scholarly journals Identification of Potential Hub Genes Related to Diagnosis and Prognosis of Hepatitis B Virus-Related Hepatocellular Carcinoma via Integrated Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Yuqin Tang ◽  
Yongqiang Zhang ◽  
Xun Hu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Hub Genes ◽  
Diagnosis And Prognosis ◽  
Prognostic Analysis ◽  
B Virus

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.

scholarly journals Identification of a five-gene signature in association with overall survival for hepatocellular carcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11273
Author(s):  
Lei Yang ◽  
Weilong Yin ◽  
Xuechen Liu ◽  
Fangcun Li ◽  
Li Ma ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Overall Survival ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Enrichment Score ◽  
Hub Genes ◽  
Cox Regression Analysis

Background Hepatocellular carcinoma (HCC) is considered to be a malignant tumor with a high incidence and a high mortality. Accurate prognostic models are urgently needed. The present study was aimed at screening the critical genes for prognosis of HCC. Methods The GSE25097, GSE14520, GSE36376 and GSE76427 datasets were obtained from Gene Expression Omnibus (GEO). We used GEO2R to screen differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed by Cytoscape in order to find hub genes by module analysis. The Metascape was performed to discover biological functions and pathway enrichment of DEGs. MCODE components were calculated to construct a module complex of DEGs. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. ONCOMINE was employed to assess the mRNA expression levels of key genes in HCC, and the survival analysis was conducted using the array from The Cancer Genome Atlas (TCGA) of HCC. Then, the LASSO Cox regression model was performed to establish and identify the prognostic gene signature. We validated the prognostic value of the gene signature in the TCGA cohort. Results We screened out 10 hub genes which were all up-regulated in HCC tissue. They mainly enrich in mitotic cell cycle process. The GSEA results showed that these data sets had good enrichment score and significance in the cell cycle pathway. Each candidate gene may be an indicator of prognostic factors in the development of HCC. However, hub genes expression was weekly associated with overall survival in HCC patients. LASSO Cox regression analysis validated a five-gene signature (including CDC20, CCNB2, NCAPG, ASPM and NUSAP1). These results suggest that five-gene signature model may provide clues for clinical prognostic biomarker of HCC.

scholarly journals Dynamic edge-based biomarker non-invasively predicts hepatocellular carcinoma with hepatitis B virus infection for individual patients based on blood testing

2019 ◽  
Vol 11 (8) ◽  
pp. 665-677 ◽  
Author(s):  
Yiyu Lu ◽  
Zhaoyuan Fang ◽  
Meiyi Li ◽  
Qian Chen ◽  
Tao Zeng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Candidate Genes ◽  
Mononuclear Cells ◽  
Prediction Models ◽  
Non Invasive ◽  
Diagnosis And Prognosis ◽  
B Virus ◽  
Edge Based

Abstract Hepatitis B virus (HBV)-induced hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths in Asia and Africa. Developing effective and non-invasive biomarkers of HCC for individual patients remains an urgent task for early diagnosis and convenient monitoring. Analyzing the transcriptomic profiles of peripheral blood mononuclear cells from both healthy donors and patients with chronic HBV infection in different states (i.e. HBV carrier, chronic hepatitis B, cirrhosis, and HCC), we identified a set of 19 candidate genes according to our algorithm of dynamic network biomarkers. These genes can both characterize different stages during HCC progression and identify cirrhosis as the critical transition stage before carcinogenesis. The interaction effects (i.e. co-expressions) of candidate genes were used to build an accurate prediction model: the so-called edge-based biomarker. Considering the convenience and robustness of biomarkers in clinical applications, we performed functional analysis, validated candidate genes in other independent samples of our collected cohort, and finally selected COL5A1, HLA-DQB1, MMP2, and CDK4 to build edge panel as prediction models. We demonstrated that the edge panel had great performance in both diagnosis and prognosis in terms of precision and specificity for HCC, especially for patients with alpha-fetoprotein-negative HCC. Our study not only provides a novel edge-based biomarker for non-invasive and effective diagnosis of HBV-associated HCC to each individual patient but also introduces a new way to integrate the interaction terms of individual molecules for clinical diagnosis and prognosis from the network and dynamics perspectives.

scholarly journals Screening and Functional Prediction of Key Candidate Genes in Hepatitis B Virus-Associated Hepatocellular Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xia Chen ◽  
Ling Liao ◽  
Yuwei Li ◽  
Hengliu Huang ◽  
Qing Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Metabolic Process ◽  
Hub Genes ◽  
Kegg Pathway Analysis ◽  
B Virus

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.

