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. 2019 Dec 6;39(12):BSR20191698. doi: 10.1042/BSR20191698

-196 to -174del, rs4696480, rs3804099 polymorphisms of Toll-like receptor 2 gene impact the susceptibility of cancers: evidence from 37053 subjects

Sheng-Lin Gao 1,*, Yi-Ding Chen 2,*, Chuang Yue 1,*, Jiasheng Chen 1, Li-Feng Zhang 1, Si-Min Wang 3,✉, Li Zuo 1,✉
PMCID: PMC6900473  PMID: 31710083

Abstract

Relationship between Toll-like receptor-2 (TLR2) and cancer risk has been illustrated in some studies, but their conclusions are inconsistent. Therefore, we designed this meta-analysis to explore a more accurate conclusion of whether TLR2 affects cancer risks. Articles were retrieved from various literature databases according to the criteria. We used STATA to calculate the odds ratio (OR) and 95% confidence interval (95% CI) to evaluate the relationship between certain polymorphism of TLR2 and cancer risk. Finally, 47 case–control studies met the criteria, comprising 15851 cases and 21182 controls. In the overall analysis, people are more likely to get cancer because of -196 to -174del in TLR2 in all five genetic models, B vs. A (OR = 1.468, 95% Cl = 1.129–1.91, P=0.005); BB vs. AA (OR = 1.716, 95% Cl = 1.178–2.5, P=0.005); BA vs. AA (OR = 1.408, 95% Cl = 1.092–1.816, P=0.008); BB+BA vs. AA (OR = 1.449, 95% Cl = 1.107–1.897, P=0.007); BB vs. BA+AA (OR = 1.517, 95% Cl = 1.092–2.107, P=0.013). Meanwhile, rs4696480 could significantly increase the risk of cancer in Caucasians, furthermore, rs3804099 significantly decreased cancer risk in overall analysis, but more subjects are necessary to confirm the results. All in all, this meta-analysis revealed that not only -196 to -174del increased the risk of among overall cancers, Caucasians are more likely to get cancer because of rs4696480, while rs3804099 polymorphism could reduce the risk of cancer in some genetic models. There is no direct evidence showing that rs5743708, rs3804100 and rs1898830 are related to cancer.

Keywords: Cancer risk, Meta-analysis, TLR2, Toll-like receptor 2

Introduction

Cancer prevalence increases rapidly and becomes a major threat to human health in today’s world. As we all know, genes are inextricably linked to the development of cancer. In many cancer studies, such as gastric cancer [1], colorectal cancer, breast cancer [2], cervical cancer [3], Toll-like receptor (TLR)-2 (TLR2) has been determined as a pathogenic factor involved in tumorigenesis. The TLR2 gene located on human chromosome 4q32, includes one coding exon and two non-coding exons [4]. TLRs are mainly expressed in immune-related cells and immune-related epithelial cells, their role in tissue resistance to microbes is achieved by identifying conserved bacterial molecules [5]. Therefore some researchers believe that TLR2 play a significant role in the innate immune response through releasing pro-inflammatory cytokines [6].

-196 to -174del is a 22-bp deletion in TLR2 gene, which has been shown to cause a decrease in the transcriptional activity of the TLR2 gene [7]. However, in the past few years, there are inconsistent conclusions about the relationship between -196 to -174del and cancer risk. One paper noted that -196 to -174del in association with Helicobacter pylori significantly increased the risk of gastric cancer in patients [1]. But Hishida et al. [8] suggested that -196 to -174del had no relationship with gastric cancer. About reproductive tumors, some literatures suggested that -196 to -174del is not associated with breast cancer [9] and cervical cancer [3], but on the contrary, Theodoropoulos et al. [10] think that -196 to -174del may produce a significant increase in the risk of breast cancer. Mandal et al. [11] revealed that -196 to -174del polymorphism in TLR2 gene seems to be associated with the upgraded prostate cancer risk, while Singh et al. [12] drew out that -196 to -174del showed a three- to five-folds risk of bladder cancer comparison with people without this mutation.

For rs3804099 (c.597T>C) and rs3804100 (c.1350T>C), Etokebe et al. [13] and Semlali et al. [14] found no association between these two SNPs and breast cancer; Tongtawee et al. [15] demonstrated that rs3804099 and rs3804100 had no relationship with gastric cancer. However, the study of Xie et al. [16] found that the risk of hepatocellular carcinoma in TLR2 rs3804099 and rs3804100 carriers was reduced. For rs4696480 (g.6686T>A), de Barros Gallo et al. [17] thought that rs4696480 was associated with oral cancer in Caucasians, but Semlali et al. [18] found no difference in rs4696480 expression between the breast cancer patients and the controls in Asians.

Therefore, considering the limitations of individual study sample sizes and the contradictions of their conclusions, we designed this meta-analysis to study the relationship between TLR2 polymorphisms. (rs3804099, rs3804100, rs4696480, rs5743708 (c.2258G> A), rs1898830 (g.8013A> G) and -196 to -174del) and cancer risk.

Materials and methods

Database searching

Up to October 2019, PubMed, Embase, Google Scholar, Web of Science, Wanfang database and CNKI database were used by two investigators for article identification. We used the following strategy for the searching of relevant citations: (TLR2 OR (Toll-like receptors-2) OR CD282) AND (cancer OR tumor OR carcinoma OR neoplasms OR malignancy) AND (polymorphism OR mutation OR variant OR SNP OR genotype). Since the present study is a meta-analysis, no institutional review board approval and patient consent were required.

Inclusion and exclusion criteria

Articles included in our research must meet the following conditions: (1) study the relationship between cancer risk and TLR2 polymorphism; (2) provide sufficient data for extraction and calculation; (3) subjects are human patients; (4) the case–control study included control group and cancer patients case group. When duplicate data appeared in different publications, only the latest publication data were used. If the study did not meet the above criteria, it was excluded.

Data extraction and quality assessment

We extracted data from these articles, such as cancer type, first author, ethnicity, source of control, publication year, number of cases and controls, etc. Any differences were resolved through group discussions until all consensus was reached. We used Newcastle–Ottawa Scale (NOS) to evaluate the quality of the article (http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp). We carefully recorded seven aspects including ‘adequacy of case definition’, ‘representativeness of the cases’, ‘selection of controls’, ‘definition of controls’, ‘comparability cases/controls’, ‘ascertainment of exposure’ and ‘ascertainment of exposure’ to evaluate.

Statistical analysis

The STATA software was used for meta-statistical analysis. The relationship between the TLR2 rs3804099, rs3804100, rs4696480, rs5743708, rs1898830, -196 to -174del and cancer risk was assessed using pooled odds ratios (ORs) with 95% confidence intervals (95% CIs) under dominant, recessive, homozygous codominance, heterozygous codominance, and allelic control genetic models. Heterogeneity was estimated using Q test and I2 statistics [19]. When heterogeneity existed (P<0.1), random-effects model was applied, otherwise, fixed-effect model was used [20]. The Hardy–Weinberg equilibrium (HWE) of the control group was calculated using the chi-square test. In addition, we performed a stratified analysis based on cancer type, race, source of control and quality score. The sensitivity analysis was used to evaluate the stability of the overall analysis and the publication bias was evaluated by Egger’s test and Begg’s funnel plot [21].

