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. 2020 Apr 7;157:11. doi: 10.1186/s41065-020-00125-1

No association between three polymorphisms (rs1800629, rs361525 and rs1799724) in the tumor necrosis factor-α gene and susceptibility to prostate cancer: a comprehensive meta-analysis

Lei Yin 1,#, Chuang Yue 2,#, Hongwei Jing 1, Hongyuan Yu 1, Li Zuo 2,✉, Tao Liu 1,✉
PMCID: PMC7137332  PMID: 32264962

Abstract

Background

Inflammation is one of the factors associated with prostate cancer. The cytokine tumor necrosis factor-alpha (TNF-α) plays an important role in inflammation. Several studies have focused on the association between TNF-α polymorphisms and prostate cancer development. Our meta-analysis aimed to estimate the association between TNF-α rs1800629 (− 308 G/A), rs361525 (− 238 G/A) and rs1799724 polymorphisms and prostate cancer risk.

Methods

Eligible studies were identified from electronic databases (PubMed, Embase, Wanfang and CNKI) using keywords: TNF-α, polymorphism, prostate cancer, until Nov 15, 2019. Odds ratios (ORs) with 95% confidence intervals (CIs) were applied to determine the association from a quantitative point-of-view. Publication bias and sensitivity analysis were also applied to evaluate the power of current study. All statistical analyses were done with Stata 11.0 software.

Results

Twenty-two different articles were included (22 studies about rs1800629; 8 studies for rs361525 and 5 studies related to rs1799724). Overall, no significant association was found between rs1800629 and rs1799724 polymorphisms and the risk of prostate cancer in the whole (such as: OR = 1.03, 95% CI = 0.92–1.16, P = 0.580 in the allele for rs1800629; OR = 0.95, 95% CI = 0.84–1.07, P = 0.381 in the allele for rs1799724). The rs361525 polymorphism also had no association with prostate cancer in the cases (OR = 0.93, 95% CI = 0.66–1.32, P = 0.684 in the allele) and ethnicity subgroup. The stratified subgroup of genotype method, however, revealed that the rs361525 variant significantly decreased the risk of prostate cancer in the Others (OR = 0.65, 95% CI = 0.47–0.89, P = 0.008, A-allele vs G-allele) and PCR-RFLP (OR = 2.68, 95% CI = 1.00–7.20, P = 0.050, AG vs GG or AA+AG vs GG) methods.

Conclusions

In summary, the findings of the current meta-analysis indicate that the TNF-α rs1800629, rs361525 and rs1799724 polymorphisms are not correlated with prostate cancer development, although there were some pooled positive results. Further well-designed studies are necessary to form more precise conclusions.

Keywords: Tumor necrosis factor-alpha, Prostate cancer, Polymorphism, Meta-analysis, Susceptibility

Background

Prostate cancer (PCA) is the second most frequent tumor in men worldwide, with 1.27 million new cases and 0.35 million deaths in 2018 [1, 2]. The incidence and mortality of PCA are correlated with increasing age, and the average age at the time of diagnosis is over 66 years in some regions. Additionally, there is also evidence of an association between ethnicity and PCA; for example, the incidence rate in African-American men is 158.3 newly diagnosed cases/100,000, which is higher than that in White men, and their mortality is about twice that of White men according to Panigrahi et al. [3]. Several factors may contribute to this disparity, such as differences in diet, habits/customs, and genetic/environmental factors.

There is growing evidence that chronic inflammation is involved in the regulation of cellular events in prostate carcinogenesis, including disruption of the immune response and regulation of the tumor microenvironment [4]. One of the best surrogates of chronic inflammation in PCA is the cytokine tumor necrosis factor alpha (TNF-α) [5, 6]. Chadha et al. indicated the median TNF-α levels in serum was significantly higher (P < 0.05) in the control group (5.12 pg/ml) than in the localized PCA group (2.20 pg/ml). Moreover, TNF-α was the strongest single predictor between localized and metastatic PCA (Area Under Curve, AUC = 0.992) and was higher than the PSA value (AUC = 0.963). Taken together, these results suggest that TNF-α may be considered a novel serum biomarker for the diagnosis of PCA [7].

