Examining the key genes and pathways in hepatocellular carcinoma development from hepatitis B virus‑positive cirrhosis

Identification of molecular subtypes based on PANoptosis-related genes and construction of a signature for predicting the prognosis and response to immunotherapy response in hepatocellular carcinoma

Background

Previous studies have demonstrated that PANoptosis is strongly correlated with cancer immunity and progression. This study aimed to develop a PANoptosis-related signature (PANRS) to explore its potential value in predicting the prognosis and immunotherapy response of hepatocellular carcinoma (HCC).

Methods

Based on the expression of PANoptosis-related genes, three molecular subtypes were identified. To construct a signature, the differentially expressed genes between different molecular subtypes were subjected to multivariate least absolute shrinkage and selection operator Cox regression analyses. The risk scores of patients in the training set were calculated using the signature. The patients were classified into high-risk and low-risk groups based on the median risk scores. The predictive performance of the signature was evaluated using Kaplan-Meier plotter, receiving operating characteristic curves, nomogram, and calibration curve. The results were validated using external datasets. Additionally, the correlation of the signature with the immune landscape and drug sensitivity was examined. Furthermore, the effect of LPCAT1 knockdown on HCC cell behavior was verified using in vitro experiments.

Results

This study developed a PANRS. The risk score obtained by using the PANRS was an independent risk factor for the prognosis of patients with HCC and exhibited good prognostic predictive performance. The nomogram constructed based on the risk score and clinical information can accurately predicted the survival probability of patients with HCC. Patients with HCC in the high-risk groups have high immune scores and tend to generate an immunosuppressive microenvironment. They also exhibited a favorable response to immunotherapy, as evidenced by high tumor mutational burden, high immune checkpoint gene expression, high human leukocyte antigen gene expression, low tumor immune dysfunction and low exclusion scores. Additionally, the PANRS enabled the identification of 15 chemotherapeutic agents, including sorafenib, for patients with HCC with different risk levels, guiding clinical treatment. The signature gene LPCAT1 was upregulated in HCC cell lines. LPCAT1 knockdown markedly decreased HCC cell proliferation and migration.

Conclusion

PANRS can accurately predict the prognosis and immunotherapy response of patients with HCC and consequently guide individualized treatment.

A systematic study on the treatment of hepatitis B-related hepatocellular carcinoma with drugs based on bioinformatics and key target reverse network pharmacology and experimental verification

BACKGROUND

Chronic hepatitis B virus (HBV) infection is the major etiology of hepatocellular carcinoma (HCC). However, the mechanism of hepatitis B-related hepatocellular carcinoma (HBV-related HCC) is still unclear. Therefore, understanding the pathogenesis and searching for drugs to treat HBV-related HCC was an effective strategy to treat this disease.

PURPOSE

Bioinformatics was used to predict the potential targets of HBV-related HCC. The reverse network pharmacology of key targets was used to analyze the clinical drugs, traditional Chinese medicine (TCM) and small molecules of TCM in the treatment of HBV-related HCC.

METHODS

In this study, three microarray datasets totally containing 330 tumoral samples and 297 normal samples were selected from the GEO database. These microarray datasets were used to screen DEGs. And the expression profile and survival of 6 key genes were analyzed. In addition, Comparative Toxicogenomics Database and Coremine Medical database were used to enrich clinical drugs and TCM of HBV-related HCC by the 6 key targets. Then the obtained TCM were classified based on the Chinese Pharmacopoeia. Among these top 6 key genes, CDK1 and CCNB1 had the most connection nodes and the highest degree and were the most significantly expressed. In general, CDK1 and CCNB1 tend to form a complex, which is conducive to cell mitosis. Hence, this study mainly studied CDK1 and CCNB1. HERB database was used to predict small molecules TCM. The inhibition effect of quercetin, celastrol and cantharidin on HepG2.2.15 cells and Hep3B cells was verified by CCK8 experiment. The effects of quercetin, celastrol and cantharidin on CDK1 and CCNB1 of HepG2.2.15 cells and Hep3B cells were determined by Western Blot.

