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The identification of key genes and pathways in hepatocellular carcinoma by bioinformatics analysis of high-throughput data

Liver cancer is a serious threat to public health and has fairly complicated pathogenesis. Therefore, the identification of key genes and pathways is of much importance for clarifying molecular mechanism of hepatocellular carcinoma (HCC) initiation and progression. HCC-associated gene expression dat...

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Autores principales: Zhang, Chaoyang, Peng, Li, Zhang, Yaqin, Liu, Zhaoyang, Li, Wenling, Chen, Shilian, Li, Guancheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5400790/
https://www.ncbi.nlm.nih.gov/pubmed/28432618
http://dx.doi.org/10.1007/s12032-017-0963-9
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author Zhang, Chaoyang
Peng, Li
Zhang, Yaqin
Liu, Zhaoyang
Li, Wenling
Chen, Shilian
Li, Guancheng
author_facet Zhang, Chaoyang
Peng, Li
Zhang, Yaqin
Liu, Zhaoyang
Li, Wenling
Chen, Shilian
Li, Guancheng
author_sort Zhang, Chaoyang
collection PubMed
description Liver cancer is a serious threat to public health and has fairly complicated pathogenesis. Therefore, the identification of key genes and pathways is of much importance for clarifying molecular mechanism of hepatocellular carcinoma (HCC) initiation and progression. HCC-associated gene expression dataset was downloaded from Gene Expression Omnibus database. Statistical software R was used for significance analysis of differentially expressed genes (DEGs) between liver cancer samples and normal samples. Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, based on R software, were applied for the identification of pathways in which DEGs significantly enriched. Cytoscape software was for the construction of protein–protein interaction (PPI) network and module analysis to find the hub genes and key pathways. Finally, weighted correlation network analysis (WGCNA) was conducted to further screen critical gene modules with similar expression pattern and explore their biological significance. Significance analysis identified 1230 DEGs with fold change >2, including 632 significantly down-regulated DEGs and 598 significantly up-regulated DEGs. GO term enrichment analysis suggested that up-regulated DEG significantly enriched in immune response, cell adhesion, cell migration, type I interferon signaling pathway, and cell proliferation, and the down-regulated DEG mainly enriched in response to endoplasmic reticulum stress and endoplasmic reticulum unfolded protein response. KEGG pathway analysis found DEGs significantly enriched in five pathways including complement and coagulation cascades, focal adhesion, ECM–receptor interaction, antigen processing and presentation, and protein processing in endoplasmic reticulum. The top 10 hub genes in HCC were separately GMPS, ACACA, ALB, TGFB1, KRAS, ERBB2, BCL2, EGFR, STAT3, and CD8A, which resulted from PPI network. The top 3 gene interaction modules in PPI network enriched in immune response, organ development, and response to other organism, respectively. WGCNA revealed that the confirmed eight gene modules significantly enriched in monooxygenase and oxidoreductase activity, response to endoplasmic reticulum stress, type I interferon signaling pathway, processing, presentation and binding of peptide antigen, cellular response to cadmium and zinc ion, cell locomotion and differentiation, ribonucleoprotein complex and RNA processing, and immune system process, respectively. In conclusion, we identified some key genes and pathways closely related with HCC initiation and progression by a series of bioinformatics analysis on DEGs. These screened genes and pathways provided for a more detailed molecular mechanism underlying HCC occurrence and progression, holding promise for acting as biomarkers and potential therapeutic targets.
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spelling pubmed-54007902017-05-08 The identification of key genes and pathways in hepatocellular carcinoma by bioinformatics analysis of high-throughput data Zhang, Chaoyang Peng, Li Zhang, Yaqin Liu, Zhaoyang Li, Wenling Chen, Shilian Li, Guancheng Med Oncol Original Paper Liver cancer is a serious threat to public health and has fairly complicated pathogenesis. Therefore, the identification of key genes and pathways is of much importance for clarifying molecular mechanism of hepatocellular carcinoma (HCC) initiation and progression. HCC-associated gene expression dataset was downloaded from Gene Expression Omnibus database. Statistical software R was used for significance analysis of differentially expressed genes (DEGs) between liver cancer samples and normal samples. Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, based on R software, were applied for the identification of pathways in which DEGs significantly enriched. Cytoscape software was for the construction of protein–protein interaction (PPI) network and module analysis to find the hub genes and key pathways. Finally, weighted correlation network analysis (WGCNA) was conducted to further screen critical gene modules with similar expression pattern and explore their biological significance. Significance analysis identified 1230 DEGs with fold change >2, including 632 significantly down-regulated DEGs and 598 significantly up-regulated DEGs. GO term enrichment analysis suggested that up-regulated DEG significantly enriched in immune response, cell adhesion, cell migration, type I interferon signaling pathway, and cell proliferation, and the down-regulated DEG mainly enriched in response to endoplasmic reticulum stress and endoplasmic reticulum unfolded protein response. KEGG pathway analysis found DEGs significantly enriched in five pathways including complement and coagulation cascades, focal adhesion, ECM–receptor interaction, antigen processing and presentation, and protein processing in endoplasmic reticulum. The top 10 hub genes in HCC were separately GMPS, ACACA, ALB, TGFB1, KRAS, ERBB2, BCL2, EGFR, STAT3, and CD8A, which resulted from PPI network. The top 3 gene interaction modules in PPI network enriched in immune response, organ development, and response to other organism, respectively. WGCNA revealed that the confirmed eight gene modules significantly enriched in monooxygenase and oxidoreductase activity, response to endoplasmic reticulum stress, type I interferon signaling pathway, processing, presentation and binding of peptide antigen, cellular response to cadmium and zinc ion, cell locomotion and differentiation, ribonucleoprotein complex and RNA processing, and immune system process, respectively. In conclusion, we identified some key genes and pathways closely related with HCC initiation and progression by a series of bioinformatics analysis on DEGs. These screened genes and pathways provided for a more detailed molecular mechanism underlying HCC occurrence and progression, holding promise for acting as biomarkers and potential therapeutic targets. Springer US 2017-04-21 2017 /pmc/articles/PMC5400790/ /pubmed/28432618 http://dx.doi.org/10.1007/s12032-017-0963-9 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Paper
Zhang, Chaoyang
Peng, Li
Zhang, Yaqin
Liu, Zhaoyang
Li, Wenling
Chen, Shilian
Li, Guancheng
The identification of key genes and pathways in hepatocellular carcinoma by bioinformatics analysis of high-throughput data
title The identification of key genes and pathways in hepatocellular carcinoma by bioinformatics analysis of high-throughput data
title_full The identification of key genes and pathways in hepatocellular carcinoma by bioinformatics analysis of high-throughput data
title_fullStr The identification of key genes and pathways in hepatocellular carcinoma by bioinformatics analysis of high-throughput data
title_full_unstemmed The identification of key genes and pathways in hepatocellular carcinoma by bioinformatics analysis of high-throughput data
title_short The identification of key genes and pathways in hepatocellular carcinoma by bioinformatics analysis of high-throughput data
title_sort identification of key genes and pathways in hepatocellular carcinoma by bioinformatics analysis of high-throughput data
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5400790/
https://www.ncbi.nlm.nih.gov/pubmed/28432618
http://dx.doi.org/10.1007/s12032-017-0963-9
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