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Prediction and analysis of weighted genes in hepatocellular carcinoma using bioinformatics analysis

The aim of the present study was to identify the differentially expressed genes (DEGs) between primary tumor tissue and adjacent non-tumor tissue of hepatocellular carcinoma (HCC) samples in order to investigate the mechanisms of HCC. The microarray data of the datasets GSE76427, GSE84005 and GSE579...

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Autores principales: Zhang, Qifan, Sun, Shibo, Zhu, Chen, Zheng, Yujian, Cai, Qing, Liang, Xiaolu, Xie, Haorong, Zhou, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6423588/
https://www.ncbi.nlm.nih.gov/pubmed/30720105
http://dx.doi.org/10.3892/mmr.2019.9929
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author Zhang, Qifan
Sun, Shibo
Zhu, Chen
Zheng, Yujian
Cai, Qing
Liang, Xiaolu
Xie, Haorong
Zhou, Jie
author_facet Zhang, Qifan
Sun, Shibo
Zhu, Chen
Zheng, Yujian
Cai, Qing
Liang, Xiaolu
Xie, Haorong
Zhou, Jie
author_sort Zhang, Qifan
collection PubMed
description The aim of the present study was to identify the differentially expressed genes (DEGs) between primary tumor tissue and adjacent non-tumor tissue of hepatocellular carcinoma (HCC) samples in order to investigate the mechanisms of HCC. The microarray data of the datasets GSE76427, GSE84005 and GSE57957 were downloaded from the Gene Expression Omnibus database. DEGs were identified using the limma package in the R programming language. Following the intersection of the DEGs screened from the three datasets, 218 genes were selected for further study. A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes database. The construction and analysis of modules were performed using Cytoscape and the module with the highest score was selected for further analysis. Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were conducted for genes involved in the PPI network and the selected subnetwork. The network of the enriched pathways and their associated genes was constructed using Cytoscape. For the genes in the global PPI network, metabolism-associated pathways were significantly enriched; whereas, for the genes in the subnetwork, ‘cell cycle’, ‘oocyte meiosis’ and ‘DNA replication’ pathways were significantly enriched. To demonstrate the portability and repeatability of the prognostic value of the weighted genes, a validation cohort was obtained from datasets of The Cancer Genome Atlas and Kaplan-Meier survival analysis was conducted. Evidence is presented that the expression levels of aldehyde dehydrogenase 2 family member, cytochrome P450 family 2 subfamily C member 8, alcohol dehydrogenase 4 (class II), pi polypeptide, alcohol dehydrogenase 1B (class I), β polypeptide and cytochrome P450 family 2 subfamily C member 9 were associated with the overall survival of patients with HCC and that the expression levels of pituitary tumor-transforming 1, cell division cycle 20, DNA topoisomerase II α and cyclin B2 were negatively associated with the overall survival of patients with HCC. In conclusion, 9 weighted genes, involved in the development and progression of HCC, were identified using bioinformatics and survival analyses.
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spelling pubmed-64235882019-03-22 Prediction and analysis of weighted genes in hepatocellular carcinoma using bioinformatics analysis Zhang, Qifan Sun, Shibo Zhu, Chen Zheng, Yujian Cai, Qing Liang, Xiaolu Xie, Haorong Zhou, Jie Mol Med Rep Articles The aim of the present study was to identify the differentially expressed genes (DEGs) between primary tumor tissue and adjacent non-tumor tissue of hepatocellular carcinoma (HCC) samples in order to investigate the mechanisms of HCC. The microarray data of the datasets GSE76427, GSE84005 and GSE57957 were downloaded from the Gene Expression Omnibus database. DEGs were identified using the limma package in the R programming language. Following the intersection of the DEGs screened from the three datasets, 218 genes were selected for further study. A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes database. The construction and analysis of modules were performed using Cytoscape and the module with the highest score was selected for further analysis. Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were conducted for genes involved in the PPI network and the selected subnetwork. The network of the enriched pathways and their associated genes was constructed using Cytoscape. For the genes in the global PPI network, metabolism-associated pathways were significantly enriched; whereas, for the genes in the subnetwork, ‘cell cycle’, ‘oocyte meiosis’ and ‘DNA replication’ pathways were significantly enriched. To demonstrate the portability and repeatability of the prognostic value of the weighted genes, a validation cohort was obtained from datasets of The Cancer Genome Atlas and Kaplan-Meier survival analysis was conducted. Evidence is presented that the expression levels of aldehyde dehydrogenase 2 family member, cytochrome P450 family 2 subfamily C member 8, alcohol dehydrogenase 4 (class II), pi polypeptide, alcohol dehydrogenase 1B (class I), β polypeptide and cytochrome P450 family 2 subfamily C member 9 were associated with the overall survival of patients with HCC and that the expression levels of pituitary tumor-transforming 1, cell division cycle 20, DNA topoisomerase II α and cyclin B2 were negatively associated with the overall survival of patients with HCC. In conclusion, 9 weighted genes, involved in the development and progression of HCC, were identified using bioinformatics and survival analyses. D.A. Spandidos 2019-04 2019-02-04 /pmc/articles/PMC6423588/ /pubmed/30720105 http://dx.doi.org/10.3892/mmr.2019.9929 Text en Copyright: © Zhang 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
Zhang, Qifan
Sun, Shibo
Zhu, Chen
Zheng, Yujian
Cai, Qing
Liang, Xiaolu
Xie, Haorong
Zhou, Jie
Prediction and analysis of weighted genes in hepatocellular carcinoma using bioinformatics analysis
title Prediction and analysis of weighted genes in hepatocellular carcinoma using bioinformatics analysis
title_full Prediction and analysis of weighted genes in hepatocellular carcinoma using bioinformatics analysis
title_fullStr Prediction and analysis of weighted genes in hepatocellular carcinoma using bioinformatics analysis
title_full_unstemmed Prediction and analysis of weighted genes in hepatocellular carcinoma using bioinformatics analysis
title_short Prediction and analysis of weighted genes in hepatocellular carcinoma using bioinformatics analysis
title_sort prediction and analysis of weighted genes in hepatocellular carcinoma using bioinformatics analysis
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6423588/
https://www.ncbi.nlm.nih.gov/pubmed/30720105
http://dx.doi.org/10.3892/mmr.2019.9929
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