RESULTS

In short, 272 DEGs (53 upregulated and 219 downregulated) were identified. Among these DEGs, 6 key genes with high degree were identified, which were AURKA, BIRC5, CCNB1, CDK1, CDKN3 and TYMS. Kaplan-Meier plotter analysis showed that higher expression levels of AURKA, BIRC5, CCNB1, CDK1, CDKN3 and TYMS were associated with poor OS. According to the first 6 key targets, a variety of drugs and TCM were identified. These results showed that clinical drugs included targeted drugs, such as sorafenib, palbociclib and Dasatinib. and chemotherapy drugs, such as cisplatin and doxorubicin. TCM, such as the TCM flavor was mainly warm and bitter, and the main meridians were liver and lung. Small molecules of TCM included flavonoids, terpenoids, alkaloids and glycosides, such as quercetin, celastrol, cantharidin, hesperidin, silymarin, casticin, berberine and ursolic acid, which have great potential in anti-HBV-related HCC. For molecular docking of chemical components, the molecules with higher scores were flavonoids, alkaloids, etc. Three representative types of TCM small molecules were verified respectively, and it was found that quercetin, celastrol and cantharidin inhibited the proliferation of HepG2.2.15 cells and Hep3B cells along concentration gradient. Quercetin, celastrol and cantharidin decreased CDK1 expression in HepG2.2.15 and Hep3B cells, but for CCNB1, only cantharidin decreased CCNB1 expression in the two strains of cells.

CONCLUSION

In conclusion, AURKA, BIRC5, CCNB1, CDK1, CDKN3 and TYMS could be potential targets for the diagnosis and prognosis of HBV-related HCC. Clinical drugs include chemotherapeutic and targeted drug, traditional Chinese medicine is mainly bitter and warm TCM. Small molecular of TCM including flavonoids, terpenoids and glycosides and alkaloids, which have great potential in anti-HBV-related HCC. This study provides potential therapeutic targets and novel strategies for the treatment of HBV-related HCC.

Genomic Relevance of FGFR2 on the Prognosis of HCV-Induced Hepatocellular Carcinoma Patients

The Fibroblast Growth Factor Receptors (FGFRs) are known to regulate cancer metabolism in different tumor types, including hepatocellular carcinoma (HCC). Several risk factors are associated with HCC, of which viral infections (Hepatitis B and C) and cirrhosis are prominent. In Pakistan as well as in highly developed countries like the United States, hepatitis C virus HCV infections are most commonly reported in HCC. Here, we aimed to investigate the clinical relevance of FGFR receptors in HCC and their role in HCV-positive HCC cases. 264 HCC samples along with their clinical information and 96 normal liver samples were collected. qPCR was done to estimate the expression of FGFR1, FGFR2, FGFR3 and FGFR4. Three independent HCV-induced HCC cohorts (containing 293 HCC samples) were used for validation. According to in vitro results, FGFR1 was upregulated in HCV+ HCC patients. However, in all three independent cohorts of HCC, significant a down-regulation of FGFR1 was observed. FGFR2 overexpression was observed in the in vitro cohort as well as in three independent HCC cohorts. Interestingly, a strong correlation of FGFR2 expression was observed between cirrhosis and HCV in all four HCC cohorts. Our study suggested that FGFR2 expression can be used to classify HCC patients based on HCV infection. This FGFR2-based classification may lead to new therapeutic strategies against HCV-positive HCC subtypes.

Mechanism of Phellodendron and Anemarrhena Drug Pair on the Treatment of Liver Cancer Based on Network Pharmacology and Bioinformatics

Background

This study aims to explore the key targets and signaling pathways of the traditional Chinese medicine Phellodendron and Anemarrhena drug pair (PADP) for the treatment of liver cancer.

Methods

Firstly, bioinformatics technology was used to analyze GSE62232 gene chip to obtain the differential genes of liver cancer. A network pharmacology technology was used to find the active components of PADP and their targets. Secondly, the differential genes were imported into STRING database to draw a PPI network, and network topology structure map combined with Cytoscape software. And the R language was used to identify differential gene targets and pathways through GO and KEGG pathway enrichment analysis. In addition, AutoDock Vina was used for molecular docking of core targets and core compounds. Moreover, GEPIA online analysis tool was used to perform survival analysis of the core target genes. Finally, RT-PCR was used to verify the changes of key target genes. CCK−8 assay was performed to detect cell proliferation. Flow cytometry was performed to detect the cell cycle and apoptotic. Transwell invasion assay was performed to detect cell invasion.

