Cargando…
Epigenetic basis of hepatocellular carcinoma: A network-based integrative meta-analysis
AIM: To identify the key epigenetically modulated genes and pathways in HCC by performing an integrative meta-analysis of all major, well-annotated and publicly available methylation datasets using tools of network analysis. METHODS: PubMed and Gene Expression Omnibus were searched for genome-wide D...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Baishideng Publishing Group Inc
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5787679/ https://www.ncbi.nlm.nih.gov/pubmed/29399289 http://dx.doi.org/10.4254/wjh.v10.i1.155 |
_version_ | 1783295981477953536 |
---|---|
author | Bhat, Venkat Srinathan, Sujitha Pasini, Elisa Angeli, Marc Chen, Emily Baciu, Cristina Bhat, Mamatha |
author_facet | Bhat, Venkat Srinathan, Sujitha Pasini, Elisa Angeli, Marc Chen, Emily Baciu, Cristina Bhat, Mamatha |
author_sort | Bhat, Venkat |
collection | PubMed |
description | AIM: To identify the key epigenetically modulated genes and pathways in HCC by performing an integrative meta-analysis of all major, well-annotated and publicly available methylation datasets using tools of network analysis. METHODS: PubMed and Gene Expression Omnibus were searched for genome-wide DNA methylation datasets. Patient clinical and demographic characteristics were obtained. DNA methylation data were integrated using the Ingenuity Pathway Analysis, a software package for visualizing and analyzing biological networks. Pathway enrichment analysis was performed using IPA, which also provides literature-driven and computationally-predicted annotations for significant association of genes to curated molecular pathways. RESULTS: From an initial 928 potential abstracts, we identified and analyzed 11 eligible high-throughput methylation datasets representing 354 patients. A significant proportion of studies did not provide concomitant clinical data. In the promoter region, HIST1H2AJ and SPDYA were the most commonly methylated, whereas HRNBP3 gene was the most commonly hypomethylated. ESR1 and ERK were central genes in the principal networks. The pathways most associated with the frequently methylated genes were G-protein coupled receptor and cAMP-mediated signalling. CONCLUSION: Using an integrative network-based analysis approach of genome-wide DNA methylation data of both the promoter and body of genes, we identified G-protein coupled receptor signalling as the most highly associated with HCC. This encompasses a diverse range of cancer pathways, such as the PI3K/Akt/mTOR and Ras/Raf/MAPK pathways, and is therefore supportive of previous literature on gene expression in HCC. However, there are novel targetable genes such as HIST1H2AJ that are epigenetically modified, suggesting their potential as biomarkers and for therapeutic targeting of the HCC epigenome. |
format | Online Article Text |
id | pubmed-5787679 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Baishideng Publishing Group Inc |
record_format | MEDLINE/PubMed |
spelling | pubmed-57876792018-02-02 Epigenetic basis of hepatocellular carcinoma: A network-based integrative meta-analysis Bhat, Venkat Srinathan, Sujitha Pasini, Elisa Angeli, Marc Chen, Emily Baciu, Cristina Bhat, Mamatha World J Hepatol Meta-Analysis AIM: To identify the key epigenetically modulated genes and pathways in HCC by performing an integrative meta-analysis of all major, well-annotated and publicly available methylation datasets using tools of network analysis. METHODS: PubMed and Gene Expression Omnibus were searched for genome-wide DNA methylation datasets. Patient clinical and demographic characteristics were obtained. DNA methylation data were integrated using the Ingenuity Pathway Analysis, a software package for visualizing and analyzing biological networks. Pathway enrichment analysis was performed using IPA, which also provides literature-driven and computationally-predicted annotations for significant association of genes to curated molecular pathways. RESULTS: From an initial 928 potential abstracts, we identified and analyzed 11 eligible high-throughput methylation datasets representing 354 patients. A significant proportion of studies did not provide concomitant clinical data. In the promoter region, HIST1H2AJ and SPDYA were the most commonly methylated, whereas HRNBP3 gene was the most commonly hypomethylated. ESR1 and ERK were central genes in the principal networks. The pathways most associated with the frequently methylated genes were G-protein coupled receptor and cAMP-mediated signalling. CONCLUSION: Using an integrative network-based analysis approach of genome-wide DNA methylation data of both the promoter and body of genes, we identified G-protein coupled receptor signalling as the most highly associated with HCC. This encompasses a diverse range of cancer pathways, such as the PI3K/Akt/mTOR and Ras/Raf/MAPK pathways, and is therefore supportive of previous literature on gene expression in HCC. However, there are novel targetable genes such as HIST1H2AJ that are epigenetically modified, suggesting their potential as biomarkers and for therapeutic targeting of the HCC epigenome. Baishideng Publishing Group Inc 2018-01-27 2018-01-27 /pmc/articles/PMC5787679/ /pubmed/29399289 http://dx.doi.org/10.4254/wjh.v10.i1.155 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 | Meta-Analysis Bhat, Venkat Srinathan, Sujitha Pasini, Elisa Angeli, Marc Chen, Emily Baciu, Cristina Bhat, Mamatha Epigenetic basis of hepatocellular carcinoma: A network-based integrative meta-analysis |
title | Epigenetic basis of hepatocellular carcinoma: A network-based integrative meta-analysis |
title_full | Epigenetic basis of hepatocellular carcinoma: A network-based integrative meta-analysis |
title_fullStr | Epigenetic basis of hepatocellular carcinoma: A network-based integrative meta-analysis |
title_full_unstemmed | Epigenetic basis of hepatocellular carcinoma: A network-based integrative meta-analysis |
title_short | Epigenetic basis of hepatocellular carcinoma: A network-based integrative meta-analysis |
title_sort | epigenetic basis of hepatocellular carcinoma: a network-based integrative meta-analysis |
topic | Meta-Analysis |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5787679/ https://www.ncbi.nlm.nih.gov/pubmed/29399289 http://dx.doi.org/10.4254/wjh.v10.i1.155 |
work_keys_str_mv | AT bhatvenkat epigeneticbasisofhepatocellularcarcinomaanetworkbasedintegrativemetaanalysis AT srinathansujitha epigeneticbasisofhepatocellularcarcinomaanetworkbasedintegrativemetaanalysis AT pasinielisa epigeneticbasisofhepatocellularcarcinomaanetworkbasedintegrativemetaanalysis AT angelimarc epigeneticbasisofhepatocellularcarcinomaanetworkbasedintegrativemetaanalysis AT chenemily epigeneticbasisofhepatocellularcarcinomaanetworkbasedintegrativemetaanalysis AT baciucristina epigeneticbasisofhepatocellularcarcinomaanetworkbasedintegrativemetaanalysis AT bhatmamatha epigeneticbasisofhepatocellularcarcinomaanetworkbasedintegrativemetaanalysis |