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. 2017 Apr 18;8(32):53873–53885. doi: 10.18632/oncotarget.17179

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

Ning Liu 1, Guang-Jing Liu 1, Juan Liu 2
PMCID: PMC5581156  PMID: 28881857

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.

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

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 [2–4]. 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, 5–9, 11–24]. 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.

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

Figure 1

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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
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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
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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, 5–9, 14, 16–24] 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
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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.

Figure 2. Forest plot for the association between TNF-α rs1800629 polymorphism and the risk of SCC under AA vs GG model.

Figure 2

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(A) Subgroup analyses based on ethnicity; (B) Subgroup analyses based on disease type.

Figure 3. Forest plot for the association between TNF-α rs1800629 polymorphism and the risk of SCC under AA vs GG+GA model.

Figure 3

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(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
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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 [11–13, 15–17]. 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
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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
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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, 5–9, 14, 16–24] 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 [11–13, 15–17]. 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 [35–37]. 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.

SUPPLEMENTARY FIGURES AND TABLES

Footnotes

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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