Results

Firstly, a total of 21,654 genes were obtained. After screening, 1019 differential genes were obtained, including 614 down-regulated genes and 405 up-regulated genes. Furthermore, after screening by ADME standards, 52 active ingredients were obtained, of which 37 were Phellodendron and 15 were Anemarrhena. And a total of 36 differential genes have been identified, including 13 up-regulated genes and 23 down-regulated genes. Moreover, through enrichment analysis, we found that PADP may treat liver cancer through multiple channels and multiple pathways including the p53 signaling pathway, IL-17 signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway and so on. Secondly, the molecular docking results showed that there was certain affinity between the core compounds and core target genes. In addition, GEPIA online analysis showed that ESR1, AR, CCNB1, CDK1, AKR1C3 and CCNA2 might become potential target genes for the survival and prognosis of PADP for the treatment of liver cancer. Finally, it was found that PADP could up regulate genes ESR1 and AR, down regulate genes CCNB1, CDK1, AKR1C3, and CCNA2. PADP could promote the apoptosis of liver cancer cells, shorten the cell cycle, and inhibit the proliferation and invasion of liver cancer cells.

Conclusion

PADP may treat liver cancer through multiple targets, multiple channels, and multiple pathways, thereby suppressing cancer cells and improving the living quality of patients.

Critical Updates on Chronic Hepatitis B Virus Infection in 2021

Chronic hepatitis B virus (HBV) infection is a global healthcare burden in the form of chronic liver disease, cirrhosis, liver failure and liver cancer. There is no definite cure for the virus and even though extensive vaccination programs have reduced the burden of liver disease in the future population, treatment options to eradicate the virus from the host are still lacking. In this review, we discuss in detail current updates on the structure and applied biology of the virus in the host, examine updates to current treatment and explore novel and state-of-the-art therapeutics in the pipeline for management of chronic HBV. Furthermore, we also specifically review clinical updates on HBV-related acute on chronic liver failure (ACLF). Current treatments for chronic HBV infection have seen important updates in the form of considerations for treating patients in the immune tolerant phase and some clarity on end points for treatment and decisions on finite therapy with nucleos(t)ide inhibitors. Ongoing cutting-edge research on HBV biology has helped us identify novel target areas in the life cycle of the virus for application of new therapeutics. Due to improvements in the area of genomics, the hope for therapeutic vaccines, vector-based treatments and focused management aimed at targeting host integration of the virus and thereby a total cure could become a reality in the near future. Newer clinical prognostic tools have improved our understanding of timing of specific treatment options for the catastrophic syndrome of ACLF secondary to reactivation of HBV. In this review, we discuss in detail pertinent updates regarding virus biology and novel therapeutic targets with special focus on the appraisal of prognostic scores and treatment options in HBV-related ACLF.

Identification of hub genes and candidate drugs in hepatocellular carcinoma by integrated bioinformatics analysis

BACKGROUND

Hepatocellular carcinoma (HCC) is the third cancer-related cause of death in the world. Until now, the involved mechanisms during the development of HCC are largely unknown. This study aims to explore the driven genes and potential drugs in HCC.

METHODS

Three mRNA expression datasets were used to analyze the differentially expressed genes (DEGs) in HCC. The bioinformatics approaches include identification of DEGs and hub genes, Gene Ontology terms analysis and Kyoto encyclopedia of genes and genomes enrichment analysis, construction of protein-protein interaction network. The expression levels of hub genes were validated based on The Cancer Genome Atlas, Gene Expression Profiling Interactive Analysis, and the Human Protein Atlas. Moreover, overall survival and disease-free survival analysis of HCC patients were further conducted by Kaplan-Meier plotter and Gene Expression Profiling Interactive Analysis. DGIdb database was performed to search the candidate drugs for HCC.

