Comparative Transcriptomic and Epigenomic Analysis Highlights Hepatitis B Virus Infection Impact on Hepatocellular Carcinoma Development

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Hunninghake, Mandi Lichtenstein, Andrea A. Perreault This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7850229/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Hepatitis B virus (HBV) infection is a major risk factor for hepatocellular carcinoma (HCC), yet the molecular events linking infection to malignant transformation remain incompletely defined. Here, we integrated transcriptomic (RNA-seq) and epigenomic (ChIP-seq for H3K27ac and H3K27me3) data across normal hepatocyte-derived cells (WT), HBV-infected cells, and HCC samples to quantify gene expression changes and epigenetic remodeling during disease progression. Transcriptomic profiling revealed distinct sets of differentially expressed genes (DEGs) between WT, HBV, and HCC states, with enrichment in pathways related to chromatin organization, signal transduction, lipid metabolism, and immune regulation. Epigenomic analyses demonstrated that HBV infection contributes to widespread redistribution of histone modifications, with reduced H3K27me3 at promoter regions and increased H3K27ac at enhancer regions, favoring activation of oncogenic programs. Differential histone regions (DHRs) overlapped with DEGs involved in ribosomal activity, ubiquitin-like protein function, and chromatin dynamics, suggesting coordinated transcriptomic and epigenomic reprogramming. Together, these results suggest that HBV infection establishes an epigenetic landscape that persists into HCC and drives altered gene expression in pathways central to lipid metabolism, chromatin structure, and cell cycle progression. This work highlights candidate biomarkers and regulatory mechanisms underlying HBV-associated hepatocarcinogenesis. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Genetics Biological sciences/Molecular biology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Hepatitis B virus (HBV) is the most chronic viral infection globally, posing a significant health burden 1 . There are multiple genotypes of the hepatitis B virus and they are known to have distinct geographical presences. In most of Africa, Europe, and the Americas, genotypes A and D are most prevalent. Genotypes B and C are predominantly found in Asia. Sub-Saharan Africa, however, is where genotype E is primarily located. Other genotypes (F, G, H, I, and J) are less common and found in even more specific regions. This distinction in geographical range has implications for health equity and access to treatment. More than 250 million people in the world have chronic hepatitis B infection and over 1 million people die each year from complications and progression of the infection. HBV infection is the leading risk factor in developing hepatocellular carcinoma. Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related deaths globally 1 . It can develop due to a variety of factors, such as patient demographics (sex, age, ethnicity) environmental conditions, existing comorbidities, and chronic hepatitis infection. HCC also has a skewed geographical distribution, with higher incidence in East Asia and sub-Saharan Africa. This is thought to stem from the ability of different HBV genotypes to infect distinct populations 2 , 3 . Varying viral replication capacities of these HBV genotypes may further contribute to the differences in clinical outcomes among patients. Therefore, the rates of global HCC are linked to the prevalence of distinct HBV genotypes. Understanding the relationship between HBV viral mechanisms, chronic infection, and HCC is critical for developing effective early diagnoses, targeted therapies, and preventative strategies to combat the high global burden of liver cancer and liver-related mortality. Research has increasingly focused on the epigenetic changes induced by HBV, particularly through the activity of the viral X protein (HBx) 4 . HBx appears to act as a master regulator of the host epigenome, altering gene expression through interactions with chromatin-modifying enzymes, DNA methyltransferases (DNMTs), histone-modifying proteins, and transcription factors 5 – 7 . These changes allow HBV to manipulate the host's regulatory machinery, promoting cell survival, immune evasion, and malignant transformation. One of the central epigenetic mechanisms through which HBx exerts its effects involves post-translational modifications of histone tails 4 . Modifications to lysine 27 on histone H3 (H3K27) have emerged as key regulatory factors in HBV-related tumorigenesis 8 . Recent literature has explored the role of epigenetic priming in the transition from chronic liver disease towards HCC 9 – 11 . This model proposes that precancerous cells acquire key epigenetic characteristics which primes them for oncogenic activity. Disruption of the balance between activating (H3K27ac) and repressive (H3K27me3) marks can lead to abnormal gene expression patterns, silencing of tumor suppressors, or activation of oncogenes. Understanding non-mutagenic factors contributes to changes in gene expression is key to a better understanding of disease progression. Other studies have continued this research to understand the sequential timing of the presence of these marks appearing and the impact on gene expression 9 , 12 . Gene expression changes from normal transcriptional programs are a key driver of cancer. Identifying epigenetic marks in HBV-infected samples and diagnosed HCC samples can provide insights to the epigenetic priming occurring during the transition from HBV infection to HCC. Subsequent investigation of changes in transcriptional programs from normal liver cells to HBV-infection to HCC will offer a deeper understanding of the critical genes involved in this transition. Together, this information is key in understanding and preventing the progression to HCC, as well as potential treatment avenues. In this study, we leverage publicly available cell line and patient-derived datasets to investigate key epigenetic factors and gene expression in normal liver, HBV-infected cells, and HCC samples. We explore the differences between HBV genotypes, and compare cell line and patient-derived samples. Using the results from our analyses, we present key genes that control disease progression as targets for future therapies. Results Gene expression varies as cells progress from healthy to HBV-infection to HCC diagnosis To understand the transcriptomic changes associated with HBV infection and the progression to HCC, we conducted a comprehensive analysis using RNA-seq data from three distinct stages. By comparing the gene expression profiles of normal HepG2 cell lines (WT), HBV-infected HepG2 cells (HBV), and HCC cell lines, we aimed to identify differentially expressed genes (DEGs) and elucidate the molecular mechanisms involved in hepatocarcinogenesis. In addition to assessing correlation of replicates (Supplementary Table 1), we performed Principle Component Analysis (Supplementary Figure 1). We did not see a clear distinction between disease states (Supplementary Figure 1A), which led us to separate data that was derived from cell lines from those that were patient-derived. This analysis provided a clear distinction between data derived from cell lines and patient samples (Supplementary Figure 1B). We keep cell-line and patient-derived analyses separate in all future analyses. We compared standardized gene expression across 4 groups—WT vs. HBV (Figure 1A), WT vs. HCC cell line data (Figure 1B), WT vs. HCC patient data (Figure 1C), and HBV vs. HCC (Figure 1D). Generally, replicates from the same disease state were clustered together. Although replicates had high correlation values, some discrepancies in individual gene expression remain. Using DESeq2, we calculated the set of DEGs that had a fold change of 2 and p-value < 1e -5 . Of the 49,224 total observations, there were 174 DEGs between WT and HBV-infected cells, 116 of which were upregulated in HBV and 58 of which were downregulated (Figure 1E). Genes that were differentially expressed between disease stages were enriched in gene ontology (GO) molecular function groups such as signal receptor activity, catalytic activity, and chromatin components (Figure 1F). When separating cell line and patient data for the comparison between WT and HCC, there were distinct sets of DEGs. In cell line data, there were 9 DEGs, 5 of which were upregulated in HCC and 4 of which were downregulated (Figure 1G). These genes were primarily enriched in structural molecule activity, protein heterodimerization activity, and key parts of chromatin structure (Figure 1H). Alternatively, there were 61 DEGs between WT and HCC patient data, 18 of which were upregulated in HCC and 43 of which were downregulated (Figure 1I). These genes were also found in protein heterodimerization activity and key parts of chromatin structure GO groups, but were also found in antigen binding functions. There were 15 DEGs between HBV and HCC, with one gene (UMPS) being less expressed in HBV and 14 genes with higher expression in HBV. These DEGs were not enriched in any specific GO molecular function groups. Histone modification location shifts as cells progress from healthy to HBV-infection to HCC diagnosis We examined the differential histone regions (DHRs) for H3K27ac and H3K27me3 between WT, HBV, and HCC due to HBx’s known role in epigenetic mechanisms. In our data, H3K27me3 was consistently found at promoter regions and distal intergenic regions in WT, HBV, and HCC (Figure 2A-E). In HBV infection, the histone marks were shifted away from promoter regions and more towards downstream and distal intergenic regions. First, we investigated a subset of the different subtypes of HBV (HBV-C, HBV-D, and HBV-A). We found no statistical differences in H3K27me3 presence (fold change of 2 and p-value < 1e -5 ) between these genetic variants of the virus (Supplemental Figure 2). These differences