False-positive report probability analysis and trial sequential analysis

We also used the false-positive report probability (FPRP) to evaluate the results; 0.2 was set as thePRP threshold and assigned a prior probability of 0.25 to detect the OR of 0.67/1.50 (protective/risk effects). The significant result with the FPRP values less than 0.2 were considered a worthy finding [22,23]. Trial sequential analysis (TSA) was conducted with the guideline of a former publication [24,25]. We set a significance of 5% for type I error, as well as a 30% significance of type II error, to calculate the required sample size, and built the TSA monitoring boundaries.

In silico analysis

For evaluating the linkage disequilibrium (LD) between different polymorphisms, we downloaded the dataset including the polymorphisms information of TLR2 gene from the 1000 Genomes Project, which contained the distribution of gene polymorphisms among CHB (Han Chinese in Beijing, China), CHS (southern Han Chinese, China), CEU (Utah residents with Northern and Western European ancestry from the CEPH collection), JPT (Japanese in Tokyo, Japan) and YRI (Yoruba in Ibadan, Nigeria), ESN (Esan in Nigeria) patients, and we used Haplpoview software to visualize the association between different polymorphisms, the relationship between them were assessed by r2 statistics. We also performed the expression quantitative trait loci (eQTL) analysis using GTEx portal website (http://www.gtexportal.org/home/) to predict potential associations between the SNPs and gene expression levels [26,27].

Results

Search results

We used online databases to find 242 articles, and found another 36 articles by reviewing the references. After removing the duplicates, we found a total of 268 records in the database. We first screened the duplicate articles and then screened 43 of the high-quality articles on the NOS (Supplementary Table S1). Of the 43 articles selected, 13 were rejected for insufficient data. At last, 30 articles met the criteria, including 47 case–control studies. The flowchart of our study selection is shown in Figure 1. This meta-analysis collected individuals with different genetic backgrounds (e.g. Asians, Africans and Caucasians). The detailed characteristics of these publications are provided in Table 1.

Figure 1. Flowchart of enrolled studies selection procedure.

Figure 1

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Table 1. Characteristics of the enrolled studies on TLR2 polymorphism and cancer.

First author Year Ethnicity Genotyping method Source of control Cancer type Cases Control
AA BA BB Total A% B% AA BA BB Total A% B% HWE
(-196 to -174del)
Tahara et al. 2007 Asian AS-PCR PB Gastric cancer 126 112 51 289 63.0% 37.0% 73 65 8 146 72.3% 27.7% Y
Pandey et al. 2009 Asian PCR PB Cervical cancer 102 43 5 150 82.3% 17.7% 114 35 1 150 87.7% 12.3% Y
Hishida et al. 2010 Asian PCR HB Gastric cancer 243 267 73 583 64.6% 35.4% 722 730 184 1636 66.4% 33.6% Y
Srivastava et al. 2010 Asian PCR-RFLP PB Gallbladder cancer 132 94 6 232 77.2% 22.8% 163 87 4 254 81.3% 18.7% N
Zeng et al. 2011a Asian DHPLC HB Gastric cancer 119 110 19 248 70.2% 29.8% 187 246 63 496 62.5% 37.5% Y
Nischalk et al. 2011 Caucasian PCR PB Hepatocellular carcinoma 115 63 11 189 77.5% 22.5% 248 92 7 347 84.7% 15.3% Y
Oliveira et al. 2012 Caucasian PCR-RFLP PB Gastric cancer 116 50 8 174 81.0% 19.0% 189 34 2 225 91.6% 8.4% Y
Mandal et al. 2012 Asian PCR PB Prostate cancer 135 54 6 195 83.1% 16.9% 193 52 5 250 87.6% 12.4% Y
Theodoropoulos et al. 2012 Caucasian PCR PB Breast cancer 120 113 28 261 67.6% 32.4% 432 46 2 480 94.8% 5.2% Y
Singh et al. 2013 Asian PCR PB Bladder cancer 110 79 11 200 74.8% 25.3% 119 73 8 200 77.8% 22.3% Y
Bi et al. 2014 Asian PCR PB Cervical cancer 40 47 15 102 62.3% 37.7% 36 50 14 100 61.0% 39.0% Y
Castano-Rodriguez et al. 2014 Asian MassARRAY HB Gastric cancer 7 44 35 86 33.7% 66.3% 19 95 106 220 30.2% 69.8% Y
Zidi et al. 2014 African PCR HB Cervical cancer 89 20 13 122 81.1% 18.9% 196 37 27 260 82.5% 17.5% N
Devi et al. 2015 Asian PCR PB Breast cancer 251 191 20 462 75.0% 25.0% 491 246 33 770 79.7% 20.3% Y
Proenca et al. 2015 African PCR PB Colorectal cancer 144 39 5 188 87.0% 13.0% 200 36 4 240 90.8% 9.2% Y
Zidi et al. 2015 African PCR PB Cervical cancer 93 26 11 130 81.5% 18.5% 196 37 27 260 82.5% 17.5% N
AL-Harras et al. 2016 African PCR-RFLP PB Breast cancer 44 22 6 72 76.4% 23.6% 61 33 6 100 77.5% 22.5% Y
Huang et al. 2018 Asian PCR PB Gastric cancer 105 124 31 260 64.2% 35.8% 132 113 15 260 72.5% 27.5% Y
rs3804099
Etokebe et al. 2009 Caucasian TaqMan PB Breast cancer 29 44 16 89 57.3% 42.7% 26 48 15 89 56.2% 43.8% Y
Slattery et al. 2012 Caucasian GoldenGate PB Colon cancer 1255 300 1555 - - 1531 425 1956 - - -
Xie et al. 2012 Asian SNaPshot HB Hepatocellular carcinoma 19 71 121 211 25.8% 74.2% 15 117 100 232 31.7% 68.3% N
Miedema et al. 2012 Caucasian AS-PCR HB Lymphoblastic leukemia 51 94 37 182 53.8% 46.2% 48 102 28 178 55.6% 44.4% N
Slattery et al. 2012 Caucasian GoldenGate PB Rectal cancer 238 372 144 754 56.2% 43.8% 299 477 183 959 56.0% 44.0% Y
Zeljic et al. 2013 Caucasian TaqMan PB Oral cancer 29 39 25 93 52.2% 47.8% 37 67 0 104 67.8% 32.2% N
Semlali et al. 2017 Asian TaqMan PB Breast cancer 35 58 32 125 51.2% 48.8% 33 71 42 146 46.9% 53.1% Y
Semlali et al. 2018 Asian TaqMan PB Colon cancer 42 50 19 111 60.4% 39.6% 28 47 27 102 50.5% 49.5% Y
Tongtawee et al. 2018 Asian TaqMan HB Gastric cancer 62 13 13 88 77.8% 22.2% 194 56 62 312 71.2% 28.8% N
Zeng et al. 2011b Asian PCR-RFLP HB Gastric cancer 132 99 17 248 73.2% 26.8% 216 231 49 496 66.8% 33.2% Y
rs3804100
Purdu et al. 2008 Caucasian TaqMan PB Non-Hodgkin lymphoma 1658 272 12 1942 92.4% 7.6% 1556 233 9 1798 93.0% 7.0% Y
Etokebe et al. 2009 Caucasian TaqMan PB Breast cancer 76 13 0 89 92.7% 7.3% 84 11 0 95 94.2% 5.8% Y
Xie et al. 2012 Asian SNaPshot HB Hepatocellular carcinoma 14 67 130 211 22.5% 77.5% 11 110 111 232 28.4% 71.6% N
Miedema et al. 2012 Caucasian AS-PCR HB Lymphoblastic leukemia 170 18 1 189 94.7% 5.3% 165 18 0 183 95.1% 4.9% Y
Castano-Rodriguez et al. 2014 Asian MassARRAY HB Gastric cancer 47 34 4 85 75.3% 24.7% 122 76 14 212 75.5% 24.5% Y
Semlali et al. 2017 Asian TaqMan PB Breast cancer 99 24 1 124 89.5% 10.5% 115 27 4 146 88.0% 12.0% Y
Semlali et al. 2018 Asian TaqMan PB Colon cancer 99 13 2 114 92.5% 7.5% 82 19 2 103 88.8% 11.2% Y
Tongtawee et al. 2018 Asian TaqMan HB Gastric cancer 66 22 0 88 87.5% 12.5% 230 70 12 312 84.9% 15.1% N
rs4696480
Miedema et al. 2012 Caucasian AS-PCR HB Hepatocellular carcinoma 42 99 44 185 49.5% 50.5% 60 83 38 181 56.1% 43.9% Y
Gallo et al. 2017 Caucasian TaqMan PB Oral cancer 12 39 24 75 42.0% 58.0% 31 34 24 89 53.9% 46.1% N
Semlali et al. 2017 Asian TaqMan PB Breast cancer 46 51 29 126 56.7% 43.3% 50 63 25 138 59.1% 40.9% Y
Semlali et al. 2018 Asian TaqMan PB Colon cancer 30 49 27 106 51.4% 48.6% 26 41 25 92 50.5% 49.5% Y
rs5743708
Nischalk et al. 2011 Caucasian PCR PB Hepatocellular carcinoma 174 15 0 189 96.0% 4.0% 319 28 0 347 96.0% 4.0% Y
Slattery et al. 2012 Caucasian GoldenGate PB Rectal cancer 727 27 754 - - 913 46 959 - -
Slattery et al. 2012 Caucasian GoldenGate PB Colon cancer 1467 88 1555 - - 1864 92 1956 - -
Kina et al. 2018 Caucasian PCR PB Glioma 32 18 70 120 34.2% 65.8% 184 35 6 225 89.6% 10.4% N
rs1898830
Xie et al. 2012 Asian SNPshot HB Hepatocellular carcinoma 47 92 72 211 44.1% 55.9% 34 118 80 232 40.1% 59.9% Y
Slattery et al. 2012 Caucasian GoldenGate PB Rectal cancer 305 363 86 754 64.5% 35.5% 410 437 111 958 65.6% 34.4% Y
Slattery et al. 2012 Caucasian GoldenGate PB Colon cancer 705 674 176 1555 67.0% 33.0% 896 833 227 1956 67.1% 32.9% Y
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Abbreviations: H-B, hospital based; P-B, population based. P>0.05 means conformed to HWE.

