Host Genetic Variations and Their Implications on HBV and HCV Infection in the Iranian Population: A Comprehensive Systematic Review

Abstract

Background:

Background: Hepatitis virus infections are among the serious emerging health issues. They are the primary causes of cirrhosis and hepatocellular carcinoma. Growing evidence shows a link between certain genomic variations and inflammation including viral infection such as HBV and HCV. Therefore, this study aimed to comprehensively review studies that analyze the effect of host genomic variations on the risk of contracting viral hepatitis in Iranian population.

Methods:

The study was conducted according to the PRISMA Statement. All Persian and English case-control articles published until the beginning of June 2023 were included in the study. Two authors reviewed the articles independently. The third author reviewed the final results. Pathway analysis and protein interactions were also performed using GO and STRING databases.

Results:

Seventy relevant studies were retrieved. Fifty-three studies examined the association of SNPs with the risk of HBV infection. In terms of genetic variations, 25 genes and 44 SNPs were identified. Tumor necrosis factor alpha, Interleukin 28B, and Interleukin 10 were the most prevalent considered genes. The most common polymorphisms were in the interleukin family. Moreover, the top five identified molecular functions were cytokine activity, cytokine receptor binding, molecular function regulator, protein binding, and signaling receptor binding.

Conclusion:

The polymorphisms of genes involved in the production of immune factors, cytokines, interleukins, and their receptors are associated with the risk of HBV and HCV infections in the Iranian population. Moreover, the extracellular and intracellular signaling pathways and the regulating molecules of these processes can be considered as important factors in liability for these viral infections.

Introduction

Hepatitis C (HCV) and B virus (HBV) infections are two serious, emerging health issues. HBV infects almost two billion individuals, whereas HCV infects 210 million subjects worldwide (1). The primary causes of hepatocellular carcinoma (HCC), liver cirrhosis (LC), and chronic hepatitis (CH) are considered as outcome of these two viral diseases. About 78% of HCC cases are related to HBV or HCV, and HCC is the third most common cancer mortality cause worldwide (2). The genetic background of people with a viral infection affects the recovery or complications. In addition, identifying genetic susceptibility to this infection, may also help to better understand the mechanism of the host’s response to the viral infections, resulting better management of hepatitis B and C patients (3).

Additionally, variations in the related genes influence the inflammation-causing factors directly. Single nucleotide polymorphisms (SNPs) are the most common genetic variations, which may impact the degree of inflammatory factor production and the strength of their reaction. Growing evidence shows a link between certain SNPs and inflammation of the liver such as chronic hepatitis, liver cirrhosis, and HCC (4, 5). Interferon (IFN), Interleukin (IL), tumor necrosis factor (TNF), and transforming growth factor (TGF) genes make up the majority of the genes. Previous research on liver cirrhosis has suggested numerous gene variants related to immune variables; however, the findings were inconsistent. Detection of genetic risk factors for liver cirrhosis might clarify the intricate nature of the illness and strengthen the evidence based treatments or proactive measures (4).

So far, scattered studies have been conducted in Iran on identifying genetic variants and hepatitis B and C. To our knowledge, there has not been a systematic review to assess polymorphism data on the infection of HBV and HCV in the Iranian population. Therefore, this study aimed to comprehensively review of studies that analyze host genetic variations and the risk of contracting hepatitis viruses.

Methods

Identification and selection of studies

Identifying genetic polymorphisms associated with hepatitis B and C virus infection in the Iranian population was the main objective of this study. To access related articles, the MeSH terms were searched in the three main international databases of Web of Science, Scopus, and PubMed, as well as the Iranian SID database. All Persian and English case-control articles published until the beginning of June 2023 were included in the study. Review, systematic review and meta-analysis, animal and/or in vitro studies, editorials, and letters to the editor were excluded.

Data extraction and quality control

The PRISMA statement was taken into consideration in writing this article (6). All articles were sorted by year of publication and then divided into two separate files. On the other hand, we conducted the Newcastle-Ottawa Scale (NOS) for all our case-control studies. Two authors (F. A. and Z. NN.) independently reviewed each file. A third author (A. KS.) resolved discrepancies by reading each file. Extracted data include authors, year, location, type of hepatitis virus, diagnostic and genotyping methods, sample size, demographic characteristics (age, sex), gene, SNP, genotype, allele frequencies, and case-control matching methods.

