Dataset Information

Integrative analysis of highly mutated genes in hepatitis B virus-related hepatic carcinoma.

ABSTRACT: Gene mutation is responsible for the development of hepatocellular carcinoma (HCC) with hepatitis B virus (HBV) infection; however, the characteristics and associated biological functions of highly mutated genes, in which the mutation frequencies are at least 5% in HCC patients with HBV infection, are not clearly evaluated. In the study, we analyzed the information regarding somatic mutation obtained by whole-exome sequencing in 280 HBV-related HCC tissues from public databases and published studies. Via integrative analysis, 78 genes, including TP53, TTN, MUC16, CTNNB1, and PCLO were summarized as highly mutated genes, and some of these mutated genes were further identified as cancer driver genes. Besides, we discovered that the highly mutated genes were enriched with various biological functions and pathways. The expression of many of highly mutated genes was found to be significantly altered in HBV-related HCC, and several highly mutated genes were related to a variety of clinical factors and associated with the poor survival of the disease. Taken together, these results could enrich our understanding of highly mutated genes and their relationships with HBV-related HCC. Some of the identified highly mutated genes might be used as novel biomarkers of disease prognosis, or as molecular targets for the treatment of HCC with HBV infection.

SUBMITTER: Kong F

PROVIDER: S-EPMC7131865 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Integrative analysis of highly mutated genes in hepatitis B virus-related hepatic carcinoma.

Kong Fanyun F Kong Delong D Yang Xiaoying X Yuan Dongchen D Zhang Ning N Hua Xuan X You Hongjuan H Zheng Kuiyang K Tang Renxian R

Cancer medicine 20200204 7

Gene mutation is responsible for the development of hepatocellular carcinoma (HCC) with hepatitis B virus (HBV) infection; however, the characteristics and associated biological functions of highly mutated genes, in which the mutation frequencies are at least 5% in HCC patients with HBV infection, are not clearly evaluated. In the study, we analyzed the information regarding somatic mutation obtained by whole-exome sequencing in 280 HBV-related HCC tissues from public databases and published stu ...[more]

PMID: 32017470

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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:Hepatocellular carcinoma (HCC) is a common malignant cancer with poor survival outcomes, and hepatitis B virus (HBV) infection is most likely to contribute to HCC. But the molecular mechanism remains obscure. Our study intended to identify the candidate potential hub genes associated with the carcinogenesis of HBV-related HCC (HBV-HCC), which may be helpful in developing novel tumor biomarkers for potential targeted therapies. Four transcriptome datasets (GSE84402, GSE25097, GSE94660, and GSE121248) were used to screen the 309 overlapping differentially expressed genes (DEGs), including 100 upregulated genes and 209 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were used to explore the biological function of DEGs. A PPI network based on the STRING database was constructed and visualized by the Cytoscape software, consisting of 209 nodes and 1676 edges. Then, we recognized 17 hub genes by CytoHubba plugin, which were further validated on additional three datasets (GSE14520, TCGA-LIHC, and ICGC-LIRI-JP). The diagnostic effectiveness of hub genes was assessed with receiver operating characteristic (ROC) analysis, and all hub genes displayed good performance in discriminating TNM stage I patient samples and normal tissue ones. For prognostic analysis, two prognostic key genes (TOP2A and KIF11) out of the 17 hub genes were screened and used to develop a prognostic signature, which showed good potential for overall survival (OS) stratification of HBV-HCC patients. Gene Set Enrichment Analysis (GSEA) was performed in order to better understand the function of this prognostic gene signature. Finally, the miRNA-mRNA regulatory relationships of all hub genes in human liver were predicted using miRNet. In conclusion, the current study gives further insight on the pathogenesis and carcinogenesis of HBV-HCC, and the identified DEGs provide a promising direction for improving the diagnostic, prognostic, and therapeutic outcomes of HBV-HCC.