Meta-analysis results

The results of pooled analysis for TLR2 polymorphism and cancer susceptibility are provided in Table 2. For -196 to -174del, we collected 18 articles containing 3943 cases and 4574 controls [1–3,6,8–12,28–36]. In the overall analysis, -196 to -174del significantly increased the risk of cancer [B vs. A (OR = 1.468, 95% Cl = 1.129–1.91, P=0.005); BB vs. AA (OR = 1.716, 95% Cl = 1.178–2.5, P=0.005); BA vs. AA (OR = 1.408, 95% Cl = 1.092–1.816, P=0.008); BB+BA vs. AA (OR = 1.449, 95% Cl = 1.107–1.897, P=0.007); BB vs. BA+AA (OR = 1.517, 95% Cl = 1.092–2.107, P=0.013)] (Figure 2). Among the subgroup of Caucasians, -196 to -174del produces a significant increase in the risk of cancer, too [B vs. A (OR = 3.291, 95% Cl = 1.139–9.51, P=0.028); BB vs. AA (OR = 9.878, 95% Cl = 1.83–53.322, P=0.008); BA vs. AA (OR = 3.156, 95% Cl = 1.034–9.634, P=0.044); BB+BA vs. AA (OR = 3.555, 95% Cl = 1.098–11.51, P=0.034); BB vs. BA+AA (OR = 7.294, 95% Cl = 1.752-30.369, P=0.006)]. During the subgroup analysis of HB, -196 to -174del was found to be associated with cancer [B vs. A (OR = 1.576, 95% Cl = 1.193–2.08, P<0.001); BB vs. AA (OR = 2.274, 95% Cl = 1.43–3.616, P<0.001); BA vs. AA (OR = 1.543, 95% Cl = 1.143–2.081, P<0.001); BB+BA vs. AA (OR = 1.624, 95% Cl = 1.186–2.223, P<0.001); BB vs. BA+AA (OR = 2.011, 95% Cl = 1.317–3.07, P=0.001)]. In addition, in the subgroup analysis of Asians, the models of BB+BA vs. AA (OR = 1.203, 95% Cl = 1.015–1.427, P=0.033) and B vs. A (OR = 1.169, 95% Cl = 1.005–1.361, P=0.043) suggested that -196 to -174del increased the risk of cancer. Meanwhile, when -196 to -174del conformed to HWE in the control group, analysis of all models showed that the deletion of these 22 genes increased the risk of cancer (Supplementary Table S2). By the way, the BA vs. AA model in the N subgroup suggested that -196 to-174del was related to the cancer risk (OR = 1.335, 95% Cl = 1.015–1.757, P=0.039).

Table 2. Results of pooled analysis for TLR2 polymorphism and cancer susceptibility.

