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An Integrated Approach to Uncover Driver Genes in Breast Cancer Methylation Genomes
BACKGROUND: Cancer cells typically exhibit large-scale aberrant methylation of gene promoters. Some of the genes with promoter methylation alterations play “driver” roles in tumorigenesis, whereas others are only “passengers”. RESULTS: Based on the assumption that promoter methylation alteration of...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3620319/ https://www.ncbi.nlm.nih.gov/pubmed/23579546 http://dx.doi.org/10.1371/journal.pone.0061214 |
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author | Shen, Xiaopei Li, Shan Zhang, Lin Li, Hongdong Hong, Guini Zhou, XianXiao Zheng, Tingting Zhang, Wenjing Hao, Chunxiang Shi, Tongwei Liu, Chunyang Guo, Zheng |
author_facet | Shen, Xiaopei Li, Shan Zhang, Lin Li, Hongdong Hong, Guini Zhou, XianXiao Zheng, Tingting Zhang, Wenjing Hao, Chunxiang Shi, Tongwei Liu, Chunyang Guo, Zheng |
author_sort | Shen, Xiaopei |
collection | PubMed |
description | BACKGROUND: Cancer cells typically exhibit large-scale aberrant methylation of gene promoters. Some of the genes with promoter methylation alterations play “driver” roles in tumorigenesis, whereas others are only “passengers”. RESULTS: Based on the assumption that promoter methylation alteration of a driver gene may lead to expression alternation of a set of genes associated with cancer pathways, we developed a computational framework for integrating promoter methylation and gene expression data to identify driver methylation aberrations of cancer. Applying this approach to breast cancer data, we identified many novel cancer driver genes and found that some of the identified driver genes were subtype-specific for basal-like, luminal-A and HER2+ subtypes of breast cancer. CONCLUSION: The proposed framework proved effective in identifying cancer driver genes from genome-wide gene methylation and expression data of cancer. These results may provide new molecular targets for potential targeted and selective epigenetic therapy. |
format | Online Article Text |
id | pubmed-3620319 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-36203192013-04-11 An Integrated Approach to Uncover Driver Genes in Breast Cancer Methylation Genomes Shen, Xiaopei Li, Shan Zhang, Lin Li, Hongdong Hong, Guini Zhou, XianXiao Zheng, Tingting Zhang, Wenjing Hao, Chunxiang Shi, Tongwei Liu, Chunyang Guo, Zheng PLoS One Research Article BACKGROUND: Cancer cells typically exhibit large-scale aberrant methylation of gene promoters. Some of the genes with promoter methylation alterations play “driver” roles in tumorigenesis, whereas others are only “passengers”. RESULTS: Based on the assumption that promoter methylation alteration of a driver gene may lead to expression alternation of a set of genes associated with cancer pathways, we developed a computational framework for integrating promoter methylation and gene expression data to identify driver methylation aberrations of cancer. Applying this approach to breast cancer data, we identified many novel cancer driver genes and found that some of the identified driver genes were subtype-specific for basal-like, luminal-A and HER2+ subtypes of breast cancer. CONCLUSION: The proposed framework proved effective in identifying cancer driver genes from genome-wide gene methylation and expression data of cancer. These results may provide new molecular targets for potential targeted and selective epigenetic therapy. Public Library of Science 2013-04-08 /pmc/articles/PMC3620319/ /pubmed/23579546 http://dx.doi.org/10.1371/journal.pone.0061214 Text en © 2013 Shen et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Shen, Xiaopei Li, Shan Zhang, Lin Li, Hongdong Hong, Guini Zhou, XianXiao Zheng, Tingting Zhang, Wenjing Hao, Chunxiang Shi, Tongwei Liu, Chunyang Guo, Zheng An Integrated Approach to Uncover Driver Genes in Breast Cancer Methylation Genomes |
title | An Integrated Approach to Uncover Driver Genes in Breast Cancer Methylation Genomes |
title_full | An Integrated Approach to Uncover Driver Genes in Breast Cancer Methylation Genomes |
title_fullStr | An Integrated Approach to Uncover Driver Genes in Breast Cancer Methylation Genomes |
title_full_unstemmed | An Integrated Approach to Uncover Driver Genes in Breast Cancer Methylation Genomes |
title_short | An Integrated Approach to Uncover Driver Genes in Breast Cancer Methylation Genomes |
title_sort | integrated approach to uncover driver genes in breast cancer methylation genomes |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3620319/ https://www.ncbi.nlm.nih.gov/pubmed/23579546 http://dx.doi.org/10.1371/journal.pone.0061214 |
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