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. 2023 Apr 15;18(1):20220580. doi: 10.1515/biol-2022-0580

Association of interleukin-10 rs1800896, rs1800872, and interleukin-6 rs1800795 polymorphisms with squamous cell carcinoma risk: A meta-analysis

Zhenxia Wei 1,#, Xiaoping Su 2,3,#, Qiurui Hu 4, Yonghui Huang 4, Cuiping Li 2,3,✉, Xuanping Huang 1,2,✉
PMCID: PMC10106975  PMID: 37077342

Abstract

The relationship between interleukin (IL)-10 and IL-6 gene polymorphisms and squamous cell carcinoma (SCC) has been demonstrated but with inconsistent conclusions. The aim of this study was to evaluate the potential associations of IL gene polymorphisms and the SCC risk. PubMed, Cochrane Library, Web of Science, China National Knowledge Infrastructure, China Biomedical Database, WanFang, and China Science and Technology Journal Database databases were searched for articles reporting the correlations of IL-10 and IL-6 gene polymorphisms with the SCC risk. Odds ratio and 95% confidence interval were calculated using Stata Version 11.2. Meta-regression, sensitivity, and publication bias were analyzed. False-positive reporting probability and Bayesian measure of the false-discovery probability were used to explore the credibility of the calculation. Twenty-three articles were included. The IL-10 rs1800872 polymorphism showed a significant correlation with the SCC risk in the overall analysis. Studies pooled by ethnicity revealed that the IL-10 rs1800872 polymorphism reduced the SCC risk in the Caucasian population. The results of this study suggest that the IL-10 rs1800872 polymorphism may confer a genetic susceptibility to SCC, particularly oral SCC, in Caucasians. However, the IL-10 rs1800896 or IL-6 rs1800795 polymorphism was not significantly associated with the SCC risk.

Keywords: squamous cell carcinoma, interleukin-10, interleukin-6, genetic polymorphism, meta-analysis

1. Introduction

Cancer is a leading cause of death worldwide. According to the 2019 World Health Organization estimates, cancer was the leading cause of death in 112 of 183 countries and may surpass cardiovascular disease as the leading cause of death in many countries [1]. Squamous cell carcinoma (SCC) is a malignant tumor that arises in tissues and organs covered by the squamous epithelium, including the skin, oral cavity, esophagus, cervix, vagina, bronchus, urinary bladder, and renal pelvis [2–4]. Currently, combination therapy is the most common treatment modality for SCC [5,6]. Regardless of the treatment modality, patients develop severe scarring, increasing their financial burden and mental stress [7–9]. Extensive epidemiological and molecular biology studies have shown that chronic inflammation, unhealthy lifestyle, viral infections, and many other risk factors increase the SCC risk [10–12]; however, the specific role of these factors in tumor development has not been elucidated.

Processes such as inflammatory cell infiltration and malignant cell metastasis are common in cancer. Cytokines might play a critical role in these processes. Interleukin (IL) is an immunomodulatory cytokine involved in cell proliferation and apoptosis that can promote tumor immune escape and accelerate the progression of malignant tumors by inhibiting the anti-tumor immune response in the tumor microenvironment [13]. T-helper cytokines are utilized by IL-10 to regulate the growth and differentiation of natural immune cells, keratinocytes, and endothelial cells and inhibit the activation and effector functions of T cells [14]. IL-6 can participate in the differentiation regulation of B cells and promote the release of antibodies by B cells. It can be produced and secreted by tumor cells, involved in the proliferation and differentiation of malignant tumor cells, and expressed at high levels in serum and tumor tissues of most cancers [15]. IL-10 and IL-6 genes are located on chromosomes 10 and 7, respectively, and polymorphic in the region where the gene begins transcription. In recent years, the relationship between IL-10 and IL-6 gene polymorphisms and cancer have attracted great attention. Gene polymorphisms are closely related to changes in the IL expression level, leading to the occurrence of many cancers. Gene polymorphisms in the IL-10 and IL-6 promoter regions might affect the expression of gene-encoded proteins associated with the risk and prognosis of SCC [16,17]. The correlations of IL-10 rs1800896(-1082) and rs1800872(-592) and IL-6 rs1800795(-174) promoter-region polymorphisms with the SCC risk have been extensively studied. The polymorphisms are located near the transcription factor binding site and related to the pathogenesis of SCC, including cervical SCC [18]. However, the study results have been inconclusive and inconsistent [19,20].

The advantage of meta-analyses is reduction in random errors through quantitative and comprehensive analyses of all eligible research data. To date, no studies have focused on the correlations of IL-10 or IL-6 gene polymorphisms with the SCC risk. Therefore, the aim of this meta-analysis was to clarify the correlations of the IL-10 rs1800896 and rs1800872 and IL-6 rs1800795 gene polymorphisms with the SCC risk, including subgroup analyses by ethnicity, control source, and cancer type. The risk assessment was expected to be more detailed and accurate compared to previous studies. At the same time to provide ideas for cancer prevention and clinical treatment.

2. Materials and methods

2.1. Literature search

We extracted articles reporting the correlations of IL-10 rs1800896 and rs1800872 and IL-6 rs1800795 gene polymorphisms with the SCC risk from PubMed, Cochrane Library, Web of Science, China National Knowledge Infrastructure, China Biomedical Database, WanFang, and China Science and Technology Journal Database databases. The search terms were (“Interleukin-10” OR “IL-10” OR “IL10”) (“Interleukin-6” OR “IL-6” OR “IL6”) AND (“squamous cell carcinoma” OR “squamous cancer” OR “squamous cell tumor”) AND (“polymorphism” OR “genetic polymorphism” OR “polymorphisms”). Moreover, references of relevant studies, including meta-analyses, were searched manually to screen more studies for inclusion. We limited our search to human studies and did not specify any minimum number of cases or controls required or the year of publication. Articles published in English or Chinese were likely to be included.

2.2. Inclusion and exclusion criteria

Inclusion criteria were (1) case–control study reporting the associations of rs1800896, rs1800872, and rs1800795 polymorphisms in the promoter regions of IL-10 and IL-6 genes with the SCC risk, (2) information available regarding the distribution of cases and controls, allowing calculation of the odds ratio (OR) with 95% confidence interval (CI), and (3) full text available without duplication. Exclusion criteria were (1) original study design other than case–control or study without genotype data, (2) cancer not specified as SCC, (3) case report, (4) non-human study, (5) review (including meta-analysis), and (6) duplicate or overlapping data.

2.3. Study design and extracted information

We extracted the following information: surname of the first author, date of publication, country, participants’ ethnicity (Asian, Caucasian, or mixed descent), control source (population-, or hospital-based), cancer type (e.g., oral SCC), sample sizes of case and control groups, and detailed data on the genetics and genotyping of case and control studies. Two investigators (Z.W. and X.S.) independently extracted information based on the constituted standards, and a third investigator (Q.H.) reviewed the information. Disagreements were resolved through a discussion among the three investigators, ensuring more accurate data extraction. Investigators selected studies by reviewing the abstracts and full text based on the aforementioned eligibility criteria. We manually searched the references in selected studies, including meta-analyses, to screen more studies for inclusion. Among similar studies, those with the largest sample size or most recent publication were selected.

2.4. Statistical analysis

The correlations of IL-10 rs1800896 and rs1800872 and IL-6 rs1800795 gene polymorphisms with the SCC risk were estimated using 95% CI and OR. The significance of OR was determined with the z-test. A p-value <0.05 was set to indicate statistical significance. The combined OR was evaluated using four genetic models, including homozygote comparison (GG/AA; CC/AA; CC/GG), heterozygote comparison (AG/AA; AC/AA; GC/GG), dominant (AG + GG/AA; AC + CC/AA; GC + CC/GG), and recessive (GG/AA + AG; CC/AA + AC; CC/GG + GC) models in IL-10 rs1800896 and rs1800872 and IL-6 rs1800795. Subgroup analyses were conducted by cancer type, ethnicity, and control source. I 2 statistics were used to evaluate the heterogeneity among studies. Studies were homogenous at I 2 < 50%, and the fixed-effects model was used to combine the OR and 95% CI; otherwise, the random-effects model was used.

The occurrence of publication bias was determined with Egger’s and Begg’s tests. Stata version 11.2 (Stata Corporation, College Station, TX) was used, with the significance level set as a bilateral α of 0.05. A p-value <0.05 was considered to indicate publication bias.

Three predefined sources of heterogeneity were detected using meta-regression analyses: publication year, ethnicity, and control source. The rationality of the meta-analysis results was checked with sensitivity analyses. Excluding one study each time and combining the remaining studies revealed no substantial changes in the corresponding combined OR; thus, our results were considered to be statistically robust. In addition, the false-positive reporting probability (FPRP) was evaluated. We confirmed a FPRP <0.2 and appointed prior probabilities of 0.25, 0.1, 0.01, 0.001, and 0.0001 to examine an OR of 1.5 associated with the SCC risk. The results were significant at FPRP <0.2 [21]. The Bayesian measure of false-discovery probability (BFDP) was estimated using Excel computed tables to evaluate the reliability of the statistically significant associations [22]. The results were significant at BFDP <0.8.

3. Results

3.1. Study characteristics

After applying the eligibility criteria, 222 articles were retrieved. After reading the titles and abstracts, 154 articles were excluded for reasons such as irrelevant study topic, meta-analysis or review design, and Greek language. After reviewing the full text of the remaining 67 articles, 44 articles without genotypic data of patients with SCC were excluded. Finally, 23 articles met all requirements for inclusion in this study. Figure 1 shows a flowchart of the study selection process.

Figure 1.

Figure 1

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Whole flow diagram of the study selection process.

Fifteen studies, involving 3,311 cases and 4,756 controls, investigated the association of the IL-10 rs1800896 polymorphism with the SCC risk [16,19,20,23–34]. Eleven studies, involving 3,069 cases and 4,265 controls, investigated the association of the IL-10 rs1800872 polymorphism with the SCC risk [16,20,24,26,27,29,30,34–37]. Eight studies, involving 1,315 cases and 1,905 controls, investigated the association of the IL-6 rs1800795 polymorphism with the SCC risk [20,33,38–43]. Nine and six of the 15 included studies on IL-10 rs1800896 involved the Caucasian and Asian ethnicities, respectively. Control sources were hospital-based in six studies and population-based in nine studies. A total of six cancer types were reported. Oral and cervical SCC were reported in five and three articles, respectively. Lung, head and neck, laryngeal, and esophageal SCC were reported in one article each. Table 1 shows the characteristics of the included studies on IL-10 rs1800896.

