Cargando…

Bioinformatics analysis of aberrantly methylated-differentially expressed genes and pathways in hepatocellular carcinoma

AIM: To discover methylated-differentially expressed genes (MDEGs) in hepatocellular carcinoma (HCC) and to explore relevant hub genes and potential pathways. METHODS: The data of expression profiling GSE25097 and methylation profiling GSE57956 were gained from GEO Datasets. We analyzed the differen...

Descripción completa

Detalles Bibliográficos
Autores principales: Sang, Liang, Wang, Xue-Mei, Xu, Dong-Yang, Zhao, Wen-Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Baishideng Publishing Group Inc 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6021769/
https://www.ncbi.nlm.nih.gov/pubmed/29962817
http://dx.doi.org/10.3748/wjg.v24.i24.2605
_version_ 1783335533181665280
author Sang, Liang
Wang, Xue-Mei
Xu, Dong-Yang
Zhao, Wen-Jing
author_facet Sang, Liang
Wang, Xue-Mei
Xu, Dong-Yang
Zhao, Wen-Jing
author_sort Sang, Liang
collection PubMed
description AIM: To discover methylated-differentially expressed genes (MDEGs) in hepatocellular carcinoma (HCC) and to explore relevant hub genes and potential pathways. METHODS: The data of expression profiling GSE25097 and methylation profiling GSE57956 were gained from GEO Datasets. We analyzed the differentially methylated genes and differentially expressed genes online using GEO2R. Functional and enrichment analyses of MDEGs were conducted using the DAVID database. A protein-protein interaction (PPI) network was performed by STRING and then visualized in Cytoscape. Hub genes were ranked by cytoHubba, and a module analysis of the PPI network was conducted by MCODE in Cytoscape software. RESULTS: In total, we categorized 266 genes as hypermethylated, lowly expressed genes (Hyper-LGs) referring to endogenous and hormone stimulus, cell surface receptor linked signal transduction and behavior. In addition, 161 genes were labelled as hypomethylated, highly expressed genes (Hypo-HGs) referring to DNA replication and metabolic process, cell cycle and division. Pathway analysis illustrated that Hyper-LGs were enriched in cancer, Wnt, and chemokine signalling pathways, while Hypo-HGs were related to cell cycle and steroid hormone biosynthesis pathways. Based on PPI networks, PTGS2, PIK3CD, CXCL1, ESR1, and MMP2 were identified as hub genes for Hyper-LGs, and CDC45, DTL, AURKB, CDKN3, MCM2, and MCM10 were hub genes for Hypo-HGs by combining six ranked methods of cytoHubba. CONCLUSION: In the study, we disclose numerous novel genetic and epigenetic regulations and offer a vital molecular groundwork to understand the pathogenesis of HCC. Hub genes, including PTGS2, PIK3CD, CXCL1, ESR1, MMP2, CDC45, DTL, AURKB, CDKN3, MCM2, and MCM10, can be used as biomarkers based on aberrant methylation for the accurate diagnosis and treatment of HCC.
format Online
Article
Text
id pubmed-6021769
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Baishideng Publishing Group Inc
record_format MEDLINE/PubMed
spelling pubmed-60217692018-06-29 Bioinformatics analysis of aberrantly methylated-differentially expressed genes and pathways in hepatocellular carcinoma Sang, Liang Wang, Xue-Mei Xu, Dong-Yang Zhao, Wen-Jing World J Gastroenterol Basic Study AIM: To discover methylated-differentially expressed genes (MDEGs) in hepatocellular carcinoma (HCC) and to explore relevant hub genes and potential pathways. METHODS: The data of expression profiling GSE25097 and methylation profiling GSE57956 were gained from GEO Datasets. We analyzed the differentially methylated genes and differentially expressed genes online using GEO2R. Functional and enrichment analyses of MDEGs were conducted using the DAVID database. A protein-protein interaction (PPI) network was performed by STRING and then visualized in Cytoscape. Hub genes were ranked by cytoHubba, and a module analysis of the PPI network was conducted by MCODE in Cytoscape software. RESULTS: In total, we categorized 266 genes as hypermethylated, lowly expressed genes (Hyper-LGs) referring to endogenous and hormone stimulus, cell surface receptor linked signal transduction and behavior. In addition, 161 genes were labelled as hypomethylated, highly expressed genes (Hypo-HGs) referring to DNA replication and metabolic process, cell cycle and division. Pathway analysis illustrated that Hyper-LGs were enriched in cancer, Wnt, and chemokine signalling pathways, while Hypo-HGs were related to cell cycle and steroid hormone biosynthesis pathways. Based on PPI networks, PTGS2, PIK3CD, CXCL1, ESR1, and MMP2 were identified as hub genes for Hyper-LGs, and CDC45, DTL, AURKB, CDKN3, MCM2, and MCM10 were hub genes for Hypo-HGs by combining six ranked methods of cytoHubba. CONCLUSION: In the study, we disclose numerous novel genetic and epigenetic regulations and offer a vital molecular groundwork to understand the pathogenesis of HCC. Hub genes, including PTGS2, PIK3CD, CXCL1, ESR1, MMP2, CDC45, DTL, AURKB, CDKN3, MCM2, and MCM10, can be used as biomarkers based on aberrant methylation for the accurate diagnosis and treatment of HCC. Baishideng Publishing Group Inc 2018-06-28 2018-06-28 /pmc/articles/PMC6021769/ /pubmed/29962817 http://dx.doi.org/10.3748/wjg.v24.i24.2605 Text en ©The Author(s) 2018. Published by Baishideng Publishing Group Inc. All rights reserved. http://creativecommons.org/licenses/by-nc/4.0/ This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial.
spellingShingle Basic Study
Sang, Liang
Wang, Xue-Mei
Xu, Dong-Yang
Zhao, Wen-Jing
Bioinformatics analysis of aberrantly methylated-differentially expressed genes and pathways in hepatocellular carcinoma
title Bioinformatics analysis of aberrantly methylated-differentially expressed genes and pathways in hepatocellular carcinoma
title_full Bioinformatics analysis of aberrantly methylated-differentially expressed genes and pathways in hepatocellular carcinoma
title_fullStr Bioinformatics analysis of aberrantly methylated-differentially expressed genes and pathways in hepatocellular carcinoma
title_full_unstemmed Bioinformatics analysis of aberrantly methylated-differentially expressed genes and pathways in hepatocellular carcinoma
title_short Bioinformatics analysis of aberrantly methylated-differentially expressed genes and pathways in hepatocellular carcinoma
title_sort bioinformatics analysis of aberrantly methylated-differentially expressed genes and pathways in hepatocellular carcinoma
topic Basic Study
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6021769/
https://www.ncbi.nlm.nih.gov/pubmed/29962817
http://dx.doi.org/10.3748/wjg.v24.i24.2605
work_keys_str_mv AT sangliang bioinformaticsanalysisofaberrantlymethylateddifferentiallyexpressedgenesandpathwaysinhepatocellularcarcinoma
AT wangxuemei bioinformaticsanalysisofaberrantlymethylateddifferentiallyexpressedgenesandpathwaysinhepatocellularcarcinoma
AT xudongyang bioinformaticsanalysisofaberrantlymethylateddifferentiallyexpressedgenesandpathwaysinhepatocellularcarcinoma
AT zhaowenjing bioinformaticsanalysisofaberrantlymethylateddifferentiallyexpressedgenesandpathwaysinhepatocellularcarcinoma