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Review

Multimodal Studies in Hepatitis B Virus Associated Hepatocellular Carcinoma

Xie, Runze1; You, Maojun1; Wang, Xin1; Du, Shunda2; Wang, Fu-Sheng3; Yang, Pengyuan1,∗

Editor(s): Wang, Haijuan

Author Information
Infectious Diseases & Immunity: July 2022 - Volume 2 - Issue 3 - p 204-209
doi: 10.1097/ID9.0000000000000052
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Abstract

Introduction

Hepatocellular carcinoma (HCC) accounts for the fourth most common tumor worldwide, and causes the second highest death rate among all cancer types due to the late diagnosis and postsurgical recurrence.[1,2] HCC occurs mostly in developing countries with a 5-year overall survival rate of only 18% worldwide and 12% in China.[1,3] Of note, portal vein tumor thrombus increases the risk of intrahepatic metastasis and esophageal varix hemorrhage, leading to poor prognosis.[4,5] The major risk factors of HCC are chronic infections of hepatitis B virus (HBV) and hepatitis C virus, alcohol, obesity, and chemical carcinogens including aflatoxin and aristolochic acid. Among these etiological factors, the HBV infection is the most prevalent one that contributes to 50% to 80% of HCC cases worldwide.[6] Under the stress of persistent HBV infection, HCC often develops from a long-term chronic inflammation and/or cirrhosis.[7] The carcinogenetic processing initiated by endoplasmic reticulum stress signaling pathways, abatement expression of tumor suppressors, dysregulation of tumor-associated genes, and improper transcriptional factor (TF) activations has been scrutinized and reviewed [Figure 1].[2]

F1
Figure 1:
HBV drives carcinogenesis through multiple pathways. cccDNA: Covalently closed circular DNA; ER: endoplasmic reticulum; HBV: Hepatitis B virus; HBx: Hepatitis B virus X protein; HCC: Hepatocellular carcinoma; pgRNA: Pre-genome RNA; S/pre S: Hepatitis B virus S/preS envelope protein.

In the HCC microenvironment, researchers have revealed decreased number of natural killer (NK) cells and impaired CD8+ T cell activities that correlate the biomarkers and pathways of T cell exhaustion.[8–10] However, resistance to immune checkpoint blockage therapy is still commonly observed in a majority of HCC patients.[11] This is probably due to the complexity of liver tumor microenvironment which is difficult to capture with traditional biochemical experiments. It is even more complicated in HBV+ HCC, for HBV integrates viral DNA into the host genome and persists in hepatocytes, resulting in genomic instability and insertion mutagenesis of the host genome.[12] In addition, previous studies have defined the diverse phenotypes of T cell populations in HBV-associated tumor microenvironment, which requires further classification and identification.[13,14]

To fully understand the underlying mechanism of HBV infection initialed carcinogenesis, multimodal methodologies are employed to illustrate the integration of HBV DNA into host genome and associated immune escape. Rather than mere inquiry on a particular gene or pathway, multimodal studies focus on systemic alteration of genome, epigenome, transcriptome, and proteome simultaneously, allowing us to observe system-level events [Table 1]. This review introduces multimodal methods implemented in HBV+ HCC studies and their discoveries.

Table 1 - Technologies commonly used to investigate system-level events in HBV related research
Level Representative technology Applicable scope
Genomic Whole genomic sequencing HBV integrations and genetic mutations [26–33]
Epigenetic ChIP-seq Trans-regulation of gene expression [16]
ATAC-seq Chromatin accessibility [43,45]
MeRIP-seq HBV post-transcriptional modification [46]
Transcriptomic Bulk RNA sequencing and single cell RNA sequencing Immune cell populations Gene expression and immune landscape [13,14,30]
Proteomic Mass spectrum derived technologies Differentially expressed proteins during HBV infection Biomarker identification [49–51]
HBV: Hepatitis B virus; ChIP-seq: Chromatin Immunoprecipitation sequencing; ATAC-seq: Assay for transposase-accessible chromatin using sequencing; MeRIP-seq: Methylated RNA immunoprecipitation with next-generation sequencing; RNA: Ribonucleic acid.

