Identification of key exosomes-related genes in hepatitis B virus-related hepatocellular carcinoma

Abstract

One of the primary risk factors for hepatocellular carcinoma (HCC) is the hepatitis B virus (HBV). Exosomes have a significant impact on the dissemination of HBV-infected HCC. This study aimed to screen HBV exosome-related hub genes in HCC for a better understanding of the HCC pathogenic mechanism. First, multiple HBV-induced HCC datasets were collected from the Gene Expression Omnibus (GEO) database, and the exosome-related gene set was obtained from relevant literature. Nine HBV-related HCC exosome hub genes (HP, C9, APOA1, PON1, TTR, LPA, FCN2, FCN3, and MBL2) were selected through differential analysis and network analysis. An analysis of the receiver operation characteristic (ROC) revealed that these genes had good diagnostic value. These hub genes were primarily enriched in biological processes such as the citrate cycle tca cycle, phenylalanine metabolism, and fatty acid metabolism, according to gene set enrichment analysis (GSEA). Furthermore, this study predicted the miRNA (hsa-miR-590-5p) targeting LPA, as well as 12 lncRNAs (AL121655, SAP30-DT, LINC00472, etc.) targeting hsa-miR-590-5p. Finally, nelarabine, methylprednisolone, and methylprednisolone were predicted to be possible medications that target the hub gene based on the CellMiner database. To sum up, this work was crucial for discovering new biomarkers and comprehending the function of exosome-related genes in the growth of HBV-infected HCC.

Keywords

hepatocellular carcinoma hepatitis B virus exosome bioinformatics analysis hub gene

1 Introduction

As a major global public health issue, hepatocellular carcinoma (HCC) is a common and extremely deadly tumor.¹ HCC mainly occurs in liver cells and is usually associated with liver diseases such as hepatitis virus infection,² alcohol abuse,³ non-alcoholic steatohepatitis,⁴ and cirrhosis.⁵ In addition, changes like gene mutations,⁶ epigenetic modifications,⁷ and aberrant activation of cellular signaling pathways⁸ are also essential factors influencing the HCC occurrence. The primary pathogen causing hepatitis B is the hepatitis B virus (HBV), which is also one of the risk factors for HCC.⁹ HBV infection may lead to key gene mutations and abnormal expression, aberrantly activating multiple cellular signaling pathways such as Wnt/β-catenin,¹⁰ PI3K/AKT,¹¹ and RAS/MAPK,¹² to stimulate tumor formation. HBV infection is also related to the formation and maintenance of HCC stem cells. It increases the number and activity of HCC stem cells and facilitates HCC progression.¹³ Thus, it is imperative to comprehend the underlying mechanisms between HBV and HCC.

Exosomes are a type of small vesicles produced by cells, wrapped in cell membranes and carrying intracellular substances such as proteins, nucleic acids, and metabolites.¹⁴ Exosomes were originally recognized as a form of cell waste, but recent studies have proved that exosomes are involved in various activities such as intercellular communication, neovascularization, cancer metastasis, and drug resistance.^15–18 In addition, researchers believed that many viruses rely on exosomes as a transmission pathway, such as hepatitis E virus (HEV),¹⁹ influenza A virus (IAV),²⁰ and hepatitis C virus (HCV).²¹ Studies on HCV infection have revealed that exosomes isolated from the plasma of HCV-infected patients contain HCV RNA, and these exosomes effectively transmit HCV to liver cells in a receptor-independent manner.²² Moreover, the significance of exosomes has been identified in the study of HBV infection.²³ Yang et al.²⁴ evidenced that during chronic HBV infection, HBV-positive exosomes impair NK cell functions (including interferon-gamma production, cytolytic activity, NK cell proliferation, and survival), repress the expression of pattern recognition receptors on NK cells, thereby causing the NK cell dysfunction. Furthermore, recent research by Todorova et al.²⁵ has discovered some exosomal microRNAs and exosomal differentially expressed proteins in HBV-related HCC cells. However, the detailed biological function of exosomes had not been completely determined.

To better understand the pathogenic mechanisms of HCC, this study thoroughly investigated the biological functions of HBV exosome-related genes (ERGs) in HCC with potential miRNAs and lncRNAs that may regulate these genes. Simultaneously, some potential drug candidates that may target hub genes were predicted. In conclusion, the findings of this work could aid in the discovery of novel HCC biomarkers as well as the comprehension of the function of HBV infection in HCC development.

2 Materials and methods

2.1 Data acquisition

Expression data (mRNA) and miRNA dataset (GSE69580) of the training set (GSE14520 and GSE55092) and validation set (GSE121248) were obtained from the Gene Expression Omnibus (GEO) database. Due to the inclusion of two different batches of expression profile chip data sets in the GSE14520 dataset, the filtering and selection were performed according to the previous research method,²⁶ and the Affymetrix HT human genome U133A array data set containing most patients was selected. The final GSE14520 data set used in this study contained 212 chronic hepatitis B-induced HCC tumor samples and 220 paired non-tumor samples. Similarly, the GSE55092 data set was screened with reference to previous research methods,^27,28 and finally, 39 cases of microarray data from the HCC tumor region induced by HBV and 81 cases of microarray data from the non-tumor region were obtained. GSE121248 contained microarray data from 70 chronic hepatitis B-induced HCC tissues and 37 adjacent non-tumor tissues. The miRNA dataset GSE69580 included HBV-related HCC tumors (n = 5) and adjacent non-tumor samples (n = 5). 2946 ERGs were collected from the literatures.^29–31