Statistical analysis

In the control group, the X² statistics was used to evaluate the allele frequencies for Hardy-Weinberg equilibrium testing. The analysis of GO function has been implemented to categorize the selected genes into three independent categories, namely biological process, cellular component, and molecular function. In the results of this database, the significance level for the FDR scale was less than 5%, and the lowest value was considered the most relevant cellular pathway or function. In order to better understand the interactions between potential genes that were obtained from databases, the “Search Tool for the Retrieval of Interacting Genes/Proteins” (STRING; ver. 12.0; http://stringdb.org/) was employed to create a protein-protein interaction (PPI) network. This network represents the interactions between the proteins encoded by the potential genes we identified earlier. In the PPI network, each node represents a protein, and the edges between them represent their interactions. A node with many connections is known as a hub node, as it is a central point of the network with many interactions. Power of the studies was also calculated online (www.clincalc.com) according to each study’s sample size of case and control groups.

Results

Study identification

The initial search with the mentioned terms identified 1160 (PubMed=148, Scopus=786, Web of science=226) articles. After removing the duplicate articles, the title and abstract of the remaining 1121 studies were reviewed for the primary detection of unrelated and relevant articles. At this stage, 981 studies were excluded, and the full texts of 140 articles were received and reviewed to confirm their relevance to the current systematic topic. Finally, 70 case-control studies evaluating the association of one or more polymorphisms with hepatitis B and C virus infection were included in the analysis (Fig. 1).

Fig. 1:

Diagram of the systematic review process

On the other hand, we assessed the power of each study with positive associations. Among all studies, just nine (7–15) had a power of more than 80% (Table 1). Furthermore, regarding quality control, only four studies (8–10, 16) had a grade of 5 or 6 and good quality (Table S1), In contrast, other studies have a score of 4 or less, indicating their results’ lack of reliability.

Table 1:

Distribution of genotypes and alleles investigated in association with the hepatitis B and C virus infection in Iran