Table 1.

Characteristics of included case–control studies on IL-10 rs1800896 polymorphism and squamous cell carcinoma

No. Author Year Ethnicity Country Cancer type Source of control Sample size of case Sample size of control Genotype distribution MAF Genotyping method
Case Control
AA AG GG AA AG GG
1 Pasvenskaite et al. 2021 Caucsian Lithuania Laryngeal SCC HB 300 533 70 163 67 148 269 116 0.48 PCR
2 Mao et al. 2021 Asian China Oral SCC HB 125 110 109 16 0 98 12 0 0.06 PCR
3 Chen et al. 2020 Asian China Esophageal SCC HB 721 1,208 625 84 4 1,061 136 4 0.06 PCR
4 Goud et al. 2019 Asian Malaysia Oral SCC PB 41 48 37 4 0 39 9 0 0.07 PCR-RFLP
5 Sharma et al. 2018 Caucsian India Oral SCC PB 100 150 51 36 13 100 50 0 0.22 PCR
6 Hussain et al. 2016 Caucsian India Oral SCC HB 232 221 69 158 5 127 93 1 0.29 PCR-RFLP
7 Torres-Poveda et al. 2016 Caucsian Mexico Cervical SCC PB 200 200 121 70 9 110 78 12 0.24 PCR
8 Zhou et al. 2014 Asian China Laryngeal SCC PB 146 119 115 26 5 107 11 1 0.09 PCR-RFLP
9 Torres-Poveda et al. 2012 Caucsian Mexico Cervical SCC HB 204 166 125 66 13 92 62 12 0.24 PCR
10 Jeong et al. 2010 Asian Korea Head and neck SCC HB 290 358 238 38 2 304 45 1 0.07 PCR
11 Vairaktaris et al. 2008 Caucsian Greece and Germany Oral SCC PB 144 141 46 96 2 81 60 0 0.28 PCR
12 Guo et al. 2005 Asian China Esophageal SCC PB 203 443 117 81 5 267 164 12 0.22 PCR-RFLP
13 Zoodsma et al. 2005 Caucsian Holland Cervical SCC PB 512 606 121 242 149 130 307 169 0.47 PCR
14 Seifart et al. 2005 Caucsian Germany Lung SCC PB 40 243 13 17 10 86 115 42 0.42 PCR
15 El-Omar et al. 2003 Caucsian United States Esophageal SCC PB 53 210 16 28 9 59 103 48 0.47 PCR-TaqMan
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Abbreviations: PB, population-based; HB, hospital-based; SCC, squamous cell carcinoma; MAF, minor allele frequency.

Seven and four of the 11 included studies on IL-10 rs1800872 involved the Caucasian and Asian populations, respectively. Control sources were population-based in six studies and hospital-based in five studies. A total of four cancer types were reported. Esophageal, cervical, oral, and lung SCC were reported in four, three, two, and two articles, respectively. Table 2 shows the characteristics of the included studies on IL-10 rs1800872.

Table 2.

Characteristics of included case–control studies on IL-10 rs1800872 polymorphism and squamous cell carcinoma

No. Author Year Ethnicity Country Cancer type Source of control Sample size of case Sample size of control Genotype distribution MAF Genotyping method
Case Control
AA AC CC AA AC CC
1 Pasvenskaite et al. 2021 Caucsian Lithuania Laryngeal SCC HB 300 533 21 102 177 33 179 321 0.23 PCR
2 Chen et al. 2020 Asian China Esophageal SCC HB 721 1,208 349 301 65 550 523 128 0.31 PCR
3 Sharma et al. 2018 Caucsian India Oral SCC PB 100 150 25 54 21 18 88 44 0.47 PCR
4 Torres-Poveda et al. 2016 Caucsian Mexico Cervical SCC HB 200 200 58 98 44 30 85 85 0.47 PCR
5 Singh et al. 2017 Caucsian India Oral SCC PB 250 250 39 168 43 14 173 63 0.45 PCR-RFLP
6 Zhou et al. 2014 Asian China Laryngeal SCC PB 146 119 63 70 13 64 39 16 0.32 PCR-RFLP
7 Sun et al. 2013 Asian China Esophageal SCC HB 380 380 162 163 31 191 141 33 0.28 PCR
8 Torres-Poveda et al. 2012 Caucsian Mexico Cervical SCC HB 204 166 49 105 50 30 70 66 0.45 PCR
9 Wang et al. 2006 Asian China Esophageal SCC PB 203 443 95 88 20 182 196 65 0.35 PCR-RFLP
10 Zoodsma et al. 2005 Caucsian Holland Cervical SCC PB 512 606 25 172 300 26 175 405 0.20 PCR
11 El-Omar et al. 2003 Caucsian United States Esophageal SCC PB 53 210 3 15 35 13 70 127 0.22 PCR-TaqMan
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Abbreviations: PB, population-based; HB, hospital-based; SCC, squamous cell carcinoma; MAF, minor allele frequency.

Six and two of the nine included studies on IL-6 rs1800795 involved the Caucasian and Asian populations, respectively. Control sources were population-based in six studies and hospital-based in two studies. A total of five cancer types were reported. Oral and laryngeal cancer were reported in three and two articles, respectively. Lung, cervical, and esophageal SCC were reported in article each. Table 3 shows the characteristics of the included studies on IL-6 rs1800795.

Table 3.

Characteristics of included case–control studies on IL-10 rs1800795 polymorphism and squamous cell carcinoma

No. Author Year Ethnicity Country Cancer type Source of control Sample size of case Sample size of control Genotype distribution MAF Genotyping method
Case Control
GG GC CC GG GC CC
1 Pasvenskaite et al. 2020 Caucsian Lithuania Laryngeal SCC HB 352 538 69 182 102 132 261 145 0.47 PCR
2 Candan Demiröz Abakay 2020 Caucsian Turkey Laryngeal SCC PB 80 50 38 31 11 29 15 5 0.30 PCR
3 Fernández-Mateos et al. 2019 Caucsian Spanish Oral SCC PB 70 70 12 33 25 8 23 39 0.34 PCR
4 Shi et al. 2014 Asian China Cervical SCC HB 418 518 131 201 86 181 259 78 0.42 PCR-RFLP
5 Gaur et al. 2011 Asian India Oral SCC PB 140 120 98 35 7 65 41 14 0.23 PCR-RFLP
6 Vairaktaris et al. 2008 Caucsian Greece and Germany Oral SCC PB 162 156 42 102 18 90 60 6 0.33 PCR
7 Seifart et al. 2005 Caucsian Germany Lung SCC PB 40 243 17 19 4 90 107 46 0.40 PCR
8 El-Omar et al. 2003 Caucsian United States Esophageal SCC PB 53 210 13 6 5 83 98 28 0.32 PCR-TaqMan
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Abbreviations: PB, population-based; HB, hospital-based; SCC, squamous cell carcinoma; MAF, minor allele frequency.

3.2. Quantitative synthesis

Table 4 shows the relationship between the IL-10 rs1800896 gene polymorphism and the SCC risk. Data of 15 studies combined and analyzed according to four models revealed no significant association between the IL-10 rs1800896 gene polymorphism and the SCC risk (GG/AA: OR = 1.18, 95% CI: 0.96–1.45, p = 0.109; AG/AA: OR = 1.27, 95% CI: 0.99–1.61, p = 0.057; AG + GG/AA: OR = 1.27, 95% CI: 0.99–1.61, p = 0.057; GG/AA + AG: OR = 1.14, 95% CI: 0.96–1.35, p = 0.149; Figure 2). Subgroup analyses by ethnicity and control source revealed no significant correlation between the IL-10 rs800896 gene polymorphism and the SCC risk in any model. The subgroup analysis by cancer type revealed that the IL-10 rs1800896 gene polymorphism significantly increased the oral SCC risk in all four models (GG/AA: OR = 19.09, 95% CI: 4.48–81.45, p = 0.000; AG/AA: OR = 1.76, 95% CI: 1.05–2.95, p = 0.045; AG + GG/AA: OR = 1.94, 95% CI: 1.21–3.13, p = 0.006; GG/AA + AG: OR = 12.70, 95% CI: 3.09–52.16, p = 0.000). In addition, the IL-10 rs1800896 gene polymorphism increased the risk of laryngeal SCC (AG/AA: OR = 1.41, 95% CI: 1.04–1.93, p = 0.030), esophageal SCC (AG + GG/AA: OR = 0.48, 95% CI: 0.32–0.72, p = 0.000). Two gene models (AG/AA: I 2 = 74.10%, p = 0.000; AG + GG/AA: I 2 = 76.80%, p = 0.000). No positive results were found other than those aforementioned.

Table 4.