Comparison Subgroup n Cases Controls PH PZ HR (95% CI)
(-196 to -174del)
B vs. A Overall 18 3943 6394 <0.001 0.005* 1.468 (1.129–1.91)
BB vs. AA Overall 18 3943 6394 <0.001 0.005* 1.716 (1.178–2.5)
BA vs. AA Overall 18 3943 6394 <0.001 0.008* 1.408 (1.092–1.816)
BB+BA vs. AA Overall 18 3943 6394 <0.001 0.007* 1.449 (1.107–1.897)
BB vs. BA+ AA Overall 18 3943 6394 <0.001 0.013* 1.517 (1.092–2.107)
B vs. A Asian 11 2807 4482 <0.001 0.043* 1.169 (1.005–1.361)
BB vs. AA Asian 11 2807 4482 0.003 0.098 1.373 (0.943–2)
BA vs. AA Asian 11 2807 4482 0.039 0.054 1.168 (0.997–1.367)
BB+BA vs. AA Asian 11 2807 4482 0.008 0.033* 1.203 (1.015–1.427)
BB vs. BA+ AA Asian 11 2807 4482 0.005 0.177 1.256 (0.902–1.748)
B vs. A Caucasian 3 624 1052 <0.001 0.028* 3.291 (1.139–9.51)
BB vs. AA Caucasian 3 624 1052 0.007 0.008* 9.878 (1.83–53.322)
BA vs. AA Caucasian 3 624 1052 <0.001 0.044* 3.156 (1.034–9.634)
BB+BA vs. AA Caucasian 3 624 1052 <0.001 0.034* 3.555 (1.098–11.51)
BB vs. BA+ AA Caucasian 3 624 1052 0.029 0.006* 7.294 (1.752–30.369)
B vs. A African 4 512 860 0.653 0.159 1.163 (0.943–1.436)
BB vs. AA African 4 512 860 0.796 0.746 1.076 (0.693–1.67)
BA vs. AA African 4 512 860 0.652 0.075 1.296 (0.974–1.724)
BB+BA vs. AA African 4 512 860 0.72 0.106 1.232 (0.956–1.586)
BB vs. BA+AA African 4 512 860 0.755 0.897 1.029 (0.666–1.59)
B vs. A PB 14 2904 3782 <0.001 0.001* 1.576 (1.193–2.08)
BB vs. AA PB 14 2904 3782 <0.001 0.001* 2.274 (1.43–3.616)
BA vs. AA PB 14 2904 3782 <0.001 0.005* 1.543 (1.143–2.081)
BB+BA vs. AA PB 14 2904 3782 <0.001 0.002* 1.624 (1.186–2.223)
BB vs. BA+AA PB 14 2904 3782 0.001 0.001* 2.011 (1.317–3.07)
B vs. A HB 4 1039 2612 0.016 0.502 0.92 (0.721–1.173)
BB vs. AA HB 4 1039 2612 0.048 0.552 0.866 (0.54–1.39)
BA vs. AA HB 4 1039 2612 0.122 0.841 0.984 (0.837–1.156)
BB+BA vs. AA HB 4 1039 2612 0.038 0.716 0.942 (0.684–1.298)
BB vs. BA+AA HB 4 1039 2612 0.121 0.43 0.917 (0.739–1.138)
B vs. A Gastric cancer 6 1640 2983 <0.001 0.194 1.22 (0.904–1.647)
BB vs. AA Gastric cancer 6 1640 2983 <0.001 0.176 1.565 (0.818–2.995)
BA vs. AA Gastric cancer 6 1640 2983 0.002 0.309 1.171 (0.864–1.586)
BB+BA vs. AA Gastric cancer 6 1640 2983 <0.001 0.216 1.246 (0.879–1.764)
BB vs. BA+AA Gastric cancer 6 1640 2983 <0.001 0.223 1.401 (0.814–2.411)
B vs. A Breast cancer 3 795 1350 <0.001 0.212 2.31 (0.621–8.593)
BB vs. AA Breast cancer 3 795 1350 <0.001 0.2 4.049 (0.478–34.306)
BA vs. AA Breast cancer 3 795 1350 <0.001 0.197 2.347 (0.642–8.58)
BB+BA vs. AA Breast cancer 3 795 1350 <0.001 0.2 2.52 (0.613–10.36)
BB vs. BA+AA Breast cancer 3 795 1350 <0.001 0.233 3.176 (0.476–21.196)
B vs. A Cervical cancer 4 504 770 0.474 0.269 1.121 (0.916–1.372)
BB vs. AA Cervical cancer 4 504 770 0.453 0.782 1.061 (0.696–1.618)
BA vs. AA Cervical cancer 4 504 770 0.554 0.177 1.215 (0.916–1.613)
BB+BA vs. AA Cervical cancer 4 504 770 0.586 0.207 1.177 (0.914–1.515)
BB vs. BA+AA Cervical cancer 4 504 770 0.456 0.848 1.041 (0.692–1.566)
B vs. A Y 15 3459 5620 <0.001 0.008* 1.447 (1.103–1.897)
BB vs. AA Y 15 3459 5620 <0.001 0.004* 1.915 (1.227–2.991)
BA vs. AA Y 15 3459 5620 <0.001 0.02* 1.422 (1.057–1.915)
BB+BA vs. AA Y 15 3459 5620 <0.001 0.013* 1.494 (1.088–2.052)
BB vs. BA+AA Y 15 3459 5620 <0.001 0.009* 1.673 (1.137–2.461)
B vs. A N 3 484 774 0.709 0.14 1.168 (0.951–1.434)
BB vs. AA N 3 484 774 0.597 0.84 1.05 (0.655–1.681)
BA vs. AA N 3 484 774 0.872 0.039* 1.335 (1.015–1.757)
BB+BA vs. AA N 3 484 774 0.839 0.07 1.258 (0.981–1.613)
BB vs. BA+AA N 3 484 774 0.615 0.959 0.988 (0.62–1.575)
rs3804099
B vs. A Overall 9 1901 2618 0.001 0.723 0.967 (0.806–1.162)
BB vs. AA Overall 9 1901 2618 0.029 0.29 0.84 (0.609–1.16)
BA vs. AA Overall 9 1901 2618 0.643 0.008* 0.827 (0.717–0.952)
BB+BA vs. AA Overall 9 1901 2618 0.446 0.016* 0.85 (0.744–0.97)
BB vs. BA+AA Overall 10 3456 4574 0.001 0.946 0.991 (0.768–1.28)
B vs. A Asian 5 783 1288 0.013 0.177 0.838 (0.648–1.083)
BB vs. AA Asian 5 783 1288 0.721 0.005* 0.65 (0.482–0.877)
BA vs. AA Asian 5 783 1288 0.892 0.001* 0.69 (0.55–0.867)
BB+BA vs. AA Asian 5 783 1288 0.994 <0.001 0.684 (0.555–0.843)
BB vs. BA+AA Asian 5 783 1288 0.005 0.559 0.869 (0.542–1.393)
B vs. A Caucasian 4 1118 1330 0.025 0.3 1.147 (0.885–1.486)
BB vs. AA Caucasian 4 1118 1330 0.024 0.455 1.283 (0.667–2.47)
BA vs. AA Caucasian 4 1118 1330 0.819 0.425 0.929 (0.774–1.114)
BB+BA vs. AA Caucasian 4 1118 1330 0.87 0.866 0.985 (0.829–1.171)
BB vs. BA+AA Caucasian 5 2673 3286 0.01 0.647 1.082 (0.771–1.518)
B vs. A Breast cancer 2 214 235 0.647 0.364 0.885 (0.68–1.152)
BB vs. AA Breast cancer 2 214 235 0.611 0.399 0.796 (0.47–1.351)
BA vs. AA Breast cancer 2 214 235 0.887 0.302 0.792 (0.509–1.233)
BB+BA vs. AA Breast cancer 2 214 235 0.765 0.276 0.793 (0.523–1.203)
BB vs. BA+AA Breast cancer 2 214 235 0.621 0.713 0.921 (0.592–1.432)
B vs. A Gastric Cancer 2 336 808 0.831 0.002* 0.728 (0.594–0.893)
BB vs. AA Gastric Cancer 2 336 808 0.75 0.026* 0.605 (0.389–0.942)
BA vs. AA Gastric Cancer 2 336 808 0.926 0.018* 0.706 (0.529–0.942)
BB+BA vs. AA Gastric Cancer 2 336 808 0.956 0.004* 0.681 (0.524–0.886)
BB vs. BA+AA Gastric Cancer 2 336 808 0.928 0.083 0.683 (0.444–1.051)
BB vs. BA+ AA Colon Cancer 2 1666 2058 0.243 0.034* 0.841 (0.716–0.987)
B vs. A PB 5 1172 1400 0.004 0.985 0.997 (0.759–1.311)
BB vs. AA PB 5 1172 1400 0.01 0.762 0.912 (0.502–1.658)
BA vs. AA PB 5 1172 1400 0.764 0.252 0.901 (0.754–1.077)
BB+BA vs. AA PB 5 1172 1400 0.468 0.385 0.928 (0.785–1.098)
BB vs. BA+AA PB 6 2727 3356 0.021 0.549 0.915 (0.683–1.225)
B vs. A HB 4 729 1218 0.007 0.658 0.934 (0.691–1.263)
BB vs. AA HB 4 729 1218 0.29 0.155 0.794 (0.577–1.091)
BA vs. AA HB 4 729 1218 0.624 0.005* 0.713 (0.564–0.902)
BB+BA vs. AA HB 4 729 1218 0.679 0.005* 0.734 (0.591–0.912)
BB vs. BA+AA HB 4 729 1218 0.012 0.782 1.073 (0.65–1.772)
B vs. A Y 5 1327 1792 0.13 0.036* 0.895 (0.807–0.993)
BB vs. AA Y 5 1327 1792 0.233 0.087 0.828 (0.668–1.028)