RESULTS

A total of 197 DEGs were identified. The protein-protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes software, 10 genes were selected by Cytoscape plugin cytoHubba and served as hub genes. These 10 genes were all closely related to the survival of HCC patients. DGIdb database predicted 29 small molecules as the possible drugs for treating HCC.

CONCLUSION

Our study provides some new insights into HCC pathogenesis and treatments. The candidate drugs may improve the efficiency of HCC therapy in the future.

Identification of Key Genes Associated With the Process of Hepatitis B Inflammation and Cancer Transformation by Integrated Bioinformatics Analysis

Background

Hepatocellular carcinoma (HCC) has become the main cause of cancer death worldwide. More than half of hepatocellular carcinoma developed from hepatitis B virus infection (HBV). The purpose of this study is to find the key genes in the transformation process of liver inflammation and cancer and to inhibit the development of chronic inflammation and the transformation from disease to cancer.

Methods

Two groups of GEO data (including normal/HBV and HBV/HBV-HCC) were selected for differential expression analysis. The differential expression genes of HBV-HCC in TCGA were verified to coincide with the above genes to obtain overlapping genes. Then, functional enrichment analysis, modular analysis, and survival analysis were carried out on the key genes.

Results

We identified nine central genes (CDK1, MAD2L1, CCNA2, PTTG1, NEK2) that may be closely related to the transformation of hepatitis B. The survival and prognosis gene markers composed of PTTG1, MAD2L1, RRM2, TPX2, CDK1, NEK2, DEPDC1, and ZWINT were constructed, which performed well in predicting the overall survival rate.

Conclusion

The findings of this study have certain guiding significance for further research on the transformation of hepatitis B inflammatory cancer, inhibition of chronic inflammation, and molecular targeted therapy of cancer.

Integrated Transcriptomics and Reverse Pharmacophore Mapping-based Network Pharmacology to Explore the Mechanisms of Natural Compounds against Doxorubicin-induced Cardiotoxicity

BACKGROUND

Doxorubicin-induced cardiotoxicity (DIC) has greatly limited the clinical benefits of this frontline drug in oncotherapy. Drug combination with natural compounds (NCs) that possess potency against DIC is considered as a promising intervention strategy. However, the mechanisms of action (MoAs) underlying such drug interactions remain poorly understood. The aim of this study was to systematically pursuit of the molecular mechanisms of NCs against DIC.

METHODS

First, the gene expression signatures of DIC were characterized from transcriptomics datasets with doxorubicin-treated and untreated cardiomyocytes using differentially expressed gene identification, functional enrichment analysis, and protein-protein interaction network analysis. Secondly, reverse pharmacophore mapping-based network pharmacology was employed to illustrate the MoAs of 82 publicly reported NCs with anti-DIC potency. Cluster analysis based on their enriched pathways was performed to gain systematic insights into the anti-DIC mechanisms of the NCs. Finally, the typical compounds were validated using gene set enrichment analysis (GSEA) of the relevant gene expression profiles from a public gene expression database.

RESULTS

Based on their anti-DIC MoAs, the 82 NCs could be divided into four groups, which corresponded to ten MoA clusters. GSEA and literature evidence on these compounds were provided to validate the MoAs identified through this bioinformatics analysis. The results suggested that NCs exerted potency against DIC through both common and different MoAs.

CONCLUSION

This strategy integrating different types of bioinformatics approaches is expected to create new insights for elucidating the MoAs of NCs against DIC.

Host pharmacogenetic factors that may affect liver neoplasm incidence upon using direct-acting antivirals for treating hepatitis C infection

Introduction

Direct-acting antivirals (DAAs) represent a breakthrough in hepatitis C virus (HCV) treatment as they directly inhibit HCV nonstructural (NS) proteins (NS3/4A, NS5A, and NS5B). However, ongoing debates exist regarding their relationship with hepatocellular carcinoma (HCC) whose incidence is widely debated among investigators. This study was conducted to identify host pharmacogenetic factors that may influence HCC incidence upon using HCV DAAs.