in genotypes are based on sequence-level differences, not epigenetic variations, which is supported by our data. This highlights the importance of investigating both genetic differences (mutations, genomics rearrangements, etc.) in addition to epigenetic modifications when studying disease. HBV-C is associated with the greatest risk of liver disease and progression to HCC. Therefore, subsequent analysis focused on data from HBV-C. Following our gene expression analysis, we used DESeq2 (fold change of 2 and p-value < 1e -10 ) to identify DHRs between the disease states. Between WT and HBV, there was significantly less methylation throughout the genome, suggesting the activation of genes once HBV infection has occurred (Figure 2F). The 93 regions of differential methylation were enriched for ribosome activity, transcription regulation, and ubiquitin-like protein activity GO molecular functions (Figure 2G). When comparing WT and HCC, 164 of the total 174 DHRs had decreasing methylation (Figure 2H), and these regions were similarly enriched for chromatin organization, ubiquitin-like protein activity, and ribosome activity GO molecular function (Figure 2I). Interestingly, when looking at acetylation differences between WT and HCC, we observed more increases in acetylation (145 of 201 total regions) (Figure 2J). These regions were also enriched for ribosome activity, as well as different types of channel activity (Figure 2K). We observed the smallest number of DHRs between HBV and HCC (16), similar to our results from RNA-seq analysis (Figure 2L). Despite the small number of differential regions, these were still enriched for ribosome activity, transcriptional regulation, and ubiquitin protein activity (Figure 2M). This suggests that the epigenetic landscape established during HBV infection persists into HCC diagnosis and is a potential driver for the disease progression. Key genes in lipid metabolism and cell cycle progression are differentially expressed in disease stages Once we had an overview of the transcriptomic program in WT, HBV, and HCC, we explored specific gene examples that fell into several categories. The first category was genes that decreased in expression from WT to HBV infection to HCC. Overall, there were 58 and 15 genes identified by DESeq2 that were less expressed in each comparison in cell line data (WT vs. HBV infection and HBV infection vs. HCC, respectively). An example of a gene in this category is APOB, which functions in transporting fats and cholesterol in the bloodstream 13 (Figure 3A). A continually decreasing expression of this key liver function gene impacts the development of HCC. The second category was genes that decreased between WT and HCC patient samples. Overall, there were 43 genes that decreased. None of these overlapped with genes that decreased in the cell line samples. One example is CRHBP (Figure 3B). Corticotropin releasing hormone (CRH) binding protein (CRHBP) is a glycoprotein known to regulate the interaction between CRH and its receptors 14 . Several studies have begun to explore its role in various cancers, specifically HCC. Third, we explored genes that increased in expression during disease progression. One example includes ZYX, which continually increased from WT to HBV-infection to HCC (Figure 3C). Zyxin is a critical component of the cytoskeleton and localizes to focal adhesions. It also plays a crucial role in signal transduction 15 . Both of these cellular functions contribute to zyxin’s potential role in HBV infection progressing to HCC. In patient-derived data, SERPINE1 increased from WT to HCC (Figure 3D). This gene is an inhibitor of a serine protease, while also contributing to cell adhesion and cell migration. Studies have found increased SERPINE1 expression leads to more aggressive tumors and poor patient outcome 16 . Finally, we wanted to identify genes that were most highly expressed in HBV-infected samples. BHLHE40 increases significantly from WT and HBV-infection before dropping again in HCC. It functions in circadian control through its interaction with ARNTL and PER1. More importantly, it has been linked to cell differentiation and cell cycle progression, and associated with a wide variety of cancers. DHRs are found at cancer-related and cellular differentiation genes We next to identified specific DHRs and investigated their potential involvement in disease progression. First, we considered differential acetylation regions. The MAF promoter had increased acetylation in HCC, suggesting this gene is highly activated in HCC (Figure 4A). MAF is a transcription factor that acts in activation and repression of genes, and has been implicated in a variety of cancer types 17–19 . Specifically, MAFB expression has been identified as highly overexpressed in malignant HCC lines 20 . This may be because MAFB expression alters immune microenvironments, which are critical to tumor development and patient outcome. Alternatively, we found that IRX3 had decreased acetylation in HCC (Figure 4B). This indicates an inactivation of this gene in HCC, contributing to oncogenesis. The IRX gene family is critical for embryonic development and research has also linked them to cancer 21 . Next, we investigated DHRs with highest methylation in HBV-infected samples. This set of regions would provide information about unique genes not expressed in HBV-infection but possibly expressed in WT or HCC samples. MUC3A had high methylation in HBV-infected data compared to WT, suggesting that is this gene is inactivated once HBV infection occurs (Figure 4C). MUC3A plays a key role in maintaining a protective barrier on mucosal membranes and promotes cell migration. There is no research that links MUC3A with HBV infection, but there is a body of literature that has linked MUC3A expression with cancer progression 22–24 . Finally, we wanted to explore regions that had shifts in both H3K27ac and H3K27me3 at the same time. TNFRSF19 is such a gene, where methylation minimally increases and acetylation decreases (Figure 4D). TNFRSF19 is part of the Tumor Necrosis Factor Receptor Superfamily and mainly functions in cell fate determination. It has been used as a biomarker in certain tumor types 25–28 . Differential RNA-seq and ChIP-seq align at cellular function genes After investigating these data types individually, we integrated the transcriptomic and epigenomic data to characterize regions with both DEGs and DHRs (specifically H3K27me3). It is known that histone modifications contribute to the regulation of gene expression, but the temporal dynamics of this interaction are still being studied 29,30 . Using the DESeq2 cutoff values of fold change of 1.5 and adjusted p-value < 0.05, we identified a set of differentially transcribed genes and regions with differential H3K27me3. There were 593 differential genes when comparing wild type and HBV-infected cells and 1211 differential H3K27me3 peaks (Figure 5A). 35 regions (5.9%) were found in both of these categories. Specifically, we found that SMG1P1 had increasing H3K27me3 and therefore decreased transcription during HBV infection (Figure 5B). SMG1P1 is a pseudogene for SMG-1, which functions in the elimination of faulty mRNAs. SMG-1 expression is significantly decreased in HCC, and has been explored in other cancer types as well 31 . Next, we investigated the differential genes and peaks when comparing wild type and HCC. There were 60 differential genes and 4220 differential peaks, with 10 regions overlapping the two categories (Figure 5C). The majority of the 16.7% genes included genes that are transcribed and translated in histone protein components. For example, gene H2AC12 had decreased H3K27me3 and increased expression in HCC (Figure 5D). H2AC12, also known as HIST1H2AH, is an intron-less gene and encodes a replication-dependent histone that is a member of the histone H2A family. A study by Verma et al. found that there was an increase in H2A isoforms, specifically HIST1H2AH, due to polyadenylation. This process also activates MAP kinases, p38, ERK, and JNK in HCC liver tumor tissues 32 . When comparing HBV-infected cells and HCC, there were 71 differential genes and 930 differential H3K27me3 peaks (Figure 5E). Even though there was only a 5.6% overlap, we found genes related to cell signaling and were genes often dysregulation in disease. Specifically, the WNK2 gene had increased H3K27me3 and decreased expression (Figure 5F). In a cohort of Chinese patients, Liu et al. found that high methylation at WNK2 was linked to increased risk of HCC development 33,34 . Similar results were found in a different study with Chinese patients and a additional patient cohort from Taiwan 35 . Finally, we explored the differences between gene expression and H3K27ac presence between wild type and HCC. We found 60 differential genes and 3833 differential peaks (Figure 5G). 