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Screening of potential biomarkers in hepatitis C virus-induced hepatocellular carcinoma using bioinformatic analysis

Evidence suggests that hepatitis C virus (HCV) infection is among the main causes of hepatocellular carcinoma (HCC). In addition, HCV-induced HCC (HCV-HCC) exhibits adverse clinical outcomes and limited therapeutic treatments are available for this condition. To investigate key biomarkers in the occ...

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Autores principales: Liu, Jun, Ma, Zhanzhong, Liu, Yanming, Wu, Liangyin, Hou, Zhiwei, Li, Wenli
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6676667/
https://www.ncbi.nlm.nih.gov/pubmed/31452738
http://dx.doi.org/10.3892/ol.2019.10578
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author Liu, Jun
Ma, Zhanzhong
Liu, Yanming
Wu, Liangyin
Hou, Zhiwei
Li, Wenli
author_facet Liu, Jun
Ma, Zhanzhong
Liu, Yanming
Wu, Liangyin
Hou, Zhiwei
Li, Wenli
author_sort Liu, Jun
collection PubMed
description Evidence suggests that hepatitis C virus (HCV) infection is among the main causes of hepatocellular carcinoma (HCC). In addition, HCV-induced HCC (HCV-HCC) exhibits adverse clinical outcomes and limited therapeutic treatments are available for this condition. To investigate key biomarkers in the occurrence and development of HCV-HCC, microarray datasets GSE62232, GSE69715 and GSE107170 were downloaded from the Gene Expression Omnibus database for analysis. The differentially expressed genes between HCV-HCC and normal tissue were identified using the GEO2R online tool. The function enrichment analyses including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were performed using the Database for Annotation, Visualization and Integrated Discovery online tool. A protein-protein interaction network was constructed using the Search Tool for the Retrieval of Interacting Genes database and visualized using Cytoscape. A total of 368 DEGs were identified, and the top 10 hub genes with a high degree of connectivity were selected for further analysis. Subsequently, overall survival and disease-free survival analysis revealed that there was a significant association between altered expression of HMMR, CCNB1 and KIF20A, and poor clinical outcome. In summary, these results indicate that HMMR, CCNB1 and KIF20A are potential targets for diagnosis and therapy of HCV-HCC.
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spelling pubmed-66766672019-08-26 Screening of potential biomarkers in hepatitis C virus-induced hepatocellular carcinoma using bioinformatic analysis Liu, Jun Ma, Zhanzhong Liu, Yanming Wu, Liangyin Hou, Zhiwei Li, Wenli Oncol Lett Articles Evidence suggests that hepatitis C virus (HCV) infection is among the main causes of hepatocellular carcinoma (HCC). In addition, HCV-induced HCC (HCV-HCC) exhibits adverse clinical outcomes and limited therapeutic treatments are available for this condition. To investigate key biomarkers in the occurrence and development of HCV-HCC, microarray datasets GSE62232, GSE69715 and GSE107170 were downloaded from the Gene Expression Omnibus database for analysis. The differentially expressed genes between HCV-HCC and normal tissue were identified using the GEO2R online tool. The function enrichment analyses including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were performed using the Database for Annotation, Visualization and Integrated Discovery online tool. A protein-protein interaction network was constructed using the Search Tool for the Retrieval of Interacting Genes database and visualized using Cytoscape. A total of 368 DEGs were identified, and the top 10 hub genes with a high degree of connectivity were selected for further analysis. Subsequently, overall survival and disease-free survival analysis revealed that there was a significant association between altered expression of HMMR, CCNB1 and KIF20A, and poor clinical outcome. In summary, these results indicate that HMMR, CCNB1 and KIF20A are potential targets for diagnosis and therapy of HCV-HCC. D.A. Spandidos 2019-09 2019-07-05 /pmc/articles/PMC6676667/ /pubmed/31452738 http://dx.doi.org/10.3892/ol.2019.10578 Text en Copyright: © Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Articles
Liu, Jun
Ma, Zhanzhong
Liu, Yanming
Wu, Liangyin
Hou, Zhiwei
Li, Wenli
Screening of potential biomarkers in hepatitis C virus-induced hepatocellular carcinoma using bioinformatic analysis
title Screening of potential biomarkers in hepatitis C virus-induced hepatocellular carcinoma using bioinformatic analysis
title_full Screening of potential biomarkers in hepatitis C virus-induced hepatocellular carcinoma using bioinformatic analysis
title_fullStr Screening of potential biomarkers in hepatitis C virus-induced hepatocellular carcinoma using bioinformatic analysis
title_full_unstemmed Screening of potential biomarkers in hepatitis C virus-induced hepatocellular carcinoma using bioinformatic analysis
title_short Screening of potential biomarkers in hepatitis C virus-induced hepatocellular carcinoma using bioinformatic analysis
title_sort screening of potential biomarkers in hepatitis c virus-induced hepatocellular carcinoma using bioinformatic analysis
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6676667/
https://www.ncbi.nlm.nih.gov/pubmed/31452738
http://dx.doi.org/10.3892/ol.2019.10578
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