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Integrative analysis with expanded DNA methylation data reveals common key regulators and pathways in cancers
The integration of genomic and DNA methylation data has been demonstrated as a powerful strategy in understanding cancer mechanisms and identifying therapeutic targets. The TCGA consortium has mapped DNA methylation in thousands of cancer samples using Illumina Infinium Human Methylation 450 K BeadC...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6358616/ https://www.ncbi.nlm.nih.gov/pubmed/30729033 http://dx.doi.org/10.1038/s41525-019-0077-8 |
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author | Fan, Shicai Tang, Jianxiong Li, Nan Zhao, Ying Ai, Rizi Zhang, Kai Wang, Mengchi Du, Wei Wang, Wei |
author_facet | Fan, Shicai Tang, Jianxiong Li, Nan Zhao, Ying Ai, Rizi Zhang, Kai Wang, Mengchi Du, Wei Wang, Wei |
author_sort | Fan, Shicai |
collection | PubMed |
description | The integration of genomic and DNA methylation data has been demonstrated as a powerful strategy in understanding cancer mechanisms and identifying therapeutic targets. The TCGA consortium has mapped DNA methylation in thousands of cancer samples using Illumina Infinium Human Methylation 450 K BeadChip (Illumina 450 K array) that only covers about 1.5% of CpGs in the human genome. Therefore, increasing the coverage of the DNA methylome would significantly leverage the usage of the TCGA data. Here, we present a new model called EAGLING that can expand the Illumina 450 K array data 18 times to cover about 30% of the CpGs in the human genome. We applied it to analyze 13 cancers in TCGA. By integrating the expanded methylation, gene expression, and somatic mutation data, we identified the genes showing differential patterns in each of the 13 cancers. Many of the triple-evidenced genes identified in majority of the cancers are biomarkers or potential biomarkers. Pan-cancer analysis also revealed the pathways in which the triple-evidenced genes are enriched, which include well known ones as well as new ones, such as axonal guidance signaling pathway and pathways related to inflammatory processing or inflammation response. Triple-evidenced genes, particularly TNXB, RRM2, CELSR3, SLC16A3, FANCI, MMP9, MMP11, SIK1, and TRIM59 showed superior predictive power in both tumor diagnosis and prognosis. These results have demonstrated that the integrative analysis using the expanded methylation data is powerful in identifying critical genes/pathways that may serve as new therapeutic targets. |
format | Online Article Text |
id | pubmed-6358616 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-63586162019-02-06 Integrative analysis with expanded DNA methylation data reveals common key regulators and pathways in cancers Fan, Shicai Tang, Jianxiong Li, Nan Zhao, Ying Ai, Rizi Zhang, Kai Wang, Mengchi Du, Wei Wang, Wei NPJ Genom Med Article The integration of genomic and DNA methylation data has been demonstrated as a powerful strategy in understanding cancer mechanisms and identifying therapeutic targets. The TCGA consortium has mapped DNA methylation in thousands of cancer samples using Illumina Infinium Human Methylation 450 K BeadChip (Illumina 450 K array) that only covers about 1.5% of CpGs in the human genome. Therefore, increasing the coverage of the DNA methylome would significantly leverage the usage of the TCGA data. Here, we present a new model called EAGLING that can expand the Illumina 450 K array data 18 times to cover about 30% of the CpGs in the human genome. We applied it to analyze 13 cancers in TCGA. By integrating the expanded methylation, gene expression, and somatic mutation data, we identified the genes showing differential patterns in each of the 13 cancers. Many of the triple-evidenced genes identified in majority of the cancers are biomarkers or potential biomarkers. Pan-cancer analysis also revealed the pathways in which the triple-evidenced genes are enriched, which include well known ones as well as new ones, such as axonal guidance signaling pathway and pathways related to inflammatory processing or inflammation response. Triple-evidenced genes, particularly TNXB, RRM2, CELSR3, SLC16A3, FANCI, MMP9, MMP11, SIK1, and TRIM59 showed superior predictive power in both tumor diagnosis and prognosis. These results have demonstrated that the integrative analysis using the expanded methylation data is powerful in identifying critical genes/pathways that may serve as new therapeutic targets. Nature Publishing Group UK 2019-02-01 /pmc/articles/PMC6358616/ /pubmed/30729033 http://dx.doi.org/10.1038/s41525-019-0077-8 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Fan, Shicai Tang, Jianxiong Li, Nan Zhao, Ying Ai, Rizi Zhang, Kai Wang, Mengchi Du, Wei Wang, Wei Integrative analysis with expanded DNA methylation data reveals common key regulators and pathways in cancers |
title | Integrative analysis with expanded DNA methylation data reveals common key regulators and pathways in cancers |
title_full | Integrative analysis with expanded DNA methylation data reveals common key regulators and pathways in cancers |
title_fullStr | Integrative analysis with expanded DNA methylation data reveals common key regulators and pathways in cancers |
title_full_unstemmed | Integrative analysis with expanded DNA methylation data reveals common key regulators and pathways in cancers |
title_short | Integrative analysis with expanded DNA methylation data reveals common key regulators and pathways in cancers |
title_sort | integrative analysis with expanded dna methylation data reveals common key regulators and pathways in cancers |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6358616/ https://www.ncbi.nlm.nih.gov/pubmed/30729033 http://dx.doi.org/10.1038/s41525-019-0077-8 |
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