35% of these regions overlapped, highlighting the link between transcription and this activation mark. Specifically, we found the HLF gene had decreased H3K27ac and decreased transcription when transitioning from wild type to HCC. HLF encodes the Hepatic Leukemia Factor, which is critical for hematopoietic stem cell (HSC) development. HLF is expressed in order to maintain HSC quiescence. Indeed, a study on HLF in prostate cancer found that cells with overexpression of HLF had reduced proliferative, migratory, and invasive activity 36 . A strong downregulation of HLF was found in a majority of tumors during a pan-cancer analysis 37 . A study on triple negative breast cancer found that HLF was regulated by a feed-forward loop driven by transforming growth factor-beta1 (TGF-β1) and the JAK2/STAT3 pathway induced by tumor-associated macrophages, indicating HLF’s importance in cell signaling and cancer development 38 . Discussion In the present study, we highlight the transcriptomic shifts that occur from normal to HBV infection to HCC (Fig. 1 ). We also found distinct transcriptomic signatures between HepG2 and Huh7 cell lines and patient data (Fig. 1 B-C). Remodeling of the epigenetic landscape, specifically H3K27ac and H3K27me3, accompanies this disease progression (Fig. 2 ). The idea of epigenetic priming has been explored previously, and our research contributes to the knowledge that HBV-induced epigenetic changes may contribute to HCC progression and are maintained during this transition. Looking at the regions marked by these opposing chromatin marks provides unique insights into the push-and-pull that occurs during gene regulation. We identified specific genes as potential biomarkers and therapeutic targets that are supported by current literature (Figs. 3 , 4 , 5 ). APOB encodes two different protein products—ApoB-40 and ApoB-100 which are synthesized in the intestines and liver, respectively. ApoB-100 plays a critical role in forming very-low-density lipoproteins (VLDL) and secreting it from the liver 13 . The shift in lipid metabolism and localization within the body prevents proper levels of lipids, contributing to tumor formation. This altered lipid metabolism has downstream effects of potentially activating oncogenic pathways and key oncogenes, like TP53. Studies have suggested that APOB could be used as a biomarker for HCC prognosis based on its decreased transcript and protein levels in various groups, specifically indicating cancer stage and tumor grade 13 . One study found that CRHBP expression was much lower in HCC patient tissue samples 39 . More recently, Wang et al. found through a series of studies that CRHBP suppresses proliferation and promotes apoptosis 40 . It also has anti-angiogenic properties 40 . Our analysis supports this claim—we see a marked decrease in CRHBP in HCC patient samples compared to WT, suggesting that CRHBP cannot inhibit cellular proliferation and apoptosis. Uncontrolled growth and evading cell death are key cancer hallmarks. Zyxin is key to forming focal adhesions, which create a connection between cells and the extracellular matrix. A key cancer hallmark is cellular migration and the increase in epithelial-mesenchymal transition, which are both supported by an increase in Zyxin gene expression 41 . It also plays a crucial role in signal transduction 15 . Zyxin overexpression increased the viability, colony forming capacity, and amount of time cells spent in S phase, suggesting this gene accelerates cell progression through the cell cycle 41 . Another cancer hallmark is uncontrolled cell growth and replication. Studies have found a link between increased zyxin expression and an activation of the AKT/mTOR signaling pathway in HCC 41 . Interestingly, research has suggested a dual role for zyxin—in certain tissues it acts as an oncogene, while in others it is a tumor suppressor 42 . The SERPINE1 gene encodes the plasminogen activator inhibitor-1 (PAI-1) protein, which regulates the plasminogen pathway that regulates homeostasis of the extracellular matrix structure 43 . If there is too much PAI-1 protein, this can lead to fibrosis, which is a contributor of HCC progression. Indeed, research has found an increase in PAI-1 expression in chronic Hepatitis-C patients with HCC and liver cirrhosis 44 . PAI-1 has also been associated with lipid metabolism, which is dysregulated in HCC 45 . Our data supports the claim that SERPINE1 and PAI-1 can be used as a biomarker for HCC progression. BHLHE40 has dual roles in circadian rhythm and cell fate determination, as well as being linked to a variety of cancers. In colorectal cancer, BHLHE40 was identified as the driver of the epithelial-mesenchymal transition through increased proliferation and invasion of cells, which is a known cancer hallmark 46 . In endometrial cancer, BHLHE40 expression decreased in higher grade and advanced disease cases. That study found that BHLHE40 is critical to the phosphorylation of key dehydrogenase activity 47 . BHLHE40 is linked to the immune system and has potential to be a therapeutic target in pancreatic and thyroid cancers 48 , 49 . The IRX family has been associated with a variety of cancers. Wang et al. found that IRX3 was upregulated in HCC 21 . However, our data shows decreased acetylation. This is an instance where the temporal dynamics of histone marks and resulting gene expression should be further explored. Certain cancers, like lung and triple negative breast cancers, have reduced expression of TNFRSF19, while others, like HCC and colorectal cancer, have increased expression 25 ,26 27, 28 . The push-and-pull of methylation and acetylation create an interesting dynamic at this gene region that needs to be further studied to explain why and how this gene has differential expression in distinct cancer types. Lipid metabolism is a critical function of the liver, and we see genes related to this function dysregulated in HBV infection and HCC. Comparing WT and HBV, there are 6 genes (APOB, PNPLA2, SREBF2, AGPAT2, FABP1, and ABCA3) that have a strong role in lipid metabolism, while 7 others have indirect or lipid-related cellular functions. Again, when investigating differentially expressed genes between WT and HCC, it is important to distinguish cell line and patient-derived data. For cell line data, 5 out of 9 differential genes have direct or indirect links to lipid metabolism. In patient data, 11 out of 61 differential genes have lipid-related functions. However, many of these differential genes are critical for immune response and detoxifications, which are both cellular functions that are impacted by lipid homeostasis. Between HBV infection and HCC, there are 4 out of 15 significantly differential genes that are directly related to lipid metabolism (ACACA, SLC2A1, SERPINA7, and SPP1). This highlights the improper function of the main impacted organ for both HBV infection and HCC, leading to potential links between these disease states. As with most cancers, we also saw dysregulation of tumor suppressor genes and oncogenic drivers. For example, tumor suppressor genes like CDH1 and WNK2 were found to be differentially regulated in disease states. Classical oncogenes like EGFR, BRD4, JAK3, and MAF were also found to be dysregulated in either RNA-seq or ChIP-seq datasets. These genes should be functionally validated to confirm their impact on HCC development, as current literature has been explored on other cancer contexts. This study provides a unique insight into HBV-infection and its progression to HCC by investigating gene expression and histone modifications simultaneously. Understanding the role of histone modifications in gene regulation is well studied, but there is more to learn about the temporal dynamics of these interactions. For example, Metz Reed et al. conducted a sophisticated time course study with a variety of sequencing data types to unravel the sequence of events between chromatin interactions and gene expression 12 . This type of study would be incredibly informative when studying HBV infection and subsequent HCC diagnosis, as it is still unclear which genes drive this progression and the timing of the transcriptomic program shift. Additionally, integrating other genomics data sets, such as ATAC-seq to identify regions of open chromatin, Hi-C to identify chromatin loops, and genome wide association studies (GWAS) to link genetic variation to identified genetic mutations would provide a more comprehensive picture of the disease. Additionally, the comparison between data generated from cell lines and derived from patient samples is critical for understanding the differences between these model systems and the care that must be taken when analyzing and interpreting data for future studies. Cell lines are used as proxy for many physiological and disease states. However, it is important to relate results back to patient information, especially when suggesting the results can be used for treatment and therapeutic development. This study specifically investigated the differences between wild type and HCC transcriptomic programs in cell line and patient data. There were no similar genes in our differential analysis, suggesting that the differentially regulated genes are entirely different. This could have a large impact on researchers targeting a specific gene for HCC treatment. Using publicly available data, we are limited by what is available. This might also contribute to sample heterogeneity and impact our comparisons. To mitigate this, we leveraged datasets from papers that had multiple conditions. Cell to cell variations can be lost in population-based averages, as has been discussed by many in the field 50 – 52 . This has driven researchers to move towards single cell sequencing strategies to more accurately capture single cell gene expression patterns. This would have been especially insightful when investigating replicates that had high correlation values, but discrepancies in individual replicate gene expression. Single cell strategies are a method to generate more specific data, which ultimately could provide a strong pipeline for targeted treatment development. Overall, this study provides a unique perspective on disease progression from HBV infection to HCC through the integration of transcriptomic and epigenetic data. The genes discussed here have high translational potential that can be targeted at the HBV infection stage to prevent malignant transformation. Methods Data download & access RNA-seq and ChIP-seq datasets were downloaded from NCBI GEO and ENCODE according to the following accession numbers: Data type Cell line SRX RNA-seq HepG2 ENCFF713MNU RNA-seq HepG2 ENCFF936SLY RNA-seq Patient SRX6686963 RNA-seq Patient SRX6686965 RNA-seq Patient SRX6686967 RNA-seq HepG2 (modified) SRX21301832 RNA-seq HepG2 (modified) SRX21301833 RNA-seq Huh7 (modified) SRX21301828 RNA-seq Huh7 (modified) SRX21301829 RNA-seq Huh7 (modified) SRX15421400 RNA-seq Huh7 (modified) SRX15421401 RNA-seq Huh7 (modified) SRX15421402 RNA-seq Huh7 SRX28839540 RNA-seq Huh7 SRX28839541 RNA-seq Huh7 SRX28839542 RNA-seq Patient SRX6686964 RNA-seq Patient SRX6686966 RNA-seq Patient SRX6686968 RNA-seq HepG2 (modified) SRX22749070 RNA-seq HepG2 (modified) SRX22749071 ChIP-seq (H3K27ac) HepG2 ENCFF745JCH ChIP-seq (H3K27ac) HepG2 ENCFF926NHE ChIP-seq (H3K27ac) HepG2 ENCFF862NDZ ChIP-seq (H3K27me3) HepG2 ENCFF027OKJ ChIP-seq (H3K27me3) HepG2 ENCFF093BIB ChIP-seq (H3K27me3) HepG2 ENCFF369DOB ChIP-seq (H3K27ac) Patient SRX3832999 ChIP-seq (H3K27ac) Patient SRX3833001 ChIP-seq (H3K27me3) Patient SRX3833009 ChIP-seq (H3K27me3) Patient SRX3833011 ChIP-seq (H3K27me3) Patient SRX4014898 ChIP-seq (H3K27me3) Patient SRX4014909 ChIP-seq (H3K27me3) Patient SRX4014869 ChIP-seq (H3K27me3) Patient SRX4014875 ChIP-seq (H3K27me3) Patient SRX4014885 ChIP-seq (H3K27me3) Patient SRX4014827 ChIP-seq (H3K27me3) Patient SRX4014828 ChIP-seq (H3K27me3) Patient SRX4014961 ChIP-seq (H3K27me3) Patient SRX4014946 ChIP-seq (H3K27me3) Patient SRX4014861 Data was downloaded onto our lab server using SRA toolkit in command line. Data processing & analysis The sequence reads were aligned to the human hg38 reference genome using BWA-MEM algorithm with default parameters 53 . Because the patterns described here were evident among individual biological replicates and replicates were well correlated (Supplementary Table 1), we merged all reads from biological replicate data sets at the bam file stage for final analyses presented here. To further explore the relationship between replicates and sample types, we performed Principal Component Analysis (PCA). MACS3 was used to call peaks in ChIP-seq data with the following parameters: -f BAM -g hs --keep-dup all -B --SPMR --nomodel --shift 0 --extsize 200 -q 0.01 54 . Raw sequencing tags were smoothed (20bp bin, 100bp sliding window) and normalized to reads per kilobase per million (RPKM) using deepTOOLS 55 . Differential analysis was performed using DESeq2 56 . Differentially expressed genes (DEGs) and differential histone regions (DHRs) were identified using Wald tests, with statistical significance thresholds set at a log2 fold change ≥ 2 and 1.5 (see text) and varying adjusted p-value (see text). Gene set enrichment analysis (GSEA) was conducted on DEGs and DHRs using clusterProfiler 57 . Data visualization bigwig files were visualized in R using plotgardener 58 . Heatmaps for DEGs were generated using ggplots. Volcano plots for DEGs and DHRs were generated using EnhancedVolcano 59 . PCA plots were generated using ggplots. Gene set enrichment analyses ridge plots were generated using enrichplot. ChIP peak binding site plots were generated using ChIPSeeker 60 . Declarations Acknowledgements We would like to thank the Biology Department and Undergraduate Research Program at Elon University for support of A.V.H. and M.L. We would also like to thank Mary Lauren Benton for providing helpful comments and feedback on manuscript drafts. Author contributions A.V.H. and M.L. were involved in conceptualization, methodology, data curation, software, formal analysis, and writing an original draft of the manuscript. A.A.P. was involved in supervision, project administration, validation, formal analysis, visualization, writing and editing the manuscript. Data availability All data used in this study can be found on NCBI GEO (GSE135631, GSE297369, GSE249278, GSE112221, GSE113879) and ENCODE (ENCFF713MNU, ENCFF936SLY, ENCFF745JCH, ENCFF926NHE, ENCFF862NDZ, ENCFF027OKJ, ENCFF093BIB, ENCFF369DOB). See table in the Methods section for specific SRR access numbers used from each of these projects. Funding A.A.P was supported by Elon University’s Faculty Research & Development Hultquist Stipend Funds. References Shoraka, S., Hosseinian, S. M., Hasibi, A., Ghaemi, A. & Mohebbi, S. R. The role of hepatitis B virus genome variations in HBV-related HCC: effects on host signaling pathways. Front. Microbiol. 14 , 1213145 (2023). Mann, E. 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ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. https://dx.doi.org/10.1093/bioinformatics/btv145. Xia, H.-B. et al. Decreased CRHBP expression is predictive of poor prognosis in patients with hepatocellular carcinoma. Oncol. Lett. 16 , 3681–3689 (2018). Additional Declarations No competing interests reported. Supplementary Files SupplementalInfo20251013.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":920467,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene expression varies as cells progress from healthy to HBV-infection to HCC diagnosis\u003c/strong\u003e. Heatmaps of gene expression for WT vs. HBV infection (A), cell line data for WT vs. HCC (B), patient-derived data for WT vs. HCC (C), and HBV infection vs. HCC (D). Volcano plot (E and GSEA molecular function results (F) for WT vs. HBV infection. Volcano plot (G) and GSEA molecular function results (H) for cell line data comparing WT vs. HCC. Volcano plot (I) and GSEA molecular function results (J) for patient data comparing WT vs. HCC. Volcano plot (K) comparing HBV infection vs. HCC.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7850229/v1/550791e60a4e86a56784a9fb.jpeg"},{"id":93989609,"identity":"485c10ea-58ec-45d6-abe4-2f1adb35fb5f","added_by":"auto","created_at":"2025-10-21 05:12:47","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":938773,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHistone modification location shifts as cells progress from healthy to HBV-infection to HCC diagnosis.\u003c/strong\u003e Genomic distribution of WT H3K27me3 (A), WT H3K27ac (B), HBV H3K27me3 (C), HCC H3K27me3 (D), and HCC H3K27ac (E). Volcano plot (F) and GSEA molecular function results (G) for WT vs. HBV H3K27me3 presence. Volcano plot (H) and GSEA molecular function results (I) for WT vs. HCC H3K27me3 presence. Volcano plot (J) and GSEA molecular function results (K) for WT vs. HCC H3K27ac presence. Volcano plot (L) and GSEA molecular function results (M) for HBV vs. HCC H3K27me3 presence.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7850229/v1/ed6e5e1f31778149d5ee66c1.jpeg"},{"id":93990180,"identity":"2234bff9-c34b-4c60-b818-47496c64e1c4","added_by":"auto","created_at":"2025-10-21 05:28:47","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":596471,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKey genes in lipid metabolism and cell cycle progression are differentially expressed in disease stages.\u003c/strong\u003e Screenshot of cell line (A, C, E) and patient-derived (B, D) RNA-seq data at key gene regions.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7850229/v1/2697ca74bcb6c64334b0866e.jpeg"},{"id":93989629,"identity":"bd85d1e1-364b-4950-a5c9-1d721a59b7ea","added_by":"auto","created_at":"2025-10-21 05:12:51","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":627007,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDHRs are found at cancer-related and cellular differentiation genes\u003c/strong\u003e. Screenshot of H3K27ac (A, B) and H3K27me3 (C, D) ChIP-seq data at key gene regions.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7850229/v1/8f7faa5e315d929f6db8e51f.jpeg"},{"id":93989630,"identity":"ad6438a7-a5c7-40e9-9451-50b9e531e0b6","added_by":"auto","created_at":"2025-10-21 05:12:52","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":756316,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential RNA-seq and ChIP-seq align at cellular function genes.\u003c/strong\u003e Venn diagram comparing differential RNA-seq (blue) and ChIP-seq peaks(orange) for WT and HBV H3K27me3 (A), WT and HCC H3K27me3 (C), HBV and HCC H3K27me3 (E), and WT and HCC H3K27ac (G). Screenshots of gene regions that overlap differential data (B, D, F, H).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7850229/v1/91fd660f9fcddfdd49529b02.jpeg"},{"id":94094037,"identity":"17c72f74-85cb-4551-8011-db35200b54f0","added_by":"auto","created_at":"2025-10-22 09:24:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4881947,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7850229/v1/092bb8a8-d284-4de6-9e54-8162bddfba20.pdf"},{"id":93990179,"identity":"66944d4c-472d-4d5a-8fae-ac44f6c5edac","added_by":"auto","created_at":"2025-10-21 05:28:47","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":402270,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalInfo20251013.docx","url":"https://assets-eu.researchsquare.com/files/rs-7850229/v1/77b2108f6eae9ff997c9005d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Transcriptomic and Epigenomic Analysis Highlights Hepatitis B Virus Infection Impact on Hepatocellular Carcinoma Development","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatitis B virus (HBV) is the most chronic viral infection globally, posing a significant health burden \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. There are multiple genotypes of the hepatitis B virus and they are known to have distinct geographical presences. In most of Africa, Europe, and the Americas, genotypes A and D are most prevalent. Genotypes B and C are predominantly found in Asia. Sub-Saharan Africa, however, is where genotype E is primarily located. Other genotypes (F, G, H, I, and J) are less common and found in even more specific regions. This distinction in geographical range has implications for health equity and access to treatment. More than 250\u0026nbsp;million people in the world have chronic hepatitis B infection and over 1\u0026nbsp;million people die each year from complications and progression of the infection. HBV infection is the leading risk factor in developing hepatocellular carcinoma.\u003c/p\u003e\u003cp\u003eHepatocellular carcinoma (HCC) is the second leading cause of cancer-related deaths globally\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. It can develop due to a variety of factors, such as patient demographics (sex, age, ethnicity) environmental conditions, existing comorbidities, and chronic hepatitis infection. HCC also has a skewed geographical distribution, with higher incidence in East Asia and sub-Saharan Africa. This is thought to stem from the ability of different HBV genotypes to infect distinct populations \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Varying viral replication capacities of these HBV genotypes may further contribute to the differences in clinical outcomes among patients. Therefore, the rates of global HCC are linked to the prevalence of distinct HBV genotypes. Understanding the relationship between HBV viral mechanisms, chronic infection, and HCC is critical for developing effective early diagnoses, targeted therapies, and preventative strategies to combat the high global burden of liver cancer and liver-related mortality.