
==== Front
Virol J
Virol J
Virology Journal
1743-422X
BioMed Central London

39090742
2446
10.1186/s12985-024-02446-3
Research
Analysis of host factor networks during hepatitis B virus infection in primary human hepatocytes
Hwangbo Suhyun 1
Kim Gahee 24
Choi Yongwook 2
Park Yong Kwang 2
Bae Songmee 2
Ryu Jae Yong 3
Hur Wonhee wonhee.her@gmail.com

2
1 https://ror.org/01z4nnt86 grid.412484.f 0000 0001 0302 820X Department of Genomic Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080 Korea
2 https://ror.org/00qdsfq65 grid.415482.e 0000 0004 0647 4899 Division of Chronic Viral Diseases, Center for Emerging Virus Research, Korea National Institute of Health, 187 Osongsaengmyeong 2-ro, Cheongju, 363951 Korea
3 https://ror.org/01h6frr69 grid.410884.1 0000 0004 0532 6173 Department of Biotechnology, Duksung Women’s University, Seoul, 01369 Korea
4 https://ror.org/02wnxgj78 grid.254229.a 0000 0000 9611 0917 Department of Pharmacy, Chungbuk National University, Cheongju, 28644 Korea
1 8 2024
1 8 2024
2024
21 17025 4 2024
23 7 2024
© The Author(s) 2024, corrected publication 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

Chronic hepatitis B virus (HBV) infection affects around 250 million people worldwide, causing approximately 887,000 deaths annually, primarily owing to cirrhosis and hepatocellular carcinoma (HCC). The current approved treatments for chronic HBV infection, such as interferon and nucleos(t)ide analogs, have certain limitations as they cannot completely eradicate covalently closed circular DNA (cccDNA). Considering that HBV replication relies on host transcription factors, focusing on host factors in the HBV genome may provide insights into new therapeutic targets against HBV. Therefore, understanding the mechanisms underlying viral persistence and hepatocyte pathogenesis, along with the associated host factors, is crucial. In this study, we investigated novel therapeutic targets for HBV infection by identifying gene and pathway networks involved in HBV replication in primary human hepatocytes (PHHs). Importantly, our study utilized cultured primary hepatocytes, allowing transcriptomic profiling in a biologically relevant context and enabling the investigation of early HBV-mediated effects.

Methods

PHHs were infected with HBV virion particles derived from HepAD38 cells at 80 HBV genome equivalents per cell (Geq/cell). For transcriptomic sequencing, PHHs were harvested 1, 2-, 3-, 5-, and 7 days post-infection (dpi). After preparing the libraries, clustering and sequencing were conducted to generate RNA-sequencing data. This data was processed using Bioinformatics tools and software to analyze DEGs and obtain statistically significant results. Furthermore, qRT-PCR was performed to validate the RNA-sequencing results, ensuring consistent findings.

Results

We observed significant alterations in the expression patterns of 149 genes from days 1 to 7 following HBV infection (R2 > 0.7, q < 0.05). Functional analysis of these genes identified RNA-binding proteins involved in mRNA metabolism and the regulation of alternative splicing during HBV infection. Results from qRT-PCR experiments and the analysis of two validation datasets suggest that RBM14 and RPL28 may serve as potential biomarkers for HBV-associated HCC.

Conclusions

Transcriptome analysis of gene expression changes during HBV infection in PHHs provided valuable insights into chronic HBV infection. Additionally, understanding the functional involvement of host factor networks in the molecular mechanisms of HBV replication and transcription may facilitate the development of novel strategies for HBV treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12985-024-02446-3.

Keywords

Hepatitis B virus
Primary human hepatocytes
Transcriptome analysis
RNA-binding proteins
Korea National Institute of Health (NIH)2022-NG-003-01 2022-NG-003-01 2022-NG-003-01 2022-NG-003-01 2022-NG-003-01 Kim Gahee Choi Yongwook Park Yong Kwang Bae Songmee Hur Wonhee issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
==== Body
pmcBackground

Hepatitis B virus (HBV), a prototypical member of the Hepadnaviridae family, is responsible for inducing acute and chronic hepatitis B, cirrhosis, and hepatocellular carcinoma (HCC) [1]. Despite the availability of safe and highly effective vaccines against HBV, chronic infection remains a significant public health challenge, affecting an estimated 296 million individuals worldwide. It is the second-leading cause of cancer-related mortality worldwide [2]. HBV is an enveloped DNA virus that contains a small, partially double-stranded 3.2 kb DNA genome, or relaxed circular DNA (rcDNA). The rcDNA transforms into covalently closed circular DNA (cccDNA), serving as a template for viral transcription. cccDNA persists within the nuclei of infected hepatocytes in an episomal state via the intervention of cellular enzymes [3]. Current treatments include nucleos(t)ide analogs (NAs), such as tenofovir or entecavir, which effectively target viral reverse transcriptase activity and reduce viral replication. However, the challenge persists as these treatments do not directly eradicate cccDNA, a pivotal obstacle to achieving a complete virological or sterilizing cure for HBV infection.

The viral transcription produces multiple-length viral RNAs (3.5, 2.4, 2.1, and 0.7 kb) under the control of four viral promoters and two enhancers. Additionally, numerous host cellular transcription factors, such as nuclear receptors and hepatocyte-enriched and ubiquitous transcription factors, participate in the HBV life cycle [4]. Recent reports have highlighted the biological significance of HBV RNA regulation, potentially impacting splicing regulation in a cell type-specific manner [5]. Therefore, a more extensive exploration is needed to understand the biological significance of HBV RNA regulation in hepatocytes, encompassing viral replication, host range, and intricate regulatory mechanisms governing post-transcriptional processing events within the HBV life cycle. Furthermore, understanding the multitude of host factors involved in the HBV life cycle holds promise for uncovering insights guiding the discovery of anti-HBV therapies, focusing on drugs targeting cellular factors.

In this study, we performed RNA-sequencing (RNA-seq) experiment to identify differentially expressed and co-regulated genes in HBV-infected primary human hepatocytes (PHHs). Additionally, to validate our transcriptome data analysis, we compared it with eligible HBV gene expression datasets collected from the public repository Gene Expression Omnibus (GEO). Using this approach, we aim to identify the specific gene expression patterns that could serve as an accurate clinical tool for predicting prognosis and adjuvant therapy responses in HBV infection.