HBV infection drives multi-omics changes in human liver cells

HBV integrates its DNA into host cells, causing directly genetic mutations. Therefore, we first review studies of HBV DNA integration. As is pointed out by C. H. Waddington, epigenetics influences cell fate without changing DNA sequences, analogous to gravity exerted on a specific landscape.[15] Epigenetics acts as pivotal driving forces of malignancy in HBV+ HCC, for instance, HBx protein interacts directly with epigenetic enzymes, mostly DNA and histone methyltransferases, and induces a pretumor phenotype in host cells. HBV DNA interacts with host DNA and alters viral genome stability and host genes expression.[16–19] In a nutshell, we review epigenomic studies in HBV+ HCC in 3 dimensions: DNA/histone level, chromatin level, and RNA level. Finally, HBV infection may also dysregulate transcription and protein translation, which will be both reviewed in this article.

HBV DNA integration leads to mutations of host genome

Whole genome sequencing (WGS) and whole exome sequencing (WES) are widely used technologies to study the HBV DNA integration events and related host genome alterations.[20,21] Bioinformatics tools such as Manta, INDELseek, and SNVSniffer have also been developed to identify the single nucleotide mutations, indels, structural variations, copy number variations (CNVs), and paralogs.[22–24] WGS/WES has gained its clinical Utility in uncovering driving forces of malignancy, analyzing cancer-related pathways, and screening new therapy targets.[25]

Genome-wide surveys of HBV+ HCC suggest that recurrent HBV integration occurs at regions of FN1, TERT, MLL4, and CCNE1 genes, and also increases CNVs events at HBV breakpoint locations.[26–29] HBV insertion site was also found in the proximity of telomeres in tumors, with a higher frequency in chromosome 17, consistent with the data from single cell genome sequencing in another study.[30,31] Kan et al. detected 399 genomic HBV integration events that affect 115 coding genes and other somatic structure alteration, for instance, gene fusion.[32] Beta-catenin and TP53 are the most frequently mutated oncogene (15.9%) and tumor suppressor (35.2%) respectively, and the related signaling pathways such as Wnt/ beta-catenin and JAK/STAT pathways alter in a way that favors carcinogenesis in HBV+ HCC.[32,33] These findings indicate that HBV integration plays an oncogenic role in HCC development.

Overall, WGS/WES data reveals HBV integration sites and the consequent gene mutations, which helps clarify the mechanism of HBV promoting HCC and more importantly, demonstrates the heterogeneity among patients, and this is critical for target therapy in clinic.[25,34] Because of the heterogeneity in HBV+ HCC, it is recommended to analyze whole genome data in larger cohort (>1000 patients) to obtain more accurate conclusion. Further, genomic survey itself is not sufficient enough to explain the complex biochemical changes in cells and thus is required to be combined with other multimodal analyses.

HBV mediates DNA/histone modification in host cells

As a regulatory protein, HBx is considered to contribute to HBV carcinogenicity. In HBV+ HCC, CHIP-seq showed that the levels of HBx binding to HBV cccDNA fluctuated parallel with HBV replication, and a number of histone acetyltransferases and deacetyltransferases are recruited to cccDNA to regulate its transcription.[16] Multiple epigenetic components interacting with HBx have been uncovered. For example, HBx binds to methyltransferase PRMT1 and inhibits its methylation activity, directly relieves the repression of HBV transcription.[17] Tumor suppressor genes such as IGFNP3, are silenced through HBx-mediated DNA methyltransferase DNMT3A recruitment.[19] Besides, HBx attenuates the repression of SETDB1-mediated H3K9me3 and HP1-induced silence of HBV cccDNA transcription, resulting in increased numbers of HBV transcripts.[18] In light of the close relationship between DNA/histone modifications and HCC progression, researchers attempted to utilize circulating tumor DNA (ctDNA) methylation profiles as diagnosis and prognosis markers in clinic.[35] Apart from studying the function of single protein like HBx, genome-wide DNA methylation interrogation derived from bisulfite genomic sequencing analysis and single cell methylation profiling methods, such as snmC-seq2, which has not yet been used in HBV studies, may help to recognize novel epigenetic patterns that underlie HCC development, progression, recurrence, and metastasis.[36–38]

HBV DNA communicates with host chromatin

HBV DNAs, including cccDNA and integrated HBV DNA, are identified as the primary contributors to the development and progression of HBV associated liver diseases. Therefore, identifying the 3D networks of different viral DNAs and host DNA interaction at chromatin level will uncover the mechanism of HBV promoting HCC. Hi-C and Capture Hi-C are effective tools to investigate spatial nuclear organization. Using these methods, Moreau et al. find that cccDNA prefers to contact host DNA at open chromatin regions enriched with active histone modifications.[39] Performing another highly sensitive technology to globally identify HBV-Host interactions in cells, named 3C-high-throughput genome-wide translocation sequencing (3C-HTGTS), researchers got the identical conclusion.[40] In addition, integrated HBV DNA appears in the transcriptional active regions and influences host gene expression.[40] The 3D landscape depiction indicates that contact sites are important for virus replication and probably contribute to the dysregulation of host genes. Regulatory information of significance may hide in 3D chromatin conformation and therefore, novel data interpretation methodologies are demanded to demystify data of such scale.