\u003c/p\u003e\u003cp\u003eResearch has increasingly focused on the epigenetic changes induced by HBV, particularly through the activity of the viral X protein (HBx) \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. HBx appears to act as a master regulator of the host epigenome, altering gene expression through interactions with chromatin-modifying enzymes, DNA methyltransferases (DNMTs), histone-modifying proteins, and transcription factors \u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. These changes allow HBV to manipulate the host's regulatory machinery, promoting cell survival, immune evasion, and malignant transformation. One of the central epigenetic mechanisms through which HBx exerts its effects involves post-translational modifications of histone tails \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Modifications to lysine 27 on histone H3 (H3K27) have emerged as key regulatory factors in HBV-related tumorigenesis \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Recent literature has explored the role of epigenetic priming in the transition from chronic liver disease towards HCC \u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. This model proposes that precancerous cells acquire key epigenetic characteristics which primes them for oncogenic activity. Disruption of the balance between activating (H3K27ac) and repressive (H3K27me3) marks can lead to abnormal gene expression patterns, silencing of tumor suppressors, or activation of oncogenes. Understanding non-mutagenic factors contributes to changes in gene expression is key to a better understanding of disease progression.\u003c/p\u003e\u003cp\u003eOther studies have continued this research to understand the sequential timing of the presence of these marks appearing and the impact on gene expression \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Gene expression changes from normal transcriptional programs are a key driver of cancer. Identifying epigenetic marks in HBV-infected samples and diagnosed HCC samples can provide insights to the epigenetic priming occurring during the transition from HBV infection to HCC. Subsequent investigation of changes in transcriptional programs from normal liver cells to HBV-infection to HCC will offer a deeper understanding of the critical genes involved in this transition. Together, this information is key in understanding and preventing the progression to HCC, as well as potential treatment avenues.\u003c/p\u003e\u003cp\u003eIn this study, we leverage publicly available cell line and patient-derived datasets to investigate key epigenetic factors and gene expression in normal liver, HBV-infected cells, and HCC samples. We explore the differences between HBV genotypes, and compare cell line and patient-derived samples. Using the results from our analyses, we present key genes that control disease progression as targets for future therapies.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eGene expression varies as cells progress from healthy to HBV-infection to HCC diagnosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo understand the transcriptomic changes associated with HBV infection and the progression to HCC, we conducted a comprehensive analysis using RNA-seq data from three distinct stages. By comparing the gene expression profiles of normal HepG2 cell lines (WT), HBV-infected HepG2 cells (HBV), and HCC cell lines, we aimed to identify differentially expressed genes (DEGs) and elucidate the molecular mechanisms involved in hepatocarcinogenesis.\u003c/p\u003e\n\u003cp\u003eIn addition to assessing correlation of replicates (Supplementary Table 1), we performed Principle Component Analysis (Supplementary Figure 1). We did not see a clear distinction between disease states (Supplementary Figure 1A), which led us to separate data that was derived from cell lines from those that were patient-derived. This analysis provided a clear distinction between data derived from cell lines and patient samples (Supplementary Figure 1B). We keep cell-line and patient-derived analyses separate in all future analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe compared standardized gene expression across 4 groups\u0026mdash;WT vs. HBV (Figure 1A), WT vs. HCC cell line data (Figure 1B), WT vs. HCC patient data (Figure 1C), and HBV vs. HCC (Figure 1D). Generally, replicates from the same disease state were clustered together. Although replicates had high correlation values, some discrepancies in individual gene expression remain. Using DESeq2, we calculated the set of DEGs that had a fold change of 2 and p-value \u0026lt; 1e\u003csup\u003e-5\u003c/sup\u003e. Of the 49,224 total observations, there were 174 DEGs between WT and HBV-infected cells, 116 of which were upregulated in HBV and 58 of which were downregulated (Figure 1E). Genes that were differentially expressed between disease stages were enriched in gene ontology (GO) molecular function groups such as signal receptor activity, catalytic activity, and chromatin components (Figure 1F). When separating cell line and patient data for the comparison between WT and HCC, there were distinct sets of DEGs. In cell line data, there were 9 DEGs, 5 of which were upregulated in HCC and 4 of which were downregulated (Figure 1G). These genes were primarily enriched in structural molecule activity, protein heterodimerization activity, and key parts of chromatin structure (Figure 1H). Alternatively, there were 61 DEGs between WT and HCC patient data, 18 of which were upregulated in HCC and 43 of which were downregulated (Figure 1I). These genes were also found in protein heterodimerization activity and key parts of chromatin structure GO groups, but were also found in antigen binding functions. There were 15 DEGs between HBV and HCC, with one gene (UMPS) being less expressed in HBV and 14 genes with higher expression in HBV. These DEGs were not enriched in any specific GO molecular function groups. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHistone modification location shifts as cells progress from healthy to HBV-infection to HCC diagnosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe examined the differential histone regions (DHRs) for H3K27ac and H3K27me3 between WT, HBV, and HCC due to HBx\u0026rsquo;s known role in epigenetic mechanisms. In our data, H3K27me3 was consistently found at promoter regions and distal intergenic regions in WT, HBV, and HCC (Figure 2A-E). In HBV infection, the histone marks were shifted away from promoter regions and more towards downstream and distal intergenic regions.\u003c/p\u003e\n\u003cp\u003eFirst, we investigated a subset of the different subtypes of HBV (HBV-C, HBV-D, and HBV-A). \u0026nbsp;We found no statistical differences in H3K27me3 presence (fold change of 2 and p-value \u0026lt; 1e\u003csup\u003e-5\u003c/sup\u003e) between these genetic variants of the virus (Supplemental Figure 2). These differences in genotypes are based on sequence-level differences, not epigenetic variations, which is supported by our data. This highlights the importance of investigating both genetic differences (mutations, genomics rearrangements, etc.) in addition to epigenetic modifications when studying disease. HBV-C is associated with the greatest risk of liver disease and progression to HCC. Therefore, subsequent analysis focused on data from HBV-C.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFollowing our gene expression analysis, we used DESeq2 (fold change of 2 and p-value \u0026lt; 1e\u003csup\u003e-10\u003c/sup\u003e) to identify DHRs between the disease states. Between WT and HBV, there was significantly less methylation throughout the genome, suggesting the activation of genes once HBV infection has occurred (Figure 2F). The 93 regions of differential methylation were enriched for ribosome activity, transcription regulation, and ubiquitin-like protein activity GO molecular functions (Figure 2G). When comparing WT and HCC, 164 of the total 174 DHRs had decreasing methylation (Figure 2H), and these regions were similarly enriched for chromatin organization, ubiquitin-like protein activity, and ribosome activity GO molecular function (Figure 2I). Interestingly, when looking at acetylation differences between WT and HCC, we observed more increases in acetylation (145 of 201 total regions) (Figure 2J). These regions were also enriched for ribosome activity, as well as different types of channel activity (Figure 2K). We observed the smallest number of DHRs between HBV and HCC (16), similar to our results from RNA-seq analysis (Figure 2L). Despite the small number of differential regions, these were still enriched for ribosome activity, transcriptional regulation, and ubiquitin protein activity (Figure 2M). This suggests that the epigenetic landscape established during HBV infection persists into HCC diagnosis and is a potential driver for the disease progression. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKey genes in lipid metabolism and cell cycle progression are differentially expressed in disease stages\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnce we had an overview of the transcriptomic program in WT, HBV, and HCC, we explored specific gene examples that fell into several categories. The first category was genes that decreased in expression from WT to HBV infection to HCC. Overall, there were 58 and 15 genes identified by DESeq2 that were less expressed in each comparison in cell line data (WT vs. HBV infection and HBV infection vs. HCC, respectively). An example of a gene in this category is APOB, which functions in transporting fats and cholesterol in the bloodstream \u003csup\u003e13\u003c/sup\u003e (Figure 3A). A continually decreasing expression of this key liver function gene impacts the development of HCC.