2.2 Selection and enrichment analysis of differentially expressed ERGs in HBV-related HCC

mRNA data sets (GSE14520, GSE55092, GSE121248) were used to obtain differentially expressed mRNAs (DE-mRNAs), and miRNA data sets (GSE69580) were used to obtain differentially expressed miRNAs (DE-miRNAs). The “limma” R package was used for differential expression analyses. FDR < 0.05 and |log (FC)|>1 were used as the threshold for DE-mRNAs. P < 0.05 and |log (FC)|>0.585 were used as the threshold for DE-miRNAs. The integrated and deduplicated ERGs were intersected with differentially expressed genes from GSE14520 and GSE55092 to obtain HBV-related HCC differentially expressed ERGs (HBV-HCC-DE-ERGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses of HBV-HCC-DE-ERGs were performed using the “clusterProfiler” R package to elucidate potential relevant functional pathways, and the results were visualized by the “enrichplot” R package.

2.3 Identification and analysis of hub genes in HBV-HCC-De-ERGs

HBV-HCC-DE-ERGs were inputted into the Search Tool for the Retrieval of Interacting Genes (STRING) database to construct a protein-protein interaction (PPI) network related to HBV-HCC-DE-ERGs (confidence level of 0.4). Based on the PPI network results, the top 10 genes were calculated using the MCC method in the CytoHubba plugin of Cytoscape 3.10.0 and integrated with the results of MCODE algorithm screening to identify hub genes. The Pearson correlation coefficient of hub genes was calculated, and a correlation chord diagram was drawn using the “circlize” R package. The “pROC” R package was used to draw the receiver operation characteristic (ROC) curve of the hub gene, and the area under the curves (AUCs) was calculated to evaluate the predictive ability of the hub genes. Subsequently, the validation was performed using the GSE121248 dataset. Enrichment analysis of hub genes in the GSE55092 dataset was performed using GSEA4.3.2 software (NOM p-val <0.05, FDR q-val <0.25).

2.4 Construction of ceRNA network

Hub genes and DE-miRNAs (Supplementary Table 1) were inputted into the mirDIP database to search for DE-miRNAs that interacted with hub genes through Bidirectional Search. The ENCORI database was used to predict lncRNAs based on miRNA, and the final ceRNA network was developed through Cytoscape.

2.5 Drug prediction

The CellMiner database was used for drug prediction of hub genes, relevant drugs were screened, and the results were visualized using the “ggplot2” package.

3 Results

3.1 Identification and enrichment analysis of HBV-HCC-De-ERGs

By conducting the Venn analysis, 43 genes were obtained by intersecting differentially expressed genes from GSE14520 and GSE55092 with integrated and deduplicated ERGs, namely HBV-HCC-DE-ERGs (Figure 1A). Afterward, KEGG and GO enrichment analysis revealed that these HBV-HCC-DE-ERGs were mainly enriched in GO items including complement activation, lectin pathway, reactive oxygen species metabolic process, response to toxic substance, humoral immune response, positive regulation of phagocytosis, calcium-dependent protein binding, serine-type endopeptidase activity, and antioxidant activity (Figure 1B), and KEGG pathways such as Biosynthesis of amino acids, Cholesterol metabolism, Arginine biosynthesis, Renin-angiotensin system, Complement and coagulation cascades, Fructose and mannose metabolism, Alanine, aspartate and glutamate metabolism, and Carbon metabolism (Figure 1C).

Figure 1.

Identification and enrichment analysis of HBV-HCC-DE-ERGs. (A): HBV-HCC-DE-ERGs identified by Venn analysis. (B): GO enrichment analysis of HBV-HCC-DE-ERGs. (C): KEGG enrichment analysis of HBV-HCC-DE-ERGs.

3.2 Hub genes identification in HBV-HCC-De-ERGs

The HBV-HCC-DE-ERGs were inputted into the STRING database to construct the corresponding PPI network (confidence level of 0.4). The results revealed that 30 of the 43 genes had interactive relationships (Figure 2). Furthermore, based on the PPI network results, the MCC method in the CytoHubba plugin of Cytoscape 3.10.0 was used for integration with the MCODE algorithm to identify hub genes. 10 hub genes with interacting relationships were selected by the MCC method (Figure 3A). 9 hub genes with interacting relationships were identified by the MCODE algorithm (Figure 3B). The intersection of the results from MCC and MCODE yielded 9 intersecting genes, thus confirming these 9 intersecting genes (HP, C9, APOA1, PON1, TTR, LPA, FCN2, FCN3, and MBL2) as hub genes in HBV-HCC-DE-ERGs (Figure 3C).

Figure 2.

Protein-protein interaction network of HBV-HCC-DE-ERGs.

Figure 3.

Identification of hub genes in HBV-HCC-DE-ERGs. (A): 10 hub genes with interaction relationships identified by the MCC method. (B): 9 hub genes with interaction relationships identified by MCODE algorithm. (C): Venn analysis of the MCC and MCODE.

3.3 Correlation analysis and diagnostic value evaluation of hub genes in HBV-HCC-De-ERGs

The correlation chord diagrams of hub genes in HBV-HCC-DE-ERGs demonstrated strong positive correlations in three datasets (GSE14520, GSE55092, and GSE121248) (Figure 4A–C). The ROC curves of GSE14520, GSE55092, and GSE121248 revealed that the AUC values of these hub genes were between 0.859–0.979, 0.817–1, and 0.683–0.952, respectively (Figure 5). These findings suggested that these hub genes had good diagnostic performance. In addition, the enrichment analysis results of hub genes indicated that C9 was mainly enriched in the citrate cycle tca cycle, FCN3 was mainly enriched in olfactory transduction, HP was mainly enriched in arginine and proline metabolism, PON1 was mainly enriched in ubiquitin-mediated proteolysis, MBL2 was mainly enriched in phenylalanine metabolism, and LPA was mainly enriched in fatty acid metabolism (Figure 6).