Conventionally, DNase I hypersensitive sites sequencing (DNase-seq), formaldehyde-assisted isolation or regulatory elements sequencing (FAIRE-seq), digital genomic footprinting (DGF), and the nucleosome occupancy and methylome sequencing (NOME-seq) are used in inquiring chromatin accessibility by assessing either nucleosome occupancy or chromatin openness.[41] These technologies could profile epigenetic landscape reshaped by HBV infection or a certain drug candidate.[42] Recently, assay for transposase-accessible chromatin using sequencing (ATAC-seq) gains its popularity in studying chromatin accessibility, due to its lower sample input, simpler experiment procedure, and higher sensitivity.[43] Using a combination of ATAC-seq, chromatin immunoprecipitation, and micrococcal nuclease assays, a recent study concluded that transcriptional activation of cccDNA rendered it more vulnerable and accessible to APOBEC3A, while transcriptional inactivation with interferon-α or HBx deletion protected cccDNA from the attack of APOBEC3A and engineered CRISPR-Cas9, which suggests that epigenetic sensitization of cccDNA may contribute to its clearance.[44]

Additionally, technology advances have made epigenetic research of chromatin accessibility possible in single-cell resolution, which may be helpful in searching for new cancer related genetic elements and pathways.[45]

RNA modification regulates HBV transcription

RNA post-transcriptional modifications are vital for the regulation of RNA stability, splicing, and translation. For example, a common modification of eukaryotic mRNA, N6-methyladenosine (m6A) addition, is also identified in HBV transcripts.[46] Using methylated RNA immunoprecipitation with next-generation sequencing (MeRIP-seq), researchers brought to light its dual regulatory function for HBV RNA: m6A methylation at 5’ epsilon loop of pgRNA is required for efficient reverse transcription while m6A methylation at 3’ epsilon loop stabilize all HBV transcripts.[46]

HBV infection triggers transcriptional changes in host cells

DNA mutations and epigenetic changes trigger dislocated gene expression, which could be easily detected by multiple RNA sequencing approaches. In addition, single cell RNA sequencing (scRNA-seq) enables us to determine cell subpopulations, which could not only compare hepatocytes with chronic hepatitis B infection (or HBV+ HCC hepatocytes) to non-viral HCC hepatocytes in order to study viral impacts in cancer progression, but also help find common cancer-related pathways with and without the presence of HBV. Transcriptome sequencing could also detect chimeric transcripts originating from HBV integration, which might be translated into aberrant fusion proteins, or regulate gene expression through direct interaction with DNA. In recent years, a number of researches suggested we should consider serum HBV RNA as biomarker for HBV infection and progression.[47] For example, a combination of hepatitis B core-related antigen measurement and HBV RNA quantification with real-time quantitative PCR (RACE-qPCR) is able to predict severe alanine transaminase flares after treatment withdrawal and HBV-DNA reactivation.[48]

HBV infection induces proteomic alterations in host cells

Due to the complicated post-transcriptional regulatory mechanism, protein expression is not always in proportion to transcript abundance, making proteomics studies essential. A variety of mass spectrum derived proteomics technologies, including quantitative proteomics, targeted proteomics, and post-translational modification analysis, are able to identify protein composition and differentially expressed proteins or metabolites in different samples. Using these methods, distinct protein profiles of HCC patient sera with different etiologies were delineated, and ficolin 3 expression decreased in HBV+ HCC patients while other 7 proteins, including a-2-glycoprotein and transthyretin, increased.[49] Other proteomics studies identified PYCR2 and ADH1A as prognostic biomarkers in HBV+ HCC, and CD14 as a detective marker for early stage cancer.[50,51] Interestingly, protein profile itself was able to classify patients into groups with distinct clinical attributes.[50]