\u003c/p\u003e\n\u003cp\u003eThe second category was genes that decreased between WT and HCC patient samples. Overall, there were 43 genes that decreased. None of these overlapped with genes that decreased in the cell line samples. One example is CRHBP (Figure 3B). Corticotropin releasing hormone (CRH) binding protein (CRHBP) is a glycoprotein known to regulate the interaction between CRH and its receptors \u003csup\u003e14\u003c/sup\u003e. Several studies have begun to explore its role in various cancers, specifically HCC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThird, we explored genes that increased in expression during disease progression. One example includes ZYX, which continually increased from WT to HBV-infection to HCC (Figure 3C). Zyxin is a critical component of the cytoskeleton and localizes to focal adhesions. It also plays a crucial role in signal transduction \u003csup\u003e15\u003c/sup\u003e. Both of these cellular functions contribute to zyxin\u0026rsquo;s potential role in HBV infection progressing to HCC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn patient-derived data, SERPINE1 increased from WT to HCC (Figure 3D). This gene is an inhibitor of a serine protease, while also contributing to cell adhesion and cell migration. Studies have found increased SERPINE1 expression leads to more aggressive tumors and poor patient outcome \u003csup\u003e16\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, we wanted to identify genes that were most highly expressed in HBV-infected samples. BHLHE40 increases significantly from WT and HBV-infection before dropping again in HCC. It functions in circadian control through its interaction with ARNTL and PER1. More importantly, it has been linked to cell differentiation and cell cycle progression, and associated with a wide variety of cancers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDHRs are found at cancer-related and cellular differentiation genes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003eWe next to identified specific DHRs and investigated their potential involvement in disease progression. First, we considered differential acetylation regions. The MAF promoter had increased acetylation in HCC, suggesting this gene is highly activated in HCC (Figure 4A). MAF is a transcription factor that acts in activation and repression of genes, and has been implicated in a variety of cancer types \u003csup\u003e17\u0026ndash;19\u003c/sup\u003e. Specifically, MAFB expression has been identified as highly overexpressed in malignant HCC lines \u003csup\u003e20\u003c/sup\u003e. This may be because MAFB expression alters immune microenvironments, which are critical to tumor development and patient outcome.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlternatively, we found that IRX3 had decreased acetylation in HCC (Figure 4B). This indicates an inactivation of this gene in HCC, contributing to oncogenesis. The IRX gene family is critical for embryonic development and research has also linked them to cancer \u003csup\u003e21\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, we investigated DHRs with highest methylation in HBV-infected samples. This set of regions would provide information about unique genes not expressed in HBV-infection but possibly expressed in WT or HCC samples. MUC3A had high methylation in HBV-infected data compared to WT, suggesting that is this gene is inactivated once HBV infection occurs (Figure 4C). MUC3A plays a key role in maintaining a protective barrier on mucosal membranes and promotes cell migration. There is no research that links MUC3A with HBV infection, but there is a body of literature that has linked MUC3A expression with cancer progression \u003csup\u003e22\u0026ndash;24\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, we wanted to explore regions that had shifts in both H3K27ac and H3K27me3 at the same time. TNFRSF19 is such a gene, where methylation minimally increases and acetylation decreases (Figure 4D). TNFRSF19 is part of the Tumor Necrosis Factor Receptor Superfamily and mainly functions in cell fate determination. It has been used as a biomarker in certain tumor types \u003csup\u003e25\u0026ndash;28\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential RNA-seq and ChIP-seq align at cellular function genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter investigating these data types individually, we integrated the transcriptomic and epigenomic data to characterize regions with both DEGs and DHRs (specifically H3K27me3). It is known that histone modifications contribute to the regulation of gene expression, but the temporal dynamics of this interaction are still being studied \u003csup\u003e29,30\u003c/sup\u003e. Using the DESeq2 cutoff values of fold change of 1.5 and adjusted p-value \u0026lt; 0.05, we identified a set of differentially transcribed genes and regions with differential H3K27me3. There were 593 differential genes when comparing wild type and HBV-infected cells and 1211 differential H3K27me3 peaks (Figure 5A). 35 regions (5.9%) were found in both of these categories. Specifically, we found that SMG1P1 had increasing H3K27me3 and therefore decreased transcription during HBV infection (Figure 5B). SMG1P1 is a pseudogene for SMG-1, which functions in the elimination of faulty mRNAs. SMG-1 expression is significantly decreased in HCC, and has been explored in other cancer types as well \u003csup\u003e31\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, we investigated the differential genes and peaks when comparing wild type and HCC. There were 60 differential genes and 4220 differential peaks, with 10 regions overlapping the two categories (Figure 5C). The majority of the 16.7% genes included genes that are transcribed and translated in histone protein components. For example, gene H2AC12 had decreased H3K27me3 and increased expression in HCC (Figure 5D). H2AC12, also known as HIST1H2AH, is an intron-less gene and encodes a replication-dependent histone that is a member of the histone H2A family. A study by Verma et al. found that there was an increase in H2A isoforms, specifically HIST1H2AH, due to polyadenylation. This process also activates MAP kinases, p38, ERK, and JNK in HCC liver tumor tissues \u003csup\u003e32\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen comparing HBV-infected cells and HCC, there were 71 differential genes and 930 differential H3K27me3 peaks (Figure 5E). Even though there was only a 5.6% overlap, we found genes related to cell signaling and were genes often dysregulation in disease. Specifically, the WNK2 gene had increased H3K27me3 and decreased expression (Figure 5F). In a cohort of Chinese patients, Liu et al. found that high methylation at WNK2 was linked to increased risk of HCC development \u003csup\u003e33,34\u003c/sup\u003e. Similar results were found in a different study with Chinese patients and a additional patient cohort from Taiwan \u003csup\u003e35\u003c/sup\u003e. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, we explored the differences between gene expression and H3K27ac presence between wild type and HCC. We found 60 differential genes and 3833 differential peaks (Figure 5G). 35% of these regions overlapped, highlighting the link between transcription and this activation mark. Specifically, we found the HLF gene had decreased H3K27ac and decreased transcription when transitioning from wild type to HCC. HLF encodes the Hepatic Leukemia Factor, which is critical for hematopoietic stem cell (HSC) development. HLF is expressed in order to maintain HSC quiescence. Indeed, a study on HLF in prostate cancer found that cells with overexpression of HLF had reduced proliferative, migratory, and invasive activity \u003csup\u003e36\u003c/sup\u003e. A strong downregulation of HLF was found in a majority of tumors during a pan-cancer analysis \u003csup\u003e37\u003c/sup\u003e. A study on triple negative breast cancer found that HLF was regulated by a feed-forward loop driven by transforming growth factor-beta1 (TGF-\u0026beta;1) and the JAK2/STAT3 pathway induced by tumor-associated macrophages, indicating HLF\u0026rsquo;s importance in cell signaling and cancer development \u003csup\u003e38\u003c/sup\u003e. \u0026nbsp; \u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, we highlight the transcriptomic shifts that occur from normal to HBV infection to HCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We also found distinct transcriptomic signatures between HepG2 and Huh7 cell lines and patient data (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-C). Remodeling of the epigenetic landscape, specifically H3K27ac and H3K27me3, accompanies this disease progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The idea of epigenetic priming has been explored previously, and our research contributes to the knowledge that HBV-induced epigenetic changes may contribute to HCC progression and are maintained during this transition. Looking at the regions marked by these opposing chromatin marks provides unique insights into the push-and-pull that occurs during gene regulation.