BA vs. AA Y 5 1327 1792 0.484 0.058 0.856 (0.729–1.005)
BB+BA vs. AA Y 5 1327 1792 0.258 0.028* 0.844 (0.725–0.982)
BB vs. BA+ AA Y 5 1327 1792 0.437 0.265 0.898 (0.742–1.086)
B vs. A N 4 574 826 0.004 0.37 1.179 (0.823–1.688)
BB vs. AA N 4 574 826 0.008 0.596 1.262 (0.534–2.98)
BA vs. AA N 4 574 826 0.628 0.042* 0.73 (0.54–0.988)
BB+BA vs. AA N 4 574 826 0.469 0.315 0.87 (0.663–1.142)
BB vs. BA+AA N 4 574 826 0.002 0.242 1.564 (0.739–3.308)
rs3804100
B vs. A Overall 8 2842 3081 0.422 0.254 1.076 (0.949–1.219)
BB vs. AA Overall 8 2842 3081 0.682 0.412 0.823 (0.516–1.311)
BA vs. AA Overall 8 2842 3081 0.487 0.603 1.041 (0.896–1.209)
BB+BA vs. AA Overall 8 2842 3081 0.758 0.641 1.035 (0.894–1.199)
BB vs. BA+AA Overall 8 2842 3081 0.243 0.061 1.343 (0.987–1.827)
B vs. A Asian 5 622 1005 0.152 0.71 1.037 (0.856–1.257)
BB vs. AA Asian 5 622 1005 0.66 0.153 0.655 (0.366–1.17)
BA vs. AA Asian 5 622 1005 0.276 0.543 0.917 (0.692–1.213)
BB+BA vs. AA Asian 5 622 1005 0.688 0.391 0.888 (0.677–1.165)
BB vs. BA+AA Asian 5 622 1005 0.105 0.079 1.346 (0.966–1.875)
B vs. A Caucasian 3 2220 2076 0.937 0.237 1.105 (0.937–1.304)
BB vs. AA Caucasian 3 2220 2076 0.618 0.494 1.337 (0.582–3.075)
BA vs. AA Caucasian 3 2220 2076 0.87 0.317 1.095 (0.917–1.308)
BB+BA vs. AA Caucasian 3 2220 2076 0.908 0.268 1.104 (0.927–1.315)
BB vs. BA+AA Caucasian 3 2220 2076 0.612 0.51 1.323 (0.576–3.039)
B vs. A PB 4 2269 2142 0.365 0.555 1.049 (0.896–1.228)
BB vs. AA PB 4 2269 2142 0.471 0.91 0.959 (0.465–1.977)
BA vs. AA PB 4 2269 2142 0.402 0.495 1.061 (0.894–1.26)
BB+BA vs. AA PB 4 2269 2142 0.384 0.514 1.057 (0.894–1.251)
BB vs. BA+ AA PB 4 2269 2142 0.479 0.911 0.96 (0.466–1.978)
B vs. A HB 4 573 939 0.308 0.266 1.124 (0.915–1.381)
BB vs. AA HB 4 573 939 0.512 0.336 0.74 (0.4–1.368)
BA vs. AA HB 4 573 939 0.346 0.872 0.975 (0.715–1.329)
BB+BA vs. AA HB 4 573 939 0.83 0.829 0.967 (0.715–1.308)
BB vs. BA+AA HB 4 573 939 0.146 0.033* 1.449 (1.031–2.036)
B vs. A Breast cancer 2 213 241 0.429 0.886 0.968 (0.617–1.517)
BA vs. AA Breast cancer 2 213 241 0.663 0.662 1.118 (0.679–1.839)
BB+BA vs. AA Breast cancer 2 213 241 0.533 0.867 1.042 (0.641–1.695)
B vs. A Gastric cancer 2 173 524 0.493 0.598 0.918 (0.669–1.261)
BB vs. AA Gastric cancer 2 173 524 0.259 0.168 0.481 (0.17–1.362)
BA vs. AA Gastric cancer 2 173 524 0.88 0.531 1.129 (0.772–1.652)
BB+BA vs. AA Gastric cancer 2 173 524 0.675 0.927 1.018 (0.703–1.473)
BB vs. BA+AA Gastric cancer 2 173 524 0.27 0.142 0.463 (0.165–1.295)
B vs. A Y 6 2543 2537 0.666 0.546 1.045 (0.905–1.207)
BB vs. AA Y 6 2543 2537 0.706 0.824 0.935 (0.516–1.695)
BA vs. AA Y 6 2543 2537 0.683 0.436 1.065 (0.909–1.248)
BB+BA vs. AA Y 6 2543 2537 0.688 0.467 1.059 (0.907–1.237)
BB vs. BA+AA Y 6 2543 2537 0.693 0.771 0.916 (0.508–1.653)
B vs. A N 2 299 544 0.075 0.741 1.091 (0.652–1.824)
BB vs. AA N 2 299 544 0.188 0.308 0.674 (0.316–1.439)
BA vs. AA N 2 299 544 0.108 0.507 0.855 (0.537–1.36)
BB+BA vs. AA N 2 299 544 0.563 0.499 0.855 (0.543–1.346)
BB vs. BA+AA N 2 299 544 0.073 0.789 0.716 (0.062–8.24)
rs4696480
B vs. A Overall 4 492 500 0.323 0.03* 1.216 (1.019–1.452)
BB vs. AA Overall 4 492 500 0.344 0.032* 1.463 (1.034–2.069)
BA vs. AA Overall 4 492 500 0.059 0.167 1.407 (0.867–2.281)
BB+BA vs. AA Overall 4 492 500 0.076 0.115 1.415 (0.919–2.179)
BB vs. BA+AA Overall 4 492 500 0.836 0.296 1.169 (0.872–1.568)
B vs. A Asian 2 232 230 0.628 0.772 1.039 (0.801–1.348)
BB vs. AA Asian 2 232 230 0.563 0.692 1.106 (0.671–1.824)
BA vs. AA Asian 2 232 230 0.711 0.77 0.939 (0.616–1.433)
BB+BA vs. AA Asian 2 232 230 0.981 0.968 0.992 (0.672–1.465)
BB vs. BA+AA Asian 2 232 230 0.382 0.596 1.125 (0.728–1.738)
B vs. A Caucasian 2 260 270 0.424 0.007* 1.393 (1.094–1.775)
BB vs. AA Caucasian 2 260 270 0.406 0.009* 1.903 (1.171–3.091)
BA vs. AA Caucasian 2 260 270 0.252 0.001* 1.984 (1.307–3.012)
BB+BA vs. AA Caucasian 2 260 270 0.261 0.001* 1.95 (1.317–2.887)
BB vs. BA+AA Caucasian 2 0.848 0.351 1.208 (0.812–1.798)
B vs. A PB 3 307 319 0.21 0.176 1.167 (0.933–1.458)
BB vs. AA PB 3 307 319 0.217 0.152 1.369 (0.891–2.105)
BA vs. AA PB 3 307 319 0.044 0.421 1.322 (0.67–2.611)
BB+BA vs. AA PB 3 307 319 0.056 0.349 1.336 (0.729–2.449)
BB vs. BA+AA PB 3 307 319 0.652 0.408 1.167 (0.809–1.681)
B vs. A Y 3 417 411 0.463 0.158 1.15 (0.947–1.396)
BB vs. AA Y 3 417 411 0.502 0.163 1.31 (0.897–1.916)
BA vs. AA Y 3 417 411 0.183 0.238 1.211 (0.881–1.665)
BB+BA vs. AA Y 3 417 411 0.227 0.158 1.239 (0.921–1.666)
BB vs. BA+AA Y 3 427 411 0.677 0.412 1.146 (0.827–1.588)
rs5743708
B vs. A Overall 2 309 572 <0.001 0.321 4.076 (0.255–65.24)
BA vs. AA Overall 2 309 572 0.022 0.338 1.697 (0.575–5.011)
BB+BA vs. AA Overall 4 2618 3487 <0.001 0.312 1.651 (1.348–2.022)
rs1898830
B vs. A Overall 3 2520 3146 0.391 0.939 1.003 (0.928–1.085)
BB vs. AA Overall 3 2520 3146 0.323 0.646 0.961 (0.809–1.14)
BA vs. AA Overall 3 2520 3146 0.056 0.806 0.971 (0.768–1.227)
BB+BA vs. AA Overall 3 2520 3146 0.075 0.813 0.975 (0.791–1.202)
BB vs. BA+AA Overall 3 2520 3146 0.998 0.77 0.977 (0.835–1.143)
B vs. A Caucasian 2 2309 2914 0.623 0.655 1.019 (0.939–1.106)
BB vs. AA Caucasian 2 2309 2914 0.779 0.972 1.003 (0.837–1.202)
BA vs. AA Caucasian 2 2309 2914 0.515 0.355 1.056 (0.941–1.187)
BB+BA vs. AA Caucasian 2 2309 2914 0.518 0.433 1.045 (0.936–1.167)
BB vs. BA+AA Caucasian 2 2309 2914 0.955 0.777 0.975 (0.822–1.158)
B vs. A PB 2 2309 2914 0.623 0.655 1.019 (0.939–1.106)
BB vs. AA PB 2 2309 2914 0.779 0.972 1.003 (0.837–1.202)
BA vs. AA PB 2 2309 2914 0.515 0.355 1.056 (0.941–1.187)
BB+BA vs. AA PB 2 2309 2914 0.518 0.433 1.045 (0.936–1.167)
BB vs. BA+AA PB 2 2309 2914 0.955 0.777 0.975 (0.822–1.158)
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Abbreviations: n, polymorphisms did not conform to HWE in the control group; P-B, population based; PH, P-value of Q test for heterogeneity test; PZ, means statistically significant (P<0.05); Y, polymorphisms conformed to HWE in the control group.