Protein level data is often associated with transcriptome data due to their close biological relationship, suggesting that integration analysis is of necessity.[50] For example, combining quantitative mass spectrometry with RNA sequencing and ribosome profiling, Yuan et al. identified differential expression of 35 canonical genes and 15 non-canonical open reading frames in cell-based HBV replication system. Furthermore, HBV replication is abated through direct binding of SIRT6 to the minichromosome and consequent deacetylation of H3K9ac and H3K56ac, suggesting the potential clinical utility of SIRT6.[52] Machine learning is a potent way to interpret integrated data with respect to classification and predicting HBV associated HCC progression, since persistent presence of HBV may import extra features into machine learning models.[53] In addition, single-cell proteomics, which has not yet been introduced to HBV and HBV+ HCC research, may generate novel knowledge in this field.[54]

HBV infection orchestrates the immunosuppressive microenvironment in HBV+ HCC

As is observed in nearly all types of cancer, immune system defects in an immunosuppressive tumor microenvironment.[55] Several studies focused on T cells in HBV associated immune response owing to their central role of immune regulation and tumor regression [Figure 2].

F2
Figure 2:
Multimodal methods used in studying T cells in hepatitis B virus (HBV) infection and HBV+ hepatocellular carcinoma. Technologies like assay for transposase-accessible chromatin using sequencing (ATAC-seq) describe the openness of chromatin, thus allowing identification of transcriptionally active genes and related transcription factors. RNA sequencing provides gene expression profile of T cells, which can be used to determine T cell subpopulations and developmental trajectories. T cell receptor (TCR) sequencing enables us to find T cells that go through clonal expansion and predict antigen sequence.

Multimodal studies have enabled us to discover new T cell subtypes and related regulatory networks. On the one hand, Zheng et al. identified 11 T cell subgroups in HCC tumor environment and immune-suppressive role of LAYN by means of scRNA-seq and subsequent research illustrated a comprehensive landscape of CD45+ immune cells.[13,14] On the other hand, T cells could be classified on the basis of spatial location, as sometimes, among T cells scattered in different regions, only those who contact directly with tumor exert significant impact in spite of their similar transcriptome profile. CD4+ T cells are correlated with liver damage and viral clearance in patients with chronic HBV infection.[56] However, despite of their role of immune activation, they could also compromise tumor immunity mediated by CD8+ T cells when it comes to the HBV-specific CD4+ T cells.[57] Liver CD8+ T cells also showed high level expression of exhaustion markers and reduced ability to produce effector cytokines in HBV associated cases, and immunosuppression likely begins during chronic hepatitis B infection period.[58] RNA-seq data could help to distinguish distinct profiles of T cell exhaustion and anergy in terms of this immunosuppression.[59] Peptide-MHC (pMHC) tetramer is useful in probing antigen specific T cells.[60] A recent study captured CD8+ T cells binding neoantigen, HBV, tumor associated and unrelated antigens with highly multiplexed combinatorial pMHC tetramer library in 46 HCC patients.[61] As a result, tumor-infiltrating HBV-specific CD8+ T cells whose presence is correlated with unrelated antigen-specific T cells are not terminally exhausted and associate with prolonged patient relapse-free survival. TCR sequencing was used to detect clonal expansion in this study, as it may capture “clonal expansion specific” features that RNA sequencing would fail to grasp.[62] Another study using a combination of TCR-seq and RNA-seq showed that HCC microenvironment displayed strong heterogeneity, which might be fueled by regional clonal immune response.[63] HBV+ HCC tumor microenvironment seems to be more immunosuppressive than non-viral-related HCC, for regulatory T cells (Treg) and resident memory cells (Trm) are reported to enrich in HBV+ HCC and exhibit more suppressive features in a multidimensional study.[64]

Technologically, T cell studies are evolving in three dimensions. The first is larger data input, especially more cell numbers (>104) used in scRNA-seq analysis to increase confidence and lower noise, resulting in the discovery of rare cell groups. The second is to improve existent methods. For instance, nanometer resolution in situ RNA sequencing brings about subcellular localization of RNA, making it a promising tool for spatial RNA sequencing study in future.[65,66] The third is the integration of different omics methods. One of the integration attempts is to jointly profile chromatin accessibility and transcriptome in the same single cell, in which chromatin data provides information of gene regulation while RNA data offers cell clusters of higher resolution.[67] However, multimodal research in the same cell is still arduous both experimentally and analytically, and relevant methodologies are in imminent need for integrative research.