\u003c/p\u003e\u003cp\u003eWe identified specific genes as potential biomarkers and therapeutic targets that are supported by current literature (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). APOB encodes two different protein products\u0026mdash;ApoB-40 and ApoB-100 which are synthesized in the intestines and liver, respectively. ApoB-100 plays a critical role in forming very-low-density lipoproteins (VLDL) and secreting it from the liver \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The shift in lipid metabolism and localization within the body prevents proper levels of lipids, contributing to tumor formation. This altered lipid metabolism has downstream effects of potentially activating oncogenic pathways and key oncogenes, like TP53. Studies have suggested that APOB could be used as a biomarker for HCC prognosis based on its decreased transcript and protein levels in various groups, specifically indicating cancer stage and tumor grade \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOne study found that CRHBP expression was much lower in HCC patient tissue samples \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. More recently, Wang et al. found through a series of studies that CRHBP suppresses proliferation and promotes apoptosis \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. It also has anti-angiogenic properties \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Our analysis supports this claim\u0026mdash;we see a marked decrease in CRHBP in HCC patient samples compared to WT, suggesting that CRHBP cannot inhibit cellular proliferation and apoptosis. Uncontrolled growth and evading cell death are key cancer hallmarks.\u003c/p\u003e\u003cp\u003eZyxin is key to forming focal adhesions, which create a connection between cells and the extracellular matrix. A key cancer hallmark is cellular migration and the increase in epithelial-mesenchymal transition, which are both supported by an increase in Zyxin gene expression \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. It also plays a crucial role in signal transduction \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Zyxin overexpression increased the viability, colony forming capacity, and amount of time cells spent in S phase, suggesting this gene accelerates cell progression through the cell cycle \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Another cancer hallmark is uncontrolled cell growth and replication. Studies have found a link between increased zyxin expression and an activation of the AKT/mTOR signaling pathway in HCC \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Interestingly, research has suggested a dual role for zyxin\u0026mdash;in certain tissues it acts as an oncogene, while in others it is a tumor suppressor \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe SERPINE1 gene encodes the plasminogen activator inhibitor-1 (PAI-1) protein, which regulates the plasminogen pathway that regulates homeostasis of the extracellular matrix structure \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. If there is too much PAI-1 protein, this can lead to fibrosis, which is a contributor of HCC progression. Indeed, research has found an increase in PAI-1 expression in chronic Hepatitis-C patients with HCC and liver cirrhosis \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. PAI-1 has also been associated with lipid metabolism, which is dysregulated in HCC \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Our data supports the claim that SERPINE1 and PAI-1 can be used as a biomarker for HCC progression.\u003c/p\u003e\u003cp\u003eBHLHE40 has dual roles in circadian rhythm and cell fate determination, as well as being linked to a variety of cancers. In colorectal cancer, BHLHE40 was identified as the driver of the epithelial-mesenchymal transition through increased proliferation and invasion of cells, which is a known cancer hallmark \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. In endometrial cancer, BHLHE40 expression decreased in higher grade and advanced disease cases. That study found that BHLHE40 is critical to the phosphorylation of key dehydrogenase activity \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. BHLHE40 is linked to the immune system and has potential to be a therapeutic target in pancreatic and thyroid cancers \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe IRX family has been associated with a variety of cancers. Wang et al. found that IRX3 was upregulated in HCC \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. However, our data shows decreased acetylation. This is an instance where the temporal dynamics of histone marks and resulting gene expression should be further explored.\u003c/p\u003e\u003cp\u003eCertain cancers, like lung and triple negative breast cancers, have reduced expression of TNFRSF19, while others, like HCC and colorectal cancer, have increased expression \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,26 27,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The push-and-pull of methylation and acetylation create an interesting dynamic at this gene region that needs to be further studied to explain why and how this gene has differential expression in distinct cancer types.\u003c/p\u003e\u003cp\u003eLipid metabolism is a critical function of the liver, and we see genes related to this function dysregulated in HBV infection and HCC. Comparing WT and HBV, there are 6 genes (APOB, PNPLA2, SREBF2, AGPAT2, FABP1, and ABCA3) that have a strong role in lipid metabolism, while 7 others have indirect or lipid-related cellular functions. Again, when investigating differentially expressed genes between WT and HCC, it is important to distinguish cell line and patient-derived data. For cell line data, 5 out of 9 differential genes have direct or indirect links to lipid metabolism. In patient data, 11 out of 61 differential genes have lipid-related functions. However, many of these differential genes are critical for immune response and detoxifications, which are both cellular functions that are impacted by lipid homeostasis. Between HBV infection and HCC, there are 4 out of 15 significantly differential genes that are directly related to lipid metabolism (ACACA, SLC2A1, SERPINA7, and SPP1). This highlights the improper function of the main impacted organ for both HBV infection and HCC, leading to potential links between these disease states.\u003c/p\u003e\u003cp\u003eAs with most cancers, we also saw dysregulation of tumor suppressor genes and oncogenic drivers. For example, tumor suppressor genes like CDH1 and WNK2 were found to be differentially regulated in disease states. Classical oncogenes like EGFR, BRD4, JAK3, and MAF were also found to be dysregulated in either RNA-seq or ChIP-seq datasets. These genes should be functionally validated to confirm their impact on HCC development, as current literature has been explored on other cancer contexts.\u003c/p\u003e\u003cp\u003eThis study provides a unique insight into HBV-infection and its progression to HCC by investigating gene expression and histone modifications simultaneously. Understanding the role of histone modifications in gene regulation is well studied, but there is more to learn about the temporal dynamics of these interactions. For example, Metz Reed et al. conducted a sophisticated time course study with a variety of sequencing data types to unravel the sequence of events between chromatin interactions and gene expression \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. This type of study would be incredibly informative when studying HBV infection and subsequent HCC diagnosis, as it is still unclear which genes drive this progression and the timing of the transcriptomic program shift. Additionally, integrating other genomics data sets, such as ATAC-seq to identify regions of open chromatin, Hi-C to identify chromatin loops, and genome wide association studies (GWAS) to link genetic variation to identified genetic mutations would provide a more comprehensive picture of the disease.\u003c/p\u003e\u003cp\u003eAdditionally, the comparison between data generated from cell lines and derived from patient samples is critical for understanding the differences between these model systems and the care that must be taken when analyzing and interpreting data for future studies. Cell lines are used as proxy for many physiological and disease states. However, it is important to relate results back to patient information, especially when suggesting the results can be used for treatment and therapeutic development. This study specifically investigated the differences between wild type and HCC transcriptomic programs in cell line and patient data. There were no similar genes in our differential analysis, suggesting that the differentially regulated genes are entirely different. This could have a large impact on researchers targeting a specific gene for HCC treatment.\u003c/p\u003e\u003cp\u003eUsing publicly available data, we are limited by what is available. This might also contribute to sample heterogeneity and impact our comparisons. To mitigate this, we leveraged datasets from papers that had multiple conditions. Cell to cell variations can be lost in population-based averages, as has been discussed by many in the field \u003csup\u003e\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. This has driven researchers to move towards single cell sequencing strategies to more accurately capture single cell gene expression patterns. This would have been especially insightful when investigating replicates that had high correlation values, but discrepancies in individual replicate gene expression. Single cell strategies are a method to generate more specific data, which ultimately could provide a strong pipeline for targeted treatment development.