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An analysis about heterogeneity among cancers based on the DNA methylation patterns

BACKGROUND: It is generally believed that DNA methylation, as one of the most important epigenetic modifications, participates in the regulation of gene expression and plays an important role in the development of cancer, and there exits epigenetic heterogeneity among cancers. Therefore, this study...

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Autores principales: Liu, Yang, Gu, Yue, Su, Mu, Liu, Hui, Zhang, Shumei, Zhang, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6937830/
https://www.ncbi.nlm.nih.gov/pubmed/31888612
http://dx.doi.org/10.1186/s12885-019-6455-x
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author Liu, Yang
Gu, Yue
Su, Mu
Liu, Hui
Zhang, Shumei
Zhang, Yan
author_facet Liu, Yang
Gu, Yue
Su, Mu
Liu, Hui
Zhang, Shumei
Zhang, Yan
author_sort Liu, Yang
collection PubMed
description BACKGROUND: It is generally believed that DNA methylation, as one of the most important epigenetic modifications, participates in the regulation of gene expression and plays an important role in the development of cancer, and there exits epigenetic heterogeneity among cancers. Therefore, this study tried to screen for reliable prognostic markers for different cancers, providing further explanation for the heterogeneity of cancers, and more targets for clinical transformation studies of cancer from epigenetic perspective. METHODS: This article discusses the epigenetic heterogeneity of cancer in detail. Firstly, DNA methylation data of seven cancer types were obtained from Illumina Infinium HumanMethylation 450 K platform of TCGA database. Then, differential methylation analysis was performed in the promotor region. Secondly, pivotal gene markers were obtained by constructing the DNA methylation correlation network and the gene interaction network in the KEGG pathway, and 317 marker genes obtained from two networks were integrated as candidate markers for the prognosis model. Finally, we used the univariate and multivariate COX regression models to select specific independent prognostic markers for each cancer, and studied the risk factor of these genes by doing survival analysis. RESULTS: First, the cancer type-specific gene markers were obtained by differential methylation analysis and they were found to be involved in different biological functions by enrichment analysis. Moreover, specific and common diagnostic markers for each type of cancer was sorted out and Kaplan-Meier survival analysis showed that there was significant difference in survival between the two risk groups. CONCLUSIONS: This study screened out reliable prognostic markers for different cancers, providing a further explanation for the heterogeneity of cancer at the DNA methylation level and more targets for clinical conversion studies of cancer.
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spelling pubmed-69378302019-12-31 An analysis about heterogeneity among cancers based on the DNA methylation patterns Liu, Yang Gu, Yue Su, Mu Liu, Hui Zhang, Shumei Zhang, Yan BMC Cancer Research Article BACKGROUND: It is generally believed that DNA methylation, as one of the most important epigenetic modifications, participates in the regulation of gene expression and plays an important role in the development of cancer, and there exits epigenetic heterogeneity among cancers. Therefore, this study tried to screen for reliable prognostic markers for different cancers, providing further explanation for the heterogeneity of cancers, and more targets for clinical transformation studies of cancer from epigenetic perspective. METHODS: This article discusses the epigenetic heterogeneity of cancer in detail. Firstly, DNA methylation data of seven cancer types were obtained from Illumina Infinium HumanMethylation 450 K platform of TCGA database. Then, differential methylation analysis was performed in the promotor region. Secondly, pivotal gene markers were obtained by constructing the DNA methylation correlation network and the gene interaction network in the KEGG pathway, and 317 marker genes obtained from two networks were integrated as candidate markers for the prognosis model. Finally, we used the univariate and multivariate COX regression models to select specific independent prognostic markers for each cancer, and studied the risk factor of these genes by doing survival analysis. RESULTS: First, the cancer type-specific gene markers were obtained by differential methylation analysis and they were found to be involved in different biological functions by enrichment analysis. Moreover, specific and common diagnostic markers for each type of cancer was sorted out and Kaplan-Meier survival analysis showed that there was significant difference in survival between the two risk groups. CONCLUSIONS: This study screened out reliable prognostic markers for different cancers, providing a further explanation for the heterogeneity of cancer at the DNA methylation level and more targets for clinical conversion studies of cancer. BioMed Central 2019-12-30 /pmc/articles/PMC6937830/ /pubmed/31888612 http://dx.doi.org/10.1186/s12885-019-6455-x Text en © The Author(s). 2019 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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Liu, Yang
Gu, Yue
Su, Mu
Liu, Hui
Zhang, Shumei
Zhang, Yan
An analysis about heterogeneity among cancers based on the DNA methylation patterns
title An analysis about heterogeneity among cancers based on the DNA methylation patterns
title_full An analysis about heterogeneity among cancers based on the DNA methylation patterns
title_fullStr An analysis about heterogeneity among cancers based on the DNA methylation patterns
title_full_unstemmed An analysis about heterogeneity among cancers based on the DNA methylation patterns
title_short An analysis about heterogeneity among cancers based on the DNA methylation patterns
title_sort analysis about heterogeneity among cancers based on the dna methylation patterns
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6937830/
https://www.ncbi.nlm.nih.gov/pubmed/31888612
http://dx.doi.org/10.1186/s12885-019-6455-x
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