Materials and methods

Details regarding 16 HCV DAAs were collected from literature and DrugBank database. Digital structures of these drugs were fed into the pharmacogenomics/pharmacovigilance in-silico pipeline (PHARMIP) to predict the genetic factors that may underpin HCC development.

Results

We identified 184 unique genes and 40 unique variants that may have key answers for the DAA/HCC paradox. These findings could be used in different methods to aid in the precise application of HCV DAAs and minimize the proposed risk for HCC. All results could be accessed at: https://doi.org/10.17632/8ws8258hn3.2.

Discussion

All the identified factors are evidence related to HCC and significantly predicted by PHARMIP as DAA targets. We discuss some examples of the methods of using these results to address the DAA/HCC controversy based on the following three primary levels: 1 - individual DAA drug, 2 - DAA subclass, and 3 - the entire DAA class. Further wet laboratory investigation is required to evaluate these results.

Recent Advances in Understanding, Diagnosing, and Treating Hepatitis B Virus Infection

Chronic hepatitis B virus (HBV) infection affects 292 million people worldwide and is associated with a broad range of clinical manifestations including cirrhosis, liver failure, and hepatocellular carcinoma (HCC). Despite the availability of an effective vaccine HBV still causes nearly 900,000 deaths every year. Current treatment options keep HBV under control, but they do not offer a cure as they cannot completely clear HBV from infected hepatocytes. The recent development of reliable cell culture systems allowed for a better understanding of the host and viral mechanisms affecting HBV replication and persistence. Recent advances into the understanding of HBV biology, new potential diagnostic markers of hepatitis B infection, as well as novel antivirals targeting different steps in the HBV replication cycle are summarized in this review article.

Integrated Bioinformatic Analysis Identifies Networks and Promising Biomarkers for Hepatitis B Virus-Related Hepatocellular Carcinoma

Chronic infection with hepatitis B virus (HBV) has long been recognized as a dominant hazard factor for hepatocellular carcinoma (HCC) and accounts for at least half of HCC instances globally. However, the underlying molecular mechanism of HBV-linked HCC has not been completely elucidated. Here, three microarray datasets, totally containing 170 tumoral samples and 181 adjacent normal tissues from the liver of patients suffering from HBV-related HCC assembled from the Gene Expression Omnibus (GEO) database, were subjected to integrated analysis of differentially expressed genes (DEGs). Subsequently, the analysis of function and pathway enrichment as well as the protein-protein interaction network (PPI) was performed. The ten hub genes screened out from the PPI network were further subjected to expression profile and survival analysis. Overall, 329 DEGs (67 upregulated and 262 downregulated) were identified. Ten DEGs with the highest degree of connectivity included cyclin-dependent kinase 1 (CDK1), cyclin B1 (CCNB1), cyclin B2 (CCNB2), PDZ-binding kinase (PBK), abnormal spindle microtubule assembly (ASPM), nuclear division cycle 80 (NDC80), aurora kinase A (AURKA), targeting protein for xenopus kinesin-like protein 2 (TPX2), kinesin family member 2C (KIF2C), and centromere protein F (CENPF). Kaplan-Meier analysis unveiled that overexpression levels of KIF2C and TPX2 were relevant to both the poor overall survival and relapse-free survival. In summary, the hub genes validated in the present study may provide promising targets for the diagnosis, prognosis, and therapy of HBV-associated HCC. Additionally, our work uncovers various crucial biological components (e.g., extracellular exosome) and signaling pathways that participate in the progression of HCC induced by HBV, serving comprehensive knowledge of the mechanisms regarding HBV-related HCC.