Materials and methods

Cell culture

HepAD38 cells were described in our previous studied [6]. Cells were cultured with or without 5 μg/ml tetracycline (Sigma-Aldrich, St Louis, MO, USA) in Dulbecco’s modified Eagle’s medium (DMEM)/F-12 (Gibco, Grand Island, NY, USA) supplemented with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin–streptomycin (Gibco) at 37 °C under a humidified atmosphere containing 5% CO2. Cultured HepAD38 cells were used to produce an HBV inoculum for infection experiments as previously described [6]. PHHs were purchased from Corning (Tewksbury, MA, USA) and maintained in the hepatocyte-specific medium (Corning) with 0.01 µg/mL EGF according to the manufacturer recommendations.

HBV production and infection

For the infection experiments, the HBV inoculum was prepared from freshly collected supernatants of HepAD38 cells as described previously [6]. The titer of the HBV solution was adjusted to 5 × 108 viral genome equivalents (GEq) per mL. PHHs (8 × 105 cells/well) were seeded onto six-well plates coated with collagen I (Gibco) and were inoculated with 80 GEq/cell of HBV in the presence of 4% PEG 8000 (Sigma). After inoculum removal and washing, the cells were incubated in fresh medium. The culture medium was collected and replaced every two days.

Detection of HBsAg and HBeAg

The levels of HBV surface antigen (HBsAg) and HBV e antigen (HBeAg) in the culture medium were determined using an ELISA kit (Wantai Bio-Pharm, Beijing, China) according to the manufacturer’s instructions. The absorbance was measured at 450 nm using a spectrophotometer (Synergy H1; BioTek, Winooski, VT, USA). All experiments were performed a minimum of three times.

Quantitative real-time PCR analysis of intracellular HBV DNA

Total genomic DNA was extracted from intracellular HBV rcDNA using a QIAamp DNA Mini Kit (Qiagen, Venlo, Netherlands) according to the manufacturer’s instructions. To assess HBV rcDNA levels, quantitative real-time PCR (qRT-PCR) was performed using a Power SYBR green PCR master mix (Applied Biosystems, Warrington, UK) with primers for HBV rcDNA and amplified using the QuantStudio 3.0 program (Applied Biosystems). The primer pairs for HBV DNA were forward primer (nt 256 to 274): 5′-CTCGTGGTGGACTTCTCTC-3′; and reverse primer (nt 404 to 421): 5′-CTGCAGGATGAAGAGGAA-3′. Relative gene expression levels were normalized against those of β-actin.

RNA-sequencing data generation and analysis

Total RNA was extracted using the TRIzol reagent (Invitrogen, CA, USA) according to the manufacturer’s instructions. Isolated RNA was evaluated using an Agilent RNA 6000 Pico kit (Agilent, Santa Clara, CA, USA), and the concentration was measured using a BioPhotometer® spectrophotometer (Eppendorf, Hamburg, Germany). RNA samples with an absorbance ratio at 260/280 nm between 1.8 and 2.0 and structural integrity verified before being used in the sequencing library preparation. A cDNA library was generated using the QIAseq FX Single Cell RNA Library Kit (Qiagen) according to the manufacturer’s protocol. In this protocol polyA-selected mRNA was converted in cDNA, and then enzymatic fragmentation (incubation time for “fragment size = 200–500 bp” was used) and library preparation were performed using 1 μg cDNA. The cDNA concentration was measured using the LightCycle qPCR (Roche, Penz Agilent High Sensitivity D5000 ScreenTape System berg, Germany), and the size of library was checked using an Agilent High Sensitivity D5000 ScreenTape System (Santa Clara, CA). RNA-seq was conducted by GnCBio (Daejeon, Korea) using HiSeq X (Illumina, CA, USA), as previously reported [7–9]. Low-quality sequence reads from the raw sequence data were filtered using Trim Galore software (https://github.com/FelixKrueger/TrimGalore). High-quality sequence reads with a base quality > 30 and length > 50 were selected and mapped to the Homo sapiens reference genome using the Bowtie2 aligner tool [10]. These preprocessing procedures were performed for both HBV-infected and HBV-uninfected PHH samples. Differential expression analysis was conducted between the two groups (HBV-infected and uninfected PHHs) using read count data (See Additional file 1).

In this study, we focused on identifying protein-coding genes related to pathogenesis, and 17,613 protein-coding genes were analyzed. Each gene in the RNA-seq data was measured on days 3, 5, and 7 after HBV infection. For each group, measurements per gene were fitted to a linear regression model. R2 > 0.7 and false discovery rate (FDR)-rate-adjusted p-values (i.e., q-values) < 0.05 were used as significance thresholds to select genes showing dynamic changes. In addition to within-group comparisons, between-group comparisons were performed using the Wilcoxon rank-sum test at a significance level of 0.05.

Dataset collection on HBV-infected primary human hepatocytes

To prevent the selection of candidate genes specific to the dataset, validation analysis was carried out using the GSE72068 dataset with a study design similar to ours [11]. The GSE72068 dataset contains microarray expression profiling data showing the gene expression response at various time points (i.e., 4 and 8 h and 1, 6, and 12 days) in HBV-infected PHHs. Among the 18,036 protein-coding genes identified in the GSE72068 dataset, 13,862 overlapped with the those in main dataset generated through RNA-Seq experiments in this study. The same statistical methods used to analyze the main dataset were also applied in the validation analysis.

The GSE25097 dataset was used to identify candidate genes as potential biomarkers for HBV-associated HCC. The GSE25097 dataset is a microarray expression profile designed to identify prognostic genetic markers between 268 HCC tumor samples and 6 healthy liver samples. Among the 18,076 protein-coding genes identified in the GSE25097 dataset, 14,143 genes overlapped with the main dataset used in this study. Comparison of the expression levels between HCC and healthy samples was performed using the Wilcoxon rank-sum test.

Furthermore, to gain insight into the mechanisms of potential biomarkers, we performed a functional enrichment analysis using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) [12].

Statistical analysis

The data are expressed as the mean ± standard deviation. Statistical analysis was performed using the unpaired t test (GraphPad Prism 8) to determine statistically significant differences between groups. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 1 × 10–15 were considered statistically significant.