\u003c/p\u003e\u003cp\u003eOverall, this study provides a unique perspective on disease progression from HBV infection to HCC through the integration of transcriptomic and epigenetic data. The genes discussed here have high translational potential that can be targeted at the HBV infection stage to prevent malignant transformation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eData download \u0026amp; access\u003c/h2\u003e\u003cp\u003eRNA-seq and ChIP-seq datasets were downloaded from NCBI GEO and ENCODE according to the following accession numbers:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eData type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" 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colname=\"c3\"\u003e\u003cp\u003eSRX28839542\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRNA-seq\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSRX6686964\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRNA-seq\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSRX6686966\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRNA-seq\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSRX6686968\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRNA-seq\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHepG2 (modified)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSRX22749070\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRNA-seq\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHepG2 (modified)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSRX22749071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChIP-seq (H3K27ac)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHepG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eENCFF745JCH\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChIP-seq (H3K27ac)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHepG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eENCFF926NHE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChIP-seq (H3K27ac)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHepG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eENCFF862NDZ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChIP-seq (H3K27me3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHepG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eENCFF027OKJ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChIP-seq (H3K27me3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHepG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eENCFF093BIB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChIP-seq (H3K27me3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHepG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eENCFF369DOB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChIP-seq (H3K27ac)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSRX3832999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChIP-seq (H3K27ac)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSRX3833001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChIP-seq (H3K27me3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSRX3833009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChIP-seq (H3K27me3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSRX3833011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChIP-seq (H3K27me3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSRX4014898\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChIP-seq (H3K27me3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSRX4014909\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChIP-seq (H3K27me3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSRX4014869\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChIP-seq (H3K27me3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSRX4014875\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChIP-seq (H3K27me3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSRX4014885\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChIP-seq (H3K27me3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSRX4014827\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChIP-seq (H3K27me3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSRX4014828\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChIP-seq (H3K27me3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSRX4014961\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChIP-seq (H3K27me3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSRX4014946\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChIP-seq (H3K27me3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSRX4014861\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eData was downloaded onto our lab server using SRA toolkit in command line.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eData processing \u0026amp; analysis\u003c/h2\u003e\u003cp\u003eThe sequence reads were aligned to the human hg38 reference genome using BWA-MEM algorithm with default parameters \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Because the patterns described here were evident among individual\u003c/p\u003e\u003cp\u003ebiological replicates and replicates were well correlated (Supplementary Table\u0026nbsp;1), we merged all reads from biological replicate data sets at the bam file stage for final analyses presented here. To further explore the relationship between replicates and sample types, we performed Principal Component Analysis (PCA). MACS3 was used to call peaks in ChIP-seq data with the following parameters: -f BAM -g hs --keep-dup all -B --SPMR --nomodel --shift 0 --extsize 200 -q 0.01 \u003csup\u003e54\u003c/sup\u003e. Raw sequencing tags were smoothed (20bp bin, 100bp sliding window) and normalized to reads per kilobase per million (RPKM) using deepTOOLS\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Differential analysis was performed using DESeq2\u003csup\u003e56\u003c/sup\u003e. Differentially expressed genes (DEGs) and differential histone regions (DHRs) were identified using Wald tests, with statistical significance thresholds set at a log2 fold change\u0026thinsp;\u0026ge;\u0026thinsp;2 and 1.5 (see text) and varying adjusted p-value (see text). Gene set enrichment analysis (GSEA) was conducted on DEGs and DHRs using clusterProfiler \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eData visualization\u003c/h2\u003e\u003cp\u003ebigwig files were visualized in R using plotgardener \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Heatmaps for DEGs were generated using ggplots. Volcano plots for DEGs and DHRs were generated using EnhancedVolcano \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. PCA plots were generated using ggplots. Gene set enrichment analyses ridge plots were generated using enrichplot. ChIP peak binding site plots were generated using ChIPSeeker \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the Biology Department and Undergraduate Research Program at Elon University for support of A.V.H. and M.L. We would also like to thank Mary Lauren Benton for providing helpful comments and feedback on manuscript drafts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.V.H. and M.L. were involved in conceptualization, methodology, data curation, software, formal analysis, and writing an original draft of the manuscript. A.A.P. was involved in supervision, project administration, validation, formal analysis, visualization, writing and editing the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this study can be found on NCBI GEO (GSE135631, GSE297369, GSE249278, GSE112221, GSE113879) and ENCODE (ENCFF713MNU, ENCFF936SLY, ENCFF745JCH, ENCFF926NHE, ENCFF862NDZ, ENCFF027OKJ, ENCFF093BIB, ENCFF369DOB). See table in the Methods section for specific SRR access numbers used from each of these projects.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.A.P was supported by Elon University\u0026rsquo;s Faculty Research \u0026amp; Development Hultquist Stipend Funds. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShoraka, S., Hosseinian, S. M., Hasibi, A., Ghaemi, A. \u0026amp; Mohebbi, S. R. 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Lett.\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 3681\u0026ndash;3689 (2018).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7850229/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7850229/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHepatitis B virus (HBV) infection is a major risk factor for hepatocellular carcinoma (HCC), yet the molecular events linking infection to malignant transformation remain incompletely defined. Here, we integrated transcriptomic (RNA-seq) and epigenomic (ChIP-seq for H3K27ac and H3K27me3) data across normal hepatocyte-derived cells (WT), HBV-infected cells, and HCC samples to quantify gene expression changes and epigenetic remodeling during disease progression. Transcriptomic profiling revealed distinct sets of differentially expressed genes (DEGs) between WT, HBV, and HCC states, with enrichment in pathways related to chromatin organization, signal transduction, lipid metabolism, and immune regulation. Epigenomic analyses demonstrated that HBV infection contributes to widespread redistribution of histone modifications, with reduced H3K27me3 at promoter regions and increased H3K27ac at enhancer regions, favoring activation of oncogenic programs. Differential histone regions (DHRs) overlapped with DEGs involved in ribosomal activity, ubiquitin-like protein function, and chromatin dynamics, suggesting coordinated transcriptomic and epigenomic reprogramming. Together, these results suggest that HBV infection establishes an epigenetic landscape that persists into HCC and drives altered gene expression in pathways central to lipid metabolism, chromatin structure, and cell cycle progression. This work highlights candidate biomarkers and regulatory mechanisms underlying HBV-associated hepatocarcinogenesis.\u003c/p\u003e","manuscriptTitle":"Comparative Transcriptomic and Epigenomic Analysis Highlights Hepatitis B Virus Infection Impact on Hepatocellular Carcinoma Development","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-21 05:12:42","doi":"10.21203/rs.3.rs-7850229/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c6dc850f-cb16-4c3d-9e6a-dc41c3a74e9c","owner":[],"postedDate":"October 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56307610,"name":"Biological sciences/Cancer"},{"id":56307611,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":56307612,"name":"Biological sciences/Genetics"},{"id":56307613,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2025-10-22T09:23:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-21 05:12:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7850229","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7850229","identity":"rs-7850229","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}