Figure 4.

The correlation chord diagrams of the hub genes. (A): Correlation chord diagram of hub genes in GSE14520. (B): Correlation chord diagram of hub genes in GSE55092. (C): Correlation chord diagram of hub genes in GSE121248.

Figure 5.

ROC curves of hub gene. (A): ROC curve analysis of hub genes in GSE14520. (B): ROC curve analysis of hub genes in GSE55092. (C): ROC curve analysis of hub genes in GSE121248.

Figure 6.

GSEA enrichment analysis diagram of hub genes.

3.4 Construction of ceRNA network

Furthermore, we analyzed the miRNAs and lncRNAs that potentially targeted the hub genes in the HBV-HCC-DE-ERGs using the mirDIP and ENCORI databases. Through comprehensive analysis, we only obtained a miRNA targeting LPA (hsa-miR-590-5p), as well as 12 lncRNAs targeting hsa-miR-590-5p (AL121655.1, SNHG1, OTUD6B-AS1, AC108449.2, XIST, AC000120.1, RNF216P1, SNHG14, LINC00472, AL136040.1, SAP30-DT, and N4BP2L2-IT2) (Figure 7).

Figure 7.

ceRNA network targeting LPA.

3.5 Potential drug prediction

Some potential drugs targeting hub genes were predicted through the CellMiner database. The results indicated that C9 had significant positive correlations with Nelarabine (Cor = 0.971), Methylprednisolone (Cor = 0.818), Sapacitabine (Cor = 0.699), Chelerythrine (Cor = 0.655), Fluphenazine (Cor = 0.620), Ribavirin (Cor = 0.590) and Dexamethasone Decadron (Cor =0.553). PON1 had a significant positive correlation with Methylprednisolone (Cor = 0.822), Nelarabine (Cor = 0.793), Ribavirin (Cor = 0.630) and Fluphenazine (Cor = 0.609). APOA1 had a significant positive correlation with ST-3595 (Cor = 0.560) (P < 0.001) (Figure 8). These findings suggested that these drugs may have a regulatory effect on C9, PON1, and APOA1.

Figure 8.

Potential drugs targeting hub genes predicted by the cellMiner database.

4 Discussion

HBV-induced liver cancer ranks third leading cause of cancer-related fatalities globally.³² According to the World Health Organization, approximately 296 million people worldwide are infected with HBV, with approximately 1.5 million new cases occurring each year.^33,34 Hepatitis B vaccination is considered to be one of the effective measures to effectively prevent and control hepatitis B and its long-term sequelae and is conducive to reducing the disease burden.^35,36 Investigations in infants vaccinated against hepatitis B have found that hepatitis B vaccine shows excellent antiviral infection, and protection can be maintained for 20 years. The vaccine was also associated with a risk of cancer-related death.³⁷ Wong et al.³⁸ conducted a retrospective observational cohort study of continuous adult subjects born between 1970 and 2002 and examined for hepatitis B surface antigen (HBsAg), revealing that universal vaccination against HBV significantly reduces the prevalence of chronic HBV infection, which may help reduce the incidence of liver cancer. Taken together, HBV and HCC are closely related. It is necessary to further explore the relationship.

Patients’ survival outcomes are impacted by the various roles that exosomes play at different stages of HBV-induced HCC progression.³⁹ Currently, researchers have preliminarily explored the microRNA and protein profiles of exosomal HBV-related HCC cancer cells.²⁵ The role and function of the HBV-related HCC exosomal gene, however, were not entirely understood. Therefore, in this project, multiple HBV-induced HCC datasets were collected from the GEO database, HBV-related HCC exosome hub genes were screened through bioinformatics analysis, and the potential biological functions and diagnostic value of these hub genes were explored. Meanwhile, we predicted the miRNAs and lncRNAs that may regulate hub genes, as well as potential drugs that interacted with them. In conclusion, the understanding of the role of HBV infection in HCC development and the creation of new biomarkers both benefited greatly from this study.

