Association between five types of Tumor Necrosis Factor-α Gene Polymorphism and Hepatocellular Carcinoma Risk: A Meta-Analysis

Abstract

Background: Research focusing on the relationship between five types of tumor necrosis factor-alpha (TNF-α) SNPs and the risk of hepatocellular carcinoma (HCC) were still controversial. Hereby, we performed a meta-analysis to determine the association between TNF-α promoter SNPs: -1031 T/C, -863 C/A, -857 C/T, -308 G/A, and -238 G/A with HCC risk.

Methods: We interrogated articles from journal database: PubMed, Pro-Quest, EBSCO, Science Direct, and Springer to determine the relationship between five types of SNPs in TNF-α gene with HCC risk. RevMan 5.3 software was used for analysis in fixed/random effect models.

Results: This meta-analysis included 23 potential articles from 2004-2018 with 3,237 HCC cases and 4,843 controls. We found that SNP -863 C/A were associated with a significantly increased HCC risk (A vs C, OR=1.31, 95% CI=1.03-1.67; CA/AA vs CC, OR=1.19, 95% CI=1.03-1.36). Similar results were obtained in -857 C/T (TT/CT vs CC, OR=1.31, 95% CI=1.06-1.62), -308 G/A (G vs A, OR=1.98, 95% CI=1.62-2.42; AA/GA vs GG, OR=1.95, 95% CI=1.53-2.49; GG/GA vs AA, OR=2.52, 95% CI=1.69-3.76; AA vs GG, OR=3.14, 95% CI=2.06-4.79; and GA vs GG, OR=2.07, 95% CI=1.60-2.68), and -238 G/A (A vs G, OR=1.50, 95% CI=1.16-1.94; AA vs GG, OR=3.87, 95% CI=1.32-11.34; GA/GG vs AA, OR=2.67, 95% CI=1.17-6.10). While no associations were observed between SNP TNF-α -1031 T/C and HCC risk.

Conclusions: The present meta-analysis showed that TNFα SNPs -863C/A, -857 C/T, -308 G/A, and -238 G/A were associated with the risk of HCC.

Background

Hepatocellular carcinoma (HCC) or liver cancer accounts for the cancer with the fourth highest mortality rate in the world. In 2018, there are estimated to be 841,080 of HCC new cases worldwide [1]. Patients with HCC usually have poor prognosis and high mortality rates, even in developed countries [2]. Until now, the pathogenesis of HCC has not been fully understood, but it is known so far that it is influenced by hepatitis B and C virus infections and also influenced by environmental factors (smoking, alcohol, aflatoxin B1) [3]. It is known that there are differences in the risk of HCC within each person, in which case the host factor has an important role [4].

Tumor Necrosis Factor-α (TNF-α) is an important inflammatory cytokine in the development of liver disease. This cytokine can cause hepatic injury, cirrhosis and eventually promote hepatocellular carcinoma [5, 6]. Several previous studies have identified some Single Nucleotide Polymorphism (SNP)s in TNF-α gene, especially in the promoter region. SNPs of TNF-α -1031T/C, -863C/A, -857C/T, -308G/A, and − 238G/A are SNPs in the TNF-α promoter site that have often been investigated regarding their association with HCC in several previous studies [7, 8]. It was also said that those SNPs could affect TNF-α production at the transcription level [9, 10].

High production of TNF-is related to the increase of pro-inflammatory cytokine secretion, the activation of proto oncogenes and several genes associated with cell growth, invasion, and cancer cells metastasis [11, 12]. Excessive production of TNFα can also induce the generation of free radicals in the form of Reactive Oxygen Species which can cause further liver damage and genomic instability [13]. It is also said that high TNF-α expression is an independent predictor of poor survival in HCC patients [14].

The results of various previous studies regarding the relationship between TNF-α polymorphism and HCC across various ethnicities and populations show mixed results, related [15–17] or there is no relationship [18, 19]. Research on TNF-α gene SNPs in patients with Hepatitis B Virus (HBV) infection is specific for each ethnicity and shows different results in each population [20]. As the results regarding the role of these five SNPs of TNF-α genes against HCC are still controversial, we conducted this meta-analysis to determine the relationship between those TNF-α gene polymorphisms and HCC.

