs GG, and carrier A vs G model. It is possible that the GA genotype of rs361525 is associated with the decreased risk of LSCC. However, well-powered studies and stratified analyses by more factors are required to confirm our findings.

TNF-α is an important multifunctional pro-inflammatory cytokine, which is closely linked to the occurrence, progression, metastasis, prevention and therapy of many types of human cancer [3537]. Alterations in TNF-α gene expression or TNF-α cytokine release lead to a variety of cancers [2, 38]. Genetic variation has been considered as a disease susceptibility or resistance factor [2]. The rs1800629 G/A polymorphism, located in the promoter region (-308 site) of human TNF-α gene, can lead to the substitution from G common allele to A rare allele [2]. In vitro experiments showed that the “A” rare allele of rs1800629 could increase TNF-α transcription [39, 40]. The frequency of “A” allele also positively correlates with high TNF-α levels in patients with oral cancer [28]. TNF-α rs1800629 was found to be positively associated with distant metastases of triple negative breast cancer patients [36]. However, no association was found between rs1800629 and TNF-α gene expression in gastric cancer patients [41, 42]. Here, we observed a positive correlation between the AA genotype of rs1800629 and the risks of LSCC/OSCC. However, we did not find any significant association between the A allele and the SCC risks. It is possible that the “A” rare allele functions in an allele dosage-dependent manner. TNF-α was found to increase the efficiency of chemotherapy and radiotherapy against breast cancer cells [43]. The carriage of the A rare allele of rs1800629 may be involved in this process, through inducing TNF-α transcription and protein expression. It may be meaningful to analyze the effect of combined mutations of TNF-α and other genes, including TNF-beta and interleukin-6, on the carcinogenesis and SCC cancer therapy, since this may lead to the discovery of potential novel biomarkers for SCC.

In conclusion, our meta-analysis indicates that the AA genotype of TNF-α rs1800629 polymorphism may serve as a prognostic biomarker for SCC, especially for LSCC and OSCC in the Asian population. The rs361525 polymorphism does not seem to be a genetic risk factor for SCC. In conjunction with other studies, these results provide a scientific support for the prognostic value of TNF-α rs1800629 polymorphisms in predicting the SCC risk.

MATERIALS AND METHODS

Database retrieval

The related articles published before January 17th, 2017 were searched in the electronic databases, including PUBMED, WOS, EMBASE, WANFANG, CNKI and SCOPUS, without any language restrictions. The present meta-analysis followed “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) [44], as shown in Supplementary Table 4.

Article selection

Duplicated articles were removed by EndNote X7 software (Thomson Reuters). The following articles were excluded: 1) Reviews, theses, cases and trials; 2) Meeting abstracts and posters; 3) Cell or animal studies; 4) Other genes or diseases; 5) Meta-analyses; 6) Non-mutation data; 7) Data without detailed genotype; 8) Lack of control data; 9) Studies without specific oral cancer type information; 10) P value for HWE (PHWE) was less than 0.05; 11) Studies with unselected mutation sites. The selected articles provide sufficient information regarding the genotypes for TNF-α polymorphisms in case and control groups. PHWE values were obtained by the chi-squared test.

Data extraction and quality assessment

The authors extracted independently the following information: First author, publication year, country, ethnicity, number of cases/controls, source of controls, age (mean value), genotyping assay, gender (male %), SNP, genotype frequencies, disease type, χ2 and PHWE values in control group. Newcastle-Ottawa Scale (NOS) system (http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp) was used to assess the quality of the included studies; NOS score ≥7 indicates a high quality study.

Statistical analyses

Mantel-Haenszel statistics was used to estimate the values of pooled odd radios (ORs) and 95 % confidence intervals (CIs); Passociation value less than 0.05 was considered statistically significant. Six genetic models, including allele, homozygote, heterozygote, dominant recessive, or carrier models were used. Cochran Q statistic and I2 test were carried out to assess the potential heterogeneities between studies. When Pheterogeneity value of Cochran Q statistic > 0.05 or I2 value <50 %, the fixed-effect model was used. Otherwise, random-effect model was applied. To investigate the potential sources of heterogeneity, sensitivity analyses and subgroup analyses based on SCC disease type, ethnicity or source of controls were performed. Begg’s test with pseudo 95 % confidence limits and Egger’s test were also conducted to evaluate the potential publication bias. Stata/SE 12.0 (College Station, TX, USA) software was used for all statistical analyses.

Authors’ contributions

This study was conceptualized and designed by Ning Liu. Ning Liu, Guang-Jing Liu and Juan Liu extracted and analyzed the data. The manuscript was written by Ning Liu. All authors approved the final manuscript.

CONFLICTS OF INTEREST

The authors have declared that no conflicts of interest exists.

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