HP, C9, APOA1, PON1, TTR, LPA, FCN2, FCN3, and MBL2 were the 9 HBV-related HCC exosome hub genes identified in this study. Hemopexin (HP) is a polymorphic protein that primarily functions to clear hemoglobin, prevent iron loss, and prevent hemoglobin oxidation.⁴⁰ Xie et al.⁴¹ evidenced that HP is specifically expressed in HCC and HP-positive neutrophil clusters have extensive and strong intercellular communication with HCC cells, tumor endothelial cells, and cancer-related fibroblasts. These findings suggested that specific expression of HP may lead to higher tumor differentiation and tumor heterogeneity. As one of the terminal complement components (TCC) proteins, complement C9 is crucial for innate immunity.⁴² Liu et al.⁴³ demonstrated that C9 can serve as a key prognosis biomarker for gastric cancer. It is currently unknown how the prognosis of HCC and C9 are related, but Bae et al.⁴⁴ proved a significant association between the +23189 C > T polymorphism [T-G-C-a-C] of exon 4 and C9_ht2 and the clearance of HBV infection and the occurrence of HCC in genetic analysis, but the association signal was not retained after multiple testing corrections. As a key member of the apolipoprotein family, apolipoprotein A 1 (APOA1) is involved in vital biological functions, such as participating in cholesterol efflux and regulating the immune microenvironment of HCC. High APOA1 has been considered a good prognosis indicator for HCC.⁴⁵ Serum paraoxonase 1 (PON1) is a gene that is markedly downregulated in HCC. Zhang et al.⁴⁶ and Zheng et al.⁴⁷ revealed that low expression of PON1 is associated with poor survival in HCC and closely related to HCC cell cycle, DNA replication, gap junctions, and p53 downstream pathways. Zhang et al.⁴⁸ put forward that APOA1 is associated with a lower risk of liver cancer through Mendelian randomization analysis. Transthyretin (TTR) is proven to be elevated throughout the HCC progression. TTR mutations are thought to be major contributors to aberrant lipid metabolism, which influences the development of cirrhosis and HCC.⁴⁹ LPA is a class of heterogeneous, biologically stable, non-immunogenic nanoparticles produced primarily by the liver and intestine.⁵⁰ In lipid metabolism, LPA can mediate the transfer of lipids from the gut to other tissues for storage and can also transport lipids from tissues to the liver for catabolism.⁵¹ The previous study has demonstrated that LPA has anti-angiogenic and anti-tumor properties.⁵² Recent studies have revealed that lipoproteins and their metabolites are associated with the risk of breast cancer, colorectal cancer, and other cancers.^53,54 In addition, based on the nanoscale properties of LPA, in recent years, lipoprotein/lipoprotein-like nanocarriers have become an increasingly popular nanostructure for the delivery of anticancer drugs.⁵⁵

Abnormal lipid metabolism is also commonly associated with the development and progression of HCC.^56,57 The hub gene in this project, such as TTR, can cut the C-terminal of APOA1, thus ensuring normal lipid metabolism.⁴⁹ Studies have uncovered that the degree of lipid oxidation is also associated with the loss of HP in plasma samples.⁵⁸ The complement component C9 contains two distinct cysteine-rich domains that exhibit high sequence similarity to those present in the low-density lipoprotein (LDL) receptor and epidermal growth factor precursor, respectively.⁵⁹ APOA1 and PON1 can form complexes with high-density lipoprotein (HDL) and have anti-atherosclerosis, antioxidant, anti-inflammatory, and immunomodulatory effects.^46,60 Although this project obtained key genes related to exosomes of HBV-associated HCC through the PPI network, MCC algorithm, and MCODE algorithm, the interactions among the 9 key genes still required further exploration.

As a member of the multimeric lectin family, mannose-binding lectin 2 (MBL2) is essential for immune regulation and tumor development, including HCC.⁶¹ Xu et al.⁶² evidenced that elevated MBL2 expression can repress the progression of HCC cells and weaken the tumor-promoting impact induced by miR-942-3p. As key members of the Ficolin (FCN) family, FCN2 and FCN3 are primarily serum molecules. Like FCN1, FCN2 and FCN3 are involved in environmental homeostasis and innate immunity in tissues.⁶³ Wang et al.⁶⁴ proved that the low expression of FCN2 in HCC tissues is not only related to immune cell infiltration, immunomodulators, and chemokine receptors, but may also play a role through Staphylococcus aureus infection, lectins, and other pathways. According to most researchers, FCN3 is also a crucial gene that influences HCC prognosis and immunity.^65–67 Therefore, based on these findings, we speculated that the 9 hub genes identified in this study may have complex roles in the progression of HBV-induced HCC.

To further probe into the mechanisms behind the predicted hub genes, GSEA was performed to determine the pathways significantly enriched by these genes. We discovered that these hub genes were mostly enriched in biological processes such as citrate cycle tca cycle, phenylalanine metabolism, and fatty acid metabolism. The citrate cycle, also known as the tricarboxylic acid (TCA) cycle, is the main pathway for cellular bioenergetics, biosynthesis, and redox balance needs. It is associated with the progression of many cancers.⁶⁸ Researchers discovered that in HCC, raising the metabolic activity of the TCA cycle can successfully lower drug metabolism.⁶⁹ In addition, polymorphisms in key enzyme genes of the TCA cycle are also thought to be connected to the HCC early recurrence.⁷⁰ As an essential amino acid, phenylalanine takes part in several biochemical processes and metabolic pathways within the human body. Although no direct relationship has been found between phenylalanine metabolism abnormalities and HCC development, phenylalanine has been recognized as a potential biomarker for gastroesophageal malignancy.⁷¹ There is a close connection between fatty acid metabolism and HCC development and progression. The growth, invasion, and metastasis of HCC tumor cells are all intimately linked to aberrant fatty acid metabolism.^72–74 Taken together, these findings suggested that the identified hub genes in this project may affect the HCC progression by regulating metabolic processes such as the citrate cycle tca cycle, phenylalanine metabolism, and fatty acid metabolism.

