Comprehensive investigation of key biomarkers and pathways in hepatitis B virus-related hepatocellular carcinoma

Analysis of host factor networks during hepatitis B virus infection in primary human hepatocytes

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.

Machine learning and experimental screening of chromatin regulator signatures and potential drugs in hepatitis B related hepatocellular carcinoma

Many evidences have confirmed that chromatin regulator factors (CRs) are involved in the progression of cancer, but its potential mechanism of affecting hepatitis B related hepatocellular carcinoma still needs to be studied. Our study detected the CRs that affect hepatitis B related hepatocellular carcinoma (HBV-HCC) through machine learning analysis, conducted the analysis of immune cells, constructed the relevant risk model and immune function infiltration, and predicted the potential therapeutic drugs. We found that these CRs were significantly related to the immune cells of Macrophages, B cells, CD8+T cells, etc., and PBK, AURKA, TOP2A and AURKB were the potential risk CRs of HBV-HCC. The expression levels of these four CRs increased in HepG2.2.15 cells and the liver of HBV-HCC patients, consistent with the predicted risk model. Subsequently, ten potential drugs closely related to the risk CRs were finally obtained, experimental research on resveratrol has shown that it can inhibit the proliferation of HepG2.2.15 cells and potentially inhibit the occurrence and development of HBV-HCC. Our study provides novel insights into the function of CRs in HBV-HCC and certain ideas for more accurate targeted therapy.Communicated by Ramaswamy H. Sarma.

Exploring the potential targets of Sanshimao formula for hepatocellular carcinoma treatment by a method of network pharmacology combined with molecular biology

ETHNOPHARMACOLOGICAL RELEVANCE

The Sanshimao (SSM) formula is an effective prescription for hepatocellular carcinoma (HCC) therapy in the clinical setting. This prescription is made up of four herbals, Maorenshen, Shijianchuan, Shishangbai and Shidachuan, which are used for detoxification and removing blood stasis. However, its mechanism in the treatment of HCC remains ambiguous.

AIM OF THE STUDY

To explore the potential targets of SSM against HCC by network pharmacology analysis and verify the data using molecular biological methods.

MATERIALS AND METHODS

We screened active components and potential targets by data mining, constructed a network, and performed functional analysis and pathway enrichment to explore the therapeutic targets of SSM for HCC treatment. Then, the effects of SSM on HCC cells were studied to validate the data from network pharmacology analysis.

RESULTS

Eighty-eight common targets were obtained by mapping 932 HCC-related genes, and 325 targets corresponded to 11 active components of SSM. They were enriched in various biological processes, such as the response to inorganic substances, response to toxic substances and apoptotic signalling pathway, and multi-pathways involved pathways in cancer, EGFR tyrosine kinase inhibitor resistance, and AGE-RAGE signalling pathway in diabetic complications, as evaluated by the analysis of advanced functions and pathways. TP53, JUN, HSP90AA1, EGFR, AR and MAPK1 might be the core targets closely related to the effects of SSM on HCC according to PPI analysis. Treatment with SSM decreased cell viability and migration, promoted apoptosis and inhibited the EGFR/FAK/AKT signalling pathway.

CONCLUSION

This research preliminarily indicates that SSM treats HCC via multiple components and pathways. EGFR/FAK/AKT are promising therapeutic targets of SSM for HCC treatment. This provides objective evidence for further mechanistic research and the future development and clinical application of SSM in HCC patients.

Identification of a Two-lncRNA Signature with Prognostic and Diagnostic Value for Hepatocellular Carcinoma

Background

Accumulating evidence has revealed the important role of long noncoding RNAs (lncRNA) in tumorigenesis and progression of hepatocellular carcinoma (HCC). This study aimed to identify potential lncRNAs that can serve as diagnostic and prognostic signatures for HCC.

Methods

Expression profiling analysis was performed to identify differentially expressed lncRNAs (DElncRNA) between HCC and matched normal samples by integrating two independent microarray datasets. Functional Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were explored by Gene Set Variation Analysis. The prognostic and diagnostic models were developed based on two DElncRNAs. Real-time PCR was used to quantify the relative expressions of candidate lncRNAs.

Results

Two robust DElncRNAs were identified and verified by quantitative PCR between HCC and matched normal samples. Function enrichment analysis revealed that they were associated with the wound healing process. The two lncRNAs were subsequently used to construct a prognostic risk model for HCC. Patients with high-risk scores estimated by the model showed a shorter survival time than low-risk patients (P < 0.001). Besides, the two lncRNA-based HCC diagnostic models exhibited good performance in discriminating HCC from normal samples on both training and test sets. The values of area under the curve (AUC) for early (I–II) and late (III–IV) HCC detection were 0.88 and 0.93, respectively.

Conclusions

The two wound healing-related DElncRNAs showed robust performance for HCC prognostic prediction and detection, implying their potential role as diagnostic and prognostic markers for HCC.