* P-value less than 0.05 was considered as statistically significant.

Figure 2. Meta-analysis of the association between TLR2 -196 to -174 del polymorphism and cancer risk.

Figure 2

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There are nine studies on rs3804099 polymorphism including a total of 3456 cases and 4574 controls [13–16,18,37–40]. According to overall analysis, rs3804099 significantly decreased cancer risk [BA vs. AA (OR = 0.827, 95% Cl = 0.717–0.952, P=0.008), BB+BA vs. AA (OR = 0.85, 95% Cl = 0.744–0.97, P=0.016)] (Figure 3). About Asians, rs3804099 polymorphism reduced the risk of cancer in the model of BA vs. AA (OR = 0.69, 95% Cl = 0.55–0.867, P=0.001) and BB vs. AA (OR = 0.65, 95% Cl = 0.482–0.877, P=0.005). In the subgroup of gastric cancer patients, we found that rs3804099 polymorphism reduced the risk of cancer [B vs. A (OR = 0.728, 95% Cl = 0.594–0.893, P=0.002), BB vs. AA (OR = 0.605, 95% Cl = 0.389–0.942, P=0.026), BA vs. AA (OR = 0.706, 95% Cl = 0.529–0.942, P=0.018), BB+BA vs. AA (OR = 0.681, 95% Cl = 0.524–0.886, P=0.004)] and the model of BB vs. BA+AA is not associated with reduced risk of gastric cancer. Part of the model in the hospital-based analysis was associated with reduced cancer risk [BA vs. AA (OR = 0.713, 95% Cl = 0.564–0.902, P=0.005), BB+BA vs. AA (OR = 0.734, 95% Cl = 0.591–0.912, P=0.005)].

Figure 3. Meta-analysis of the association between TLR2 rs3804009 del polymorphism and cancer risk.

Figure 3

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There are four studies on rs4696480 polymorphism including a total of 492 cases and 500 controls [14,17,18,38]. In some models of the overall analysis, rs4696480 significantly increased cancer risk [B vs. A (OR = 1.216, 95% Cl = 1.019–1.452, P=0.03); BB vs. AA (OR = 1.463, 95% Cl = 1.034–2.069, P=0.032)]. It is worth mentioning that rs4696480 makes Caucasians more susceptible to cancer [B vs. A (OR = 1.393, 95% Cl = 1.094–1.775, P=0.007), BB vs. AA (OR = 1.903, 95% Cl = 1.171–3.091, P=0.009), BA vs. AA (OR = 1.984, 95% Cl = 1.307–3.012, P=0.001), BB+BA vs. AA (OR = 1.95, 95% Cl = 1.317–2.887, P=0.001)]. Thus, we can conclude that a subgroup analysis by ethnicity suggests that rs4696480 is related to cancer risk in Caucasians, but not in other ethnic groups (Table 2 and Supplementary Figure S1).

For rs3804100 polymorphism, we collected eight publications which contained 2842 cases and 3081 controls [1,13–16,18,38,41]. But only in hospital-based analysis we found the model of BB vs. BA+AA (OR = 1.449, 95% Cl = 1.031–2.036, P=0.033) added to the risk of cancer. None of the other models showed any association between rs3804100 and cancer risk, either in the analysis of overall group or in other subgroups (Table 2 and Supplementary Figure S2).

As for rs5743708 [6,37,42] and rs1898830 [16,37], they were found to have no significant correlation with cancer, either in overall analysis or in other subgroup analysis (Table 2 and Supplementary Figures S3 and S4).

Sensitivity analysis and publication bias

By the way, we removed individual study one by one when conducted the sensitivity analysis. We did not observe any significant changes in the OR and corresponding 95% CI values, so the stability of our results was confirmed. All the details of sensitivity analysis are shown in the Supplementary Table S2 and Figure S5.

We used the Begg’s test to evaluate publication bias for selected literature. These funnel plots in Figure 4 showed the relationship between the cancer risk and the TLR2 polymorphism in this meta-analysis. Among the various polymorphic sites, the funnel plots were symmetrically distributed. This showed that there was no publication bias. The Egger’s test further analyzed the publication bias, and showed that no significant evidence of publication bias was observed in our study (P=0.937 for SNP rs4696480; P=0.291 for - 196 to - 174del polymorphism; P=0.991 for SNP rs3804099) (Supplementary Table S3).

Figure 4. Begg’s funnel plot for TLR2 polymorphisms and overall cancer publication bias (B vs. A).