Stratified analyses of the IL-10 rs1800896 polymorphism on squamous cell carcinoma risk

Comparative model No. Z p OR (95% CI) Heterogeneity Z Begg's test t Egger's test FPRP p-value FPRP statistical power FPRP prior probability BEDP prior probability
Heterogeneity chi-squared p I 2 0.25 0.10 0.01 0.001 0.0001 0.010 0.001 0.000
GG/AA
Overall 15 1.60 0.109 1.182(0.963–1.451) 20.09 0.065 40.30% 2.99 0.003 2.78 0.018 0.110 0.989 0.250 0.500 0.917 0.991 0.999 0.994 0.999 1.000
Ethnicity
Caucasian 9 0.72 0.470 1.166(0.769–1.769) 17.35 0.027 53.90% 1.56 0.118 2.04 0.080 0.470 0.882 0.615 0.828 0.981 0.998 1.000 0.996 1.000 1.000
Asian 6 1.25 0.212 1.571(0.773–3.191) 1.99 0.575 0.00% 1.02 0.308 3.09 0.091 0.212 0.449 0.586 0.809 0.979 0.998 1.000 0.991 0.999 1.000
Source of control
HB 6 1.36 0.175 1.269(0.899–1.792) 4.94 0.293 19.10% 1.22 0.221 1.33 0.276 0.176 0.829 0.389 0.657 0.955 0.995 1.000 0.993 0.999 1.000
PB 9 0.71 0.477 1.214(0.711–2.073) 14.52 0.043 51.80% 1.86 0.063 2.21 0.069 0.477 0.781 0.647 0.846 0.984 0.998 1.000 0.995 0.999 1.000
Cancer types
Laryngeal SCC 2 1.29 0.195 1.305(0.872–1.951) 1.43 0.233 29.80% 0.00 1.000 . . 0.194 0.751 0.437 0.700 0.962 0.996 1.000 0.993 0.999 1.000
Oral SCC 5 3.98 0.000 19.093(4.475–81.451) 1.17 0.556 0.00% 0.00 1.000 0.44 0.734 0.000 0.000 0.408 0.674 0.958 0.996 1.000 0.873 0.986 1.000
Cervical SCC 3 0.74 0.459 0.896(0.670–1.199) 0.54 0.765 0.00% 1.04 0.296 −3.59 0.173 0.460 0.977 0.586 0.809 0.979 0.998 1.000 0.997 1.000 1.000
Esophageal SCC 3 0.29 0.771 0.913(0.495–1.686) 1.14 0.566 0.00% 1.04 0.296 44.50 0.014 0.771 0.842 0.733 0.889 0.989 0.999 1.000 0.995 0.999 1.000
Genotyping method
PCR 10 1.42 0.157 1.171(0.941–1.458) 13.47 0.097 40.60% 2.19 0.029 2.240 0.06 0.158 0.987 0.325 0.591 0.941 0.994 0.999 0.995 0.999 1.000
PCR-RFLP 4 1.29 0.199 2.670(0.597–11.936) 4.38 0.112 54.30% 1.04 0.296 4.690 0.134 0.199 0.225 0.726 0.888 0.989 0.999 1.000 0.989 0.999 1.000
AG/AA
Overall 15 1.68 0.094 1.226(0.990–1.556) 54.14 0.000 74.10% 0.40 0.692 0.27 0.792 0.094 0.951 0.228 0.470 0.907 0.990 0.999 0.992 0.999 1.000
Ethnicity
Caucasian 9 1.31 0.190 1.277(0.886–1.840) 48.04 0.000 83.30% 0.31 0.754 0.28 0.790 0.189 0.806 0.414 0.679 0.959 0.996 1.000 0.993 0.999 1.000
Asian 6 1.17 0.243 1.116(0.928–1.342) 5.18 0.395 3.40% 0.00 1.000 0.11 0.921 0.243 0.999 0.422 0.687 0.960 0.996 1.000 0.997 1.000 1.000
Source of control
HB 6 1.23 0.220 1.282(0.862–1.905) 27.41 0.000 81.80% 0.00 1.000 0.01 0.991 0.219 0.781 0.457 0.716 0.965 0.996 1.000 0.993 0.999 1.000
PB 9 1.06 0.291 1.183(0.866–1.616) 25.35 0.001 68.40% 0.10 0.917 0.49 0.638 0.291 0.932 0.484 0.737 0.969 0.997 1.000 0.995 1.000 1.000
Cancer types
Laryngeal SCC 2 2.17 0.030 1.414(1.035–1.931) 1.64 0.201 39.00% 0.00 1.000 . . 0.029 0.645 0.120 0.291 0.818 0.978 0.998 0.976 0.998 1.000
Oral SCC 5 2.12 0.034 1.757(1.045–2.954) 14.70 0.005 72.80% 1.71 0.086 −3.83 0.031 0.033 0.275 0.267 0.523 0.923 0.992 0.999 0.973 0.997 1.000
Cervical SCC 3 1.79 0.073 0.823(0.666–1.018) 0.09 0.958 0.00% 1.04 0.296 −2.96 0.207 0.073 0.974 0.183 0.401 0.881 0.987 0.999 0.991 0.999 1.000
Esophageal SCC 3 0.65 0.513 1.073(0.869–1.325) 0.14 0.932 0.00% 0.00 1.000 −0.36 0.782 0.513 0.999 0.606 0.822 0.981 0.988 1.000 0.998 1.000 1.000
Genotyping method
PCR 10 0.98 0.325 1.119(0.894–1.400) 23.43 0.005 61.60% 0.89 0.371 0.79 0.455 0.325 0.995 0.495 0.746 0.970 0.997 1.000 0.997 1.000 1.000
PCR-RFLP 4 1.22 0.222 1.555(0.766–3.158) 19.37 0.000 84.50% −0.34 1.000 −0.3 0.791 0.221 0.460 0.591 0.813 0.979 0.998 1.000 0.991 0.999 1.000
AG+GG/AA
Overall 15 1.91 0.057 1.266(0.993–1.614) 60.24 0.000 76.80% 0.99 0.322 0.51 0.619 0.057 0.914 0.157 0.359 0.860 0.984 0.998 0.988 0.999 1.000
Ethnicity
Caucasian 9 1.51 0.131 1.328(0.919–1.917) 53.02 0.000 84.90% 0.52 0.602 0.58 0.582 0.130 0.742 0.344 0.612 0.945 0.994 0.999 0.991 0.999 1.000
Asian 6 1.40 0.162 1.138(0.950–1.363) 6.33 0.276 21.00% 0.38 0.707 0.16 0.879 0.160 0.999 0.325 0.591 0.941 0.994 0.999 0.996 1.000 1.000
Source of control
HB 6 1.27 0.204 1.291(0.870–1.916) 28.60 0.000 82.50% 0.00 1.000 0.08 0.940 0.205 0.772 0.443 0.705 0.963 0.996 1.000 0.993 0.999 1.000
PB 9 1.31 0.190 1.266(0.993–1.614) 30.76 0.000 74.00% 0.73 0.466 0.67 0.523 0.057 0.914 0.157 0.359 0.860 0.984 0.998 0.988 0.999 1.000
Cancer types
Laryngeal SCC 2 0.25 0.802 1.202(0.286–5.053) 12.77 0.000 92.20% 0.00 1.000 . . 0.802 0.619 0.795 0.921 0.992 0.999 1.000 0.992 0.999 1.000
Oral SCC 5 2.42 0.016 1.374(1.062–1.777) 6.12 0.191 34.60% 1.71 0.086 −2.21 0.114 0.015 0.748 0.058 0.157 0.672 0.954 0.995 0.964 0.996 1.000
Cervical SCC 3 1.12 0.263 0.892(0.730–1.090) 1.09 0.579 0.00% 1.04 0.296 1.31 0.416 0.264 0.998 0.442 0.704 0.963 0.996 1.000 0.997 1.000 1.000
Esophageal SCC 3 3.58 0.000 0.482(0.323–0.719) 5.29 0.071 62.20% 1.04 0.296 −4.80 0.131 0.000 0.056 0.018 0.053 0.381 0.861 0.984 0.505 0.911 0.999
Genotyping method
PCR 10 1.32 0.187 1.171(0.926–1.481) 27.73 0.001 67.50% 1.79 0.074 1.11 0.3 0.187 0.981 0.365 0.633 0.950 0.995 0.999 0.995 1.000 1.000
PCR-RFLP 4 1.26 0.209 1.591(0.772–3.280) 21.03 0.000 85.70% −0.34 1.000 −0.23 0.838 0.208 0.437 0.589 0.811 0.979 0.998 1.000 0.990 0.999 1.000
GG/AA+AG
Overall 15 1.44 0.149 1.136(0.955–1.352) 15.65 0.208 23.30% 2.01 0.044 2.37 0.037 0.151 0.999 0.312 0.576 0.937 0.993 0.999 0.996 1.000 1.000
Ethnicity
Caucasian 9 1.21 0.228 1.117(0.933–1.336) 13.10 0.109 38.90% 0.94 0.348 1.66 0.140 0.226 0.999 0.404 0.670 0.957 0.996 1.000 0.997 1.000 1.000
Asian 6 1.12 0.263 1.496(0.739–3.031) 1.94 0.584 0.00% 1.02 0.308 3.23 0.084 0.264 0.503 0.611 0.825 0.981 0.998 1.000 0.992 0.999 1.000
Source of control
HB 6 0.60 0.548 1.096(0.813–1.476) 3.08 0.545 0.00% 1.22 0.221 2.15 0.121 0.546 0.981 0.626 0.834 0.982 0.998 1.000 0.997 1.000 1.000
PB 9 1.34 0.179 1.158(0.935–1.434) 12.67 0.081 44.70% 1.36 0.174 1.50 0.184 0.179 0.991 0.351 0.619 0.947 0.994 0.999 0.995 1.000 1.000
Cancer types
Laryngeal SCC 2 0.48 0.633 1.085(0.777–1.514) 1.58 0.209 36.50% 0.00 1.000 . . 0.631 0.972 0.661 0.854 0.985 0.998 1.000 0.997 1.000 1.000
Oral SCC 5 3.53 0.000 12.702(3.093–52.156) 1.94 0.380 0.00% 0.00 1.000 0.49 0.709 0.000 0.002 0.454 0.714 0.965 0.996 1.000 0.923 0.992 1.000
Cervical SCC 3 0.13 0.895 1.016(0.800–1.290) 0.74 0.691 0.00% 1.04 0.296 −3.83 0.163 0.896 0.999 0.729 0.890 0.989 0.999 1.000 0.998 1.000 1.000
Esophageal SCC 3 0.51 0.610 0.863(0.489–1.521) 1.21 0.545 0.00% 1.04 0.296 5.60 0.112 0.610 0.814 0.692 0.871 0.987 0.999 1.000 0.995 0.999 1.000
Genotyping method
PCR 10 1.45 0.148 1.145(0.953–1.376) 1.88 0.209 26.50% 1.15 0.251 2.13 0.071 0.148 0.998 0.309 0.573 0.937 0.993 0.999 0.995 1.000 1.000
PCR-RFLP 4 1.32 0.18 1.696(0.774–3.717) 2.93 0.231 31.70% 0.00 1.000 16.96 0.037 0.186 0.380 0.596 0.816 0.980 0.998 1.000 0.990 0.999 1.000
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Abbreviations: OR, odds ratio; CI, confidence interval; PB, population-based; HB, hospital-based; SCC, squamous cell carcinoma; FPRP, false positive report probability; BFDP, Bayesian false discovery probability. The results in bold represented that there was statistically significant noteworthiness at 0.2 level by FPRP or 0.8 level by BFDP calculations.

Figure 2.