Perspectives

Despite of above-mentioned achievement, multimodal studies are faced with a major challenge: different technologies generate data with distinct data structure, making it difficult for integrative analysis. Besides, some new technologies, for instance ATAC-seq, have not yet been incorporated in HBV related studies. We expect to see novel knowledge generated by these methods, as well as advances in multidimensional technologies.

As is shown by multimodal studies, the etiology of HBV infection and HBV+ HCC is extremely complicated. Nevertheless, the idea that conducting multimodal studies to identify molecular targets with wide biological impact across different omes is tempting, for perturbation of such targets may bring about large-scale systemic effects. Moreover, a well-designed, multimodal study-based combination of therapeutic drugs may hopefully reshape tumor microenvironment, delay cancer progression, and even lead to a cure towards this deadly disease.

Conflicts of Interest

None.

Editor note: Fu-Sheng Wang is the Editor of Infectious Diseases & Immunity. The article was subject to the journal's standard procedures, with peer review handled independently of this member and his research group.

References

[1]. Craig AJ, von Felden J, Garcia-Lezana T, et al. Tumour evolution in hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol 2020;17(3):139–152. doi: 10.1038/s41575-019-0229-4.
[2]. Jia L, Gao Y, He Y, et al. HBV induced hepatocellular carcinoma and related potential immunotherapy. Pharmacol Res 2020;159:104992. doi: 10.1016/j.phrs.2020.104992.
[3]. Yang P, Markowitz GJ, Wang XF. The hepatitis B virus-associated tumor microenvironment in hepatocellular carcinoma. Natl Sci Rev 2014;1(3):396–412. doi: 10.1093/nsr/nwu038.
[4]. Cerrito L, Annicchiarico BE, Iezzi R, et al. Treatment of hepatocellular carcinoma in patients with portal vein tumor thrombosis: beyond the known frontiers. World J Gastroenterol 2019;25(31):4360–4382. doi: 10.3748/wjg.v25.i31.4360.
[5]. Chan SL, Chong CC, Chan AW, et al. Management of hepatocellular carcinoma with portal vein tumor thrombosis: review and update at 2016. World J Gastroenterol 2016;22(32):7289–7300. doi: 10.3748/wjg.v22.i32.7289.
[6]. Chen Y, Tian Z. HBV-induced immune imbalance in the development of HCC. Front Immunol 2019;10:2048. doi: 10.3389/fimmu.2019.02048.
[7]. Yang JD, Hainaut P, Gores GJ, et al. A global view of hepatocellular carcinoma: trends, risk, prevention and management. Nat Rev Gastroenterol Hepatol 2019;16(10):589–604. doi: 10.1038/s41575-019-0186-y.
[8]. Sun C, Sun H, Zhang C, et al. NK cell receptor imbalance and NK cell dysfunction in HBV infection and hepatocellular carcinoma. Cell Mol Immunol 2015;12(3):292–302. doi: 10.1038/cmi.2014.91.
[9]. Heim K, Neumann-Haefelin C, Thimme R, et al. Heterogeneity of HBV-specific CD8+ T-cell failure: Implications for immunotherapy. Front Immunol 2019;10:2240. doi: 10.3389/fimmu.2019.02240.
[10]. Fisicaro P, Barili V, Montanini B, et al. Targeting mitochondrial dysfunction can restore antiviral activity of exhausted HBV-specific CD8 T cells in chronic hepatitis B. Nat Med 2017;23(3):327–336. doi: 10.1038/nm.4275.
[11]. Xu F, Jin T, Zhu Y, et al. Immune checkpoint therapy in liver cancer. J Exp Clin Cancer Res 2018;37(1):110. doi: 10.1186/s13046-018-0777-4.
[12]. Nassal M. HBV cccDNA: viral persistence reservoir and key obstacle for a cure of chronic hepatitis B. Gut 2015;64(12):1972–1984. doi: 10.1136/gutjnl-2015-309809.
[13]. Zheng C, Zheng L, Yoo JK, et al. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell 2017;169(7):1342–1356. e16. doi: 10.1016/j.cell.2017.05.035.