Study Year	Gene name	Polymorphism	Risky allele	Genotype frequency						Allele frequency				HWE*	Powerd
				Cases			Controls			Cases (%)		Controls (%)
				11a	12b	22c	11	12	22	1	2	1	2
Alizadeh (2006) (37)	CD14	rs2569190	T	39	22	19	16	94	16	62.5	37.5	50.0	50.0	0.000	0.369
Alizadeh (2006) (38)	CTLA-4	rs5742909	T	41	10	0	134	10	6	90.2	9.8	92.7	7.3	0.000	0.057
Sepahi (2017) (39)		rs4553808	G	5	57	5	23	38	4	50.0	50.0	66.6	35.4	0.080	0.412
Khanizadeh (2012) (40)	IFNGR1	−611 A/G	G	82	115	3	76	117	17	69.7	30.3	67.2	32.8	0.010	0.063
		−56 T/C	C	74	86	40	42	103	55	58.5	41.5	46.7	53.3	0.888	0.620
Nasiri Ahmadabadi (2012) (7)	IL-10	rs1800872	A	31	24	2	22	55	23	75.4	24.6	49.5	50.5	0.721	0.869
Mirfakhar (2018) (41)		rs1800871	T	48	62	12	80	45	13	64.8	35.2	74.3	25.7	0.227	0.334
Sepahi (2014) (20)		rs1800896	G	15	20	15	6	39	5	50.0	50.0	51.0	49.0	0.00039	0.031
Moudi (2016) (19)				72	118	31	10	84	16	59.3	40.7	71.0	29.0	0.961	0.675
Romani (2014) (42)	IL-16	rs4072111	T	181	58	6	316	74	4	85.7	14.3	89.6	10.4	0.990	0.279
		rs11556218	G	43	147	55	124	215	55	47.6	52.4	58.8	41.2	0.044	0.766
Behelgardi (2015) (33)		rs1131445	C	116	126	20	138	104	27	68.3	31.7	70.6	29.4	0.536	0.070
Mousavi Nasab(2015) (18)	IFNL3 / IL-28B	rs12979860	T	57	74	16	37	34	9	63.9	36.1	67.5	32.5	0.961	0.058
Koolivand (2020) (17)				40	52	28	60	39	21	55.0	45.0	66.3	33.7	0.011	0.381
Larijani (2016) (43)	IL-28B	rs8099917	G	53	33	7	43	13	1	74.7	25.3	86.8	13.2	0.999	0.337
Attar (2016) (8)	IL-6	rs1800795	C	181	102	14	175	146	47	67.4	32.6	78.1	21.9	0.175	0.852
Azar (2016) (9)	TNF-α	rs1800629	A	114	215	80	228	233	22	54.2	45.8	71.3	28.7	0.00042	0.999
Heidari (2016) (10)				92	6	2	64	29	7	95.0	5.0	78.5	21.5	0.371	0.905
		−857 C/T	T	80	12	8	72	7	21	86.0	14.0	75.5	24.5	0.000	0.398
		−863 A/C	C	44	44	12	61	34	5	66.0	34.0	78.0	22.0	0.996	0.409
Larijani (2016) (44)		rs1799724	A	70	18	1	35	40	1	88.8	11.2	72.4	27.6	0.022	0.701
Ghavami (2018) (45)		rs1799964	C	176	78	4	161	108	8	83.3	16.7	77.6	22.4	0.127	0.398
Abdolmohammadi (2016) (11)	CCR5	CCR5Δ32 (rs333)	Del	352	5	0	397	51	7	99.3	0.7	92.8	7.2	0.004	0.996
Moudi (2019) (14)		rs1799987	A	22	36	42	40	39	21	60.0	40.0	40.5	59.5	0.162	0.818
Moudi (2016) (46)	MIF	rs755622	C	130	67	24	132	62	6	74.0	26.0	81.5	18.5	0.924	0.406
Eskandari (2017) (47)	HLA-G	14-bp Ins/Del	Del	90	104	5	53	144	0	71.4	28.6	63.5	36.5	0.00	0.348
Eskandari (2017) (28)	TGF-β1	rs1800472	T	130	39	0	147	16	0	89.0	11.0	95.0	5.0	0.805	0.446
		rs1800470	T	55	118	23	49	103	46	58.0	42.0	51.0	49.0	0.849	0.220
Fatemipour (2017) (12)	STAT3	rs1053004	T	18	5	10	4	14	32	62.1	37.9	22.0	78.0	0.428	0.943
Fattahi (2018) (48)	CYP2E1	rs4646421	T	33	14	7	61	25	3	74.0	26.0	82.0	17.0	0.976	0.157
Moudi (2018) (49)	LAMC1	rs20558	C	33	45	22	51	42	7	55.5	44.5	72.0	28.0	0.917	0.627
		rs20563	G	33	45	22	51	42	7	55.5	44.5	72.0	28.0	0.917	0.627
		rs10911193	T	44	44	12	61	34	5	66.0	34.0	78.0	22.0	0.996	0.409
Moudi (2019) (13)	BIRC5	rs3764383	G	42	41	17	82	54	4	62.5	37.5	77.9	22.1	0.373	0.606
		rs8073069	G	54	40	6	41	57	42	74.0	26.0	49.6	50.4	0.089	0.932
		rs9904341	C	31	47	22	74	59	7	54.5	45.5	73.9	26.1	0.544	0.779
		rs17878467	T	26	51	23	65	59	16	51.5	48.5	67.5	32.5	0.897	0.581
Ayatollahi (2019) (50)	IL-12	rs568408	A	105	35	8	37	34	9	82.8	17.2	95.2	4.8	0.961	0.725
Khanizadeh (2019) (15)	miRNA-146a	rs2910164	G	42	52	172	94	74	98	25.6	74.4	49.2	50.8	0.00	1.000
Moudi (2019) (14)	VDR	rs731236	C	33	45	22	51	42	7	55.5	44.5	72.0	28.0	0.917	0.627
		rs7975232	C	33	45	22	51	42	7	55.5	44.5	72.0	28.0	0.917	0.627
	MCP1	rs1024611	G	35	46	19	53	40	7	58.0	42.0	73.0	27.0	0.989	0.549
Zangi (2019) (51)	IL-13	110 A/G (rs20541)	A	106	170	26	135	130	18	63.2	36.7	70.6	39.3	0.191	0.441
Heidari (2020) (52)	INFG	rs2430561	A	35	108	78	48	109	43	51.2	48.8	40.3	59.7	0.439	0.573
Bahrami (2020) (53)	VDR	rs2228570	T	33	49	18	24	41	35	44.5	55.5	57.5	42.5	0.236	0.396
Sarrafnia (2020) (54)	IL-18	rs360719	G	64	53	3	51	50	19	75.4	24.6	62.9	37.1	0.530	0.499
Tayefinasrabadi (2020) (55)	IL-17A	rs10484879	A	110	54	8	109	73	17	78.4	21.6	77.6	22.4	0.641	0.029