The TNF-α gene, also termed DIF/TNFSF2/TNLG1F, is located in the class III region of the major histocompatibility complex (MHC III) and mapped to chromosome 6p21.33 with 4 exons [8, 9]. Several single nucleotide polymorphisms (SNPs) in this gene have been widely reported and have been associated with the risk of several cancers, such as PCA, breast cancer, and lung cancer [10–12]. Rs1800629 is one of the most common SNPs, with a G to A transition at the − 308 nucleotide in the promoter of the transcription initiation site, which may affect the serum expression of TNF-α [13]. Another common SNP named rs361525 is located at the − 238 site, where a G to A substitution is shown, and may influence TNF-α in the serum [14]. The rs1799724 (C to T transition) and rs1799964 (T to C transition) SNPs have been reported in recent years [15, 16]; however, to date, it is not known whether these two SNPs can affect the expression of TNF-α.

Previously, two meta-analyses focused on TNF-α polymorphisms and PCA risk have been published: Cai et al. identified 12 case-control studies and concluded that the rs1800629 polymorphism had an increased association with PCA risk in the GA vs. GG genetic model (OR = 1.19, 95% CI = 1.04–1.37) [17]. Ma et al., however, suggested that the rs361525 polymorphism was not associated with PCA, and the rs1800629 polymorphism, which is also the susceptible SNP for PCA, only had a significant association in healthy volunteers (AG vs. GG: OR = 1.47, 95% CI = 1.04–2.08) [18]. Due to these inconclusive results, as well as the publication of some additional studies, it was necessary to re-combine all of the articles, including 22 different case-control studies [15, 16, 19–36], to conduct an updated meta-analysis.

Methods

Literature search and inclusion criteria

We performed a literature search for all eligible articles regarding the association between four TNF-α polymorphisms and PCA risk on multiple electronic databases, including PubMed, Embase, Wanfang and CNKI, using the following keywords: ‘tumor necrosis factor alpha OR TNF-α’ AND ‘polymorphism OR variation OR mutation’ AND ‘prostate cancer OR carcinoma OR neoplasm OR tumor’ until Nov 15, 2019.

Relevant studies were selected based on the following inclusion criteria: (1) case-control studies addressing the correlation between a TNF-α polymorphism and PCA risk; (2) studies containing sufficient genotype data on both the cases and controls; and (3) the largest sample sizes were selected among articles with overlapping study groups. The exclusion criteria were (1) conference abstracts, case reports, reviews and duplicated information; and (2) inadequate genotype data.

Data extraction

The following data were gathered from each eligible study: the first author’s name, publication year, country, sample size for the case and control groups, source of control, Hardy-Weinberg equilibrium (HWE) of the controls, genotyping techniques and the genotype of the cases and controls.

Statistical analysis

The strength of the association between the four TNF-α polymorphisms and PCA susceptibility was measured by the odds ratio (OR) with 95% confidence interval (CI) in 3 (allele, heterozygous and dominant) genetic models. The significance of the pooled OR was assessed by the Z-test, and P < 0.05 was considered to be statistically significant. The between-study heterogeneity was evaluated by the Q-test. In cases where significant heterogeneity was detected, if P < 0.1, indicating the presence of heterogeneity, a random-effects model was selected; otherwise, a fixed-effects model was applied [37, 38]. Publication bias was inspected using Begg’s test, and Egger’s test was used to measure the degree of asymmetry. In both tests, P < 0.05 was considered statistically significant [39]. The HWE of the control group was specified through the chi-square test, where P < 0.05 was considered significant [40]. Sensitivity analyses were done to evaluate whether a single study influenced the overall pooled results by omitting each study in turn. All statistical tests used in this study were performed using Stata (version 11.0; StataCorp LP, College Station, TX).

Results

Characteristics of selected studies

A total of 168 published articles were retrieved from the PubMed, Embase, Wanfang and CNKI databases in accordance with the selection criteria. Finally, 20 different articles (22 case-control studies) were included in our meta-analysis (Table 1, Fig. 1) [15, 16, 19–36]. Of the 22 studies, TNF-α rs1800629 was analyzed in 22 studies; rs361525, in 8 studies; rs1799724, in 5 studies; and rs1799964, in 3 studies. Only three available reports investigated rs1799964 and PCA susceptibility, so we did not analyze this association. Table 1 shows the features and related information of the included studies. In addition, we checked the Minor Allele Frequency (MAF) reported for the five main worldwide populations in the 1000 Genomes Browser for each SNP: East Asian (EAS), European (EUR), African (AFR), American (AMR), and South Asian (SAS) (Fig. 2).