Figure 2

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Statistical relationship between IL-10 rs1800896 gene polymorphism and squamous cell carcinoma susceptibility in four models: (a) GG vs AA, (b) AG vs AA, (c) AG + GG vs AA, and (d) GG vs AA + AG. Abbreviations: OR: odds ratio; CI: confidence interval.

Table 5 summarizes the relationship between the IL-10 rs1800872 gene polymorphism and the SCC risk. The overall analysis revealed that the IL-10 rs1800872 gene polymorphism and the SCC risk were significantly associated in both models (CC/AA: OR = 0.59, 95% CI: 0.44–0.81, p = 0.001; CC/AA + AC: OR = 0.71, 95% CI: 0.59–0.86, p = 0.000; Figure 3). However, the IL-10 rs1800872 gene polymorphism and the SCC risk showed no significant association in the other two models (AC/AA: OR = 0.87, 95% CI: 0.69–1.11, p = 0.266; AC + CC/AA: OR = 0.78, 95% CI: 0.60–1.00, p = 0.050; Figure 3). The subgroup analysis by ethnicity revealed a significantly increased SCC risk in Caucasians in all models (CC/AA: OR = 0.48, 95% CI: 0.31–0.74, p = 0.001; AC/AA: OR = 0.68, 95% CI: 0.54–0.86, p = 0.001; AC + CC/A: OR = 0.58, 95% CI: 0.46–0.72, p = 0.000; CC/AA + AC: OR = 0.68, 95% CI: 0.52–0.89, p = 0.004). Similarly, Asians showed an increased risk (CC/AA + AC: OR = 0.80, 95% CI: 0.63–1.00, p = 0.049). The subgroup analysis by control source revealed a significantly increased SCC risk among population-based controls in both models (CC/AA: OR = 0.55, 95% CI: 0.41–0.73, p = 0.000; CC/AA + AC: OR = 0.73, 95% CI: 0.61–0.87, p = 0.000) and a significantly increased risk among hospital-based controls in only one model (CC/AA + AC: OR = 0.69, 95% CI: 0.48–0.98, p = 0.036). The subgroup analysis by cancer type revealed a significantly increased oral SCC risk (CC/AA: OR = 0.28, 95% CI: 0.17–0.49, p = 0.000; AC/AA: OR = 0.39, 95% CI: 0.24–0.62, p = 0.000; AC + CC/AA: OR = 0.36, 95% CI: 0.23–0.57, p = 0.000; CC/AA + AC: OR = 0.63, 95% CI: 0.44–0.89, p = 0.000). The risk of cervical SCC was also significantly increased (CC/AA: OR = 0.46, 95% CI: 0.25–0.84, p = 0.011; AC + CC/AA: OR = 0.62, 95% CI: 0.46–0.83, p = 0.001; CC/AA + AC: OR = 0.54, 95% CI: 0.35–0.83, p = 0.005). All gene models showed heterogeneity (CC/AA: I 2 = 61.40%, p = 0.002; AC/AA: I 2 = 65.10%, p = 0.001; AC + CC/AA: I 2 = 72.20%, p = 0.000; CC/AA + AC: I 2 = 51.80%, p = 0.023).

Table 5.

Stratified analyses of the IL-10 rs1800872 polymorphism on squamous cell carcinoma risk

Comparative model No. Z p OR (95% CI) Heterogeneity Z Begg's test t Egger's test FPRP p-value FPRP Statistical power FPRP prior probability BEDP prior probability
Heterogeneity chi-squared p I 2 0.250 0.10 0.01 0.001 0.0001 0.01 0.001 0.000001
CC/AA
Overall 11 3.34 0.001 0.595(0.438–0.807) 27.83 0.002 61.40% 0.93 0.350 −0.83 0.428 0.001 0.232 0.011 0.032 0.264 0.783 0.973 0.677 0.955 1.000
Ethnicity
Caucasian 7 3.35 0.001 0.481(0.314–0.738) 16.55 0.011 63.80% 0.00 1.000 0.14 0.895 0.001 0.068 0.035 0.097 0.541 0.923 0.992 0.676 0.955 1.000
Asian 4 1.77 0.076 0.807(0.636–1.023) 2.57 0.462 0.00% −0.34 1.000 0.01 0.991 0.076 0.943 0.196 0.422 0.889 0.988 0.999 0.991 0.999 1.000
Source of control
HB 5 1.93 0.054 0.635(0.401–1.007) 16.71 0.002 76.10% 0.73 0.462 −0.73 0.518 0.054 0.418 0.278 0.536 0.927 0.992 0.999 0.981 0.998 1.000
PB 6 4.12 0.000 0.550(0.413–0.730) 9.85 0.080 49.20% 0.38 0.707 0.14 0.895 0.000 0.091 0.001 0.003 0.036 0.276 0.792 0.114 0.566 0.992
Cancer types
Laryngeal SCC 2 0.67 0.505 0.852(0.533–1.363) 0.01 0.924 0.00% 0.00 1.000 – – 0.504 0.847 0.641 0.843 0.983 0.998 1.000 0.995 1.000 1.000
Oral SCC 2 4.61 0.000 0.284(0.167–0.485) 0.38 0.538 0.00% 0.00 1.000 – – 0.000 0.001 0.013 0.039 0.310 0.819 0.978 0.040 0.298 0.977
Cervical SCC 3 2.54 0.011 0.458(0.250–0.836) 6.60 0.037 69.70% 0.00 1.000 −0.15 0.906 0.011 0.111 0.229 0.472 0.908 0.990 0.999 0.945 0.994 1.000
Esophageal SCC 4 1.63 0.102 0.817(0.640–1.041) 2.90 0.408 0.00% −0.34 1.000 0.40 0.727 0.102 0.950 0.244 0.492 0.914 0.991 0.999 0.992 0.999 1.000
Genotyping method
PCR 7 2.60 0.009 0.613(0.423–0.887) 19.63 0.003 69.40% 1.50 0.133 −1.19 0.287 0.009 0.328 0.079 0.206 0.740 0.966 0.997 0.938 0.993 1.000
PCR-RFLP 3 2.1 0.036 0.490(0.252–0.953) 5.54 0.063 63.90% 0.00 1.000 −0.07 0.953 0.035 0.182 0.369 0.637 0.951 0.995 0.999 0.974 0.997 1.000
AC/AA
Overall 11 1.11 0.266 0.872(0.686–1.110) 28.64 0.001 65.10% 0.93 0.350 −0.84 0.423 0.266 0.985 0.447 0.708 0.964 0.996 1.000 0.996 1.000 1.000
Ethnicity
Caucasian 7 3.24 0.001 0.679(0.537–0.858) 9.85 0.131 39.10% 0.60 0.548 −0.16 0.880 0.001 0.561 0.006 0.019 0.173 0.678 0.955 0.757 0.969 1.000
Asian 4 0.77 0.438 1.126(0.835–1.518) 10.38 0.016 71.10% 1.02 0.308 1.37 0.304 0.436 0.970 0.574 0.802 0.978 0.998 1.000 0.996 1.000 1.000
Source of control
HB 5 0.43 0.670 0.946(0.734–1.220) 8.51 0.075 53.00% −0.24 1.000 −0.37 0.736 0.669 0.996 0.668 0.858 0.985 0.999 1.000 0.998 1.000 1.000
PB 6 0.94 0.349 0.792(0.487–1.289) 19.28 0.002 74.10% 0.38 0.707 −0.47 0.663 0.348 0.756 0.580 0.806 0.979 0.998 1.000 0.994 0.999 1.000
Cancer types
Laryngeal SCC 2 0.73 0.463 1.298(0.647–2.603) 3.07 0.080 67.40% 0.00 1.000 – – 0.463 0.658 0.678 0.863 0.986 0.999 1.000 0.993 0.999 1.000
Oral SCC 2 3.95 0.000 0.387(0.241–0.620) 0.24 0.624 0.00% 0.00 1.000 – – 0.000 0.012 0.020 0.056 0.397 0.869 0.985 0.258 0.778 0.997
Cervical SCC 3 1.32 0.188 0.808(0.588–1.110) 2.10 0.350 4.60% 1.04 0.296 1.57 0.361 0.188 0.882 0.390 0.658 0.955 0.995 1.000 0.994 0.999 1.000
Esophageal SCC 4 0.15 0.878 0.988(0.852–1.146) 5.54 0.136 45.80% 0.34 0.734 0.21 0.850 0.873 1.000 0.724 0.887 0.989 0.999 1.000 0.999 1.000 1.000
Genotyping method
PCR 7 0.94 0.347 0.889(0.695–1.137) 13.24 0.039 54.70% 0.60 0.548 −0.92 0.402 0.348 0.989 0.514 0.760 0.972 0.997 1.000 0.997 1.000 1.000
PCR-RFLP 3 0.44 0.658 0.835(0.376–1.853) 15.33 0.000 86.90% 0.00 1.000 −0.26 0.835 0.657 0.710 0.735 0.893 0.989 0.999 1.000 0.994 0.999 1.000
AC+CC/AA
Overall 11 1.96 0.050 0.775(0.601–1.000) 35.98 0.000 72.20% 1.09 0.276 −1.17 0.272 0.050 0.877 0.146 0.339 0.850 0.983 0.998 0.986 0.999 1.000
Ethnicity
Caucasian 7 4.83 0.000 0.579(0.463–0.722) 10.94 0.090 45.20% 0.30 0.764 0.29 0.784 0.000 0.105 0.000 0.000 0.001 0.011 0.104 0.006 0.058 0.861
Asian 4 0.36 0.717 1.052(0.799–1.384) 9.77 0.021 69.30% 0.34 0.734 1.02 0.415 0.717 0.994 0.684 0.867 0.986 0.999 1.000 0.997 1.000 1.000
Source of control
HB 5 1.17 0.241 0.822(0.592–1.141) 15.91 0.003 74.90% −0.24 1.000 −0.78 0.495 0.241 0.895 0.447 0.708 0.964 0.996 1.000 0.995 0.999 1.000
PB 6 1.38 0.167 0.724(0.457–1.144) 18.78 0.002 73.40% 0.00 1.000 −0.46 0.669 0.166 0.638 0.439 0.701 0.963 0.996 1.000 0.991 0.999 1.000
Cancer types
Laryngeal SCC 2 0.60 0.548 1.182(0.685–2.041) 2.15 0.143 53.50% 0.00 1.000 – – 0.549 0.804 0.972 0.860 0.985 0.999 1.000 0.995 0.999 1.000
Oral SCC 2 4.37 0.000 0.358(0.226–0.567) 0.27 0.606 0.00% 0.00 1.000 – – 0.000 0.004 0.009 0.026 0.227 0.748 0.967 0.069 0.426 0.987
Cervical SCC 3 3.20 0.001 0.615(0.457–0.829) 3.44 0.179 41.80% 1.04 0.296 1.59 0.357 0.001 0.298 0.014 0.041 0.320 0.826 0.979 0.767 0.971 1.000
Esophageal SCC 4 0.20 0.843 0.975(0.762–1.249) 6.44 0.092 53.40% 0.34 0.734 0.30 0.793 0.841 0.999 0.716 0.883 0.988 0.999 1.000 0.998 1.000 1.000
Genotyping method
PCR 7 1.78 0.076 0.767(0.573–1.028) 21.01 0.002 71.40% 0.60 0.548 −1.37 0.229 0.075 0.826 0.216 0.453 0.901 0.989 0.999 0.989 0.999 1.000
PCR-RFLP 3 0.75 0.451 0.751(0.357–1.581) 14.70 0.001 86.40% 0.00 1.000 −0.36 0.778 0.451 0.623 0.685 0.867 0.986 0.999 1.000 0.993 0.999 1.000
CC/AA+AC
Overall 11 3.59 0.000 0.711(0.590–0.857) 20.75 0.023 51.80% 0.00 1.000 −0.72 0.487 0.000 0.750 0.001 0.004 0.043 0.314 0.821 0.539 0.922 0.999
Ethnicity
Caucasian 7 2.85 0.004 0.677(0.518–0.885) 18.33 0.005 67.30% 0.00 1.000 −0.64 0.553 0.004 0.545 0.023 0.067 0.440 0.888 0.988 0.900 0.989 1.000
Asian 4 1.97 0.049 0.796(0.634–0.999) 1.66 0.647 0.00% 0.34 0.734 −0.87 0.474 0.049 0.937 0.136 0.320 0.838 0.981 0.998 0.987 0.999 1.000
Source of control
HB 5 2.09 0.036 0.687(0.484–0.976) 16.55 0.002 75.80% 0.24 0.806 −1.15 0.332 0.036 0.567 0.161 0.365 0.863 0.985 0.998 0.978 0.998 1.000
PB 6 3.54 0.000 0.729(0.612–0.868) 4.19 0.522 0.00% 0.75 0.452 −0.05 0.961 0.000 0.842 0.001 0.004 0.043 0.314 0.821 0.575 0.932 0.999
Cancer types
Laryngeal SCC 2 0.73 0.463 0.904(0.690–1.184) 0.95 0.329 0.00% 0.00 1.000 – – 0.463 0.987 0.585 0.809 0.979 0.998 1.000 0.997 1.000 1.000
Oral SCC 2 2.62 0.000 0.625(0.440–0.888) 0.01 0.920 0.00% 0.00 1.000 – – 0.009 0.359 0.068 0.179 0.706 0.960 0.996 0.935 0.993 1.000
Cervical SCC 3 2.79 0.005 0.538(0.348–0.831) 8.23 0.016 75.70% 0.00 1.000 −3.11 0.198 0.005 0.167 0.085 0.219 0.755 0.969 0.997 0.902 0.989 1.000
Esophageal SCC 4 1.34 0.181 0.860(0.689–1.073) 2.91 0.406 0.00% 0.34 0.734 0.48 0.678 0.182 0.988 0.355 0.623 0.948 0.995 0.999 0.995 1.000 1.000
Genotyping method
PCR 7 2.93 0.003 0.700(0.551–0.888) 16.81 0.01 64.30% 0.30 0.764 −1.10 0.321 0.003 0.656 0.015 0.043 0.332 0.834 0.980 0.883 0.987 1.000
PCR-RFLP 3 2.98 0.003 0.625(0.459–0.852) 0.01 0.996 0.00% 0.00 1.000 0.67 0.626 0.003 0.342 0.025 0.072 0.461 0.896 0.989 0.859 0.984 1.000
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Abbreviations: OR, odds ratio; CI, confidence interval; PB, population-based; HB, hospital-based; SCC, squamous cell carcinoma; FPRP, false positive report probability; BFDP, Bayesian false discovery probability. The results in bold represented that there was statistically significant noteworthiness at 0.2 level by FPRP or 0.8 level by BFDP calculations.

