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

ABSTRACT: To identify the key genes and pathways in the development of hepatocellular carcinoma (HCC) from hepatitis B virus (HBV)?positive liver cirrhosis, differentially expressed genes (DEGs) between HCC and liver cirrhosis tissue samples from the GSE17548 gene expression profile dataset were screened. A total of 1,845 DEGs were identified, including 1,803 upregulated and 42 downregulated genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein?protein interaction (PPI) network analyses were performed. It was identified that the 'cell cycle' and 'progesterone?mediated oocyte maturation' KEGG pathways were significantly enriched in the DEGs. In addition, the high expression of the hub genes from the PPI network (including cyclin dependent kinase 1, cyclin B1, cyclin B2, mitotic arrest deficient 2 like 1, BUB1 mitotic checkpoint serine/threonine kinase and cyclin A2; P=0.00116, 0.00021, 0.04889, 0.00222, 0.00015 and 0.00647, respectively) was associated with a decrease in overall survival for patients with HCC as identified using survival and expression data from The Cancer Genome Atlas. The identified hub genes and pathways may help to elucidate the molecular mechanisms of HCC progression from HBV?positive liver cirrhosis. Additionally, they may be useful as therapeutic targets or serve as novel biomarkers for HCC prognosis prediction.

SUBMITTER: Chen QF

PROVIDER: S-EPMC6236263 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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

Chen Qi-Feng QF Xia Jin-Guo JG Li Wang W Shen Lu-Jun LJ Huang Tao T Wu Peihong P

Molecular medicine reports 20180919 6

To identify the key genes and pathways in the development of hepatocellular carcinoma (HCC) from hepatitis B virus (HBV)‑positive liver cirrhosis, differentially expressed genes (DEGs) between HCC and liver cirrhosis tissue samples from the GSE17548 gene expression profile dataset were screened. A total of 1,845 DEGs were identified, including 1,803 upregulated and 42 downregulated genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein‑protein interaction (PPI) network ...[more]

PMID: 30272310

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Project description:BackgroundDespite the recent identification of several prognostic gene signatures, the lack of common genes among experimental cohorts has posed a considerable challenge in uncovering the molecular basis underlying hepatocellular carcinoma (HCC) recurrence for application in clinical purposes. To overcome the limitations of individual gene-based analysis, we applied a pathway-based approach for analysis of HCC recurrence.ResultsBy implementing a permutation-based semi-supervised principal component analysis algorithm using the optimal principal component, we selected sixty-four pathways associated with hepatitis B virus (HBV)-positive HCC recurrence (p < 0.01), from our microarray dataset composed of 142 HBV-positive HCCs. In relation to the public HBV- and public hepatitis C virus (HCV)-positive HCC datasets, we detected 46 (71.9%) and 18 (28.1%) common recurrence-associated pathways, respectively. However, overlap of recurrence-associated genes between datasets was rare, further supporting the utility of the pathway-based approach for recurrence analysis between different HCC datasets. Non-supervised clustering of the 64 recurrence-associated pathways facilitated the classification of HCC patients into high- and low-risk subgroups, based on risk of recurrence (p < 0.0001). The pathways identified were additionally successfully applied to discriminate subgroups depending on recurrence risk within the public HCC datasets. Through multivariate analysis, these recurrence-associated pathways were identified as an independent prognostic factor (p < 0.0001) along with tumor number, tumor size and Edmondson's grade. Moreover, the pathway-based approach had a clinical advantage in terms of discriminating the high-risk subgroup (N = 12) among patients (N = 26) with small HCC (<3 cm).ConclusionsUsing pathway-based analysis, we successfully identified the pathways involved in recurrence of HBV-positive HCC that may be effectively used as prognostic markers.

Project description:BackgroundIncidence of hepatitis C virus (HCV) induced hepatocellular carcinoma (HCC) has been increasing in the United States and Europe during recent years. Although HCV-associated HCC shares many pathological characteristics with other types of HCC, its molecular mechanisms of progression remain elusive.MethodsTo investigate the underlying pathology, we developed a systematic approach to identify deregulated biological networks in HCC by integrating gene expression profiles with high-throughput protein-protein interaction data. We examined five stages including normal (control) liver, cirrhotic liver, dysplasia, early HCC and advanced HCC.ResultsAmong the five consecutive pathological stages, we identified four networks including precancerous networks (Normal-Cirrhosis and Cirrhosis-Dysplasia) and cancerous networks (Dysplasia-Early HCC, Early-Advanced HCC). We found little overlap between precancerous and cancerous networks, opposite to a substantial overlap within precancerous or cancerous networks. We further found that the hub proteins interacted with HCV proteins, suggesting direct interventions of these networks by the virus. The functional annotation of each network demonstrates a high degree of consistency with current knowledge in HCC. By assembling these functions into a module map, we could depict the stepwise biological functions that are deregulated in HCV-induced hepatocarcinogenesis. Additionally, these networks enable us to identify important genes and pathways by developmental stage, such as LCK signalling pathways in cirrhosis, MMP genes and TIMP genes in dysplastic liver, and CDC2-mediated cell cycle signalling in early and advanced HCC. CDC2 (alternative symbol CDK1), a cell cycle regulatory gene, is particularly interesting due to its topological position in temporally deregulated networks.ConclusionsOur study uncovers a temporal spectrum of functional deregulation and prioritizes key genes and pathways in the progression of HCV induced HCC. These findings present a wealth of information for further investigation.

Project description:Background:The molecular mechanism by which hepatitis B virus (HBV) induces hepatocellular carcinoma (HCC) is still unknown. The genomic expression profile and bioinformatics methods were used to investigate the potential pathogenesis and therapeutic targets for HBV-associated HCC (HBV-HCC). Methods:The microarray dataset GSE55092 was downloaded from the Gene Expression Omnibus (GEO) database. The data was analyzed by the bioinformatics software to find differentially expressed genes (DEGs). Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, ingenuity pathway analysis (IPA), and protein-protein interaction (PPI) network analysis were then performed on DEGs. The hub genes were identified using Centiscape2.2 and Molecular Complex Detection (MCODE) in the Cytoscape software (Cytoscape_v3.7.2). The survival data of these hub genes was downloaded from the Gene Expression Profiling Interactive Analysis (GEPIA). Results:A total of 2264 mRNA transcripts were differentially expressed, including 764 upregulated and 1500 downregulated in tumor tissues. GO analysis revealed that these DEGs were related to the small-molecule metabolic process, xenobiotic metabolic process, and cellular nitrogen compound metabolic process. KEGG pathway analysis revealed that metabolic pathways, complement and coagulation cascades, and chemical carcinogenesis were involved. Diseases and biofunctions showed that DEGs were mainly associated with the following diseases or biological function abnormalities: cancer, organismal injury and abnormalities, gastrointestinal disease, and hepatic system disease. The top 10 upstream regulators were predicted to be activated or inhibited by Z-score and identified 25 networks. The 10 genes with the highest degree of connectivity were defined as the hub genes. Cox regression revealed that all the 10 genes (CDC20, BUB1B, KIF11, TTK, EZH2, ZWINT, NDC80, TPX2, MELK, and KIF20A) were related to the overall survival. Conclusion:Our study provided a registry of genes that play important roles in regulating the development of HBV-HCC, assisting us in understanding the molecular mechanisms that underlie the carcinogenesis and progression of HCC.

Dataset Information

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

Publications

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

Similar Datasets

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