Methods

Criteria For Inclusion And Exclusion

All included studies should meet the following criteria: (1) investigate the association between any of the five SNPs (-1031T/C, -863C/A, -857C/T, -308G/A, and − 238G/A) in TNF-α gene and risk of HCC; (2) have cohort or case-control study design; (3) risk of HCC was reported as RR (relative risk) and/or OR (odds ratio) with 95% Confidence Interval (CI) or provide sufficient data to extract RR and/or OR with 95% CI data; (4) include human subjects; (6) in English language. We excluded studies with the following criteria: (1) studies with design other than case-control or cohort; (2) duplicated studies; (3) studies with unmeasurable population and not qualified data; (4) using non-English language; (5) Studies in which the full text or main data could not be obtained.

Study Selection

Two investigators independently performed the electronic search and retrieved the articles that matched with our searched terms. Any disagreement was settled by discussion and consensus with all the authors. Final decision was merely based on the agreements of all authors.

Data Extraction

A standardized reporting form was used to extract the data from each article which included the first author’s name, year of publication, population country, TNF-α SNP type, study design, etiology of HCC, SNP genotyping method, controls, and frequencies of SNP. Hardy–Weinberg equilibrium test was performed and its significance of the control groups was calculated when the original information was not provided.

Quality Assessment

Newcastle - Ottawa quality assessment scale (NOQS) was used for measuring the quality of the included studies. This scale was designed through collaboration between the Universities of Newcastle, Australia and Ottawa, Canada. The purpose of this scale was to assess the quality of observational studies for producing good meta-analysis. The studies were qualified as high quality (9 stars), medium quality (7–8 stars), and low quality (less than 7 stars) [21].

Results

Association Between Snp Tnf-α -1031 And Hcc Risk

Only five studies regarding the association between SNP TNF-α -1031 and HCC risk with 825 cases and 1518 controls were available. The number of cases for CC and TC genotypes was reported together in the studies by Niro et al [39] and Jin et al [32] which could only be used for dominant-model analysis (CC/TC vs TT; Table 2). As the heterogeneity among studies for all models (I₂) was less than 60% and p-value for the heterogeneity was more than 0.05, fixed-effects models were applied. However, we did not obtain any significant association between SNP TNF-α -1031 and HCC risk in all model analyses. For the estimation of publication bias, we found no visual asymmetry in Funnel Plot analysis.

Association Between Snp Tnf-α -863 And Hcc Risk

We included eight studies with 1642 cases and 2746 controls to determine the association between SNP TNF-α -863 and HCC risk. The numbers of cases for CA and AA genotypes were reported together in the study by Niro et al [39], thus it could only be used for dominant-model analysis (AA/CA vs CC; Table 2). In studies with heterogeneity among studies (I₂) more than 60% and p-value for the heterogeneity was less than 0.05, we applied random-effects models. We found significant association between the allele model of A versus C of TNF-α C/A SNP with the risk of HCC (OR = 1.31, 95% CI = 1.03–1.67, p = 0.03). The dominant-model analysis (CA/AA vs CC) also showed significant association between SNP TNF-α 863 C/A and HCC risk (OR = 1.19, 95% CI = 1.03–1.36, p = 0.02; Fig. 2). As heterogeneity was found in the statistical analyses, we did sensitivity analyses to evaluate the sources of heterogeneity. We found that heterogeneity between studies was mainly caused by the study by Kummee et al [34], as after this study was excluded, no significant heterogeneity was found.

Association Between Snp Tnf-α -857 And Hcc Risk

We only obtained five studies about the association between SNP TNF-α -857 and HCC risk with 716 cases and 1005 controls. The number of cases for TT and CT genotypes was reported together in the studies by Jin et al [32] which could only be used for dominant-model analysis (TT/CT vs CC; Table 2). As the heterogeneity among studies for all models (I₂) was less than 60% and p-value for the heterogeneity was more than 0.05, fixed-effects models were applied. A significant association between SNP TNF-α -857 C/T and HCC risk was found in dominant-model analyses (OR = 1.31, 95% CI = 1.06–1.62, p = 0.01; Fig. 3). We did not observe any visual asymmetry in Funnel Plot analysis regarding the publication bias.