ANTXR1 as a potential prognostic biomarker for hepatitis B virus-related hepatocellular carcinoma identified by a weighted gene correlation network analysis

BACKGROUND

With high incidence and mortality rates, hepatocellular carcinoma (HCC) is one of the most prevalent malignant tumors worldwide. Chronic hepatitis B virus (HBV) infection is a leading cause of HCC, especially for Asians and blacks. However, the molecular mechanisms underlying HBV-related HCC are unclear. This study sought to identify novel prognostic biomarkers and explore the potential pathogenesis of HBV-related HCC.

METHODS

The gene expression profiles and corresponding clinical information of HCC from The Cancer Genome Atlas Liver Hepatocellular Carcinoma data set were analyzed by a weighted gene co-expression network analysis. Correlations between the co-expression modules and clinical traits were calculated. Next, key modules associated with HBV infection were identified. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were conducted for the genes in the key modules. The hub genes were identified based on the protein-protein interaction (PPI) network via the Cytoscape. Finally, an overall survival (OS) analysis was performed.

RESULTS

The two modules (i.e., the brown and yellow modules) most relevant to HBV infection were constructed. A functional enrichment analysis revealed that the genes in the two modules were mainly enriched in HCC-related pathways, such as the phosphatidylinositol-3-kinase and protein kinase B signaling pathway, focal adhesion, human papillomavirus infection, the Rap1 signaling pathway, and the cyclic guanosine monophosphate-dependent protein kinase (cGMP-PKG) signaling pathway. Ten hub genes [i.e., COL3A1, ANTXR1, COL14A1, THBS2, ADAMTS2, AEBP1, PRELP, EMILIN1, DCN and PODN] in the brown module, and 10 hub genes [i.e., USP34, SEC24C, ZNF770, STAG1, TSTD2, PKD1P6, CCNK, GFT2I, NT5C2 and SMG6] in the yellow module were identified. Among the hub genes, ANTXR1 (Anthrax-toxin receptor 1) was significantly correlated with HBV-related HCC patients' OS.

CONCLUSIONS

ANTXR1 represents a potential therapeutic target for HBV-related HCC. This study offers novel insights into the molecular mechanisms of HBV-induced tumorigenesis, which needs to be further validated by basic experiments and large-scale cohort studies.

Identification of genes predicting unfavorable prognosis in hepatitis B virus-associated hepatocellular carcinoma

Background

To identify potential key genes predicting unfavorable prognosis in hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC).

Methods

Gene expression profiles of GSE121248, GSE62232, and GSE55092 from the GEO database were obtained and analyzed. Differentially expressed genes (DEGs) between HBV-associated HCC tissues and adjacent normal tissues were screened by the limma package and Venn diagram software. Functional assessment of DEGs was performed by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Hub genes were selected by the protein-protein interaction (PPI) network and further validated by GSE14520 clinical data.

Results

A total of 26 up-regulated genes and 76 down-regulated genes were identified by analyzing three databases. GO and KEGG analysis demonstrated that these genes were involved in cell division, metabolism-related biological processes, the p53 pathway, and the cell cycle, among others. PPI network suggested that 14 hub DEGs (TOP2A, HMMR, DTL, CCNB1, NEK2, PBK, RACGAP1, PRC1, CDK1, RRM2, ECT2, BUB1B, ANLN, and ASPM) were most dysregulated and had potential to distinguish between HBV-associated HCC and noncancerous tissues. Further survival analysis of hub genes demonstrated that high expression of TOP2A was significantly associated with poor clinical outcomes of HBV-associated HCC.

Conclusions

TOP2A might serve as a key gene for prognosis and as a therapeutic target for HBV-associated HCC.

Prognostic Value and Molecular Mechanisms of Proteasome 26S Subunit, Non-ATPase Family Genes for Pancreatic Ductal Adenocarcinoma Patients after Pancreaticoduodenectomy

Objective: Pancreatic cancer (PC) is an extremely malignant tumor with similar morbidity and mortality and lack of an effective treatment. This study explored the prognostic value and molecular mechanisms of proteasome 26S subunit, non-ATPase (PSMD) family genes in pancreatic ductal adenocarcinoma (PDAC).Methods: Survival analyses were performed to elucidate the relationship between prognosis and the level of PSMD expression. ROC curves and nomograms were constructed to predict the prognosis. A bioinformatics analysis was used to explore the co-expression and complex interaction networks of PSMDs. The potential mechanisms were further explored via gene set enrichment analysis (GSEA).Results: We find high levels of PSMD6, PSMD9, PSMD11, and PSMD14 expression were significantly associated with a poorer OS. High PSMD6 and PSMD11 expression was associated with a poorer relapse-free survival (RFS). A risk score model was constructed based on prognosis-related genes. The area under ROC curves (AUC) was 53.3%, 59.3%, and 62.9% for 1-, 2-, 3 years, respectively.Conclusion: GSEA revealed that PSMD6 and PSMD11 play a role in PDAC through various biological processes and signaling pathways, including TP53, CDKN2A, MYC pathway, DNA repair, KRAS, cell cycle checkpoint, NIK, NF-κB signaling pathway, and proteasomes. This study demonstrated that PSMD6 and PSMD11 could serve as a potential prognostic and diagnostic biomarkers for patients with early-stage PDAC after pancreaticoduodenectomy.