Results

Establishment and characteristics of HBV-infected primary human hepatocytes

PHHs were infected with HBV particles collected from HepAD38 supernatant. Successful HBV infection was observed over 7 days, as confirmed by the detection of secreted HBV antigens (HBsAg and HBeAg) and intracellular HBV rcDNA at the indicated time points (Fig. 1A). As shown in Fig. 1B, the levels of HBsAg and HBeAg were found to plateau in HBV-infected cells at approximately 7 days post-infection (dpi). Next, intracellular HBV rcDNA was detected using qRT-PCR (Fig. 1C). The expression of HBV rcDNA increased continuously until 7 dpi. Therefore, PHH infected with HBV constitutes an effective model for studying the cellular effects of post-infection stages of the HBV life cycle. Subsequently, samples were collected at these time points and subjected to RNA-seq analysis.Fig. 1 A Workflow for HBV infection. B Levels of HBsAg and HBeAg were analyzed by ELISA at the indicated time points. The data compare the antigens secreted from HBV-infected cells (Red bar) and uninfected cells (Blue bar). Statistical significance is indicated (***: p-value < 0.001). ELISA data are presented as bar charts (n = 4). C Expression of HBV DNA detected by qRT-PCR

Changes in gene expression patterns after HBV infection in primary human hepatocytes

To comprehensively identify host gene expression changes in HBV-infected PHHs, we analyzed the dynamic alterations in gene expression patterns following HBV infection. We observed significant alterations in the expression patterns of 149 genes for 7 dpi (R2 > 0.7, q < 0.05; Fig. 2A). Among these genes, the expression of a majority of 141 genes (95%; Fig. 2B) showed a decreasing trend over time, whereas a smaller subset of 8 genes (5%; Fig. 2C) exhibited an increasing pattern. The rate of decrease or increase in the expression patterns of these 149 genes was more pronounced in HBV-infected cells compared to uninfected controls (Supplementary Fig. 1). Specifically, our analysis revealed that while both decreasing and increasing expression patterns were observed in uninfected controls over the 7 day period, the magnitude of these changes was significantly greater in HBV-infected cells (Supplementary Fig. 1B). These findings highlight the distinct expression dynamics induced by HBV infection.Fig. 2 Expression patterns of 149 genes showing significant dynamic changes. A Expression patterns over time in HBV-infected cells. Expression levels were normalized for each gene and then used as input to the heatmap. B, C Boxplots showing the differences between HBV-infected and uninfected groups for patterns of decrease and increase. The expression patterns of the two groups were compared by date. D Scatter plot with R2 as the y-axis and the fold change at day 7 after infection as the x-axis. Fold change was defined as the average expression level in HBV-infected cells divided by the average expression level in uninfected cells. Green or orange indicates genes with 1.5-fold decreased or increased expression levels, respectively, in HBV-infected cells compared with those in uninfected cells. (E) Expression levels over time for representative genes exhibiting distinct patterns, as determined by RNA-seq data analysis. Genes shown display more than a 1.5-fold difference in expression levels between HBV-infected and uninfected groups at indicated time points

Gene expression patterns in HBV-infected cells exhibited notable and statistically significant differences 3–7 dpi. Notably, by 7 dpi, over half of the genes (58%) displayed a 1.5-fold decrease or increase in expression in HBV-infected cells than in uninfected cells. The scatter plot illustrates both upregulated and downregulated genes across all datasets (Fig. 2D). The most significantly downregulated genes included RPL18, RPL28, RBM14, ABCF1, HMGA1, RBM10, and PABPC4, whereas the corresponding upregulated genes consisted of GPAM and LDLRAD4.

Cross-dataset validation and analysis of HBV-associated gene expression patterns

To prevent bias toward HBV-associated genes specific to one dataset, we conducted a cross-dataset validation analysis using the GSE72068 dataset, mirroring the study design. We focused on the patterns observed in 149 genes that exhibited statistical significance in the main dataset generated through RNA-Seq experiments in this study. Among the 149 genes, there were 112 genes in the GSE72068 dataset (Fig. 3A–C). Within the GSE72068 dataset, the expression of 112 genes showed distinct patterns of decreases or increases in HBV-infected cells, whereas no discernible patterns emerged in uninfected cells over time (Fig. 3D, E). Despite the clear temporal patterns observed in the HBV-infected cells, differences were observed in the gene composition of each pattern. Specifically, 51 of the 112 genes displayed consistent expression patterns in both datasets (Fig. 3B, C). Relative to the expression levels measured 4 h after HBV infection, the expression levels of these 51 genes began to exhibit significant differences starting from the 6 dpi (p = 2.7 × 10–7 for the decreasing pattern and p = 0.0047 for the increasing pattern). Notably, the degree of expression reduction between HBV-infected and uninfected cells began to display statistically significant differences at 12 dpi (Fig. 3F). Although the R2 values for the 51 genes were relatively modest, RPL28 and GPAM emerged among the top genes in terms of fold change (FC), which was consistent with the findings from the primary dataset (Fig. 3G).Fig. 3 Expression patterns for 149 genes in the GSE72068 dataset. A Venn diagram showing the relationship between the 149 candidate genes in the main dataset and the designed genes in the GSE72068 dataset. B Venn diagram showing the number of genes with a decreasing pattern for each dataset around 112 candidate genes. C Venn diagram showing the number of genes with an increasing pattern for each dataset around 112 candidate genes. D, E Expression pattern over time for 112 genes in HBV-infected and uninfected cells in the GSE72068 dataset. Expression levels were normalized for each gene and then used as input for the heatmap. The order of the genes is the same. F Boxplots showing the differences between HBV-infected and uninfected groups for both patterns. We focused on 51 genes that showed common patterns with the main dataset. The expression patterns of the two groups were compared by date. G Scatter plot with R2 as the y-axis and the fold change at day 12 after infection as the x-axis. Fold change was defined as the average expression level in HBV-infected cells divided by the average expression level in uninfected cells. Green or orange indicates genes with 1.1-fold decreased or increased expression levels, respectively, in HBV-infected cells compared with those in uninfected cells