Figure 3.

Figure 3

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Statistical relationship between IL-10 rs1800872 gene polymorphism and squamous cell carcinoma susceptibility in four models: (a) CC vs AA, (b) AC vs AA, (c) AC + CC vs AA, and (d) CC vs AA + AC.

Table 6 shows the association between the IL-6 rs1800795 gene polymorphism and the SCC risk. The association between the IL-6 rs1800795 gene polymorphism and the SCC risk was explored in all models (CC/GG: OR = 1.11, 95% CI: 0.66–1.87, p = 0.702; GC/GG: OR = 1.13, 95% CI: 0.74–1.73, p = 0.58; GC + CC/GG: OR = 1.09, 95% CI: 0.70–1.70, p = 0.697; CC/GG + GC: OR = 1.01, 95% CI: 0.67–1.53, p = 0.958; Figure 4). The subgroup analysis by ethnicity showed no significant association in any model. The subgroup analysis by control source revealed a significantly increased risk among hospital-based controls in both models (CC/GG: OR = 1.43, 95% CI: 1.09–1.88, p = 0.009; GC + CC/GG: OR = 1.24, 95% CI: 1.01–1.53, p = 0.044). The subgroup analysis by cancer type revealed a significantly increased laryngeal SCC risk in one model (GC + CC/GG: OR = 1.38, 95% CI: 1.02–1.86, p = 0.035). All gene models showed heterogeneity (CC/GG: I 2 = 73.40%, p = 0.000; GC/GG: I 2 = 79.40%, p = 0.000; GC + CC/GG: I 2 = 83.40%, p = 0.000; CC/GG + GC: I 2 = 68.00%, p = 0.003). No other results were statistically significant.

Table 6.

Stratified analyses of the IL-6 rs1800795 polymorphism on squamous cell carcinoma risk

