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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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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. |
format | Online Article Text |
id | pubmed-6937830 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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