Table 1.

Characteristics of the studies eligible for current meta-analysis

Author Year Country Ethnicity Case Control SOC Cases Controls HWE Genotype
MM MW WW MM MW WW
rs1800629
 Jones 2013 USA African-American 279 535 HB 5 103 171 14 153 368 0.687 Illumina’s Golden gate
 Zabaleta 2008 USA African-American 67 130 HB 2 9 56 3 33 94 0.958 Sequence
 Berhane 2012 India Asian 150 150 HB 6 24 120 1 18 131 0.662 ARMS-PCR
 Wu 2003 China-Taiwan Asian 96 126 HB 2 20 74 1 22 103 0.882 PCR-RFLP
 Alidoost 2019 Iran Asian 100 110 HB 0 16 84 0 14 96 0.476 PCR-RFLP/ARMS-PCR
 Kesarwani 2009 India Asian 197 256 HB 1 21 175 4 37 215 0.115 PCR-RFLP
 Ali 2019 Iraq Asian 30 30 PB 12 18 0 24 6 0 0.543 PCR-RFLP
 Ge 2007 China Asian 245 245 HB 2 39 204 2 48 195 0.609 TaqMan
 Dluzniewski 2012 USA Caucasian 468 468 HB 14 113 341 6 126 336 0.125 MassArray
 Pardo 2019 Venezuela Caucasian 40 40 HB 0 6 34 0 11 29 0.313 PCR-RFLP
 Zabaleta 2008 USA Caucasian 479 400 HB 9 148 322 10 118 272 0.505 Sequence
 Sáenz-López 2008 Spain Caucasian 296 310 PB 5 70 221 2 52 256 0.714 TaqMan
 Moore 2009 USA Caucasian 949 857 PB 21 228 700 11 205 641 0.231 TaqMan
 Danforth 2008 USA Caucasian 1155 1380 PB 26 336 793 45 418 926 0.795 TaqMan/MGBEclipse assay
 Danforth 2008 USA Caucasian 1111 1125 PB 25 294 792 33 286 806 0.217 TaqMan/MGBEclipse assay
 Ribeiro 2012 Portugal Caucasian 449 557 PB 8 115 326 7 143 407 0.155 TaqMan
 Wang 2009 USA Caucasian 251 250 PB 12 79 160 9 69 172 0.529 TaqMan
 Bandil 2017 India Asian 105 115 HB 9 15 81 4 7 104 < 0.001 ARMS-PCR
 Omrani 2008 Iran Asian 41 105 HB 0 36 5 3 99 3 < 0.001 ASO-PCR
 McCarron 2002 United Kingdom Caucasian 239 220 HB 6 66 167 13 57 150 0.023 ARMS-PCR
 OH 2000 USA Caucasian 73 73 HB 0 53 20 0 53 20 < 0.001 allele-specific PCR
 Zhang 2010 USA Caucasian 116 128 PB 116 128 CBMALD-TOF-MS
rs361525
 Pardo 2019 Venezuela Caucasian 40 40 HB 0 4 36 0 1 39 0.936 PCR-RFLP
 OH 2000 USA Caucasian 73 73 HB 0 23 50 0 23 50 0.11 allele-specific PCR
 Zabaleta 2008 USA Caucasian 471 385 HB 6 41 424 0 39 346 0.295 Sequence
 Alidoost 2019 Iran Asian 100 110 HB 0 10 90 0 5 105 0.807 PCR-RFLP/ARMS-PCR
 Danforth 2008 USA Caucasian 1114 1126 PB 1 121 992 3 100 1023 0.737 TaqMan/MGBEclipse assay
 Ge 2007 China Asian 245 245 HB 0 10 235 0 22 223 0.461 TaqMan
 Zabaleta 2008 USA African-American 64 126 HB 0 6 58 2 10 114 0.006 Sequence
 Bandil 2017 India Asian 105 115 HB 12 60 33 20 86 9 < 0.001 ARMS-PCR
rs1799724
 Danforth 2008 USA Caucasian 1139 1378 PB 13 203 923 14 254 1110 0.9 TaqMan/MGBEclipse assay
 Danforth 2008 USA Caucasian 1108 1101 PB 17 183 908 19 220 862 0.257 TaqMan/MGBEclipse assay
 Kesarwani 2009 India Asian 197 256 HB 4 57 136 4 56 196 1 PCR-RFLP
 Zabaleta 2008 USA African-American 464 372 HB 6 59 399 8 41 323 < 0.001 Sequence
 Zabaleta 2008 USA Caucasian 6 14 HB 3 0 3 7 0 7 < 0.001 Sequence
rs1799964
 Danforth 2008 USA Caucasian 1142 1375 PB 60 361 721 58 441 876 0.791 TaqMan/MGBEclipse assay
 Danforth 2008 USA Caucasian 1143 1155 PB 54 370 719 64 377 714 0.129 TaqMan/MGBEclipse assay
 Kesarwani 2009 India Asian 197 256 HB 90 64 43 83 91 82 < 0.001 PCR-RFLP
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HB hospital-based, PB population-based, SOC source of control, PCR-FLIP polymerase chain reaction and restrictive fragment length polymorphism; ARMS amplification refractory mutation system, HWE Hardy–Weinberg equilibrium of control group, W wild type-allele, M mutant-allele