ASTN1 is associated with immune infiltrates in hepatocellular carcinoma, and inhibits the migratory and invasive capacity of liver cancer via the Wnt/β‑catenin signaling pathway

Astrotactin 1 (ASTN1) is known to serve a physiological role in neuronal migration; however its role in liver cancer remains to be determined. In the present study, ASTN1 levels were lower in liver cancer tissues compared with those in matching normal tissue. ASTN1 levels were negatively associated with microscopic vascular invasion, advanced clinical stage and a less favorable prognosis in patients with hepatocellular carcinoma (HCC). Furthermore, ASTN1 overexpression in a liver cancer cell line reduced the migratory and invasive capacity of the cells. Based on bioinformatics analysis, ASTN1 levels were negatively associated with the Wnt signaling pathway. In addition, ASTN1 downregulated the protein expression levels of β-catenin, T-cell factor (TCF)1, TCF4, Jun proto-oncogene (C-jun), Myc proto-oncogene (C-myc), cyclooxygenase-2 (COX2), metalloproteinase (MMP)2, MMP9 and vascular endothelial growth factor (VEGF) protein levels, indicative of suppression of Wnt signaling. Furthermore, XAV939-induced Wnt signaling suppression reversed the ASTN1-mediated inhibition of invasion and migration in cells. Overexpression of ASTN1 in xenografts reduced cancer development as well as Wnt signaling. TIMER analysis showed that ASTN1 expression was negatively correlated with B cell, macrophage and neutrophil infiltrating levels in HCC. Together, the results of the present study showed that ASTN1 reduced the migratory and invasive capacity of liver cancer cells, potentially served as a candidate biomarker for diagnosis and prediction of the prognosis of HCC, and was associated with immune infiltration. Understanding the underlying mechanisms of action of ASTN1 may facilitate the development of novel strategies for prevention and treatment of liver cancer.

Bioinformatics analysis reveals meaningful markers and outcome predictors in HBV-associated hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of malignant neoplasm of the liver with high morbidity and mortality. Extensive research into the pathology of HCC has been performed; however, the molecular mechanisms underlying the development of hepatitis B virus-associated HCC have remained elusive. Thus, the present study aimed to identify critical genes and pathways associated with the development and progression of HCC. The expression profiles of the GSE121248 dataset were downloaded from the Gene Expression Omnibus database and the differentially expressed genes (DEGs) were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) analyses were performed by using the Database for Annotation, Visualization and Integrated Discovery. Subsequently, protein-protein interaction (PPI) networks were constructed for detecting hub genes. In the present study, 1,153 DEGs (777 upregulated and 376 downregulated genes) were identified and the PPI network yielded 15 hub genes. GO analysis revealed that the DEGs were primarily enriched in ‘protein binding’, ‘cytoplasm’ and ‘extracellular exosome’. KEGG analysis indicated that DEGs were accumulated in ‘metabolic pathways’, ‘chemical carcinogenesis’ and ‘fatty acid degradation’. After constructing the PPI network, cyclin-dependent kinase 1, cyclin B1, cyclin A2, mitotic arrest deficient 2 like 1, cyclin B2, DNA topoisomerase IIα, budding uninhibited by benzimidazoles (BUB)1, TTK protein kinase, non-SMC condensin I complex subunit G, NDC80 kinetochore complex component, aurora kinase A, kinesin family member 11, cell division cycle 20, BUB1B and abnormal spindle microtubule assembly were identified as hub genes based on the high degree of connectivity by using Cytoscape software. In addition, overall survival (OS) and disease-free survival (DFS) analyses were performed using the Gene Expression Profiling Interactive Analysis online database, which revealed that the increased expression of all hub genes were associated with poorer OS and DFS outcomes. Receiver operating characteristic curves were constructed using GraphPad prism 7.0 software. The results confirmed that 15 hub genes were able to distinguish HCC form normal tissues. Furthermore, the expression levels of three key genes were analyzed in tumor and normal samples of the Human Protein Atlas database. The present results may provide further insight into the underlying mechanisms of HCC and potential therapeutic targets for the treatment of this disease.

Identification of potential key genes and pathways in hepatitis B virus-associated hepatocellular carcinoma by bioinformatics analyses