Abbreviations. CD14: cluster of differentiation 14; CTLA-4: Cytotoxic T-Lymphocyte Associated Protein 4; IFNGR1: Interferon Gamma Receptor 1; IL: Interleukin; IFNL3: Interferon Lambda 3; TNF- α: Tumor necrosis factor alpha; HLA: human leucocyte antigen; CCR5: C-C Motif Chemokine Receptor 5; MIF: Macrophage Migration Inhibitory Factor; TGF-β1: transforming growth factor-beta; STAT3: Signal transducer and activator of transcription 3; CYP2E1: Cytochrome P450 2E1; LAMC1: Laminin Subunit Gamma 1; BIRC5: Baculoviral IAP repeat containing 5; VDR: Vitamin D Receptor; MCP1: monocyte chemoattractant protein 1; INFG: Interferon Gamma;

^*The P-value of Hardy-Weinberg in controls

^aWild type genotypes

^bHeterozygote genotype

^CMutant homozygote genotype

^dThe power of studies was calculated with SigmaPlot software version 14.0. (Alpha=0.050)

Data extraction

The selected studies included 11789 infected subjects and 11406 healthy controls (Table S1). The central part of Iran had the largest number of samples. Most studies (n=53) investigated the association of genetic polymorphisms with the risk of HBV infection, and seventeen studies included 3311 patients with the HCV virus and 2407 controls. The mean age was 28–61 in total subjects. Patients with HCV were mostly in their fourth decade of life. The case-control studies indicated a considerably varied sample size (ranging from 50 to 1063 individuals).

Evaluation of methodology indicated that most studies had used ELISA, RT-PCR, or a combination of these methods to diagnose patients. However, seven articles did not report the diagnostic procedure. Moreover, seven other articles had just reported diagnosis by medical records. The variation detection method was PCR-based, including PCR-RFLP, ARMS-PCR, SSP-PCR (Single Specific Primer-PCR), and TaqMan. In the matching process, confounding factors, including age and gender, were also considered.

Investigating genetic variations, 25 genes and 44 polymorphisms were identified to be assessed in the Iranian population in association with HBV or HCV infection. Tumor necrosis factor alpha (TNF-α; rs1800629) (9, 10), Interleukin 28B (IL-28B; rs12979860) (17, 18), and Interleukin 10 (IL-10; rs1800896) (19, 20) were the most prevalent considered genes. The most common polymorphisms were in the interleukin family. IL-10 and IL-16 genes had three related genetic variations, and other interleukin genes included 6, 12, 13, 17, 18, and 28 (Table 1).

Moreover, the top five identified molecular functions were cytokine activity (TGFB1, CCL2, TNF, IFNG, MIF, IFNL3, IL6/10/13/16/17A/18), cytokine receptor binding (TGFB1, CCL2, STAT3, TNF, IFNG, MIF, IL6/10/13/18), molecular function regulator (IFNL3, MIF, VDR, IFNG, TNF, CCL2, STAT3, TGFB1, BIRC5, IL6/10/13/16/17A/18), protein binding (CYP2E1, IFNL3, IFNGR1, IL12RB1, HLA-G, IFNG, TNF, MIF, CCR5, CCL2, STAT3, TGFB1, VDR, BIRC5, IL6/10/13/16/17A/18), and signaling receptor binding (IFNL3, HLA-G, IFNG, TNF, MIF, CCL2, STAT3, TGFB1, VDR, IL6/10/13/16/17A/18). Moreover, the biological pathways of negative regulation of primary miRNA processing (STAT3 and IL6), regulation of chronic inflammatory response to antigenic stimulus (IL10 and TNF), and chronic inflammatory response to antigenic stimulus (IL10 and TNF) were ranked first to third in the list, respectively. On the other hand, the position of CCR5, IL12RB1, IL17A/13, CD14, CTLA4, TNF, and HLA-G gene products are on the external side of the plasma membrane. Protein interactions are shown in Fig. 2.

Fig. 2:

Interaction between the proteins of the genes associated with HBV and HCV infections. Red: Cytokine activity, Pink: Protein binding, Blue: Cytokine receptor binding, Green: Signaling receptor binding, Yellow: Molecular function regulator activity.