Fig. 1.

Fig. 1

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A flowchart illustrating the search strategy about TNF-α rs1800629, rs361525, rs1799724 and rs1799964 polymorphisms and PCA risk was shown

Fig. 2.

Fig. 2

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MAF for the TNF-α rs1800629, rs361525 and rs1799724 polymorphsms from 1000 Genomes Browser. Vertical line, MAF; Horizontal line, ethnicity type. EAS: East Asian; EUR: European; AFR: African; AMR: American; SAS: South Asian

Pooled analysis results

Overall, the findings did not support an association between the TNF-α rs1800629 polymorphism and PCA susceptibility in the allele (OR = 1.03, 95% CI = 0.92–1.16, P = 0.580, Fig. 3a), heterozygous (OR = 1.04, 95% CI = 0.93–1.17, P = 0.486) and dominant (OR = 1.06, 95% CI = 0.94–1.18, P = 0.353) genetic models. To evaluate the power and stability, some studies not consistent with HWE were excluded, and similar results were obtained. Stratified analyses by ethnicity, source of control and genotyping methods were conducted, and no significant association was detected (Table 3).

Fig. 3.

Fig. 3

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Meta-analysis. a. Forest plots of TNF-α rs1800629 polymorphism and PCA risk (A-allele vs. G-allele). b. Forest plot of TNF-α rs1799724 polymorphism and PCA risk (T-allele vs. C-allele). c. Forest plot of TNF-α rs361525 polymorphism and PCA risk (AA vs. GG). d. Forest plot of TNF-α rs361525 polymorphism and PCA risk (A-allele vs. G-allele) on subgroup of genotyping method (Others). e. Forest plot of TNF-α rs361525 polymorphism and PCA risk (A-allele vs. G-allele) on subgroup of genotyping method (PCR-RFLP)

Table 3.

Publication bias tests (Begg’s funnel plot and Egger’s test for publication bias test) for rs1800629 and rs361525 polymorphisms

Egger’s test Begg’s test
Genetic type Coefficient Standard error t P value 95%CI of intercept z P value
rs1800629
 A-allele vs. G-allele 0.009 0.681 0.01 0.989 (−1.418–1.437) 0.21 0.833
 AG vs. GG 0.331 0.528 0.63 0.539 (−0.779–1.440) 0.1 0.922
 AA+AG vs. GG 0.046 0.619 0.07 0.941 (−1.249–1.341) 0.33 0.74
rs361525
 A-allele vs. G-allele −0.216 1.259 0.17 0.87 (−2.866–3.297) 0.12 0.902
 AG vs. GG −0.293 0.935 −0.3 0.765 (−2.582–1.996) − 0.12 1
 AA+AG vs. GG −0.303 0.938 −0.3 0.757 (−2.599–1.991) − 0.12 1
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For the TNF-α rs1799724 polymorphisms, no significant associations were identified in the cases and subgroups. Further, the rs1799724 polymorphism was not significantly associated with PCA in the allele (OR = 0.95, 95% CI = 0.84–1.07, P = 0.381, Fig. 3b), heterozygous (OR = 1.01, 95% CI = 0.80–1.27, P = 0.951) and dominant genetic models (OR = 0.95, 95% CI = 0.83–1.07, P = 0.390).