Comparative model No. Z p OR (95% CI) Heterogeneity Z Begg’s test t Egger’s test FPRP p-value FPRP statistical power FPRP prior probability BEDP prior probability
Heterogeneity chi-squared p I 2 0.25 0.10 0.01 0.001 0.0001 0.01 0.001 0.000001
CC/GG
Overall 8 0.38 0.702 1.107(0.657–1.867) 26.31 0.000 73.40% 0.12 0.902 −0.75 0.479 0.703 0.873 0.707 0.879 0.988 0.999 1.000 0.995 1.000 1.000
Ethnicity
Caucasian 6 0.61 0.54 1.244(0.619–2.497) 17.88 0.003 72.00% 0.00 1.000 −0.2 0.849 0.539 0.701 0.698 0.874 0.987 0.999 1.000 0.994 0.999 1.000
Asian 2 0.36 0.717 0.759(0.171–3.368) 8.39 0.004 88.10% 0.00 1.000 – – 0.717 0.568 0.791 0.919 0.992 0.999 1.000 0.992 0.999 1.000
Source of control
HB 2 2.6 0.009 1.433(1.093–1.878) 0.20 0.654 0.00% 0.00 1.000 – – 0.009 0.630 0.042 0.115 0.589 0.935 0.993 0.944 0.994 1.000
PB 6 0.08 0.938 0.963(0.374–2.479) 23.75 0.000 78.90% 0.75 0.452 0.07 0.944 0.938 0.777 0.784 0.916 0.992 0.999 1.000 0.993 0.999 1.000
Cancer types
Oral SCC 3 0.03 0.974 0.969(0.150–6.274) 21.24 0.000 90.60% 0.00 1.000 0.13 0.916 0.974 0.653 0.817 0.931 0.993 0.999 1.000 0.991 0.999 1.000
Laryngeal SCC 2 1.71 0.087 1.377(0.955–1.985) 0.13 0.723 0.00% 0.00 1.000 – – 0.086 0.677 0.277 0.535 0.927 0.992 0.999 0.988 0.999 1.000
Genotyping method
PCR 5 0.54 0.587 1.258(0.549–2.884) 17.81 0.001 77.50% −0.24 1.000 −0.15 0.892 0.587 0.661 0.727 0.889 0.989 0.999 1.000 0.993 0.999 1.000
PCR-RFLP 2 0.36 0.717 0.759(0.171–3.368) 8.39 0.004 88.10% 0.00 1.000 – – 0.72 0.568 0.791 0.919 0.992 0.999 1.000 0.988 0.999 1.000
GC/GG
Overall 8 0.55 0.584 1.127(0.735–1.730) 34.02 0.000 79.40% −0.12 1.000 −0.48 0.647 0.585 0.904 0.660 0.853 0.985 0.998 1.000 0.996 1.000 1.000
Ethnicity
Caucasian 6 0.87 0.386 1.282(0.731–2.250) 22.44 0.000 77.70% 0.38 0.707 −0.93 0.404 0.387 0.708 0.621 0.831 0.982 0.998 1.000 0.994 0.999 1.000
Asian 2 0.65 0.516 0.814(0.438–1.514) 4.06 0.044 75.30% 0.00 1.000 – – 0.516 0.736 0.678 0.863 0.986 0.999 1.000 0.994 0.999 1.000
Source of control
HB 2 1.41 0.158 1.174(0.940–1.467) 0.89 0.345 0.00% 0.00 1.000 – – 0.158 0.984 0.325 0.591 0.941 0.994 0.999 0.995 0.999 1.000
PB 6 0.15 0.880 1.059(0.503–2.230) 32.88 0.00 84.80% 0.00 1.000 −1.14 0.319 0.880 0.820 0.763 0.906 0.990 0.999 1.000 0.997 1.000 1.000
Cancer types
Oral SCC 3 0.36 0.718 1.276(0.339–4.807) 25.62 0.00 92.20% 0.00 1.000 −0.38 0.769 0.719 0.594 0.784 0.916 0.992 0.999 1.000 0.992 0.999 1.000
Laryngeal SCC 2 1.95 0.051 1.371(0.998–1.884) 0.15 0.702 0.00% 0.00 1.000 – – 0.052 0.710 0.179 0.396 0.878 0.986 0.999 0.984 0.998 1.000
Genotyping method
PCR 5 1.63 0.103 1.554(0.915–2.640) 15.12 0.004 73.50% 0.24 0.806 −0.33 0.763 0.103 0.448 0.408 0.674 0.958 0.996 1.000 0.987 0.999 1.000
PCR-RFLP 2 0.65 0.516 0.814(0.438–1.514) 4.06 0.044 75.30% 0.00 1.000 – – 0.515 0.736 0.678 0.863 0.986 0.999 1.000 0.994 0.999 1.000
GC + CC/GG
Overall 8 0.39 0.697 1.092(0.701–1.700) 42.10 0.000 83.40% 0.62 0.536 −0.66 0.536 0.697 0.920 0.694 0.872 0.989 0.999 1.000 0.996 1.000 1.000
Ethnicity
Caucasian 6 0.68 0.494 1.225(0.685–2.191) 27.83 0.000 82.00% 0.75 0.452 −0.96 0.393 0.494 0.753 0.663 0.855 0.985 0.998 1.000 0.994 0.999 1.000
Asian 2 0.55 0.584 1.092(0.701–1.700) 8.15 0.004 87.70% 0.00 1.000 – – 0.697 0.920 0.694 0.872 0.987 0.999 1.000 0.996 1.000 1.000
Source of control
HB 2 2.01 0.044 1.241(1.006–1.532) 0.35 0.556 0.00% 0.00 1.000 – – 0.045 0.961 0.122 0.294 0.821 0.979 0.998 0.987 0.999 1.000
PB 6 0.00 0.997 1.002(0.461–2.178) 41.69 0.000 88.00% 0.38 0.707 −0.99 0.380 0.996 0.846 0.779 0.914 0.991 0.999 1.000 0.994 0.999 1.000
Cancer types
Oral SCC 3 0.12 0.906 1.093(0.250–4.792) 35.92 0.000 94.40% 0.00 1.000 −0.42 0.747 0.906 0.663 0.804 0.925 0.993 0.999 1.000 0.992 0.999 1.000
Laryngeal SCC 2 2.11 0.035 1.380(1.024–1.860) 0.2 0.655 0.00% 0.00 1.000 – – 0.034 0.708 0.127 0.304 0.828 0.980 0.998 0.979 0.998 1.000
Genotyping method
PCR 5 1.10 0.269 1.408(0.767–2.586) 22.43 0.000 82.20% 0.24 0.806 −0.52 0.639 0.269 0.581 0.582 0.807 0.979 0.998 1.000 0.992 0.999 1.000
PCR-RFLP 2 0.55 0.584 0.794(0.348–1.811) 8.15 0.004 87.70% 0.00 1.000 – – 0.583 0.661 0.726 0.888 0.989 0.999 1.000 0.993 0.999 1.000
CC/GG + GC
Overall 8 0.05 0.958 1.011(0.667–1.532) 21.84 0.003 68.00% 0.12 0.902 −0.60 0.570 0.959 0.969 0.748 0.899 0.990 0.999 1.000 0.996 1.000 1.000
Ethnicity
Caucasian 6 0.19 0.846 1.055(0.614–1.811) 14.49 0.013 65.50% 0.00 1.000 0.07 0.945 0.846 0.899 0.738 0.894 0.989 0.999 1.000 0.995 1.000 1.000
Asian 2 0.30 0.765 0.825(0.233–2.921) 6.48 0.011 84.60% 0.00 1.000 – – 0.766 0.629 0.785 0.916 0.992 0.999 1.000 0.992 0.999 1.000
Source of control
HB 2 1.94 0.053 1.247(0.997–1.559) 1.51 0.219 33.70% 0.00 1.000 – – 0.053 0.948 0.143 0.334 0.846 0.982 0.998 0.988 0.999 1.000
PB 6 0.29 0.769 0.899(0.441–1.832) 16.70 0.005 70.10% 0.75 0.452 1.12 0.327 0.769 0.795 0.744 0.897 0.990 0.999 1.000 0.994 0.999 1.000
Cancer types
Oral SCC 3 0.35 0.728 0.802(0.232–2.774) 12.71 0.002 84.30% 1.04 0.296 0.66 0.629 0.727 0.615 0.780 0.914 0.992 0.999 1.000 0.992 0.999 1.000
Laryngeal SCC 2 0.77 0.441 1.120(0.839–1.494) 0.17 0.683 0.00% 0.00 1.000 – – 0.441 0.977 0.575 0.802 0.978 0.998 1.000 0.997 1.000 1.000
Genotyping method
PCR 5 0.06 0.951 0.981(0.531–1.813) 13.62 0.009 70.60% −0.24 1.000 −0.13 0.9204 0.951 0.891 0.762 0.906 0.991 0.999 1.000 0.995 1.000 1.000
PCR-RFLP 2 0.3 0.765 0.825(0.233–2.921) 6.48 0.011 84.60% 0.00 1.000 – – 0.765 0.629 0.785 0.916 0.992 0.999 1.000 0.992 0.999 1.000
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Abbreviations: OR, odds ratio; CI, confidence interval; PB, population-based; HB, hospital-based; SCC, squamous cell carcinoma; FPRP, false positive report probability; BFDP, Bayesian false discovery probability. The results in bold represented that there was statistically significant noteworthiness at 0.2 level by FPRP or 0.8 level by BFDP calculations.

Figure 4.

Figure 4

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Statistical relationship between IL-10 rs1800795 gene polymorphism and squamous cell carcinoma susceptibility in four models: (a) CC vs GG, (b) GC vs GG, (c) GC + CC vs GG, and (d) CC vs GG + GC.

3.3. Publication bias

The funnel plot revealed no significant asymmetry in the IL-10 rs1800896 gene polymorphism in any model (Figure S1). However, Egger’s test revealed a publication bias in the IL-10 rs1800896 models (p = 0.018 for GG/AA; p = 0.037 for GG/AA + AG). IL-18 rs1800872 or IL-6 rs1800795 models showed no asymmetry in the funnel shape (Figures S2 and S3). In addition, Egger’s test revealed no publication bias in IL-10 1800872 or IL-6 rs1800795 models.

3.4. Sensitivity analysis

The sensitivity analysis revealed no significant changes in the combination OR corresponding to the IL-10 rs1800896 or rs1800872 or IL-6 rs1800795 gene polymorphism, proving our results to be statistically robust (Figures S4–S6). The meta-regression analysis showed that the publication year, ethnicity, or control source did not affect the stability of the combined results (Table S1).

3.5. FPRP and BFDP tests

Tables S2, S3, and S4 show the FPRPs of IL-10 rs1800896 and rs1800872 and IL-6 rs1800795 gene polymorphisms, respectively. At prior probabilities of 0.25 and 0.1, FPRP and BFDP test results showed statistically significant results in almost all models of rs1800896, rs1800872, and rs1800795 polymorphisms, with an OR of 1.5.

4. Discussion

SCC can be caused by exposure to carcinogens, such as sunlight, tobacco, alcohol, and viral infections. It shows a high percentage of somatic genetic mutations. All SCC cases have similar mutation patterns [44–46]. The relationship between the IL-10 and IL-6 gene polymorphisms and the SCC risk has been shown. Gene polymorphisms affect their messenger RNA (mRNA) and protein levels. The IL-6 rs1800795 polymorphism may affect the IL-6 mRNA expression. The G allele of the IL-6 rs1800795 promoter single-nucleotide polymorphism is associated with elevated IL-6 mRNA transcription levels after in vitro endotoxin or IL-1 stimulation [47]. The presence of a variant allele G in tumor tissue is positively associated with elevated IL-10 mRNA levels [48]. Wang et al. analyzed the same polymorphisms and reported significantly higher IL-10 mRNA levels in patients with non-small cell lung cancer with the non-ATA haplotype, showing the association of cytokine IL-10 expression levels with tumor progression [49]. Currently, no relevant reports have been published on the polymorphism of these three genes or proteins. They may indirectly affect the protein expression after affecting the mRNA expression. Associations of IL-10 and IL-6 gene polymorphisms and oral and cervical SCC risks have been frequently demonstrated in meta-analyses [50,51]. However, no meta-analysis has revealed an association of IL-10 or IL-6 gene polymorphisms with the SCC risk. Therefore, we re-examined the relationship between IL-10 and IL-6 gene polymorphisms and the SCC risk from a comprehensive and unified perspective to draw a more accurate conclusion.

We investigated the IL-10 rs1800896 gene polymorphism to find its relationship with the SCC risk. The overall analysis revealed no positive results. However, the subgroup analysis by cancer type revealed positive results, indicating that the IL-10 rs1800896 gene polymorphism was a risk factor for oral SCC. Li et al. [50] also conducted a study on the association of the IL-10 gene polymorphism and the oral cancer risk. They reported that the IL-10 rs1800896 polymorphism increased the risk of oral cancers, including non-SCC, in both dominant and recessive genetic models. Both present and previous studies showed that the IL-10 rs1800896 gene polymorphism increased the oral cancer risk. However, the present study investigated SCC, while previous studies did not differentiate SCC and non-SCC. In addition, the present subgroup analysis showed that other cancer types, such as cervical or esophageal SCC, showed no positive results. These results were consistent with those of Ni et al.’s meta-analysis of eight studies. Furthermore, the IL-10 rs1800896 gene polymorphism affects carcinoma of the uterine cervix [52]. However, the present results were inconsistent with the results of Li et al.’s meta-analysis of seven studies, which showed that the IL-10 rs1800896 gene polymorphism could increase the risk of esophageal cancer, possibly including non-SCC [53]. In the present study, the IL-10 rs1800896 polymorphism showed different impressions in diverse organs, probably because of the IL-10 rs1800896 gene polymorphisms in different parts of the body having different distributions in different cancer types. However, the exact mechanism remains unclear. The subgroup analyses did not include a sufficient sample size. Therefore, we should exercise caution in drawing conclusions. Future, large-scale studies should be conducted. No link between the IL-10 rs1800896 gene polymorphism and the SCC risk was found in hospital- or population-based models.