Discussion

The role of genetic background in the contracting viral infections has been proven (21). According to WHO, HBV and HCV infections are spread worldwide and can cause liver problems (22). Several pathways are involved in the pathogenesis of hepatitis B and C infection, with emerging data on dissecting the genetic basis of the disease. The role of cytokines and immune system factors, including interleukin-6, 10, 12, 27, membrane cofactor CD46 protein, tumor necrosis factor-alpha, and interferon-gamma, has been documented in the development of viral infection (23). Based on Genome-Wide Association Studies (GWAS) or functional analysis, new players such as AAMDC, HLA alleles, interferon-alpha, and proteins involved in glucose metabolism have also been suggested (24, 25). Nevertheless, it is crucial to replicate and validate these findings in diverse populations to ensure their reliability and accuracy. In this context, we will briefly address some points of our findings as follows:

Cytokines, which are small proteins, play a critical role in regulating the growth and functioning of various cells in the immune system and blood. When they are released, they act as signals to prompt the immune system. Cytokines have an impact on the growth of all blood cells and other cells that contribute to the body’s immune response and ability to modulate inflammation. Overall, 25 patients with chronic HBV and HCV co-infection were collected, and the TGFβ1, CCL2, and IFNG serum levels were measured. The patients had high levels of TGFB and CCL2 after four weeks and decreased levels of IFNG compared with the baseline (26). Numerous studies have examined the variations in genes associated with this pathway. For example, an association was demonstrated between the TGFβ1 −509C>T polymorphism and an increased susceptibility to chronic hepatitis C. This polymorphism leads to elevated TGFβ1 levels, which subsequently hinder the production of IFN-γ, thereby contributing to the progression of cirrhosis (27). Although this variant was not associated with the risk of contracting hepatitis B in a study, two other variants of TGFβ1, rs1800470 and rs1800472, were associated with the risk of infection with HBV (28). Moreover, TGFβ1 rs1800471 could be a host genetic factor associated with susceptibility to HCV infection (29). Heterozygosity for CCR5Δ32 was linked to increased susceptibility to HBV-related liver disease, while VDR a/a and TNF-β A/A were associated with disease severity. Additionally, the VDR a/a allele was found to be correlated with higher viral load (30). On the other hand, CCR5Δ32 has a protective effect in resistance to HBV infection in the Iranian population. The heterozygosity for CCR5/CCR5Δ32 (W/D) in healthy controls was greater than in cases (11.2% vs 2%) (11). Furthermore, another Iranian study indicated significant associations with susceptibility to chronic HBV infection in association with CCR5-2459A/G, VDR-APa1A/C, and VDRTaq1T/C polymorphisms. In addition, no association of the CCR5D32 SNP with the disease was found (14). These findings underscore the significant influence of immunogenetic factors on the outcome of chronic HBV infection Moreover, cytokine receptors like TNF-α, IL-2R, IL-16, IL-10 and IL-10R are significantly increased in HBV-infected patients (7, 31–33) .

On the other hand, the effect of IL10 −1082A>G and TNFA −308G>A on susceptibility to HCV infection was investigated in the Brazilian population. Patients with the IL10 −1082A>G polymorphism had a higher HCV viral load. The patients with chronic HCV infection did not have TNFA −308G>A polymorphism, and its absence showed the protective role of TNF −308G>A in HCV infection (34). A study on Iranian subjects indicated a possible association between IL-10 promoter polymorphism (−1082A>G) and HCV infection (20). On the other hand, in another study on the Iraqi population, the effect of IL6 and IL4RA polymorphisms on HBV and HCV infection was investigated, and rs1800795 was significantly associated with HBV but not with HCV infection. IL6 rs1800797 had an association with both HBV and HCV infections. IL4RA rs1801275 had no association with HBV or HCV infection (35). Moreover, HBV-infected patients in India were investigated for the association of IL18 at position −607 with HBV infection. People with allele A at position −607 were protected against HBV infection. However, for position 137, no significant difference was observed between controls and cases (36).

In some of the included studies there was no enough clinical information which may affect the quality check of the studies. There are also some gaps in the information about sample size and case/control matching method. Some studies were also underpowered. These studies’ limitations affected the range of quality control because of such limited evidence.

Conclusion

The polymorphisms of genes involved in the secretion of immune factors, cytokines, interleukins, and their receptors are associated with the risk of HBV and HCV infections in the Iranian population. Moreover, the extracellular and intracellular signaling pathways and the regulating molecules of these processes can be considered important factors in the spread of these viral infections. In the future, to pave the road to the personalized medicine, studies with a larger sample size should be conducted to validate the association of polymorphisms in these genes where these results can identify populations with a higher risk of infection and help take preventive measures to reduce the spread of the disease.

Journalism Ethics considerations

Ethical issues (Including plagiarism, informed consent, misconduct, data fabrication and/or falsification, double publication and/or submission, redundancy, etc.) have been completely observed by the authors.