For the TNF-α rs361525 polymorphism, although no association was found in the allele (OR = 0.93, 95% CI = 0.66–1.32, P = 0.684), heterozygous (OR = 0.86, 95% CI = 0.52–1.41, P = 0.542, Fig. 3c) and dominant models (OR = 0.85, 95% CI = 0.52–1.39, P = 0.525), for HWE, ethnicity and source of control, pooled significant relationships were observed in genotyping subgroups, such as Others (OR = 0.65, 95% CI = 0.47–0.89, P = 0.008 for A-allele vs. G-allele, Fig. 3d) and PCR-RFLP (OR = 2.68, 95% CI = 1.00–7.20, P = 0.050, Fig. 3e).

Heterogen

Heterogeneity and publication bias

As shown in Table 2, heterogeneity among the studies was found in all three genetic comparisons for all 3 SNPs (rs1800629, rs361525 and rs1799724).

Table 2.

The pooled ORs and 95%CIs for the association between TNF polymorphisms and prostate cancer susceptibility in total and stratified analysis

Variables N Case/Control M-allele vs. W-allele
OR(95%CI) PhP 		MW vs. WW
OR(95%CI) PhP 		MM + MW vs. WW
OR(95%CI) PhP
rs1800629
 Total 22 6936/7619 1.03 (0.92–1.16)0.001 0.580 1.04 (0.93–1.17)0.040 0.486 1.06 (0.94–1.18)0.013 0.353
 HWE 18 7485/6792 1.03 (0,92–1.16)0.006 0.584 1.04 (0,93–1.16)0.091 0.509 1.05 (0,94–1.17)0.051 0.429
Ethnicity
 Asian 8 964/1137 1.03 (0.68–1.56)0.000 0.881 1.04 (0.70–1.56)0.038 0.845 1.09 (0.70–1.71)0.006 0.698
 Caucasian 12 5626/5817 1.01 (0.94–1.08)0.223 0.838 1.02 (0.94–1.11)0.525 0.672 1.02 (0.94–1.11)0.433 0.625
 African-American 2 346/665 0.93 (0.47–1.86)0.049 0.843 0.87 (0.28–2.67)0.009 0.804 0.90 (0.34–2.37)0.016 0.829
SOC
 HB 14 2579/2973 1.02 (0.86–1.22)0.012 0.787 1.00 (0.81–1.22)0.023 0.972 1.01 (0.82–1.24)0.012 0.787
 PB 8 4357/4646 1.04 (0.89–1.22)0.009 0.600 1.04 (0.94–1.14)0.298 0.483 1.04 (0.95–1.14)0.199 0.425
Genotyping
 Others 5 977/1309 1.07 (0.91–1.26)0.420 0.420 0.97 (0.62–1.53)0.021 0.900 1.07 (0.79–1.45)0.079 0.668
 Sequencing 2 546/530 0.94 (0.75–1.19)0.166 0.608 0.76 (0.34–1.70)0.055 0.505 0.80 (0.41–1.55)0.086 0.506
 TaqMan 7 4456/4733 1.04 (0.92–1.17)0.081 0.520 1.02 (0.93–1.12)0.278 0.638 1.02 (0.93–1.12)0.152 0.672
 PCR-RFLP 5 463/562 0.74 (0.43–1.28)0.030 0.280 0.90 (0.63–1.29)0.263 0.565 0.89 (0.63–1.26)0.186 0.520
 ARMS-PCR 3 494/485 1.56 (0.74–3.29)0.001 0.239 1.28 (0.93–1.78)0.163 0.135 1.54 (0.80–2.97)0.024 0.192
rs361525
 Total 8 2212/2222 0.93 (0.66–1.32)0.007 0.684 0.86 (0.52–1.41)0.000 0.542 0.85 (0.52–1.39)0.000 0.525
 HWE 6 2043/1979 1.11 (0,91–1.35)0.111 0.321 1.02 (0,69–1.52)0.055 0.905 1.05 (0,73–1.52)0.803 0.794
Ethnicity
 Asian 3 450/470 0.72 (0.34–1.50)0.039 0.380 0.55 (0.15–1.99)0.002 0.360 0.54 (0.15–2.00)0.001 0.357
 Caucasian 4 1698/1624 1.16 (0.94–1.44)0.673 0.164 1.16 (0.94–1.44)0.673 0.164 1.16 (0.94–1.44)0.673 0.164
 African-American 1 64/126 – – –
Genotyping
 Others 2 178/188 0.65 (0.47–0.89)0.111 0.008 0.44 (0.09–2.25)0.002 0.326 0.44 (0.08–2.28)0.002 0.325
 Sequencing 2 535/511 1.07 (0.72–1.57)0.595 0.746 0.90 (0.59–1.38)0.590 0.633 0.98 (0.65–1.48)0.999 0.936
 PCR-RFLP 2 140/150 2.59 (0.98–6.85)0.628 0.055 2.68 (1.00–7.20)0.626 0.050 2.68 (1.00–7.20)0.626 0.050
 TaqMan 2 1359/1371 0.77 (0.30–2.01)0.017 0.599 0.78 (0.28–2.20)0.011 0.640 0.77 (0.28–2.13)0.013 0.620
rs1799724
 Total 5 2914/3121 0.95 (0.84–1.07)0.169 0.381 1.01 (0.80–1.27)0.054 0.951 0.95 (0.83–1.07)0.120 0.390
 HWE 3 2444/2735 0.99 (0,78–1.26)0.042 0.930 0.98 (0,74–1.30)0.037 0.896 0.99 (0,75–1.30)0.032 0.931
 Caucasian 3 2253/2493 0.90 (0.79–1.03)0.403 0.115 0.88 (0.76–1.02)0.196 0.082 0.88 (0.76–1.02)0.400 0.089
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Ph: value of Q-test for heterogeneity test; P: Z-test for the statistical significance of the OR; HB hospital-based, PB population-based, SOC source of control, PCR-FLIP polymerase chain reaction and restrictive fragment length polymorphism, ARMS amplification refractory mutation system HWE, Hardy–Weinberg equilibrium of control group, W wild type-allele, M mutant-allele