Association Between Snp Tnf-α -308 And Hcc Risk

Interestingly, we found most included studies analyzing the association between SNP TNF-α -308 G/A and HCC risk. The number of cases for AA and GA genotypes was reported together in the studies by Niro et al [39] and Jin et al [32] which could only be used for dominant-model analysis (AA/GA vs GG; Table 2). In studies with heterogeneity among studies (I₂) more than 60% and p-value for the heterogeneity was less than 0.05, random-effects models were applied. All five genetic models showed significant association with risk of HCC with OR for allele model = 1.98, 95% CI = 1.62–2.42, p < 0.001; OR for dominant model = 1.95, 95% CI = 1.53–2.49, p < 0.001; OR for recessive model = 2.52, 95% CI = 1.69–3.76, p < 0.001; OR for codominant major vs minor homozygote model = 3.14, 95% CI = 2.06–4.79, p < 0.001; and OR for codominant heterozygote vs major homozygote model = 2.07, 95% CI = 1.60–2.68, p < 0.001; Fig. 4). As heterogeneity was found in the statistical analyses, we did sensitivity analyses to evaluate the sources of heterogeneity. We found that heterogeneity between studies was mainly caused by the studies by Ho et al [28] and Akkiz et al [35], as after these studies were excluded, no significant heterogeneity was found.

Association Between Snp Tnf-α -238 And Hcc Risk

Nine studies with 831 cases and 1293 controls were included to determine the association between SNP TNF-α -238 and HCC risk. Similar to the TNF-a -863 SNP, the numbers of cases for GA and AA genotypes were reported together in the study by Niro et al [39], thus it could only be used for dominant-model analysis (GA/AA vs GG; Table 2). In studies with heterogeneity among studies (I₂) more than 60% and p-value for the heterogeneity was less than 0.05, we applied random-effects models. The allele model of A versus G of SNP TNF-α -238 G/A was significantly associated with risk of HCC (OR = 1.50, 95% CI = 1.16–1.94, p = 0.002). The codominant-model analysis (AA vs GG) also showed significant association between SNP TNF-α 238 G/A and HCC risk (OR = 3.87, 95% CI = 1.32–11.34, p = 0.01). The recessive model analysis (AA vs GA + GG) proved significant association between SNP TNF-α 238 G/A and HCC risk as well (OR = 2.67, 95% CI = 1.17–6.10, p = 0.02; Fig. 5). As heterogeneity was found in the statistical analyses, we did sensitivity analyses to evaluate the sources of heterogeneity. We found that heterogeneity between studies was mainly caused by the study by Teixeira et al [38], as after this study was excluded, no significant heterogeneity was found.

Sensitivity Analysis

In this meta-analysis, there were no significant change in ORs by deleting a particular study, which indicated that no single study influenced the statistical significance of the overall results.

Discussion

The development of HCC depends on some factors such as viral infection, environmental, behavioral, metabolism, and genetics [41, 42]. The contribution of SNP as a genetic factor is widely studied related to its role in the development of HCC, in which the prevalence of SNP is different in each population [5, 43, 44]. From the host factor, TNF-α is thought to play an important role in hepatocarcinogenesis through the induction of fibrogenic factors. Tumor Necrosis Factor-α is associated with increased necroinflammatory activity and liver fibrosis. Necroinflammation of hepatocytes triggers mutagenesis and activation of oncogens from protooncogenes in host cells, causing HCC [45].

Five biallelic SNPs in the promoter region Tumor Necrosis Factor-α gene were known: -1031T/C, -863C/A, -857C/T, -308G/A, and − 238G/A [7]. There have been many studies conducted to determine the association between these five TNF-α SNPs and HCC risk. Several meta-analyses have also been performed to analyze the association between TNF-α SNP and HCC. However, to date, only few meta-analyses have analysed all these five SNPs with HCC risk. Hereby, we performed this updated meta-analysis to clarify the independent role of each of these five SNPs on HCC risk.

As research on SNP TNF-α -1031 T/C and HCC risk is still limited, we only found five studies investigating this SNP, even though many studies have shown significant association between this SNP with other diseases such as polycystic ovary syndrome and endometriosis [46, 47]. In a study conducted by Shin et al survival of HCC cases with TNF-α -1031 wild type (TT) genotype or SNP TC genotype was significantly better than those with the SNP CC genotype [8]. However, in this meta-analysis, no significant relationship was found between the SNP and HCC risk in all genetic models. Meta-analysis conducted by Wei et al also shows that there is no significant relationship between this SNP and HCC risk [48].

This study shows a significant relationship between SNP TNF-α -863 C/A with HCC risk in allele models and dominant model analysis. A meta-analysis conducted by Wei et al also showed a significant relationship betweehis SNP and HCC in dominant and codominant (CA vs CC) model analysis [48]. Polymorphism of TNF-α -863 C/A in the promoter region can influence TNF-α expression, however, the result is still conflicting. Some research suggest that it may increase TNF-α expression [49, 50], while other research show the opposite result in which it may decrease TNF-α expression [27, 51].