Potential biomarkers selection for HBV-mediated chronic liver disease

Apart from the 51 genes validated using the GSE72068 dataset, a collective of 88 genes has been identified, encompassing 37 genes exhibiting substantial evidence (high R2 and FC values), signifying potential biomarkers for HBV-associated HCC. Detailed information encompassing the main analysis results for significant 51 genes exhibiting consistent patterns across both datasets, is provided in Table 1. The 37 genes were included in the list of potential biomarkers as they could not be identified in the GSE72068 dataset due to limitations of the designed genes (see Table 2). Notably, these 37 genes had high R2 and FC values, including ELOA (R2 = 0.818) and LDLRAD4 (FC = 1.730). The DAVID functional analysis revealed that most of these genes, accounting for 80%, are known contributors to protein binding (GO:0005515; q-value = 0.0133). Within this subset, 6 genes, including RPL28, correspond to the structural elements of ribosomes (hsa03010, q-value = 0.0341). Additionally, among the statistically significant functional pathways and gene ontology terms, we focused on RNA-binding proteins (RBPs, GO:0003723, q-value = 5.6 × 10–4). Among the 88 genes, 21 (24%) were identified as RBPs, which were downregulated in HBV-infected cells relative to uninfected cells. While previously reported RBPs have shown associations with prognostic markers in HCC patients, regardless of chronic HBV infection, differences exist between these RBPs and the biomarkers identified in this study [13, 14]. Validation experiments employing qRT-PCR were conducted for 21 genes, resulting in the validation of 6 genes (ABCF1, HMGA1, RPL28, RBM10, RBM14, and PABPC4) (Fig. 4B, C). Interestingly, RBM14 and RPL28 exhibited a trend of downregulation in HBV-infected PHH and in HCC tumor tissue based on the GSE72068 and GSE25097 datasets (Fig. 4D, E). These findings, supported by evidence from HCC tumor samples, suggest the potential of RBM14 and RPL28 as biomarkers for HBV-associated HCC. As for RBM10, while not prominently highlighted in the GSE72068 and GSE25097 datasets, it had previously been identified as a downregulated tumor suppressor gene in HCC tissues through qRT-PCR [15]. Table 1 Detailed results of main dataset analysis for genes with common patterns

Gene	Transcript	Coefficient	R2	p value	q-value	Fold change	
NSFL1C	NM_016143	− 326.6	0.869	1.8E−08	2.1.E−04	0.716	
PDGFRB	NM_002609	206.4	0.860	3.1E−08	2.1.E−04	1.248	
RPL18	NM_000979	− 571.9	0.858	3.5E−08	2.1.E−04	0.461	
RPL28	NM_000991	− 448.7	0.846	6.8E−08	2.1.E−04	0.541	
RBM14	NM_006328	− 326.0	0.840	8.9E−08	2.1.E−04	0.500	
DAGLB	NM_139179	− 675.0	0.837	1.0E−07	2.1.E−04	0.538	
PRPS1L1	NM_175886	− 61.1	0.836	1.1E−07	2.1.E−04	0.593	
PSMC4	NM_006503	− 801.2	0.826	1.8E−07	2.6.E−04	0.518	
DUSP1	NM_004417	− 1802.3	0.819	2.5E−07	2.8.E−04	0.675	
ELL3	NM_025165	− 48.6	0.803	4.8E−07	3.5.E−04	0.696	
USP42	NM_032172	51.2	0.797	6.4E−07	4.1.E−04	0.708	
POLE3	NM_017443	− 195.7	0.790	8.3E−07	4.6.E−04	0.656	
ABCF1	NM_001025091	− 303.7	0.789	8.5E−07	4.6.E−04	0.526	
CALCB	NM_000728	− 21.7	0.779	1.2E−06	5.7.E−04	0.286	
HMGA1	NM_145899	− 598.8	0.775	1.5E−06	6.3.E−04	0.446	
KDM1B	NM_153042	− 115.7	0.774	1.5E−06	6.4.E−04	0.664	
SLC25A19	NM_021734	− 82.0	0.770	1.7E−06	6.8.E−04	0.869	
PRPF4	NM_004697	− 236.8	0.769	1.8E−06	6.8.E−04	0.727	
GPAM	NM_020918	4192.9	0.769	1.8E−06	6.8.E−04	1.544	
GBA2	NM_020944	− 410.5	0.763	2.2E−06	7.6.E−04	0.577	
BRMS1	NM_015399	− 69.5	0.757	2.7E−06	8.2.E−04	0.621	
RNF25	NM_022453	− 51.2	0.756	2.8E−06	8.2.E−04	0.577	
DUSP14	NM_007026	− 228.9	0.751	3.2E−06	8.6.E−04	0.607	
LAS1L	NM_031206	− 151.3	0.745	3.9E−06	9.4.E−04	0.497	
TTC4	NM_004623	− 169.3	0.742	4.4E−06	1.0.E−03	0.460	
MED26	NM_004831	− 39.4	0.741	4.5E−06	1.0.E−03	0.817	
AP4B1	NM_006594	− 99.7	0.737	5.2E−06	1.0.E−03	0.686	
MTF1	NM_005955	− 213.3	0.732	6.1E−06	1.0.E−03	0.785	
FUS	NM_004960	− 356.0	0.727	7.0E−06	1.1.E−03	0.575	
AKAP13	NM_007200	− 991.3	0.727	7.0E−06	1.1.E−03	0.816	
LSG1	NM_018385	− 292.7	0.724	7.6E−06	1.1.E−03	0.821	
RPS5	NM_001009	− 915.6	0.724	7.6E−06	1.1.E−03	0.384	
C5	NM_001735	1374.6	0.722	8.1E−06	1.1.E−03	1.107	
EID3	NM_001008394	− 38.4	0.720	8.6E−06	1.1.E−03	0.740	
RRP12	NM_015179	− 570.5	0.718	9.2E−06	1.1.E−03	0.552	
LRP4	NM_002334	28.3	0.717	9.4E−06	1.1.E−03	1.237	
CCDC86	NM_024098	− 190.4	0.716	9.5E−06	1.1.E−03	0.540	
Results are listed in descending order of R2. To make it easier to understand at a glance, we have listed the results for genes with R2 ≥ 0.716. The coefficient, R2, p-value and q-value are the results of fitting a linear regression model. Fold change was defined as the average expression level in HBV-infected cells divided by the average expression level in uninfected cells, measured at 7 days post-infection. Genes validated through qRT-PCR experiments are indicated in bold

Table 2 Detailed results from analysis of main dataset for undesigned genes in GSE72068 set