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

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Yun Ji ◽  
Yue Yin ◽  
Weizhen Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Interaction Network ◽  
Cyclin B1 ◽  
Microtubule Assembly ◽  
Integrated Analysis ◽  
Hub Genes ◽  
Poor Overall Survival ◽  
B Virus

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.

scholarly journals Characterization of Hepatitis B Virus Integrations Identified in Hepatocellular Carcinoma Genomes

Viruses ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 245
Author(s):  
Pranav Mathkar ◽  
Xun Chen ◽  
Arvis Sulovari ◽  
Dawei Li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Accurate Identification ◽  
Diagnostic Potential ◽  
B Virus ◽  
Integration Sites

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality. Almost half of HCC cases are associated with hepatitis B virus (HBV) infections, which often lead to HBV sequence integrations in the human genome. Accurate identification of HBV integration sites at a single nucleotide resolution is critical for developing a better understanding of the cancer genome landscape and of the disease itself. Here, we performed further analyses and characterization of HBV integrations identified by our recently reported VIcaller platform in recurrent or known HCC genes (such as TERT, MLL4, and CCNE1) as well as non-recurrent cancer-related genes (such as CSMD2, NKD2, and RHOU). Our pathway enrichment analysis revealed multiple pathways involving the alcohol dehydrogenase 4 gene, such as the metabolism pathways of retinol, tyrosine, and fatty acid. Further analysis of the HBV integration sites revealed distinct patterns involving the integration upper breakpoints, integrated genome lengths, and integration allele fractions between tumor and normal tissues. Our analysis also implies that the VIcaller method has diagnostic potential through discovering novel clonal integrations in cancer-related genes. In conclusion, although VIcaller is a hypothesis free virome-wide approach, it can still be applied to accurately identify genome-wide integration events of a specific candidate virus and their integration allele fractions.

scholarly journals Bioinformatic Identification and Validation of Key Genes and Biological Pathways Involved in Hepatitis B Virus-related Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Guan-Hua Ren ◽  
Fan-Biao Mei ◽  
Chao-Jun Zhang ◽  
Dong-Mei Cai ◽  
Long Long ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Disease Free Survival ◽  
The Cancer Genome Atlas ◽  
Hub Genes ◽  
Chronic Hepatitis B Virus ◽  
B Virus ◽  
Key Genes

Abstract Objectives: Hepatocellular carcinoma (HCC) is a common malignant tumor severely scathing human health. As we all know, one of the main risk factors of HCC is chronic hepatitis B virus (HBV) infection, which involved in the oncogenesis of HCC through direct and indirect mechanisms such as inflammatory injury, integration into the host genome and interaction with some special target genes. This study aimed to identify these key genes potentially associated with HBV-related HCC by bioinformatics analyses.Results: Total of 320 DEGs was identified between HBV-related HCC tissue samples and adjacent normal samples. These DGEs were strongly associated with several biological processes, such as retinol metabolism and steroid hormone biosynthesis. A PPI network was constructed and top six hub genes, including CDK1, CCNB1, CDC20, CDKN3, HMMR and MKI67, were determined. GEPIA online tool analysis validated the six key hub genes had the same expression trend as predicted in The Cancer Genome Atlas (TCGA) datasets. The overall survival and disease-free survival reflected that high expression of CDK1, CDC20, HMMR, MKI67 and CCNB1 significantly predicted poor prognosis, whereas CDKN3 expression has no statistical differences in overall survival.Conclusion: The present study identified key genes and pathways involved in HBV-related HCC, which will improve our understanding of the mechanisms underlying the development and recurrence of HCC. The six identified genes might be potential biomarkers for the diagnosis and treatment of HBV-related HCC.

Role of environmental factors in the etiology of hepatocellular carcinoma

2010 ◽  
Vol 151 (28) ◽  
pp. 1132-1136 ◽  
Author(s):  
István Tornai
Keyword(s):  
Hepatocellular Carcinoma ◽  
Hepatitis B Virus ◽  
Environmental Factors ◽  
Hepatitis B ◽  
Hepatitis A ◽  
Virus Hepatitis ◽  
B Virus

A krónikus vírushepatitisek jelentik ma a legismertebb okokat a hepatocellularis carcinoma (HCC) kialakulásában. A krónikus B- és C-vírus-hepatitis a májrákok körülbelül 40-50%-át okozza. A nyugati típusú társadalmakban a HCC előfordulása folyamatosan növekvő tendenciát mutat. Az alkohol számít a környezeti tényezők közül a legfontosabbnak, bár az alkoholfogyasztás a legtöbb országban csökken. Ez aláhúzza az egyéb környezeti tényezők fontosságát is. Az elfogyasztott alkoholmennyiséggel egyenes arányban növekszik a cirrhosis és a következményes HCC gyakorisága nőkben és férfiakban egyaránt. A kémiai anyagok közül a legismertebb a Kínában és Afrikában elterjedt aflatoxin, amely a gabonaféléket szennyező mycotoxin. Hasonló területeken endémiás, mint a hepatitis B-vírus, együtt szinergista hatást fejtenek ki. A dohányzás is egyértelműen bizonyított hepatocarcinogen hatással rendelkezik. Ez is jelentősen fokozódik, ha alkoholfogyasztással vagy vírushepatitisszel társul. Társadalmilag talán a legfontosabb az elhízás, a következményes nem alkoholos zsírmáj, illetve steatohepatitis és a 2-es típusú cukorbetegség, amelyek prevalenciája egyre fokozódik. Feltehetően ezek állnak a növekvő HCC-gyakoriság hátterében. Az inzulinrezisztencia és az oxidatív stressz képezik a legfontosabb patogenetikai lépéseket a májsejtkárosodásban. További fontos rizikótényező az orális fogamzásgátlók elterjedt használata. Egyes foglalkozások esetén a tartós szervesoldószer-expozíció is növeli a HCC rizikóját. Védelmet jelenthetnek az antioxidánsok, a szelén, a gyógyszerek közül a statinok és a feketekávé-fogyasztás.

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