The publication bias was assessed by applying Begg’s funnel plot and Egger’s test. Based on the samples and publications, we tested two SNPs, rs1800629 and rs361525. The shape of the funnel plots was symmetrical, and the Egger’s test supported no existence of publication bias in any of the three comparisons for the rs1800629 (t = 0.01, p = 0.989 for Egger’s test; z = 0.21, p = 0.833 for Begg’s test, Fig. 4a, b) and rs361525 (t = − 0.3, p = 0.765 for Egger’s test; z = − 0.12, p = 1 for Begg’s test, Fig. 4c, d) polymorphisms (Table 3).

Fig. 4.

Fig. 4

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Publication bias. a. Begg’s funnel plot for publication bias test (A-allele vs. G-allele). b. Egger’s publication bias plot (A-allele vs. G-allele). c. Begg’s funnel plot for publication bias test (A-allele vs. G-allele). d. Egger’s publication bias plot (A-allele vs. G-allele)

Sensitivity analysis

We performed sensitivity analyses to assess the effect of a specific publication on the overall estimate. Similar with publication bias, we also analyzed both rs1800629 and rs361525 (Fig. 5a, b), and no significant changes were observed when excluding each study in any of the three genetic models (allele, heterozygous and dominant). Thus, the final pooled results are both stable and reliable.

Fig. 5.