[14]. Zhang Q, He Y, Luo N, et al. Landscape and dynamics of single immune cells in hepatocellular carcinoma. Cell 2019;179(4):829–845. e20. doi: 10.1016/j.cell.2019.10.003.
[15]. Waddington CH. The Strategy of the Genes: A Discussion of Some Aspects of Theoretical Biology. London: Allen & Unwin; 1957. 1–262.
[16]. Belloni L, Pollicino T, De Nicola F, et al. Nuclear HBx binds the HBV minichromosome and modifies the epigenetic regulation of cccDNA function. Proc Natl Acad Sci U S A 2009;106(47):19975–19979. doi: 10.1073/pnas.0908365106.
[17]. Benhenda S, Ducroux A, Rivière L, et al. Methyltransferase PRMT1 is a binding partner of HBx and a negative regulator of hepatitis B virus transcription. J Virol 2013;87(8):4360–4371. doi: 10.1128/jvi.02574-12.
[18]. Rivière L, Gerossier L, Ducroux A, et al. HBx relieves chromatin-mediated transcriptional repression of hepatitis B viral cccDNA involving SETDB1 histone methyltransferase. J Hepatol 2015;63(5):1093–1102. doi: 10.1016/j.jhep.2015.06.023.
[19]. Zheng DL, Zhang L, Cheng N, et al. Epigenetic modification induced by hepatitis B virus X protein via interaction with de novo DNA methyltransferase DNMT3A. J Hepatol 2009;50(2):377–387. doi: 10.1016/j.jhep.2008.10.019.
[20]. Meienberg J, Bruggmann R, Oexle K, et al. Clinical sequencing: is WGS the better WES? Hum Genet 2016;135(3):359–362. doi: 10.1007/s00439-015-1631-9.
[21]. Chen W, Zhang K, Dong P, et al. Noninvasive chimeric DNA profiling identifies tumor-originated HBV integrants contributing to viral antigen expression in liver cancer. Hepatol Int 2020;14(3):326–337. doi: 10.1007/s12072-020-10016-2.
[22]. Chen X, Schulz-Trieglaff O, Shaw R, et al. Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics 2016;32(8):1220–1222. doi: 10.1093/bioinformatics/btv710.
[23]. Au CH, Leung AY, Kwong A, et al. INDELseek: detection of complex insertions and deletions from next-generation sequencing data. BMC Genomics 2017;18(1):16. doi: 10.1186/s12864-016-3449-9.
[24]. Liu Y, Loewer M, Aluru S, et al. SNVSniffer: an integrated caller for germline and somatic single-nucleotide and indel mutations. BMC Syst Biol 2016;10(2):47. doi: 10.1186/s12918-016-0300-5.
[25]. Ziogas DE, Kyrochristos ID, Glantzounis GK, et al. Primary liver cancer genome sequencing: translational implications and challenges. Expert Rev Gastroenterol Hepatol 2017;11(10):875–883. doi: 10.1080/17474124.2017.1356227.
[26]. Sung WK, Zheng H, Li S, et al. Genome-wide survey of recurrent HBV integration in hepatocellular carcinoma. Nat Genet 2012;44(7):765–769. doi: 10.1038/ng.2295.
[27]. Ding D, Lou X, Hua D, et al. Recurrent targeted genes of hepatitis B virus in the liver cancer genomes identified by a next-generation sequencing-based approach. PLoS Genet 2012;8(12):e1003065. doi: 10.1371/journal.pgen.1003065.
[28]. Neuveut C, Wei Y, Buendia MA. Mechanisms of HBV-related hepatocarcinogenesis. J Hepatol 2010;52(4):594–604. doi: 10.1016/j.jhep.2009.10.033.
[29]. Li W, Zeng X, Lee NP, et al. HIVID: an efficient method to detect HBV integration using low coverage sequencing. Genomics 2013;102(4):338–344. doi: 10.1016/j.ygeno.2013.07.002.
[30]. Zhao LH, Liu X, Yan HX, et al. Genomic and oncogenic preference of HBV integration in hepatocellular carcinoma. Nat Commun 2016;7:12992. doi: 10.1038/ncomms12992.
[31]. Duan M, Hao J, Cui S, et al. Diverse modes of clonal evolution in HBV-related hepatocellular carcinoma revealed by single-cell genome sequencing. Cell Res 2018;28(3):359–373. doi: 10.1038/cr.2018.11.