In the present study, we also found a significant relationship between SNP TNF-α-857 C/T with dominant model analysis. Limited studies were present regarding the relationship between this polymorphism with HCC risk, thus there were only 7 studies that came within our inclusion criteria. This is different from the meta-analysis conducted by Wei et al which showed no significant relationship between this SNP and HCC risk, however in that meta-analysis there were only 3 included studies [48].

There have been many previous studies investigating SNP TNF-α -308 G/A with HCC risk, thus we got 19 included studies. All five genetic analysis models showed a significant relationship between the SNP and HCC risk. This is in line with several previous meta-analysis studies, including those conducted by Hu et al and Tavakoulpour and Sali on allele models and dominant model analyses [16, 52], Wei et al on codominant dan dominant model analyses [48], and Xiao et al in all except recessive model analysis [17].

Studies on SNP TNF-α -238 G/A have also been extensively performed. Nevertheless, meta-analyses of these SNPs are still limited and yield conflicting results. The present study showed a significant relationship between this SNP and HCC risk. This is in line with a meta-analysis conducted by Xiao et al showing that this SNP is associated with HCC risk, although in that study only HBV-related HCC was studied [17]. Another meta-analysis conducted by Hu et al, however, shows no significant relationship between this SNP with HCC risk in Asian population [52].

This meta-analysis still had several limitations. To date, there has rarely been any meta-analysis discussing the relationship between SNPs of TNF-α -1031 T/C, -863 C/A, -857 C/T, and − 238 G/A with HCC. Most present meta-analysis only focus on SNP − 308 G/A. We also got a small number of studies on SNPs − 1031 T/C, -863 C/A, -857 C/T, due to the limited number of studies. Research on these three SNPs, both observational and meta-analysis, is further needed in the future to find out the role of each of the three SNPs on HCC so that a stronger power of study will be obtained.

The second limitation of this study was that we only included studies in English language so that it does not rule out the exclusion of any good research in non-English languages. Third, HCC is a multifactorial process involving host, agent, and environment factors. The role of other host genomes, viruses, and the environment can influence the course and risk of HCC. Variations in genes adjacent to the TNF-α gene are said to also be able to regulate TNF-α expression [53]. Epigenetic factors which include histone modification, non-coding RNA, and gene methylation can also affect TNF-α expression and contribute to HCC risk [54]. Fourth, the study populations in our included studies came from various ethnicities (Asian, African, European and South American). Further research is required to determine the effects of these various TNF-α SNPs in each different population.

Conclusion

In conclusion, this meta-analysis determined the association between SNP TNF-α -1031 T/C, -863 C/A, -857 C/T, -308 G/A, and − 238 G/A and HCC risk. Our novel data demonstrated that all these SNPs but − 1031 T/C might increase HCC risk. Therefore, genetic predisposition, especially polymorphism of TNF-α gene may play important role in the pathogenesis of HCC. Further studies in larger population and analysing gene-environment interaction are required to provide better understanding between TNF-α polymorphisms and the risk of HCC.

Abbreviations

HCC = Hepatocellular carcinoma; TNF-α = Tumor Necrosis Factor-α; SNP = Single Nucleotide Polymorphism; HBV = Hepatitis B Virus; RR = relative risk; OR = odds ratio; CI = confidence interval; NOQS = Newcastle - Ottawa quality assessment scale.

Declarations

Ethics approval and consent to participate

Not applicable 

Consent for publish

Not applicable 

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. 

Competing interests

The authors declare that they have no conflict of interest. 

Funding

All publication funds from the beginning of data collection to manuscript preparation and our publication costs are supported by Dato ’Sri Prof. Dr. Tahir through the Tahir Professorship Program, Indonesia. 

Authors’ Contributions

This study was designed by CDKW and RH, CDKW and FCA conducted the literature search, S performed statistical analysis, GIP and RH wrote the manuscript. All authors read and approved the final manuscript. 

Acknowledgments

Not applicable.

Authors’ Information

Citrawati Dyah Kencono Wungu: https://orcid.org/0000-0001-5180-957X

Fis Citra Ariyanto: https://orcid.org/0000-0001-6883-195X

Gwenny Ichsan Prabowo: -

Soetjipto: -

Retno Handajani: -

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