Gene	Transcript	Coefficient	R2	p value	q-value	Fold change	
ELOA	NM_003198	− 628.2	0.818	2.6E−07	2.8.E−04	0.795	
TMEM208	NM_014187	− 182.1	0.806	4.3E−07	3.3.E−04	0.592	
LRRC75A	NM_207387	− 187.6	0.799	5.8E−07	4.0.E−04	0.650	
TTI2	NM_025115	− 97.5	0.773	1.6E−06	6.5.E−04	0.712	
RPLP0	NM_053275	− 7236.4	0.759	2.5E−06	8.2.E−04	0.606	
RMC1	NM_013326	− 209.4	0.758	2.6E−06	8.2.E−04	0.771	
FNBP1	NM_001363755	− 59.1	0.758	2.6E−06	8.2.E−04	0.690	
SRPRB	NM_001379313	− 594.6	0.756	2.8E−06	8.2.E−04	0.795	
URB2	NM_014777	− 199.5	0.751	3.3E−06	8.7.E−04	0.545	
BRPF1	NM_001003694	− 73.5	0.747	3.7E−06	9.3.E−04	0.600	
RBM10	NM_005676	− 157.1	0.746	3.8E−06	9.4.E−04	0.524	
MEA1	NM_001318942	− 146.7	0.742	4.3E−06	9.9.E−04	0.578	
RPL8	NM_000973	− 2073.6	0.741	4.5E−06	1.0.E−03	0.441	
GEMIN5	NM_001252156	− 151.7	0.740	4.6E−06	1.0.E−03	0.780	
LDLRAD4	NM_181481	55.9	0.739	4.9E−06	1.0.E−03	1.730	
EMSY	NM_020193	− 28.0	0.738	5.0E−06	1.0.E−03	0.562	
PARP2	NM_001042618	− 98.8	0.738	5.0E−06	1.0.E−03	0.669	
FLAD1	NM_025207	− 224.2	0.736	5.4E−06	1.0.E−03	0.512	
C2orf81	NM_001145054	− 64.7	0.735	5.5E−06	1.0.E−03	0.395	
TMEM265	NM_001256829	− 81.0	0.733	5.9E−06	1.0.E−03	0.359	
KANSL2	NM_017822	− 72.2	0.732	6.0E−06	1.0.E−03	0.651	
CFAP73	NM_001144872	− 48.0	0.732	6.1E−06	1.0.E−03	0.357	
PABPC4	NM_001135653	− 828.8	0.731	6.1E−06	1.0.E−03	0.545	
CFH	NM_000186	4273.6	0.730	6.3E−06	1.0.E−03	1.100	
MIEF1	NM_019008	− 295.5	0.724	7.5E−06	1.1.E−03	0.638	
CSRNP1	NM_033027	− 153.0	0.722	8.2E−06	1.1.E−03	0.695	
COPS8	NM_006710	− 207.6	0.720	8.4E−06	1.1.E−03	0.828	
MAP3K4	NM_001291958	− 168.2	0.717	9.2E−06	1.1.E−03	0.559	
USP2	NM_171997	− 34.8	0.716	9.6E−06	1.1.E−03	0.525	
ATRIP	NM_130384	− 66.0	0.711	1.1E−05	1.2.E−03	0.718	
BUD23	NM_001202560	− 179.0	0.707	1.2E−05	1.3.E−03	0.583	
SMIM29	NM_001008703	− 51.9	0.707	1.3E−05	1.3.E−03	0.576	
ZBTB43	NM_001135776	− 212.8	0.706	1.3E−05	1.3.E−03	0.744	
ZBTB21	NM_020727	− 178.4	0.705	1.3E−05	1.3.E−03	0.876	
URGCP	NM_001077664	− 298.7	0.705	1.3E−05	1.3.E−03	0.528	
ODC1	NM_001287188	− 2034.3	0.705	1.3E−05	1.3.E−03	0.381	
GPAT3	NM_032717	− 121.6	0.700	1.5E−05	1.4.E−03	0.652	
Results are listed in descending order of R2. The coefficient, R2, p-value and q-value are the results of fitting a linear regression model. Fold change was defined as the average expression level in HBV-infected cells divided by the average expression level in uninfected cells, measured at 7 days post-infection. Genes validated through qRT-PCR experiments are indicated in bold

Fig. 4 Functional enrichment analysis and qRT-PCR-based validation analysis. A All statistically significant enriched terms are displayed (q-value < 0.05). The count indicates the number of genes belonging to each term. B, C The quantitative analysis of genes identified by qRT-PCR. Relative expression levels were calculated by normalizing β-actin expression. Significant differences between uninfected and HBV-infected primary human hepatocytes at 7 dpi are represented (*p < 0.05, **p < 0.01). D Comparison of expression levels between two groups on the last day of measurement for each dataset. Fold change (FC) is defined as the average RNA expression level in the HBV group divided by that in the control (Ctrl) group. (E) Two genes validated in the GSE25097 dataset show downregulated expression levels in real HCC samples compared with those in healthy liver samples

Discussion

HBV is a non-cytopathic, hepatotropic virus known for causing persistent infections that may lead to cirrhosis and HCC. Consequently, numerous studies have focused on characterizing the altered gene expression profiles in host cells following HBV infection [16–18]. Despite extensive research on gene expression changes to understand HBV infection in tumor-derived cell lines and hepatocyte, little is known about HBV infection in PHHs. Our study aimed to identify potential biomarkers for functionally curing HBV infection through a comprehensive understanding of the genes and pathways involved in HBV replication/transcription in PHHs. These cells used to cryopreserve PHHs in this study represent a physiologically relevant in vitro culture system for studying HBV infection. They closely mimic the characteristics of HBV-infected human hepatocytes [19].

In the HBV life cycle, HBV enters hepatocytes by binding to specific receptors on their surface. Once inside, the uncoated viral genome enters the nucleus, forming cccDNA, which acts as a template for viral transcript synthesis. Viral transcription driven by promoter and enhancer regions (ENI and ENII) generates four unspliced viral RNAs − 3.5, 2.4, 2.1, and 0.7 kb − among which the 3.5 kb RNA contains precore and pregenomic RNA species. Notably, the precore mRNA encodes the precore antigen (HBeAg), while the pregenomic RNA directs the translation of the core antigen (HBcAg) and polymerase. After encapsidation, pregenomic RNA serves as a reverse transcription template. Assembled HBV virions are secreted from hepatocytes. Each step of the HBV life cycle is heavily dependent on the host factors, such as hepatocyte-enriched and ubiquitous transcription factors, regulating viral replication and transcription via promoter and enhancer regions. Therefore, we focused on changes in gene expression and transcription dynamics of hepatocyte related with HBV replication/transcription processes after HBV infection. Our analysis suggests significant changes primarily in RNA metabolism-related genes within PHHs depending on the time following HBV infection.