With the IL-10 rs1800872 gene polymorphism, the SCC risk was low, particularly in oral SCC. Subgroup analyses by ethnicity indicated that the IL-10 rs1800872 polymorphism might be a protective factor in the Caucasian population but a risk factor in the Asian population. The IL-10 rs1800872 gene polymorphism had different effects on the Caucasian and Asian populations. The reason might be that the proportion of the gene expression differed among ethnic groups. These differences may be derived from different genetic backgrounds and environmental exposures, such as the difference in minor allele frequencies in healthy controls among the Caucasian and Asian populations. Therefore, inconsistent associations indicate the possibility of differences in the magnitude of the IL-10 rs1800872 gene polymorphism contribution to the SCC risk across different genetic backgrounds and environmental exposures [54]. The subgroup analysis by cancer type showed no association between the IL-10 rs1800872 polymorphism and the esophageal SCC risk, broadly consistent with the included independent studies [20,24,36,37].

The IL-6 rs1800795 gene polymorphism and the SCC risk showed no association. However, the subgroup analysis by control source revealed that the presence of the IL-6 rs1800795 gene polymorphism increased the SCC risk among hospital-based controls but not among population-based controls. The possible reasons are as follows: (1) some studies included hospital-based controls, which could induce an inherent selection bias because the hospital population does not represent the general population and (2) hospital-based controls may have other diseases that affect the release of interleukins, affecting the present results. Thus, appropriate and representative control populations played roles in assessing the relationship between the gene polymorphism and the disease risk. We found inconsistent results for the same cancer among independent studies. One study showed that the CC genotype of IL-6 rs1800795 may be protective in patients with oral SCC [42]. Another study showed a seven-fold increased risk of oral SCC with the CC genotype [43]. The results of the two studies were directly opposite [42,43]; therefore, we performed a subgroup analysis by cancer type. However, the results showed no significant association between the IL-6 rs1800795 polymorphism and the oral SCC risk, consistent with previous meta-analysis results [55]. A previous meta-analysis of 11 studies conducted by Rezaei et al. also confirmed that the IL-6 rs1800795 gene polymorphism was not associated with the oral cancer risk. Thus, the strength of the association between the IL-6 rs1800795 gene polymorphism and the oral cancer risk could be evaluated using meta-analyses, and the results of this study were more accurate than those of each independent study. All studies that met the eligibility criteria were included in this study, but a larger sample size would increase the reliability of the conclusions.

This meta-analysis has some advantages. First, no studies on the association between the IL-10 rs1800896 and rs1800872 and IL-6 rs1800795 gene polymorphisms and the SCC risk have been reported. Second, this was the most comprehensive study on this topic, with sufficient statistical power. However, several limitations exist. First, many studies had to be excluded because they did not report the relevant cancer type or pathologically diagnosed SCC. Further, some studies had to be excluded because they did not report the proportion of SCC by genotype. Finally, the number of included studies was small. Future, large-scale studies are required to more accurately explain the association between the studied genotypes and the SCC risk. Second, we only included articles published in English or Chinese, which might lead to a language bias. The preponderance of Asians in the original data could also be a bias. Third, the pathogenesis of SCC was affected by various factors, such as environmental changes, diet, age, and sex, which were not accounted for because of the retrospective study design. Fourth, subgroup analyses included only a few studies, reducing the statistical efficiency. Finally, the statistical heterogeneity frequently existed between the IL-10 rs1800872 and IL-6 rs1800795 gene polymorphisms and the SCC risk during statistical calculations. This could be because of the small number of included studies and the considerable heterogeneity among studies. Therefore, caution should be exercised when drawing conclusions.

5. Conclusions

This meta-analysis showed that the IL-10 rs1800872 gene polymorphism reduced the SCC risk, particularly in Caucasians. However, no IL-10 rs1800896 or IL-6 rs1800795 polymorphism was correlated with the SCC risk. Considering the limitations of this study, further carefully designed, large-scale studies are required to evaluate the association of the IL-10 and IL-6 genetic polymorphisms with the SCC risk.

Supplementary Material

Supplementary material

Footnotes

Funding information: This work was supported by the Guangxi Science and Technology Base and Talents Special Project (2021AC18031), Nanning Qingxiu District Science and Technology Plan (2021004), and Guangxi Medical and health-suitable technology development and popularization application project (S2021085).

Author contributions: The authors thank all the participants for their contributions to this study. Z.W. and X.S. collected the data. C.L. and X.H. checked the data. Q.H and Y.H. calculated the data. Z.W., X.S, C.L., and X.H. analyzed the data and wrote the manuscript. All authors read and approved the final manuscript.

Conflict of interest: Authors state no conflict of interest.

Data availability statement: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Contributor Information

Cuiping Li, Email: gxcuiping@sina.com.

Xuanping Huang, Email: hxp120@126.com.