Furthermore, this study predicted a miRNA targeting LPA (hsa-miR-590-5p) and 12 lncRNAs targeting hsa-miR-590-5p (AL121655.1, SNHG1, OTUD6B-AS1, AC108449.2, XIST, AC000120.1, RNF216P1, SNHG14, LINC00472, AL136040.1, SAP30-DT, and N4BP2L2-IT2). Recent studies have uncovered that miR-590-5p acts as an oncogene in liver cancer and can affect the malignant progression and chemotherapy resistance of liver cancer by targeting FOXO1⁷⁵ or YAP1.⁷⁶ Chen et al.⁷⁷ found that miR-590-5p could be sponged by circEPB41L2 to further hinder the development of HCC. Among the 12 lncRNAs, some genes have been preliminarily explored in HCC. For example, the TCGA database analysis demonstrated that LINC00472 is associated with the EMT process of HCC and can be applied as a potential therapeutic target for HCC.⁷⁸ Chen et al.⁷⁹ pointed out that LINC00472 could repress the malignant progression of HCC cells by targeting the miR-93-5p/PDCD4 pathway. LncRNA SNHG14 has been confirmed to be highly expressed in HCC and can repress the proliferation, migration, and invasion of HCC cells by sponging miR-206 and regulating the expression level of SOX9.⁸⁰ Zhang et al.⁸¹ also put forward that lncRNA SNHG14 plays a pro-cancer role in HCC, and it can facilitate cell proliferation and angiogenesis by up-regulating PABPC1 through H3K27 acetylation and regulating PTEN signal transduction in HCC tumorigenesis. The RING finger (RNF) protein family regulates cell activity mainly by mediating protein degradation.^82,83 Zhang et al.⁸⁴ constructed 11 prognostic models composed of RNF through the TCGA database, including RNF216P1, and pointed out that RNF216P1 is primarily highly expressed in patients with advanced HCC. For OTUD6B-AS1, Kong et al.⁸⁵ demonstrated its high expression in HCC in vitro and in vivo experiments, uncovering that it regulated the GSKIP/Wnt/β-catenin signaling pathway by targeting miR-664b-3p, which can boost the malignant phenotype of HCC cells. In this project, we preliminarily investigated the mechanism of the LINC00472/miR-590-5p/LPA pathway in HCC. The results manifested that LINC00472 could hinder the viability of HCC cells by regulating the expression of miR-590-5p/LPA. However, a subset of lncRNAs has not been mechanistically studied in HCC, although bioinformatics predicts their association with hsa-miR-590-5p in HCC. In addition, experiments are needed to further verify the targeted binding relationship between these 12 lncRNAs and miR-590-5p and explore the specific mechanism of the lncRNAs/miR-590-5p/LPA pathway in HCC.

Potential drugs targeting hub genes were predicted based on the CellMiner database, such as Nelarabine, Methylprednisolone, and Methylprednisolone. It was also speculated that these miRNAs, lncRNAs, and drug candidates may have the function of regulating hub genes and mediating their effects in HCC. However, this speculation still required extensive future research for validation.

In conclusion, this project identified 9 HBV-related HCC exosome hub genes and explored the potential biological functions and diagnostic value of these hub genes. Meanwhile, the miRNA and lncRNAs that may regulate hub genes were predicted, as well as potential drugs that interacted with them. These results not only advanced the knowledge of the function of HBV infection in HCC development, but they also served as useful resources for the creation of novel HCC biomarkers. However, it should be noted that there were certain restrictions on the current study. Firstly, the number of screened hub genes was constrained by the limited collection of HBV-related HCC ERGs. Secondly, the primary analytical method used in this study was bioinformatics analysis, and the analysis results could only be predictions. Therefore, more experimental research is required to investigate the potential mechanisms between the identified hub genes and HCC progression. Third, the results of this investigation need to be further corroborated in the clinic. Fourth, the interaction between the selected hub genes remains to be further explored.

Supplemental Material

sj-xls-1-thc-10.1177_09287329241296353 - Supplemental material for Identification of key exosomes-related genes in hepatitis B virus-related hepatocellular carcinoma

Supplemental material, sj-xls-1-thc-10.1177_09287329241296353 for Identification of key exosomes-related genes in hepatitis B virus-related hepatocellular carcinoma by Zhuoyi Wang, Jianfang Lu, Xiangyan Liu, Jingfeng Liu and Jianhui Li in Technology and Health Care

Footnotes

ORCID iD

Jianhui Li

Ethics approval and consent to participate

Not Applicable.

Authors’ contributions

Conceptualization: Zhuoyi Wang

Data curation: Jianfang Lu

Formal Analysis: Jianfang Lu

Methodology: Zhuoyi Wang and Jianhui Li

Project administration: Xiangyan Liu

Resources: Jingfeng Liu

Supervision: Xiangyan Liu

Writing – original draft: Zhuoyi Wang

Writing – review & editing: Jianhui Li

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The project was supported by National Key Research and Development Program of China (2021YFC2301805).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

The data and materials in the current study are available from the corresponding author on reasonable request.

Supplemental material

Supplemental material for this article is available online.

References

Fang

Zhang

, et al. Smart glypican-3-targeting peptide-chlorin e6 conjugates for targeted photodynamic therapy of hepatocellular carcinoma. Eur J Med Chem 2024; 264: 116047.

Shivangi

Mishra Gupta

, et al. Clinical diagnosis of viral hepatitis: current status and future strategies. Diagn Microbiol Infect Dis 2023; 108: 116151.

Yamamoto

, et al. IL-17 signaling in steatotic hepatocytes and macrophages promotes hepatocellular carcinoma in alcohol-related liver disease. J Hepatol 2020; 72: 946–959.

Liao

Xie

Wang

, et al. Formononetin promotes fatty acid beta-oxidation to treat non-alcoholic steatohepatitis through SIRT1/PGC-1alpha/PPARalpha pathway. Phytomedicine 2023; 124: 155285.

Yang

Liu

, et al. Mesenchymal stromal cells in hepatic fibrosis/cirrhosis: from pathogenesis to treatment. Cell Mol Immunol 2023; 20: 583–599.