HBV directly alters RNA metabolism by interacting with cellular RBPs, affecting RNA processing and stability in host cell [20]. A study by Chabrolles et al. revealed that HBV core protein was found to interact with RBPs, notably SRSF10. Their functional studies identified SRSF10 as the HBV RNA regulator primarily in its dephosphorylated state within infected cell nuclei, influencing nascent HBV RNA levels without affecting HBV RNA splicing [21]. Additionally, virus-induced changes in cell environment indirectly impact RNA metabolism, leading to dysregulated gene expression and altered mRNA levels crucial for immune responses, cell functions, and apoptosis during HBV infection [22–24]. In this study, we identified six RNA metabolism-associated genes involved in HBV infection, such as ABCF1, HMGA1, RPL28, RBM10, RBM14, and PABPC4. They were successfully validated through qRT-PCR experiments and analysis of two separate datasets. Notably, RBM14 and RPL28 were involved in hepatocarcinogenesis and used as potential biomarkers for HBV associated HCC [25, 26]. RBM14 plays diverse roles in maintaining the stem-like state of glioblastoma multiforme spheres and assisting PARP-dependent DNA repair at double-strand breaks. Moreover, elevated RBM14 expression in HCC modulate the M2-phenotype polarization of KCs through N-methyladenosine (m6A) methylation regulation, thereby promoting HCC cells' malignant aggressiveness [27].

RPL28 is implicated in HCC by modulating murine double minute 2 (MDM2), affecting the tumor suppressor p53's function. Its negative regulation of MDM2 inhibits p53 ubiquitination, stabilizing p53's tumor-suppressive role. Dysregulated RPL28 activity in hepatocarcinogenesis may disrupt these processes, potentially facilitating HCC cell growth [28]. However, there is insufficient research on the functions of RBM14 and RPL28 related to the HBV infection. Collectively, our results and previous reports suggested that the changed expression of RNA processing regulatory factors in HBV-associated HCC could serve as the potential biomarkers.

In this study, we revealed a modest number of significantly differentially expressed gene between HBV-infected and non-infected PHH. These unexpected results might be due to the differences in cell viability and metabolic activity of freshly isolated PHHs compared to cryopreserved PHHs due to the preservation stress incurred during freezing and thawing [29, 30]. Therefore, further validations and investigations using more natural models and clinical samples would be necessary in the future. Additional experiments are necessary to investigate differences in gene expression related to susceptibility to HBV infection in freshly isolated PHHs obtained from different donors.

In summary, our study presents comprehensive transcriptional profiling of HBV infection dynamics, validates the 10 genes related to RNA-metabolism on HBV infection. We utilized the PHHs, which are regarded as the most physiologically relevant in vitro models for studying HBV infection. PHHs closely mirror the characteristics of human liver cells, the primary target of HBV. Our findings uncovered alterations in various genes and cellular pathways linked to mRNA metabolism, alternative splicing, and spliceosomes during HBV replication. Several of these genes are associated with HBV-associated HCC. These findings hint at the potential for identifying novel biomarkers to address HBV-associated HCC.

Conclusions

Our study delineated alterations in gene expression within PHHs caused by HBV infection. We pinpointed RBPs crucial in mRNA metabolism and the regulation of alternative splicing during HBV infection. Grasping the functional roles of host factor networks in HBV infection holds promise in elucidating the molecular mechanisms behind HBV replication/transcription and could pave the way for developing therapeutic interventions against HBV infection.

Supplementary Information

Supplementary Material 2 Fig. 1. Expression patterns of 149 genes in uninfected cells compared with HBV-infected cells. (A) Expression patterns over time in uninfected cells. Expression levels were normalized for each gene and used as input for the heatmap. (B, C) Violin plots depicting differences between HBV-infected and uninfected groups on days 0 and 7. Significant differences between the two group are indicated (**P < 0.01, ***P<0.001, and ****P<1×10-15).

Supplementary Material 1.

Author contributions

Experiments were conceived and designed by W.H., J.Y.R. and mainly performed by G.K. and S.H. Data were processed and analyzed by S.H. The original draft was Written by G.K. The manuscript was reviewed and supervised by Y.C., Y.K.P. and S.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the “Korea National Institute of Health” (KNIH) research project (project No. 2022-NG-002-01, 2022-NG-003-01).

Availability of data and materials

The main datasets we generated in this study are available in Additional File 1. The two validation datasets can be download from the GEO database. (https://www.ncbi.nlm.nih.gov/gds). GEO accession ID: GSE72068: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE72068, GSE25097: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25097.

Declarations

Competing interests

The authors declare no competing interests.

Publisher's Note

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

Suhyun Hwangbo and Gahee Kim contributed equally to this work and share first authorship.

Jae Yong Ryu and Wonhee Hur contributed equally to this work and share last authorship.