Figure 4

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For Begg’s funnel plot, the x-axis is log (OR), and the y-axis is natural logarithm of OR. The horizontal line in the figure represents the overall estimated log (OR). The two diagonal lines indicate the pseudo 95% confidence limits of the effect estimate.

Results of FPRP and TSA

The FPRP values for positive findings at different prior probability levels are shown in Table 3. For -196 to -174del variant, almost all the statistical power high than 0.2, for the FPRP values, under the prior probability of 0.25, the FPRP values for each group is less than 0.2, except the five genetic models about Caucasian subgroup. Which means that the results on Caucasian subgroup are not stable, more studies are needed to illustrate the results. For the other positive results on rs3804099, rs3804100 and rs4696480, almost all the statistical power was higher than 0.5, and under the prior probability of 0.25, the FPRP values for each group is less than 0.2, which means that the results are reliable. The results of TSA are shown in Figure 5, we analyzed the required sample size of each polymorphism. The required sample size of -196 to -174del variant is approximately 39020, although the sample size in the current study did not meet the required number, we observed that the cumulative z-curve crossed the trial sequential monitoring boundary and the traditional significant boundary (Z = 1.96, α = 0.05), which means that our conclusions were robust with the sufficient evidence. For rs3804100 (required sample size: 9162) and rs4696480 (required sample size: 1984), we observed that the cumulative z-curve crossed the trial sequential monitoring boundary and the traditional significant boundary, and meet the required number. The TSA result about rs1898830 showed that the mutant allele performed the similar impact on cancer risk compare with the wild allele, no more samples are needed to confirm the result (Figure 5). However, The TSA results of rs3804099 and rs5743708 indicated that more objects are need to drag out the robust conclusion (Supplementary Figure S6).

Table 3. FPRP values for associations between the risk of cancer and the frequency of genotypes.

Comparison Subgroup n PZ OR (95% CI) Statistical power
0.25 0.1 0.01 0.001
(-196 to -174del)
B vs. A Overall 18 0.005* 1.468 (1.129–1.91) 0.564 0.022† 0.064† 0.427 0.883
BB vs. AA Overall 18 0.005* 1.716 (1.178–2.5) 0.237 0.054† 0.146† 0.652 0.950
BA vs. AA Overall 18 0.008* 1.408 (1.092–1.816) 0.683 0.035† 0.099† 0.547 0.924
BB+BA vs. AA Overall 18 0.007* 1.449 (1.107–1.897) 0.597 0.034† 0.096† 0.539 0.922
BB vs. BA+ AA Overall 18 0.013* 1.517 (1.092–2.107) 0.468 0.073† 0.192† 0.723 0.963
B vs. A Asian 11 0.043* 1.169 (1.005–1.361) 0.999 0.117† 0.285 0.814 0.978
BB+BA vs. AA Asian 11 0.033* 1.203 (1.015–1.427) 0.994 0.106† 0.262 0.796 0.975
B vs. A Caucasian 3 0.028* 3.291 (1.139–9.51) 0.073 0.532 0.773 0.974 0.997
BB vs. AA Caucasian 3 0.008* 9.878 (1.83–53.322) 0.014 0.621 0.831 0.982 0.998
BA vs. AA Caucasian 3 0.044* 3.156 (1.034–9.634) 0.096 0.577 0.804 0.978 0.998
BB+BA vs. AA Caucasian 3 0.034* 3.555 (1.098–11.51) 0.075 0.579 0.805 0.978 0.998
BB vs. BA+ AA Caucasian 3 0.006* 7.294 (1.752–30.369) 0.015 0.561 0.793 0.977 0.998
B vs. A PB 14 0.001* 1.576 (1.193–2.08) 0.364 0.011† 0.031† 0.263 0.783
BB vs. AA PB 14 0.001* 2.274 (1.43–3.616) 0.040 0.039† 0.108† 0.571 0.931
BA vs. AA PB 14 0.005* 1.543 (1.143–2.081) 0.427 0.031† 0.086† 0.510 0.913
BB+BA vs. AA PB 14 0.002* 1.624 (1.186–2.223) 0.310 0.023† 0.067† 0.441 0.888
BB vs. BA+ AA PB 14 0.001* 2.011 (1.317–3.07) 0.087 0.040† 0.111† 0.578 0.933
B vs. A Y 15 0.008* 1.447 (1.103–1.897) 0.603 0.036† 0.101† 0.551 0.925
BB vs. AA Y 15 0.004* 1.915 (1.227–2.991) 0.141 0.083† 0.214 0.750 0.968
BA vs. AA Y 15 0.02* 1.422 (1.057–1.915) 0.637 0.088† 0.224 0.760 0.970
BB+BA vs. AA Y 15 0.013* 1.494 (1.088–2.052) 0.510 0.072† 0.189 0.719 0.963
BB vs. BA+ AA Y 15 0.009* 1.673 (1.137–2.461) 0.290 0.085† 0.218 0.754 0.969
BA vs. AA N 3 0.039* 1.335 (1.015–1.757) 0.797 0.129† 0.307 0.830 0.980
rs3804099
BA vs. AA Overall 9 0.008* 0.827 (0.717–0.952) 0.999 0.024† 0.069† 0.448 0.891
BB+BA vs. AA Overall 9 0.016* 0.85 (0.744–0.97) 1.000 0.045† 0.125† 0.611 0.941
BB vs. AA Asian 5 0.005* 0.65 (0.482–0.877) 0.434 0.032† 0.091† 0.524 0.917
BA vs. AA Asian 5 0.001* 0.69 (0.55–0.867) 0.287 0.064† 0.170† 0.692 0.958
B vs. A Gastric cancer 2 0.002* 0.728 (0.594–0.893) 0.801 0.009† 0.025† 0.223 0.743
BB vs. AA Gastric cancer 2 0.026* 0.605 (0.389–0.942) 0.334 0.190† 0.413 0.886 0.987
BA vs. AA Gastric cancer 2 0.018* 0.706 (0.529–0.942) 0.652 0.076† 0.199† 0.732 0.965
BB+BA vs. AA Gastric cancer 2 0.004* 0.681 (0.524–0.886) 0.563 0.022† 0.063† 0.426 0.882
BB vs. BA+ AA Colon cancer 2 0.034* 0.841 (0.716-0.987) 0.998 0.093† 0.235 0.771 0.971
BA vs. AA HB 4 0.005* 0.713 (0.564–0.902) 0.712 0.020† 0.057† 0.400 0.871
BB+BA vs. AA HB 4 0.005* 0.734 (0.591–0.912) 0.807 0.019† 0.055† 0.391 0.867
B vs. A Y 5 0.036* 0.895 (0.807–0.993) 1.000 0.098† 0.247 0.783 0.973
BB+BA vs. AA Y 5 0.028* 0.844 (0.725–0.982) 0.999 0.078† 0.202 0.736 0.966
BA vs. AA N 4 0.042* 0.73 (0.54–0.988) 0.722 0.147† 0.341 0.851 0.983
rs3804100
BB vs. BA+ AA HB 4 0.033* 1.449 (1.031–2.036) 0.579 0.144† 0.336 0.848 0.983
rs4696480
B vs. A Overall 4 0.03* 1.216 (1.019–1.452) 0.990 0.085† 0.218 0.754 0.969
BB vs. AA Overall 4 0.032* 1.463 (1.034–2.069) 0.556 0.145† 0.337 0.848 0.983
B vs. A Caucasian 2 0.007* 1.393 (1.094–1.775) 0.725 0.029† 0.084† 0.501 0.910
BB vs. AA Caucasian 2 0.009* 1.903 (1.171–3.091) 0.168 0.143† 0.333 0.846 0.982
BA vs. AA Caucasian 2 0.001* 1.984 (1.307–3.012) 0.095 0.040† 0.110† 0.576 0.932
BB+BA vs. AA Caucasian 2 0.001* 1.95 (1.317–2.887) 0.095 0.026† 0.075† 0.470 0.899
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Statistical power was calculated using the number of observations in the subgroup and the OR and P values in this table. Abbreviations: CI, confidence interval; H-B, hospital based; HWE (Y), polymorphisms conformed to HWE in the control group.