References

  • [1].Bray F, Laversanne M, Weiderpass E, Soerjomataram I. The ever-increasing importance of cancer as a leading cause of premature death worldwide. Cancer. 2021;127(16):3029–30. [DOI] [PubMed]
  • [2].Andersen AS, Koldjaer Sølling AS, Ovesen T, Rusan M. The interplay between HPV and host immunity in head and neck squamous cell carcinoma. Int J Cancer. 2014;134(12):2755–63. [DOI] [PubMed]
  • [3].Houghton CR, Iversen T. Squamous cell carcinoma of the vagina: a clinical study of the location of the tumor. Gynecologic Oncol. 1982;13(3):365–72. [DOI] [PubMed]
  • [4].Wong-You-Cheong JJ, Woodward PJ, Manning MA, Sesterhenn IA. From the archives of the AFIP: neoplasms of the urinary bladder: radiologic-pathologic correlation. Radiographics. 2006;26(2):553–80. [DOI] [PubMed]
  • [5].Zhou N, Mitchell KG, Corsini EM, Truong VTT, Antonoff MB, Mehran RJ, et al. Analysis of trimodal and bimodal therapy in a selective-surgery paradigm for locally advanced oesophageal squamous cell carcinoma. Br J Surg. 2021;108(10):1207–15. [DOI] [PubMed]
  • [6].Luo H, Lu J, Bai Y, Mao T, Wang J, Fan Q, et al. Effect of camrelizumab vs placebo added to chemotherapy on survival and progression-free survival in patients with advanced or metastatic esophageal squamous cell carcinoma: the Escort-1st randomized clinical trial. JAMA. 2021;326(10):916–25. [DOI] [PMC free article] [PubMed]
  • [7].Ngcamphalala C, Ostensson E, Ginindza TG. The economic burden of cervical cancer in Eswatini: societal perspective. PLoS One. 2021;16(4):e0250113. [DOI] [PMC free article] [PubMed]
  • [8].Hahn VS, Zhang KW, Sun L, Narayan V, Lenihan DJ, Ky B. Heart failure with targeted cancer therapies: mechanisms and cardioprotection. Circ Res. 2021;128(10):1576–93. [DOI] [PMC free article] [PubMed]
  • [9].Park T, Hwang M. Health care use and expenditures attributable to cancer: a population-based study. Res Soc Adm Pharm. 2021;17(7):1300–5. [DOI] [PubMed]
  • [10].Magnes T, Wagner S, Kiem D, Weiss L, Rinnerthaler G, Greil R, et al. Prognostic and predictive factors in advanced head and neck squamous cell carcinoma. Int J Mol Sci. 2021;22(9):4981. [DOI] [PMC free article] [PubMed]
  • [11].Chamoli A, Gosavi AS, Shirwadkar UP, Wangdale KV, Behera SK, Kurrey NK, et al. Overview of oral cavity squamous cell carcinoma: risk factors, mechanisms, and diagnostics. Oral Oncol. 2021;121:105451. [DOI] [PubMed]
  • [12].Yang X, Suo C, Zhang T, Yin X, Man J, Yuan Z, et al. A nomogram for screening esophageal squamous cell carcinoma based on environmental risk factors in a high-incidence area of China: a population-based case-control study. BMC Cancer. 2021;21(1):343. [DOI] [PMC free article] [PubMed]
  • [13].Mirlekar B. Tumor promoting roles of IL-10, TGF-beta, IL-4, and IL-35: Its implications in cancer immunotherapy. SAGE Open Med. 2022;10:1–15. [DOI] [PMC free article] [PubMed]
  • [14].Wei H, Li B, Sun A, Guo F. Interleukin-10 family cytokines immunobiology and structure. Adv Exp Med Biol. 2019;1172:79–96. [DOI] [PubMed]
  • [15].Waldner MJ, Foersch S, Neurath MF. Interleukin-6 –  a key regulator of colorectal cancer development. Int J Biol Sci. 2012;8(9):1248–53. [DOI] [PMC free article] [PubMed]
  • [16].Pasvenskaite A, Liutkeviciene R, Gedvilaite G, Vilkeviciute A, Liutkevicius V, Uloza V. Impact of IL-10 promoter polymorphisms and il-10 serum levels on advanced laryngeal squamous cell carcinoma and survival rate. Cancer Genomics Proteom. 2021;18(1):53–65. [DOI] [PMC free article] [PubMed]
  • [17].Upadhyay R, Jain M, Kumar S, Ghoshal UC, Mittal B. Association of interleukin-6 (-174G > C) promoter polymorphism with risk of squamous cell esophageal cancer and tumor location: an exploratory study. Clin Immunol. 2008;128(2):199–204. [DOI] [PubMed]
  • [18].Datta A, Tuz Zahora F, Abdul Aziz M, Sarowar Uddin M, Ferdous M, Shalahuddin Millat M, et al. Association study of IL10 gene polymorphisms (rs1800872 and rs1800896) with cervical cancer in the Bangladeshi women. Int Immunopharmacol. 2020;89(Pt B):107091. [DOI] [PubMed]
  • [19].Guo W, Wang N, Wang YM, Li Y, Wen DG, Chen ZF, et al. Interleukin-10 -1082 promoter polymorphism is not associated with susceptibility to esophageal squamous cell carcinoma and gastric cardiac adenocarcinoma in a population of high-incidence region of north China. World J Gastroenterol. 2005;11(6):858–62. [DOI] [PMC free article] [PubMed]
  • [20].El-Omar EM, Rabkin CS, Gammon MD, Vaughan TL, Risch HA, Schoenberg JB, et al. Increased risk of noncardia gastric cancer associated with proinflammatory cytokine gene polymorphisms. Gastroenterology. 2003;124(5):1193–201. [DOI] [PubMed]
  • [21].Dubben HH. Re: Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J Natl Cancer Inst. 2004;96(22):1722, author reply 1722–1723. [DOI] [PubMed]
  • [22].Wakefield J, Bayesian A. measure of the probability of false discovery in genetic epidemiology studies. Am J Hum Genet. 2007;81(2):208–27. [DOI] [PMC free article] [PubMed]
  • [23].Mao C, Li Z, Niu Y. The association between interleukin 10 gene promoter polymorphisms and oral squamous cell carcinoma risk. J Hubei Univ Med. 2021;40(6):566–70 + 577.
  • [24].Chen S, Cao R, Liu C, Tang W, Kang M. Investigation of IL-4, IL-10, and HVEM polymorphisms with esophageal squamous cell carcinoma: a case–control study involving 1929 participants. Biosci Rep. 2020;40(8). [DOI] [PMC free article] [PubMed]
  • [25].Goud E, Malleedi S, Ramanathan A, Wong GR, Hwei Ern BT, Yean GY, et al. Association of interleukin-10 genotypes and oral cancer susceptibility in selected malaysian population: a case–control study. Asian Pac J Cancer Prev. 2019;20(3):935–41. [DOI] [PMC free article] [PubMed]
  • [26].Sharma U, Singhal P, Bandil K, Kumar A, Bose S, Ahuja P, et al. Genetic variations of IL-10: identification of novel variations and evaluation of the impact of the SNPs/haplotype in the promoter region with the progression of oral squamous cell carcinoma in Indian population. Cytokine. 2018;103:99–108. [DOI] [PubMed]
  • [27].Torres-Poveda K, Burguete-Garcia AI, Bahena-Roman M, Mendez-Martinez R, Zurita-Diaz MA, Lopez-Estrada G, et al. Risk allelic load in Th2 and Th3 cytokines genes as biomarker of susceptibility to HPV-16 positive cervical cancer: a case control study. BMC Cancer. 2016;16:330. [DOI] [PMC free article] [PubMed]
  • [28].Hussain SR, Ahmad MK, Mahdi AA, Naqvi H, Ahmad MW, Srivastava S, et al. Association of interleukin-10 (A1082G) gene polymorphism with oral squamous cell carcinoma in north Indian population. J Genet. 2016;95(2):249–55. [DOI] [PubMed]
  • [29].Zhou J, Zhang D, Chen B, Li Q, Zhou L, Liu F, et al. Association of interleukin-10 promoter polymorphisms and corresponding plasma levels with susceptibility to laryngeal squamous cell carcinoma. Oncol Lett. 2014;7(5):1721–7. [DOI] [PMC free article] [PubMed]
  • [30].Torres-Poveda K, Burguete-García AI, Cruz M, Martínez-Nava GA, Bahena-Román M, Ortíz-Flores E, et al. The SNP at -592 of human IL-10 gene is associated with serum IL-10 levels and increased risk for human papillomavirus cervical lesion development. Infect Agents Cancer. 2012;7(1):32. [DOI] [PMC free article] [PubMed]
  • [31].Jeong SW, Tae K, Lee SH, Kim KR, Park CW, Park BL, et al. Cox-2 and IL-10 polymorphisms and association with squamous cell carcinoma of the head and neck in a Korean sample. J Korean Med Sci. 2010;25(7):1024–8. [DOI] [PMC free article] [PubMed]
  • [32].Vairaktaris E, Yapijakis C, Serefoglou Z, Derka S, Vassiliou S, Nkenke E, et al. The interleukin-10 (-1082A/G) polymorphism is strongly associated with increased risk for oral squamous cell carcinoma. Anticancer Res. 2008;28(1a):309–14. [PubMed]
  • [33].Seifart C, Plagens A, Dempfle A, Clostermann U, Vogelmeier C, von Wichert P, et al. TNF-alpha, TNF-beta, IL-6, and IL-10 polymorphisms in patients with lung cancer. Dis Markers. 2005;21(3):157–65. [DOI] [PMC free article] [PubMed]
  • [34].Zoodsma M, Nolte IM, Schipper M, Oosterom E, van der Steege G, de Vries EG, et al. Interleukin-10 and Fas polymorphisms and susceptibility for (pre)neoplastic cervical disease. Int J Gynecol Cancer. 2005;15(Suppl 3):282–90. [DOI] [PubMed]
  • [35].Singh PK, Ahmad MK, Kumar V, Gupta R, Kohli M, Jain A, et al. Genetic polymorphism of interleukin-10 (-A592C) among oral cancer with squamous cell carcinoma. Arch Oral Biol. 2017;77:18–22. [DOI] [PubMed]
  • [36].Sun JM, Li Q, Gu HY, Chen YJ, Wei JS, Zhu Q, et al. Interleukin 10 rs1800872 T > G polymorphism was associated with an increased risk of esophageal cancer in a Chinese population. Asian Pac J Cancer Prev. 2013;14(6):3443–7. [DOI] [PubMed]
  • [37].Wang N, Guo W, Li Y, Wang YM, Wen DG, Chen ZF, et al. Relationship between interleukin-10 promoter polymorphisms and esophageal squamous cell carcinoma and gastric cardiac adenocarcinoma. Tumor. 2006;02:157–62.
  • [38].Abakay CD, Pashazadeh M, Ardahanli E, Oral HB. Transforming growth factor-beta1 gene polymorphism as a potential risk factor in Turkish patients with laryngeal squamous cell carcinoma. J Cancer Res Ther. 2020;16(1):144–9. [DOI] [PubMed]
  • [39].Pasvenskaite A, Vilkeviciute A, Liutkeviciene R, Gedvilaite G, Liutkevicius V, Uloza V. Associations of IL6 rs1800795, BLK rs13277113, TIMP3 rs9621532, IL1RL1 rs1041973 and IL1RAP rs4624606 single gene polymorphisms with laryngeal squamous cell carcinoma. Gene. 2020;747:144700. [DOI] [PubMed]
  • [40].Fernandez-Mateos J, Seijas-Tamayo R, Adansa Klain JC, Pastor Borgonon M, Perez-Ruiz E, Mesia R, et al. Genetic susceptibility in head and neck squamous cell carcinoma in a Spanish population. Cancers (Basel). 2019;11(4):493. [DOI] [PMC free article] [PubMed]
  • [41].Shi WJ, Liu H, Wu D, Tang ZH, Shen YC, Guo L. Stratification analysis and case–control study of relationships between interleukin-6 gene polymorphisms and cervical cancer risk in a Chinese population. Asian Pac J Cancer Prev. 2014;15(17):7357–62. [DOI] [PubMed]
  • [42].Gaur P, Mittal M, Mohanti B, Das S. Functional variants of IL4 and IL6 genes and risk of tobacco-related oral carcinoma in high-risk Asian Indians. Oral Dis. 2011;17(7):720–6. [DOI] [PubMed]
  • [43].Vairaktaris E, Yiannopoulos A, Vylliotis A, Yapijakis C, Derka S, Vassiliou S, et al. Strong association of interleukin-6 -174 G > C promoter polymorphism with increased risk of oral cancer. Int J Biol Markers. 2006;21(4):246–50. [DOI] [PubMed]
  • [44].Campbell JD, Alexandrov A, Kim J, Wala J, Berger AH, Pedamallu CS, et al. Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas. Nat Genet. 2016;48(6):607–16. [DOI] [PMC free article] [PubMed]
  • [45].Hoadley KA, Yau C, Wolf DM, Cherniack AD, Tamborero D, Ng S, et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell. 2014;158(4):929–44. [DOI] [PMC free article] [PubMed]
  • [46].Song Y, Li L, Ou Y, Gao Z, Li E, Li X, et al. Identification of genomic alterations in oesophageal squamous cell cancer. Nature. 2014;509(7498):91–5. [DOI] [PubMed]
  • [47].Cacev T, Jokic M, Loncar B, Krizanac S, Kapitanovic S. Interleukin-6-174 G/C polymorphism is not associated with IL-6 expression and susceptibility to sporadic colon cancer. DNA Cell Biol. 2010;29(4):177–82. [DOI] [PubMed]
  • [48].Miteva LD, Stanilov NS, Deliysky TS, Stanilova SA. Significance of -1082A/G polymorphism of IL10 gene for progression of colorectal cancer and IL-10 expression. Tumour Biol. 2014;35(12):12655–64. [DOI] [PubMed]
  • [49].Wang YC, Sung WW, Wu TC, Wang L, Chien WP, Cheng YW, et al. Interleukin-10 haplotype may predict survival and relapse in resected non-small cell lung cancer. PLoS One. 2012;7(7):e39525. [DOI] [PMC free article] [PubMed]
  • [50].Li F, Xu X, Xuan C, Chen WT. Association between interleukin-10 gene polymorphisms and risk of oral carcinoma: a meta-analysis. Histol Histopathol. 2020;35(11):1329–36. [DOI] [PubMed]
  • [51].Wang K, Jiao Z, Chen H, Liu X, Lu J, Liu X, et al. The association between rs1800872 polymorphism in interleukin-10 and risk of cervical cancer: a meta-analysis. Medicine. 2021;100(3):e23892. [DOI] [PMC free article] [PubMed]
  • [52].Ni J, Ye Y, Teng F, Wu Q. Interleukin 10 polymorphisms and cervical cancer risk: a meta-analysis. Int J Gynecol Cancer. 2013;23(1):126–33. [DOI] [PubMed]
  • [53].Li YF, Yang PZ, Li HF. Functional polymorphisms in the IL-10 gene with susceptibility to esophageal, nasopharyngeal, and oral cancers. Cancer Biomarkers Sect A Dis Markers. 2016;16(4):641–51. [DOI] [PubMed]
  • [54].Mashhadiabbas F, Dastgheib SA, Hashemzehi A, Bahrololoomi Z, Asadian F, Neamatzadeh H, et al. Association of IL-10 -1082A > G, -819C > T, and -592C > A polymorphisms with susceptibility to chronic and aggressive periodontitis: a systematic review and meta-analysis. Inflamm Res. 2021;70(5):509–24. [DOI] [PubMed]
  • [55].Rezaei F, Mohammadi H, Heydari M, Sadeghi M, Mozaffari HR, Khavid A, et al. Association between IL-8 (-251T/A) and IL-6 (-174G/C) polymorphisms and oral cancer susceptibility: a systematic review and meta-analysis. Medicina (Kaunas, Lithuania). 2021;57(5):405. [DOI] [PMC free article] [PubMed]

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