Project description:Chronic infections by hepatitis B virus (HBV) and hepatitis C virus (HCV) appear to be the most significant causes of hepatocellular carcinoma (HCC). Aberrant promoter methylation is known to be deeply involved in cancer, including HCC. In this study, we analyzed aberrant promoter methylation on genome-wide scale in 6 HCCs including 3 HBV-related and 3 HCV-related HCCs, 6 matched noncancerous liver tissues and 3 normal liver tissues by methylated DNA immunoprecipitation-on-chip analysis. Candidate genes with promoter methylation were detected more frequently in HCV-related HCC. Candidate genes methylated preferentially to HBV-related or HCV-related HCCs were detected and selected, and methylation levels of the selected genes were validated using 125 liver tissue samples, including 61 HCCs (28 HBV-related HCCs and 33 HCV-related HCCs) and matched 59 matched noncancerous livers, and 5 normal livers, by quantitative methylation analysis using MALDI-TOF mass spectrometry. Among analyzed genes, preferential methylation in HBV-related HCC was validated in 1 gene only. However, 15 genes were found methylated preferentially in HCV-related HCC, which was independent from age. Hierarchical clustering of HCC using these 15 genes stratified HCV-related HCC as a cluster of frequently methylated samples. The 15 genes included genes inhibitory to cancer-related signaling such as RAS/RAF/ERK and Wnt/b-catenin pathways. It was indicated that genes methylated preferentially in HCV-related HCC exist, and it was suggested that DNA methylation might play an important role in HCV-related HCC by silencing cancer-related pathway inhibitors. we analyzed aberrant promoter methylation in 6 HCC clinical samples (including 3 HBV-related HCCs and 3 HCV-related HCCs) and their matched noncancerous tissues on genome-wide scale by the method. Candidate regions of promoter methylation preferentially to HBV-related HCC and HCV-related HCC were selected, and the methylation levels of these genes were measured quantitatively using MALDI-TOF mass spectrometry. Expression levels of these 6 pairs of HCC and 4 more pairs of HCCs and surrounding noncancerous tissues were analyzed by expression array and are reported in this Series. <br><br>This experiment was reloaded in November 2010 after additional curation. this dataset is part of the TransQST collection.

Project description:BackgroundThe majority of primary liver cancers in adults worldwide are hepatocellular carcinomas (HCCs, or hepatomas). Thus, a deep understanding of the underlying mechanisms for the pathogenesis and carcinogenesis of HCC at the molecular level could facilitate the development of novel early diagnostic and therapeutic treatments to improve the approaches and prognosis for HCC patients. Our study elucidates the underlying molecular mechanisms of HBV-HCC development and progression and identifies important genes related to the early diagnosis, tumour stage, and poor outcomes of HCC.MethodsGSE55092 and GSE121248 gene expression profiling data were downloaded from the Gene Expression Omnibus (GEO) database. There were 119 HCC samples and 128 nontumour tissue samples. GEO2R was used to screen for differentially expressed genes (DEGs). Volcano plots and Venn diagrams were drawn by using the ggplot2 package in R. A heat map was generated by using Heatmapper. By using the clusterProfiler R package, KEGG and GO enrichment analyses of DEGs were conducted. Through PPI network construction using the STRING database, key hub genes were identified by cytoHubba. Finally, KM survival curves and ROC curves were generated to validate hub gene expression.ResultsBy GO enrichment analysis, 694 DEGs were enriched in the following GO terms: organic acid catabolic process, carboxylic acid catabolic process, carboxylic acid biosynthetic process, collagen-containing extracellular matrix, blood microparticle, condensed chromosome kinetochore, arachidonic acid epoxygenase activity, arachidonic acid monooxygenase activity, and monooxygenase activity. In the KEGG pathway enrichment analysis, DEGs were enriched in arachidonic acid epoxygenase activity, arachidonic acid monooxygenase activity, and monooxygenase activity. By PPI network construction and analysis of hub genes, we selected the top 10 genes, including CDK1, CCNB2, CDC20, BUB1, BUB1B, CCNB1, NDC80, CENPF, MAD2L1, and NUF2. By using TCGA and THPA databases, we found five genes, CDK1, CDC20, CCNB1, CENPF, and MAD2L1, that were related to the early diagnosis, tumour stage, and poor outcomes of HBV-HCC.ConclusionsFive abnormally expressed hub genes of HBV-HCC are informative for early diagnosis, tumour stage determination, and poor outcome prediction.

Dataset Information

Integrative analysis of highly mutated genes in hepatitis B virus-related hepatic carcinoma.

Publications

Integrative analysis of highly mutated genes in hepatitis B virus-related hepatic carcinoma.

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