Change history

9/7/2024

The Supplementary Material files have been swapped
==== Refs
References

1. Lamontagne J Mell JC Bouchard MJ Transcriptome-wide analysis of hepatitis B virus-mediated changes to normal hepatocyte gene expression PLoS Pathog 2016 12 2 e1005438 10.1371/journal.ppat.1005438 26891448
Lamontagne J, Mell JC, Bouchard MJ. Transcriptome-wide analysis of hepatitis B virus-mediated changes to normal hepatocyte gene expression. PLoS Pathog. 2016;12(2):e1005438.26891448 10.1371/journal.ppat.1005438
2. Wong MCS Huang JLW George J Huang J Leung C Eslam M The changing epidemiology of liver diseases in the Asia-Pacific region Nat Rev Gastroenterol Hepatol 2019 16 1 57 73 10.1038/s41575-018-0055-0 30158570
Wong MCS, Huang JLW, George J, Huang J, Leung C, Eslam M, et al. The changing epidemiology of liver diseases in the Asia-Pacific region. Nat Rev Gastroenterol Hepatol. 2019;16(1):57–73.30158570 10.1038/s41575-018-0055-0
3. Dandri M Petersen J cccDNA maintenance in chronic hepatitis B—targeting the matrix of viral replication Infect Drug Resist 2020 13 3873 3886 10.2147/IDR.S240472 33149632
Dandri M, Petersen J. cccDNA maintenance in chronic hepatitis B—targeting the matrix of viral replication. Infect Drug Resist. 2020;13:3873–86.33149632 10.2147/IDR.S240472
4. Bar-Yishay I Shaul Y Shlomai A Hepatocyte metabolic signalling pathways and regulation of hepatitis B virus expression Liver Int 2011 31 3 282 290 10.1111/j.1478-3231.2010.02423.x 21281428
Bar-Yishay I, Shaul Y, Shlomai A. Hepatocyte metabolic signalling pathways and regulation of hepatitis B virus expression. Liver Int. 2011;31(3):282–90.21281428 10.1111/j.1478-3231.2010.02423.x
5. Ito N Nakashima K Sun S Ito M Suzuki T Cell type diversity in hepatitis B virus RNA splicing and its regulation Front Microbiol 2019 10 207 10.3389/fmicb.2019.00207 30800119
Ito N, Nakashima K, Sun S, Ito M, Suzuki T. Cell type diversity in hepatitis B virus RNA splicing and its regulation. Front Microbiol. 2019;10:207.30800119 10.3389/fmicb.2019.00207
6. Lee HW Choi Y Lee AR Yoon CH Kim KH Choi BS Hepatocyte growth factor-dependent antiviral activity of activated cdc42-associated kinase 1 against hepatitis B virus Front Microbiol 2021 12 800935 10.3389/fmicb.2021.800935 35003030
Lee HW, Choi Y, Lee AR, Yoon CH, Kim KH, Choi BS, et al. Hepatocyte growth factor-dependent antiviral activity of activated cdc42-associated kinase 1 against hepatitis B virus. Front Microbiol. 2021;12:800935.35003030 10.3389/fmicb.2021.800935
7. Lee DH Lee HJ Lee YJ Kang HM Jeong OM Kim MC DNA barcoding techniques for avian influenza virus surveillance in migratory bird habitats J Wildl Dis 2010 46 2 649 654 10.7589/0090-3558-46.2.649 20688667
Lee DH, Lee HJ, Lee YJ, Kang HM, Jeong OM, Kim MC, et al. DNA barcoding techniques for avian influenza virus surveillance in migratory bird habitats. J Wildl Dis. 2010;46(2):649–54.20688667 10.7589/0090-3558-46.2.649
8. Ambardar S Gupta R Trakroo D Lal R Vakhlu J High throughput sequencing: an overview of sequencing chemistry Indian J Microbiol 2016 56 4 394 404 10.1007/s12088-016-0606-4 27784934
Ambardar S, Gupta R, Trakroo D, Lal R, Vakhlu J. High throughput sequencing: an overview of sequencing chemistry. Indian J Microbiol. 2016;56(4):394–404.27784934 10.1007/s12088-016-0606-4
9. Yeo SJ Than DD Park HS Sung HW Park H Molecular characterization of a novel avian influenza A (H2N9) strain isolated from wild duck in Korea in 2018 Viruses 2019 11 11 1046 10.3390/v11111046 31717636
Yeo SJ, Than DD, Park HS, Sung HW, Park H. Molecular characterization of a novel avian influenza A (H2N9) strain isolated from wild duck in Korea in 2018. Viruses. 2019;11(11):1046.31717636 10.3390/v11111046
10. Langmead B Salzberg SL Fast gapped-read alignment with Bowtie 2 Nat Methods 2012 9 4 357 359 10.1038/nmeth.1923 22388286
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9(4):357–9.22388286 10.1038/nmeth.1923
11. Ancey PB Testoni B Gruffaz M Cros MP Durand G Le Calvez-Kelm F Genomic responses to hepatitis B virus (HBV) infection in primary human hepatocytes Oncotarget 2015 6 42 44877 44891 10.18632/oncotarget.6270 26565721
Ancey PB, Testoni B, Gruffaz M, Cros MP, Durand G, Le Calvez-Kelm F, et al. Genomic responses to hepatitis B virus (HBV) infection in primary human hepatocytes. Oncotarget. 2015;6(42):44877–91.26565721 10.18632/oncotarget.6270
12. Sherman BT Hao M Qiu J Jiao X Baseler MW Lane HC DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update) Nucleic Acids Res 2022 50 W1 W216 W221 10.1093/nar/gkac194 35325185
Sherman BT, Hao M, Qiu J, Jiao X, Baseler MW, Lane HC, et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 2022;50(W1):W216–21.35325185 10.1093/nar/gkac194
13. Li M Liu Z Wang J Liu H Gong H Li S Systematic analysis identifies a specific RNA-binding protein-related gene model for prognostication and risk-adjustment in HBV-related hepatocellular carcinoma Front Genet 2021 12 707305 10.3389/fgene.2021.707305 34422009
Li M, Liu Z, Wang J, Liu H, Gong H, Li S, et al. Systematic analysis identifies a specific RNA-binding protein-related gene model for prognostication and risk-adjustment in HBV-related hepatocellular carcinoma. Front Genet. 2021;12:707305.34422009 10.3389/fgene.2021.707305
14. Lin Y Liang R Qiu Y Lv Y Zhang J Qin G Expression and gene regulation network of RBM8A in hepatocellular carcinoma based on data mining Aging (Albany NY) 2019 11 2 423 447 10.18632/aging.101749 30670676
Lin Y, Liang R, Qiu Y, Lv Y, Zhang J, Qin G, et al. Expression and gene regulation network of RBM8A in hepatocellular carcinoma based on data mining. Aging (Albany NY). 2019;11(2):423–47.30670676 10.18632/aging.101749
15. Zhao Z Li J Shen F Protective effect of the RNA-binding protein RBM10 in hepatocellular carcinoma Eur Rev Med Pharmacol Sci 2020 24 11 6005 6013 32572914
Zhao Z, Li J, Shen F. Protective effect of the RNA-binding protein RBM10 in hepatocellular carcinoma. Eur Rev Med Pharmacol Sci. 2020;24(11):6005–13.32572914
16. Liao X Yu T Yang C Huang K Wang X Han C Comprehensive investigation of key biomarkers and pathways in hepatitis B virus-related hepatocellular carcinoma J Cancer 2019 10 23 5689 5704 10.7150/jca.31287 31737106
Liao X, Yu T, Yang C, Huang K, Wang X, Han C, et al. Comprehensive investigation of key biomarkers and pathways in hepatitis B virus-related hepatocellular carcinoma. J Cancer. 2019;10(23):5689–704.31737106 10.7150/jca.31287