Wang

Kang

, et al. Rictor mediates p53 deactivation to facilitate the malignant transformation of hepatocytes and promote hepatocarcinogenesis. J Transl Med 2023; 21: 919.

Chen

Huang

, et al. SETD1A drives stemness by reprogramming the epigenetic landscape in hepatocellular carcinoma stem cells. JCI Insight 2023; 8: e168375.

Austinat

Dunsch

Wittekind

, et al. Correlation between beta-catenin mutations and expression of Wnt-signaling target genes in hepatocellular carcinoma. Mol Cancer 2008; 7: 21.

Toyoda

Koshiyama

Yasuda

, et al. Effect of previous infection with hepatitis B virus on the incidence of hepatocellular carcinoma after sustained virologic response in patients with chronic hepatitis C virus infection. J Viral Hepat 2023; 31: 137–142.

10.

Chen

Yang

Wang

, et al. beta-Sitosterol suppresses hepatocellular carcinoma growth and metastasis via FOXM1-regulated Wnt/beta-catenin pathway. J Cell Mol Med 2023; 28: e18072.

11.

Paskeh

MDA

Ghadyani

Hashemi

, et al. Biological impact and therapeutic perspective of targeting PI3K/Akt signaling in hepatocellular carcinoma: promises and challenges. Pharmacol Res 2023; 187: 106553.

12.

Sun

Zhou

, et al. Elevated expression of RIT1 hyperactivates RAS/MAPK signal and sensitizes hepatocellular carcinoma to combined treatment with sorafenib and AKT inhibitor. Oncogene 2022; 41: 732–744.

13.

Liu

Dai

Wang

, et al. Hepatitis B virus PreS1 facilitates hepatocellular carcinoma development by promoting appearance and self-renewal of liver cancer stem cells. Cancer Lett 2017; 400: 149–160.

14.

Yang

Tang

Zheng

, et al. Exosomal long noncoding RNAs serve as biomarkers for liver disease. Turk J Gastroenterol 2023; 34: 674–680.

15.

Hazrati

Soudi

Malekpour

, et al. Immune cells-derived exosomes function as a double-edged sword: role in disease progression and their therapeutic applications. Biomark Res 2022; 10: 30.

16.

Jiang

Shi

Sun

, et al. Exosomal miR-30a-5p promoted intrahepatic cholangiocarcinoma progression by increasing angiogenesis and vascular permeability in PDCD10 dependent manner. Int J Biol Sci 2023; 19: 4571–4587.

17.

Deng

Huang

, et al. Engineered exosomes-based theranostic strategy for tumor metastasis and recurrence. Asian J Pharm Sci 2023; 18: 100870.

18.

Wang

Luo

, et al. An exosome-based specific transcriptomic signature for profiling regulation patterns and modifying tumor immune microenvironment infiltration in triple-negative breast cancer. Front Immunol 2023; 14: 1295558.

19.

Glitscher

Spannaus

Behr

, et al. The protease domain in HEV pORF1 mediates the replicase's localization to multivesicular bodies and its exosomal release. Cell Mol Gastroenterol Hepatol 2024; 17: 589–605.

20.

Kwasnik

Socha

Czech

, et al. Protein-coding region derived small RNA in exosomes from influenza A virus-infected cells. Int J Mol Sci 2023; 24: 867.

21.

Yin

Zhao

Chen

, et al. Dual roles and potential applications of exosomes in HCV infections. Front Microbiol 2022; 13: 1044832.

22.

Bukong

Momen-Heravi

Kodys

, et al. Exosomes from hepatitis C infected patients transmit HCV infection and contain replication competent viral RNA in complex with Ago2-miR122-HSP90. PLoS Pathog 2014; 10: e1004424.

23.

Wang

Cao

Yang

. Exosomes in hepatitis B virus transmission and related immune response. Tohoku J Exp Med 2020; 252: 309–320.

24.

Yang

Han

Hou

, et al. Exosomes mediate hepatitis B virus (HBV) transmission and NK-cell dysfunction. Cell Mol Immunol 2017; 14: 465–475.

25.

Todorova

Byrum

Mackintosh

, et al. Exosomal MicroRNA and protein profiles of hepatitis B virus-related hepatocellular carcinoma cells. Int J Mol Sci 2023; 24: 13098.

26.

Dong

Zhang

Xia

, et al. The necroptosis related gene LGALS3 can be used as a biomarker for the adverse progression from chronic HBV infection to HCC. Front Immunol 2023; 14: 1142319.

27.

Xue

Zhao

, et al. Integrated analysis of multiple transcriptomic data identifies ST8SIA6–AS1 and LINC01093 as potential biomarkers in HBV–associated liver cancer. Oncol Lett 2023; 25: 185.

28.

Melis

Diaz

Kleiner

, et al. Viral expression and molecular profiling in liver tissue versus microdissected hepatocytes in hepatitis B virus-associated hepatocellular carcinoma. J Transl Med 2014; 12: 230.

29.

Peng

Bai

Zheng

, et al. An exosome-related lncRNA signature correlates with prognosis, immune microenvironment, and therapeutic responses in hepatocellular carcinoma. Transl Oncol 2023; 31: 101651.

30.

Qiu

Guo

Lin

, et al. An exosome-related long non-coding RNAs risk model could predict survival outcomes in patients with breast cancer. Sci Rep 2022; 12: 22322.

31.

Zhu

Tao

Yan

, et al. Machine learning identifies exosome features related to hepatocellular carcinoma. Front Cell Dev Biol 2022; 10: 1020415.

32.

Adugna

. Histomolecular characterisation of hepatitis B virus induced liver cancer. Rev Med Virol 2023; 33: e2485.