Identification of Hub Genes Associated With Hepatocellular Carcinoma Using Robust Rank Aggregation Combined With Weighted Gene Co-expression Network Analysis

Background

Bioinformatics provides a valuable tool to explore the molecular mechanisms underlying pathogenesis of hepatocellular carcinoma (HCC). To improve prognosis of patients, identification of robust biomarkers associated with the pathogenic pathways of HCC remains an urgent research priority.

Methods

We employed the Robust Rank Aggregation method to integrate nine qualified HCC datasets from the Gene Expression Omnibus. A robust set of differentially expressed genes (DEGs) between tumor and normal tissue samples were screened. Weighted gene co-expression network analysis was applied to cluster DEGs and the key modules related to clinical traits identified. Based on network topology analysis, novel risk genes derived from key modules were mined and biological verification performed. The potential functions of these risk genes were further explored with the aid of miRNA–mRNA regulatory networks. Finally, the prognostic ability of these genes was assessed by constructing a clinical prediction model.

Results

Two key modules showed significant association with clinical traits. In combination with protein–protein interaction analysis, 29 hub genes were identified. Among these genes, 19 from one module showed a pattern of upregulation in HCC and were associated with the tumor node metastasis stage, and 10 from the other module displayed the opposite trend. Survival analyses indicated that all these genes were significantly related to patient prognosis. Based on the miRNA-mRNA regulatory network, 29 genes strongly linked to tumor activity were identified. Notably, five of the novel risk genes, ABAT, DAO, PCK2, SLC27A2, and HAO1, have rarely been reported in previous studies. Gene set enrichment analysis for each gene revealed regulatory roles in proliferation and prognosis of HCC. Least absolute shrinkage and selection operator regression analysis further validated DAO, PCK2, and HAO1 as prognostic factors in an external HCC dataset.

Conclusion

Analysis of multiple datasets combined with global network information presents a successful approach to uncover the complex biological mechanisms of HCC. More importantly, this novel integrated strategy facilitates identification of risk hub genes as candidate biomarkers for HCC, which could effectively guide clinical treatments.

Gene signatures and prognostic values of m1A-related regulatory genes in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) ranks fourth in cancer-related mortality worldwide. N1-methyladenosine (m1A), a methylation modification on RNA, is gaining attention for its role across diverse biological processes. However, m1A-related regulatory genes expression, its relationship with clinical prognosis, and its role in HCC remain unclear. In this study, we utilized The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) database to investigate alterations within 10 m1A-related regulatory genes and observed a high mutation frequency (23/363). Cox regression analysis and least absolute shrinkage and selection operator were used to explore the association between m1A-related regulatory genes expression and HCC patient survival and identified four regulators that were remarkably associated with HCC patient prognosis. Additionally, an independent cohort from International Cancer Genome Consortium was studied to validate our discoveries and found to be consistent with those in the TCGA dataset. In terms of mechanism, gene set enrichment analysis linked these four genes with various physiological roles in cell division, the MYC pathway, protein metabolism, and mitosis. Kyoto Encyclopedia of Genes and Genomes analysis revealed that PI3K/Akt signaling pathway had potential relevance to m1A-related regulatory genes in HCC. These findings indicate that m1A-related regulatory genes may play crucial roles in regulating HCC progression and be exploited for diagnostic and prognostic purposes.

A novel molecular-clinicopathologic nomogram to improve prognosis prediction of hepatocellular carcinoma

BACKGROUND

Emerging evidence suggests that long non-coding RNA (lncRNA) plays a crucial part in the development and progress of hepatocellular carcinoma (HCC). The objective was to develop novel molecular-clinicopathological prediction methods for overall survival (OS) and recurrence of HCC.

RESULTS

An 8-lncRNA-based classifier for OS and a 14-lncRNA-based classifier for recurrence were developed by LASSO COX regression analysis, both of which had high accuracy. The tdROC of OS-nomogram and recurrence-nomogram indicates the satisfactory accuracy and predictive power. The classifiers and nomograms for predicting OS and recurrence of HCC were validated in the Test and GEO cohorts.

CONCLUSIONS

These two lncRNA-based classifiers could be independent prognostic factors for OS and recurrence. The molecule-clinicopathological nomograms based on the classifiers could increase the prognostic value.

METHODS

HCC lncRNA expression profiles from the cancer genome atlas (TCGA) were randomly divided into 1:1 training and test cohorts. Based on least absolute shrinkage and selection operator method (LASSO) COX regression model, lncRNA-based classifiers were established to predict OS and recurrence, respectively. OS-nomogram and recurrence-nomogram were developed by combining lncRNA-based classifiers and clinicopathological characterization to predict OS and recurrence, respectively. The prognostic value was accessed by the time-dependent receiver operating characteristic (tdROC) and the concordance index (C-index).