Fig. 5

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Sensitivity analysis. a. Sensitivity analysis for TNF-α rs1800629 polymorphism and RA risk (A-allele vs. G-allele). b. Sensitivity analysis between TNF-α rs361525 polymorphism and RA risk (A-allele vs. G-allele)

Discussion

There is evidence to suggest that chronic inflammation is prevalent in the adult prostate and may contribute to disease development in the form of promoting tumor initiation and progression [5, 41]. Therefore, chronic inflammation has been considered an enabling characteristic in the development of cancers [42], such as PCA. Several previous epidemiological studies have been explored to make a connection between inflammation and PCA development, showing evidence that associates symptomatic prostatitis with PCA risk [43–45]. For example, men with prostatitis have increased serum PSA levels, and while a medical diagnosis for prostatitis symptoms may be received initially, they may be screened for PCA and might be diagnosed with PCA in the end. Furthermore, many men with prostatic inflammation without symptoms also have increased PSA values, which may increase the odds of visits to the doctor, and they may be identified as having PCA [45]. Taken together, these observations indicate that the detection of inflammation in the prostate may be helpful for us to better identify PCA patients; however, there are no specific biomarkers of prostate inflammation to date.

Studying both pro- and anti-inflammatory cytokine genes is essential for PCA [21]. TNF-α, as a main mediator of inflammation, has a vital role in PCA development [10]. By considering the capacity of TNF-α promoter SNPs (rs1800629 and rs361525), and the influence of their gene expression [13, 14], these two SNPs have been identified as potential functional variants and as novel biomarkers for the early detection for PCA susceptibility.

Several studies and two meta-analyses have examined the association between TNF-α gene polymorphisms and PCA risk [15–36]. Nevertheless, the findings were inconsistent, possibly due to the small samples or relatively low statistical power of the included studies. Therefore, a current, updated meta-analysis with a comprehensive assessment that included more eligible studies was performed to evaluate the impact of TNF-α gene polymorphisms (rs1800629, rs361525 and rs1799724) on PCA susceptibility, which may overcome the aforementioned disadvantages [15, 16, 19–36]. For the TNF-α rs1800629 polymorphism, the findings from 20 studies, including 6936 cases and 7619 controls, did not support an association between this variant and PCA risk [15, 16, 19–36]. To the best of our knowledge, for the rs1799724 [15, 16, 35] polymorphism, which was analyzed for the first time, no significant association was detected from 3 studies, which included 2914 cases and 3121 controls. For the rs361525 polymorphism [15, 20, 21, 24, 28, 30, 35], pooled significant relationships were observed in the genotyping method subgroups. Cumulatively, we believe no association exists between the four common TNF-α polymorphisms and PCA risk based on the current evidence.

Despite a comprehensive analysis of the current associations between the four TNF-α polymorphisms and the risk of developing PCA, there are some limitations that should be considered. First, the number of samples remains insufficient, especially for the rs1799724 and rs1799964 polymorphisms and ethnicities in some polymorphisms, such as African-American, Asian, African and mixed populations, which perhaps leads to imbalance and publication bias. Second, gene-gene, SNP-SNP and gene-environment interactions should be taken into consideration. Other covariates, including prostate health index, age, family history, environmental factors, Gleason score, TNM stage and living habits, should be better observed, which will help us to draw an exact conclusion. Third, the protein expression level of TNF-α in different polymorphisms should also be observed and be reevaluated by meta-analysis in the future research.

In summary, our study presents evidence that three of the most common TNF-α polymorphisms (rs1800629, rs361525 and rs1799724) are not associated with PCA risk, which should be verified in the future, but they may be poised to become serum biomarkers in several subgroups for the detection of PCA susceptibility.

Acknowledgements

Not applicable.

Abbreviations

PCA

Prostate cancer

TNF-α

Tumor necrosis factor alpha

ORs

Odds ratios

CIs

Confidence intervals

AUC

Area under curve

MHC III

Major histocompatibility complex

SNPs

Single nucleotide polymorphisms

HWE

Hardy-Weinberg equilibrium

Authors’ contribution

TL, LY and LZ conceived the study. LZ, CY and HJ searched the databases and extracted the data. TL and HY analyzed the data. TL wrote the draft of the paper. LZ reviewed the manuscript. The author(s) read and approve the final manuscript.

Funding

This work was supported by the High-Level Medical Talents Training Project of China (NO.2016CZBJ035).

Availability of data and materials

All the data generated in the present research is contained in this manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Lei Yin and Chuang Yue contributed equally to this work.

Contributor Information

Li Zuo, Email: chenyuhuameta@sina.com.

Tao Liu, Email: renkeweijy@163.com.

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Data Availability Statement

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