33.

WHO. Global hepatitis programme. https://www.who.int/teams/global-hiv-hepatitis-and-stis-programmes/hepatitis/testing-and-diagnostics.

34.

Tourkochristou

Assimakopoulos

Thomopoulos

, et al. NAFLD And HBV interplay - related mechanisms underlying liver disease progression. Front Immunol 2022; 13: 965548.

35.

Romano

Paladini

Galli

, et al. Hepatitis B vaccination. Hum Vaccin Immunother 2015; 11: 53–57.

36.

Romano

Zanetti

. Hepatitis B vaccination: a historical overview with a focus on the Italian achievements. Viruses 2022; 14: 1515.

37.

Guo

. The impact of hepatitis B vaccination in the United States, 1999–2018. Hepatology 2022; 75: 1566–1578.

38.

Wong

Hui

Yip

, et al. Universal HBV vaccination dramatically reduces the prevalence of HBV infection and incidence of hepatocellular carcinoma. Aliment Pharmacol Ther 2022; 56: 869–877.

39.

Zhou

, et al. H-TEX-mediated signaling between hepatocellular carcinoma cells and macrophages and exosome-targeted therapy for hepatocellular carcinoma. Front Immunol 2022; 13: 997726.

40.

Delanghe

Delrue

Speeckaert

, et al. Unlocking the link between haptoglobin polymorphism and noninfectious human diseases: insights and implications. Crit Rev Clin Lab Sci 2023; 61: 1–23.

41.

Xie

Huang

, et al. Single-cell RNA sequencing revealed potential targets for immunotherapy studies in hepatocellular carcinoma. Sci Rep 2023; 13: 18799.

42.

Shen

, et al. Tracing and exploring the evolutionary origin and systematic function of fish complement C9. Mol Genet Genomics 2021; 296: 665–676.

43.

Liu

Yang

Shu

, et al. A novel prognostic and therapeutic target biomarker based on complement-related gene signature in gastric cancer. Transl Cancer Res 2023; 12: 3565–3580.

44.

Bae

Pasaje

Park

, et al. Genetic analysis of complement component 9 (C9) polymorphisms with clearance of hepatitis B virus infection. Dig Dis Sci 2011; 56: 2735–2741.

45.

Wang

Chen

Xiao

, et al. Impact of apolipoprotein A1 on tumor immune microenvironment, clinical prognosis and genomic landscape in hepatocellular carcinoma. Precis Clin Med 2023; 6: pbad021.

46.

Zhang

Ying

Zhao

, et al. Identification of protein expression changes in hepatocellular carcinoma through iTRAQ. Dis Markers 2020; 2020: 2632716.

47.

Chen

, et al. Evaluation of the prognostic value of paraoxonase 1 in the recurrence and metastasis of hepatocellular carcinoma and establishment of a liver-specific predictive model of survival. J Transl Med 2018; 16: 327.

48.

Zhang

Liu

. Lifestyle factors, glycemic traits, and lipoprotein traits and risk of liver cancer: a Mendelian randomization analysis. Sci Rep 2024; 14: 8502.

49.

Malik

, et al. Novel mutations in transthyretin gene associated with hepatocellular carcinoma. Mol Carcinog 2018; 57: 70–77.

50.

Tsimikas

. A test in context: lipoprotein(a): diagnosis, prognosis, controversies, and emerging therapies. J Am Coll Cardiol 2017; 69: 692–711.

51.

Feingold

, et al. Introduction to Lipids and Lipoproteins. In: Feingold

Anawalt

Blackman

Boyce

Chrousos

Corpas

(eds) Endotext. South Dartmouth (MA), 2000.

52.

Lippi

Franchini

Salvagno

, et al. Lipoprotein[a] and cancer: anti-neoplastic effect besides its cardiovascular potency. Cancer Treat Rev 2007; 33: 427–436.

53.

Debik

Schafer

Andreassen

, et al. Lipoprotein and metabolite associations to breast cancer risk in the HUNT2 study. Br J Cancer 2022; 127: 1515–1524.

54.

Zeljkovic

Vekic

Mihajlovic

, et al. Revealing the role of high-density lipoprotein in colorectal cancer. Int J Mol Sci 2021; 22: 3352.

55.

Chaudhary

Bower

Corbin

. Lipoprotein drug delivery vehicles for cancer: rationale and reason. Int J Mol Sci 2019; 20: 6327.

56.

Pope 3rd

Kimbrough

Vemireddy

, et al. Aberrant lipid metabolism as a therapeutic target in liver cancer. Expert Opin Ther Targets 2019; 23: 473–483.

57.

Alannan

Fayyad-Kazan

Trezeguet

, et al. Targeting lipid metabolism in liver cancer. Biochemistry 2020; 59: 3951–3964.

58.

Yalamanoglu

Deuel

Hunt

, et al. Depletion of haptoglobin and hemopexin promote hemoglobin-mediated lipoprotein oxidation in sickle cell disease. Am J Physiol Lung Cell Mol Physiol 2018; 315: L765–LL74.

59.

Tschopp

Mollnes

. Antigenic crossreactivity of the alpha subunit of complement component C8 with the cysteine-rich domain shared by complement component C9 and low density lipoprotein receptor. Proc Natl Acad Sci U S A 1986; 83: 4223–4227.

60.

Morris

Puri

Bortolasci

, et al. The role of high-density lipoprotein cholesterol, apolipoprotein A and paraoxonase-1 in the pathophysiology of neuroprogressive disorders. Neurosci Biobehav Rev 2021; 125: 244–263.