*P-value less than 0.05 was considered as statistically significant.

†The significant result with the FPRP values less than 0.2 was considered a worthy finding.

Figure 6. LD analyses for TLR2 polymorphisms in populations from 1000 genomes Phase 3.

Figure 6

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The number of each cell represents r2 and white color cells show no LD between polymorphisms.

Figure 5. TSA for TLR2 polymorphism under the allele contrast model (B vs. A).

Figure 5

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LD analyses and in-silico analysis of TLR2 expression

LD analysis was conducted to evaluate the presence of bins in different TLR2 polymorphisms, aiming to understand the internal linkages, the results of which are shown in Figure 6. Highlighted, there is significant LD between rs4696480 and rs1898830 in CEU, CHB and CHS, and JPT populations (CEU: r2 = 0.52; CHB and CHS: r2 = 0.90; JPT: r2 = 1.0). The LD between rs3804099 and rs3804100 is also remarkable in CHB and CHS and JPT populations (CHB and CHS: r2 = 0.85; JPT: r2 = 0.86) (Supplementary Table S4). According to the result on GTEx portal data, we found that the mutant allele leads to an increase expression of TLR2 mRNA in rs1898830 (P=3.5*10−17), while the mutant allele of rs3804099 (P=2.5*10−14), rs3804100 (P=9.7*10−5) and rs4696480 (P=1.2*10−5) lead to a decreased expression of TLR2 (Figure 7).

Figure 7. In-silico analysis of TLR2 expression concerned to its polymorphisms.

Figure 7

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Discussion

TLRs are expressed in mast cells and several other cell types, which could recognize microbial components and trigger inflammatory response. TLR2 is type I transmembrane transporter which plays an important role in immune inflammatory response [43], and have been shown to influence host defense and disease progression [44]. There have been four previous meta-analyses on TLR2. But two of the studies were limited to gastric cancer [45,46]. One of these articles suggested that - 196 to - 174del was associated with the rise of cancer risk and the rs3804099 can decrease cancer risk [47]. Another article suggested that -196 to -174del had no relationship with cervical cancer [48]. For assessing the real influence of TLR2 on cancer risk, we collected more samples than before. And our meta-analysis combines many types of cancers to study the relationship between TLR2 polymorphism and cancer risk as comprehensively as possible.

For -196 to -174del, it is a 22-bp deletion at the promoter region of TLR2 gene. Transcriptional reduction in the TLR2 gene due to this substitution may significantly alter the function of the promoter [49]. Chen et al.’s meta-analysis [45] thought that this polymorphism is not associated with gastric cancer. Yang et al. [48] published a meta-analysis in 2018 suggesting that -196 to -174del had nothing to do with cervical cancer. And in our calculations, we revealed that the deletion of these 22 genes does increase the risk of cancer, especially among Caucasians. However, the subgroup calculations of gastric, breast and cervical cancers had no obvious significance.

Synonymous mutations are associated with disease, such as rs3804099 and rs3804100 of TLR2 [16]. We found that rs3804099 is protective against gastric cancer which is consistent with Wang et al. [47]. As for rs3804100, unfortunately, we only came to the conclusions related to cancer in the subgroup of hospital-based. This conclusion is extremely contingent because of the small number of samples and the limitations of the source of the sample. Taking into account the vast majority of calculations and references, we reserve the conclusion that rs3804100 is not related to cancer. And we are the first meta-analysis involving rs4696480. The overall analysis of B vs. A and BB vs. AA shown that rs4696480 has increased the risk of cancer. At the same time, the calculation results also show that its influence on cancer is particularly obvious among the Caucasian population.

Although our conclusions about -196 to -174del, rs3804099 and rs3804100 are consistent with the previous two meta-analyses, we included more case–control studies, so our meta-analysis is more convincing. And we also clearly observe that ‘ethnic’ factors are critical in assessing the role of TLR2 in cancer risk. The calculation of -196 to -174del and rs4696480 both found that Caucasians make a significant increase in the cancer risk. And in the model of BB vs. AA and BA vs. AA, rs3804099 deduce the cancer risk in Asians. Furthermore, as the results showing -196 to -174del and rs4696480 are associated with the tumorigenesis, so that these polymorphisms could be a potential biomarker to remind people with the polymorphism pay more attention to the occurrence of cancer, and solve the problem as soon as possible. In the current study, we also evaluated the LD between different polymorphisms of TLR2, we found that there are significantly LD among rs4696480 and rs1898830, rs3804099 and rs3804100. Based on the results, it could guide the researchers to put these polymorphisms together when assess their effect on cancer risks or other bioscience mechanisms. At the same time, we should also be aware of some of the limitations of our article. First of all, based on the results of TSA, we found that the sample size of -196 to -174 del, rs3804100 and rs4696480 is enough to generate the reliable conclusion in the current study, however, larger number of patients are needed to confirm the effect of rs3804099, rs1898830 and rs5743708 to cancer risks. Second, we lack in-depth studies of the effects of environment, lifestyle, bacterial infections and other factors of cancer risk.

Conclusion

Our meta-analysis suggested that -196 to -174del increased the risk of cancer; rs4696480 increases the risk of cancer in Caucasians; rs3804099 reduced the risk of cancer, especially gastric cancer. While there is no direct evidence showing that rs5743708,3804100 and rs1898830 are related to cancer.

Supplementary Material

Supplementary Figure S1-S6 and Tables S1-S4
Click here for additional data file. (1.6MB, pdf)

Abbreviations

CEU

Utah residents with Northern and Western European ancestry from the CEPH collection

CHB

Han Chinese in Beijing, China

CHS

Southern Han Chinese, China

FPRP

false-positive report probability

HWE

Hardy–Weinberg equilibrium

JPT

Japanese in Tokyo, Japan

LD

linkage disequilibrium

NOS

Newcastle–Ottawa Scale

OR

odds ratio

TLR2

Toll-like receptor-2

TSA

trial sequential analysis

95% CI

95% confidence interval

Contributor Information

Si-Min Wang, Email: wsm_cmu@163.com.

Li Zuo, Email: zuoli1978@hotmail.com.

Funding

The authors declare that there are no sources of funding to be acknowledged.

Competing Interests

The authors declare that there are no competing interests associated with the manuscript.

Author Contribution

Conception and design: S.-L.G. and Y.-D.C. Collection and assembly of data: C.Y., J.C. and L.-F.Z. Data analysis and interpretation: S.-L.G., Y.-D.C. and L.Z. Manuscript writing: S.-L.G., YD.C. and S.-M.W. Final approval of manuscript: all authors.

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Associated Data

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Supplementary Materials

Supplementary Figure S1-S6 and Tables S1-S4
Click here for additional data file. (1.6MB, pdf)

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