[32]. Kan Z, Zheng H, Liu X, et al. Whole-genome sequencing identifies recurrent mutations in hepatocellular carcinoma. Genome Res 2013;23(9):1422–1433. doi: 10.1101/gr.154492.113.
[33]. Liu ZH, Lian BF, Dong QZ, et al. Whole-exome mutational and transcriptional landscapes of combined hepatocellular cholangiocarcinoma and intrahepatic cholangiocarcinoma reveal molecular diversity. Biochim Biophys Acta Mol Basis Dis 2018;1864(6):2360–2368. doi:10.1016/j.bbadis.2018.01.027.
[34]. Kyrochristos ID, Glantzounis GK, Ziogas DE, et al. From clinical standards to translating next-generation sequencing research into patient care improvement for hepatobiliary and pancreatic cancers. Int J Mol Sci 2017;18:1. doi: 10.3390/ijms18010180.
[35]. Xu RH, Wei W, Krawczyk M, et al. Circulating tumour DNA methylation markers for diagnosis and prognosis of hepatocellular carcinoma. Nat Mater 2017;16(11):1155–1161. doi: 10.1038/nmat4997.
[36]. Zheng Y, Huang Q, Ding Z, et al. Genome-wide DNA methylation analysis identifies candidate epigenetic markers and drivers of hepatocellular carcinoma. Brief Bioinform 2018;191:101–108. doi: 10.1093/bib/bbw094.
[37]. Luo C, Rivkin A, Zhou J, et al. Robust single-cell DNA methylome profiling with snmC-seq2. Nat Commun 2018;9(1):3824. doi: 10.1038/s41467-018-06355-2.
[38]. Yamada N, Yasui K, Dohi O, et al. Genome-wide DNA methylation analysis in hepatocellular carcinoma. Oncol Rep 2016;35(4):2228–2236. doi: 10.3892/or.2016.4619.
[39]. Moreau P, Cournac A, Palumbo GA, et al. Tridimensional infiltration of DNA viruses into the host genome shows preferential contact with active chromatin. Nat Commun 2018;9(1):4268. doi: 10.1038/s41467-018-06739-4.
[40]. Yang B, Li B, Jia L, et al. 3D landscape of hepatitis B virus interactions with human chromatins. Cell Discov 2020;6(1):95. doi: 10.1038/s41421-020-00218-1.
[41]. Stricker SH, Köferle A, Beck S. From profiles to function in epigenomics. Nat Rev Genet 2017;18(1):51–66. doi: 10.1038/nrg.2016.138.
[42]. Cai LY, Chen SJ, Xiao SH, et al. Targeting p300/CBP attenuates hepatocellular carcinoma progression through epigenetic regulation of metabolism. Cancer Res 2021;81(4):860–872. doi: 10.1158/0008-5472. Can-20-1323.
[43]. Buenrostro JD, Giresi PG, Zaba LC, et al. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods 2013;10(12):1213–1218. doi: 10.1038/nmeth.2688.
[44]. Wang Y, Li Y, Zai W, et al. HBV covalently closed circular DNA minichromosomes in distinct epigenetic transcriptional states differ in their vulnerability to damage. Hepatology 2021;doi: 10.1002/hep.32245. Online ahead of print.
[45]. Buenrostro JD, Wu B, Litzenburger UM, et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 2015;523(7561):486–490. doi: 10.1038/nature14590.
[46]. Imam H, Khan M, Gokhale NS, et al. N6-methyladenosine modification of hepatitis B virus RNA differentially regulates the viral life cycle. Proc Natl Acad Sci U S A 2018;115(35):8829–8834. doi: 10.1073/pnas.1808319115.
[47]. Liu S, Zhou B, Valdes JD, et al. Serum hepatitis B virus RNA: a new potential biomarker for chronic hepatitis B virus infection. Hepatology 2019;69(4):1816–1827. doi: 10.1002/hep.30325.
[48]. Carey I, Gersch J, Wang B, et al. Pregenomic HBV RNA and hepatitis B core-related antigen predict outcomes in hepatitis B e antigen-negative chronic hepatitis B patients suppressed on nucleos(T)ide analogue therapy. Hepatology 2020;72(1):42–57. doi: 10.1002/hep.31026.
[49]. Sarvari J, Mojtahedi Z, Kuramitsu Y, et al. Comparative proteomics of sera from HCC patients with different origins. Hepat Mon 2014;14(1):e13103. doi: 10.5812/hepatmon.13103.
[50]. Gao Q, Zhu H, Dong L, et al. Integrated proteogenomic characterization of HBV-related hepatocellular carcinoma. Cell 2019;179(2):561–577. e22. doi: 10.1016/j.cell.2019.08.052.