17. Xie S Jiang X Zhang J Xie S Hua Y Wang R Identification of significant gene and pathways involved in HBV-related hepatocellular carcinoma by bioinformatics analysis PeerJ 2019 7 e7408 10.7717/peerj.7408 31392101
Xie S, Jiang X, Zhang J, Xie S, Hua Y, Wang R, et al. Identification of significant gene and pathways involved in HBV-related hepatocellular carcinoma by bioinformatics analysis. PeerJ. 2019;7:e7408.31392101 10.7717/peerj.7408
18. Yang Y Zhong Z Ding Y Zhang W Ma Y Zhou L Bioinformatic identification of key genes and pathways that may be involved in the pathogenesis of HBV-associated acute liver failure Genes Dis 2018 5 4 349 357 10.1016/j.gendis.2018.02.005 30591937
Yang Y, Zhong Z, Ding Y, Zhang W, Ma Y. Zhou L Bioinformatic identification of key genes and pathways that may be involved in the pathogenesis of HBV-associated acute liver failure. Genes Dis. 2018;5(4):349–57.30591937 10.1016/j.gendis.2018.02.005
19. Hu J Lin YY Chen PJ Watashi K Wakita T Cell and animal models for studying hepatitis B virus infection and drug development Gastroenterology 2019 156 2 338 354 10.1053/j.gastro.2018.06.093 30243619
Hu J, Lin YY, Chen PJ, Watashi K, Wakita T. Cell and animal models for studying hepatitis B virus infection and drug development. Gastroenterology. 2019;156(2):338–54.30243619 10.1053/j.gastro.2018.06.093
20. Zhang T Zheng H Lu D Guan G Li D Zhang J RNA binding protein TIAR modulates HBV replication by tipping the balance of pgRNA translation Signal Transduct Target Ther 2023 8 1 346 10.1038/s41392-023-01573-7 37699883
Zhang T, Zheng H, Lu D, Guan G, Li D, Zhang J, et al. RNA binding protein TIAR modulates HBV replication by tipping the balance of pgRNA translation. Signal Transduct Target Ther. 2023;8(1):346.37699883 10.1038/s41392-023-01573-7
21. Chabrolles H Auclair H Vegna S Lahlali T Pons C Michelet M Hepatitis B virus Core protein nuclear interactome identifies SRSF10 as a host RNA-binding protein restricting HBV RNA production PLoS Pathog 2020 16 11 e1008593 10.1371/journal.ppat.1008593 33180834
Chabrolles H, Auclair H, Vegna S, Lahlali T, Pons C, Michelet M, et al. Hepatitis B virus Core protein nuclear interactome identifies SRSF10 as a host RNA-binding protein restricting HBV RNA production. PLoS Pathog. 2020;16(11):e1008593.33180834 10.1371/journal.ppat.1008593
22. Jose-Abrego A Roman S Laguna-Meraz S Panduro A Host and HBV interactions and their potential impact on clinical outcomes Pathogens 2023 12 9 1146 10.3390/pathogens12091146 37764954
Jose-Abrego A, Roman S, Laguna-Meraz S, Panduro A. Host and HBV interactions and their potential impact on clinical outcomes. Pathogens. 2023;12(9):1146.37764954 10.3390/pathogens12091146
23. Wang S Gao S Ye W Li Y Luan J Lv X The emerging importance role of m6A modification in liver disease Biomed Pharmacother 2023 162 114669 10.1016/j.biopha.2023.114669 37037093
Wang S, Gao S, Ye W, Li Y, Luan J, Lv X. The emerging importance role of m6A modification in liver disease. Biomed Pharmacother. 2023;162:114669.37037093 10.1016/j.biopha.2023.114669
24. Yang Y Yan Y Yin J Tang N Wang K Huang L O-GlcNAcylation of YTHDF2 promotes HBV-related hepatocellular carcinoma progression in an N(6)-methyladenosine-dependent manner Signal Transduct Target Ther 2023 8 1 63 10.1038/s41392-023-01316-8 36765030
Yang Y, Yan Y, Yin J, Tang N, Wang K, Huang L, et al. O-GlcNAcylation of YTHDF2 promotes HBV-related hepatocellular carcinoma progression in an N(6)-methyladenosine-dependent manner. Signal Transduct Target Ther. 2023;8(1):63.36765030 10.1038/s41392-023-01316-8
25. Sun T Zhu W Ru Q Zheng Y Silencing RPL8 inhibits the progression of hepatocellular carcinoma by down-regulating the mTORC1 signalling pathway Hum Cell 2023 36 2 725 737 10.1007/s13577-022-00852-9 36577883
Sun T, Zhu W, Ru Q, Zheng Y. Silencing RPL8 inhibits the progression of hepatocellular carcinoma by down-regulating the mTORC1 signalling pathway. Hum Cell. 2023;36(2):725–37.36577883 10.1007/s13577-022-00852-9
26. Zhang Z Gao W Liu Z Yu S Jian H Hou Z Comprehensive analysis of m6A regulators associated with immune infiltration in Hepatitis B virus-related hepatocellular carcinoma BMC Gastroenterol 2023 23 1 259 10.1186/s12876-023-02873-6 37507670
Zhang Z, Gao W, Liu Z, Yu S, Jian H, Hou Z, et al. Comprehensive analysis of m6A regulators associated with immune infiltration in Hepatitis B virus-related hepatocellular carcinoma. BMC Gastroenterol. 2023;23(1):259.37507670 10.1186/s12876-023-02873-6
27. Hu J Yang L Peng X Mao M Liu X Song J METTL3 promotes m6A hypermethylation of RBM14 via YTHDF1 leading to the progression of hepatocellular carcinoma Hum Cell 2022 35 6 1838 1855 10.1007/s13577-022-00769-3 36087219
Hu J, Yang L, Peng X, Mao M, Liu X, Song J, et al. METTL3 promotes m6A hypermethylation of RBM14 via YTHDF1 leading to the progression of hepatocellular carcinoma. Hum Cell. 2022;35(6):1838–55.36087219 10.1007/s13577-022-00769-3
28. Shi Y Wang X Zhu Q Chen G The ribosomal protein L28 gene induces sorafenib resistance in hepatocellular carcinoma Front Oncol 2021 11 685694 10.3389/fonc.2021.685694 34307151
Shi Y, Wang X, Zhu Q, Chen G. The ribosomal protein L28 gene induces sorafenib resistance in hepatocellular carcinoma. Front Oncol. 2021;11:685694.34307151 10.3389/fonc.2021.685694
29. Hengstler JG Utesch D Steinberg P Platt KL Diener B Ringel M Cryopreserved primary hepatocytes as a constantly available in vitro model for the evaluation of human and animal drug metabolism and enzyme induction Drug Metab Rev 2000 32 1 81 118 10.1081/DMR-100100564 10711408
Hengstler JG, Utesch D, Steinberg P, Platt KL, Diener B, Ringel M, et al. Cryopreserved primary hepatocytes as a constantly available in vitro model for the evaluation of human and animal drug metabolism and enzyme induction. Drug Metab Rev. 2000;32(1):81–118.10711408 10.1081/DMR-100100564
30. Illouz S Alexandre E Pattenden C Mark L Bachellier P Webb M Differential effects of curcumin on cryopreserved versus fresh primary human hepatocytes Phytother Res 2008 22 12 1688 1691 10.1002/ptr.2545 18697189
Illouz S, Alexandre E, Pattenden C, Mark L, Bachellier P, Webb M, et al. Differential effects of curcumin on cryopreserved versus fresh primary human hepatocytes. Phytother Res. 2008;22(12):1688–91.18697189 10.1002/ptr.2545