Chronic hepatitis B virus (HBV) is one of the leading causes of hepatocellular carcinoma (HCC). The precise molecular mechanisms by which HBV contributes to HCC development are not fully understood. The key genes and pathways involved in the transformation of nontumor hepatic tissues into HCC tissues in patients with HBV infection are essential to guide the treatment of HBV-associated HCC. Five datasets were collected from the Gene Expression Omnibus database to form a large cohort. Differentially expressed genes (DEGs) were identified between HCC tissues and nontumor hepatic tissues from HBV-infected patients using the ‘limma’ package. The top 50 upregulated and top 50 downregulated DEGs in HCC vs. nontumor tissues were demonstrated in subsets by heat maps. Based on the DEGs, Gene Ontology functional and Kyoto Encyclopedia of Genes and Genomes pathways enrichment analyses were performed. Several key pathways of the up- and downregulated DEGs were identified and presented by protein-protein interaction (PPI) networks. A total of 1,934 DEGs were identified. The upregulated DEGs were primarily associated with the ‘cell cycle’. Among the DEGs enriched in the ‘cell cycle’ pathway, 6 genes had a log₂-fold change >2: SFN, BUB1B, TTK, CCNB1, CDK1 and CDC20. The downregulated DEGs were primarily associated with the metabolic pathways, such as ‘carbon metabolism’, ‘glycine, serine and threonine metabolism’, ‘tryptophan metabolism’, ‘retinol metabolism’ and ‘alanine, aspartate and glutamate metabolism’. The DEGs in the ‘cell cycle’ and ‘metabolic pathways’ were presented by the PPI networks respectively. Overall, the present study provides new insights into the specific etiology of HCC and molecular mechanisms for the transformation of nontumor hepatic tissues into HCC tissues in patients with a history of HBV infection and several potential therapeutic targets for targeted therapy in these patients.

BUB1 promotes proliferation of liver cancer cells by activating SMAD2 phosphorylation

Budding uninhibited by benzimidazoles 1 (BUB1) is a mitotic checkpoint serine/threonine kinase that has been reported as an oncogene or tumor suppressor gene in various types of cancer, including breast cancer, pancreatic ductal adenocarcinoma, prostate and gastric cancers. However, its role in liver cancer remains unclear. The present study aimed to explore the biological function of BUB1 in liver cancer. The present study demonstrated that BUB1 mRNA expression levels and the intensity of immunohistochemical staining were significantly increased in liver cancer tissues compared with normal tissues. The role of BUB1 in cell proliferation was also determined. Overexpression of BUB1 significantly promoted cell proliferation, whereas knockdown of BUB1 expression inhibited the proliferation of liver cancer cell lines. In experiments investigating the underlying mechanism, overexpression of BUB1 increased the levels of SMAD2 phosphorylation, whereas knockdown of BUB1 reduced the levels of SMAD2 phosphorylation. Therefore, BUB1 may promote proliferation of liver cancer cells by activating phosphorylation of SMAD2, and BUB1 may serve as a potential target in the diagnosis and/or treatment of liver cancer.

Identification of potential hub genes related to the progression and prognosis of hepatocellular carcinoma through integrated bioinformatics analysis

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related deaths among cancer patients. Genes correlated with the progression and prognosis of HCC are critically needed to be identified. In the present study, 3 Gene Expression Omnibus (GEO) datasets (GSE46408, GSE65372 and GSE84402) were used to analyze the differentially expressed genes (DEGs) between HCC and non-tumor liver tissues. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to clarify the functional roles of DEGs. A protein-protein interaction network was established to screen the hub genes associated with HCC. The prognostic values of hub genes in HCC patients were analyzed using The Cancer Genome Atlas (TCGA) database. The expression levels of hub genes were validated based on ONCOMINE, TCGA and Human Protein Atlas (HPA) databases. Notably, 56 upregulated and 33 downregulated DEGs were markedly enriched under various GO terms and four KEGG terms. Among these DEGs, 10 hub genes with high connectivity degree were identified, including cyclin B1, cyclin A2, cyclin B2, condensin complex subunit 3, PDZ binding kinase, nucleolar and spindle-associated protein 1, aurora kinase A, ZW10 interacting kinetochore protein, protein regulator of cytokinesis 1 and kinesin family member 4A. The upregulated expression levels of these hub genes in HCC tissues were further confirmed by ONCOMINE, TCGA, and HPA databases. Additionally, the increased mRNA expression of each hub gene was related to the unfavorable disease-free survival and overall survival of HCC patients. The present study identified ten genes associated with HCC, which may help to provide candidate targets for the diagnosis and treatment of HCC.