61.

Liao

Yang

, et al. Mannose-binding Lectin 2 as a potential therapeutic target for hepatocellular carcinoma: multi-omics analysis and experimental validation. Cancers (Basel) 2023; 15: 4900.

62.

Dong

Chen

, et al. MiR-942-3p promotes the proliferation and invasion of hepatocellular carcinoma cells by targeting MBL2. Cancer Control 2019; 26: 1073274819846593.

63.

Sun

Dong

, et al. Comprehensive analysis of prognostic value and immune infiltration of ficolin family members in hepatocellular carcinoma. Front Genet 2022; 13: 913398.

64.

Wang

Zeng

Jiang

, et al. Ficolin-2: a potential immune-related therapeutic target with low expression in liver cancer. Front Oncol 2022; 12: 987481.

65.

Wang

Song

Tan

, et al. Identification and validation of hub genes associated with hepatocellular carcinoma via integrated bioinformatics analysis. Front Oncol 2021; 11: 614531.

66.

Lai

Hong

, et al. A novel gene signature based on CDC20 and FCN3 for prediction of prognosis and immune features in patients with hepatocellular carcinoma. J Immunol Res 2022; 2022: 9117205.

67.

Kaur

Dhall

Kumar

, et al. Identification of platform-independent diagnostic biomarker panel for hepatocellular carcinoma using large-scale transcriptomics data. Front Genet 2019; 10: 1306.

68.

Todisco

Convertini

Iacobazzi

, et al. TCA Cycle rewiring as emerging metabolic signature of hepatocellular carcinoma. Cancers (Basel) 2019; 12: 68.

69.

Fukuda

Ebara

Okuyama

, et al. Increased metabolizing activities of the tricarboxylic acid cycle and decreased drug metabolism in hepatocellular carcinoma. Carcinogenesis 2002; 23: 2019–2023.

70.

Wan

Zhou

, et al. Polymorphisms in genes of tricarboxylic acid cycle key enzymes are associated with early recurrence of hepatocellular carcinoma. PLoS One 2015; 10: e0124471.

71.

Wiggins

Kumar

Markar

, et al. Tyrosine, phenylalanine, and tryptophan in gastroesophageal malignancy: a systematic review. Cancer Epidemiol Biomarkers Prev 2015; 24: 32–38.

72.

Liu

Yang

, et al. Xiao-Xu-Ming decoction prevented hemorrhagic transformation induced by acute hyperglycemia through inhibiting AGE-RAGE-mediated neuroinflammation. Pharmacol Res 2021; 169: 105650.

73.

Wang

Zhang

Chen

, et al. Acyl-CoA thioesterase 9 promotes tumour growth and metastasis through reprogramming of fatty acid metabolism in hepatocellular carcinoma. Liver Int 2022; 42: 2548–2561.

74.

Zhang

Liu

, et al. MicroRNA-377-3p inhibits hepatocellular carcinoma growth and metastasis through negative regulation of CPT1C-mediated fatty acid oxidation. Cancer Metab 2022; 10: 2.

75.

Jia

Tang

Deng

, et al. miR-590-5p promotes liver cancer growth and chemotherapy resistance through directly targeting FOXO1. Am J Transl Res 2019; 11: 2181–2193.

76.

Chen

, et al. miR-590-5p suppresses hepatocellular carcinoma chemoresistance by targeting YAP1 expression. EBioMedicine 2018; 35: 142–154.

77.

Chen

Qiu

, et al. Circular RNA CircEPB41L2 functions as tumor suppressor in hepatocellular carcinoma through sponging miR-590-5p. Cancer Manag Res 2021; 13: 2969–2981.

78.

Zhang

Wang

Liu

. EMT-related long non-coding RNA in hepatocellular carcinoma: a study with TCGA database. Biochem Biophys Res Commun 2018; 503: 1530–1536.

79.

Chen

Zheng

Kang

, et al. Long non-coding RNA LINC00472 suppresses hepatocellular carcinoma cell proliferation, migration and invasion through miR-93-5p/PDCD4 pathway. Clin Res Hepatol Gastroenterol 2019; 43: 436–445.

80.

Lin

Zhan

, et al. LncRNA SNHG14 Sponges miR-206 to affect proliferation, apoptosis, and metastasis of hepatocellular carcinoma cells by regulating SOX9. Dig Dis Sci 2022; 67: 936–946.

81.

Zhang

Kurban

, et al. LncRNA SNHG14 promotes hepatocellular carcinoma progression via H3K27 acetylation activated PABPC1 by PTEN signaling. Cell Death Dis 2020; 11: 646.

82.

Liu

Tan

Zhang

, et al. NAT10 Regulates p53 activation through acetylating p53 at K120 and ubiquitinating Mdm2. EMBO Rep 2016; 17: 349–366.

83.

Wang

Dai

Zeng

, et al. E3 ubiquitin ligases in breast cancer metastasis: a systematic review of pathogenic functions and clinical implications. Front Oncol 2021; 11: 752604.

84.

Zhang

Yang

Wang

, et al. The systematic analyses of RING finger gene signature for predicting the prognosis of patients with hepatocellular carcinoma. J Oncol 2022; 2022: 2466006.

85.

Kong

Xue

, et al. The long noncoding RNA OTUD6B-AS1 enhances cell proliferation and the invasion of hepatocellular carcinoma cells through modulating GSKIP/Wnt/beta-catenin signalling via the sequestration of miR-664b-3p. Exp Cell Res 2020; 395: 112180.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.04 MB