[51]. Guo J, Jing R, Zhong JH, et al. Identification of CD14 as a potential biomarker of hepatocellular carcinoma using iTRAQ quantitative proteomics. Oncotarget 2017;8(37):62011–62028. doi: 10.18632/oncotarget.18782.
[52]. Yuan S, Liao G, Zhang M, et al. Multiomics interrogation into HBV (Hepatitis B virus)-host interaction reveals novel coding potential in human genome, and identifies canonical and non-canonical proteins as host restriction factors against HBV. Cell Discov 2021;7(1):105. doi: 10.1038/s41421-021-00337-3.
[53]. Chaudhary K, Poirion OB, Lu L, et al. Deep learning-based multi-omics integration robustly predicts survival in liver cancer. Clin Cancer Res 2018;24(6):1248–1259. doi: 10.1158/1078-0432.Ccr-17-0853.
[54]. Budnik B, Levy E, Harmange G, et al. SCoPE-MS: mass spectrometry ofsingle mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biol 2018;19(1):161. doi: 10.1186/s13059-018-1547-5.
[55]. Hinshaw DC, Shevde LA. The tumor microenvironment innately modulates cancer progression. Cancer Res 2019;79(18):4557–4566. doi: 10.1158/0008-5472.Can-18-3962.
[56]. Wang H, Luo H, Wan X, et al. TNF-(/IFN-( profile of HBV-specific CD4 T cells is associated with liver damage and viral clearance in chronic HBV infection. J Hepatol 2020;72(1):45–56. doi: 10.1016/j.jhep.2019.08.024.
[57]. Meng F, Zhen S, Song B. HBV-specific CD4+ cytotoxic T cells in hepatocellular carcinoma are less cytolytic toward tumor cells and suppress CD8+ T cell-mediated antitumor immunity. APMIS 2017;125(8):743–751. doi: 10.1111/apm.12704.
[58]. Wang X, He Q, Shen H, et al. Genetic and phenotypic difference in CD8+ T cell exhaustion between chronic hepatitis B infection and hepatocellular carcinoma. J Med Genet 2019;56(1):18–21. doi: 10.1136/jmedgenet-2018-105267.
[59]. Yang Y, Liu F, Liu W, et al. Analysis of single-cell RNAseq identifies transitional states of T cells associated with hepatocellular carcinoma. Clin Transl Med 2020;10(3):e133. doi: 10.1002/ctm2.133.
[60]. Altman JD, Moss PA, Goulder PJ, et al. Phenotypic analysis of antigen-specific T lymphocytes. Science 1996;274(5284):94–96. doi: 10.1126/science.274.5284.94.
[61]. Cheng Y, Gunasegaran B, Singh HD, et al. Non-terminally exhausted tumor-resident memory HBV-specific T cell responses correlate with relapse-free survival in hepatocellular carcinoma. Immunity 2021;54(8):1825–1840. e7. doi: 10.1016/j.immuni.2021.06.013.
[62]. Satpathy AT, Saligrama N, Buenrostro JD, et al. Transcript-indexed ATAC-seq for precision immune profiling. Nat Med 2018;24(5):580–590. doi: 10.1038/s41591-018-0008-8.
[63]. Losic B, Craig AJ, Villacorta-Martin C, et al. Intratumoral heterogeneity and clonal evolution in liver cancer. Nat Commun 2020;11(1):291. doi: 10.1038/s41467-019-14050-z.
[64]. Lim CJ, Lee YH, Pan L, et al. Multidimensional analyses reveal distinct immune microenvironment in hepatitis B virus-related hepatocellular carcinoma. Gut 2019;68(5):916–927. doi: 10.1136/gutjnl-2018-316510.
[65]. Halpern KB, Shenhav R, Matcovitch-Natan O, et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 2017;542(7641):352–356. doi: 10.1038/nature21065.
[66]. Eng CL, Lawson M, Zhu Q, et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 2019;568(7751):235–239. doi: 10.1038/s41586-019-1049-y.
[67]. Cao J, Cusanovich DA, Ramani V, et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 2018;361(6409):1380–1385. doi: 10.1126/science.aau0730.
Keywords:

Hepatitis B virus; Hepatocellular carcinoma; Multimodal study

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