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An empirical Bayes model for gene expression and methylation profiles in antiestrogen resistant breast cancer

BACKGROUND: The nuclear transcription factor estrogen receptor alpha (ER-alpha) is the target of several antiestrogen therapeutic agents for breast cancer. However, many ER-alpha positive patients do not respond to these treatments from the beginning, or stop responding after being treated for a per...

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Autores principales: Jeong, Jaesik, Li, Lang, Liu, Yunlong, Nephew, Kenneth P, Huang, Tim Hui-Ming, Shen, Changyu
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3003621/
https://www.ncbi.nlm.nih.gov/pubmed/21108837
http://dx.doi.org/10.1186/1755-8794-3-55
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author Jeong, Jaesik
Li, Lang
Liu, Yunlong
Nephew, Kenneth P
Huang, Tim Hui-Ming
Shen, Changyu
author_facet Jeong, Jaesik
Li, Lang
Liu, Yunlong
Nephew, Kenneth P
Huang, Tim Hui-Ming
Shen, Changyu
author_sort Jeong, Jaesik
collection PubMed
description BACKGROUND: The nuclear transcription factor estrogen receptor alpha (ER-alpha) is the target of several antiestrogen therapeutic agents for breast cancer. However, many ER-alpha positive patients do not respond to these treatments from the beginning, or stop responding after being treated for a period of time. Because of the association of gene transcription alteration and drug resistance and the emerging evidence on the role of DNA methylation on transcription regulation, understanding of these relationships can facilitate development of approaches to re-sensitize breast cancer cells to treatment by restoring DNA methylation patterns. METHODS: We constructed a hierarchical empirical Bayes model to investigate the simultaneous change of gene expression and promoter DNA methylation profiles among wild type (WT) and OHT/ICI resistant MCF7 breast cancer cell lines. RESULTS: We found that compared with the WT cell lines, almost all of the genes in OHT or ICI resistant cell lines either do not show methylation change or hypomethylated. Moreover, the correlations between gene expression and methylation are quite heterogeneous across genes, suggesting the involvement of other factors in regulating transcription. Analysis of our results in combination with H3K4me2 data on OHT resistant cell lines suggests a clear interplay between DNA methylation and H3K4me2 in the regulation of gene expression. For hypomethylated genes with alteration of gene expression, most (~80%) are up-regulated, consistent with current view on the relationship between promoter methylation and gene expression. CONCLUSIONS: We developed an empirical Bayes model to study the association between DNA methylation in the promoter region and gene expression. Our approach generates both global (across all genes) and local (individual gene) views of the interplay. It provides important insight on future effort to develop therapeutic agent to re-sensitize breast cancer cells to treatment.
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spelling pubmed-30036212011-01-06 An empirical Bayes model for gene expression and methylation profiles in antiestrogen resistant breast cancer Jeong, Jaesik Li, Lang Liu, Yunlong Nephew, Kenneth P Huang, Tim Hui-Ming Shen, Changyu BMC Med Genomics Research Article BACKGROUND: The nuclear transcription factor estrogen receptor alpha (ER-alpha) is the target of several antiestrogen therapeutic agents for breast cancer. However, many ER-alpha positive patients do not respond to these treatments from the beginning, or stop responding after being treated for a period of time. Because of the association of gene transcription alteration and drug resistance and the emerging evidence on the role of DNA methylation on transcription regulation, understanding of these relationships can facilitate development of approaches to re-sensitize breast cancer cells to treatment by restoring DNA methylation patterns. METHODS: We constructed a hierarchical empirical Bayes model to investigate the simultaneous change of gene expression and promoter DNA methylation profiles among wild type (WT) and OHT/ICI resistant MCF7 breast cancer cell lines. RESULTS: We found that compared with the WT cell lines, almost all of the genes in OHT or ICI resistant cell lines either do not show methylation change or hypomethylated. Moreover, the correlations between gene expression and methylation are quite heterogeneous across genes, suggesting the involvement of other factors in regulating transcription. Analysis of our results in combination with H3K4me2 data on OHT resistant cell lines suggests a clear interplay between DNA methylation and H3K4me2 in the regulation of gene expression. For hypomethylated genes with alteration of gene expression, most (~80%) are up-regulated, consistent with current view on the relationship between promoter methylation and gene expression. CONCLUSIONS: We developed an empirical Bayes model to study the association between DNA methylation in the promoter region and gene expression. Our approach generates both global (across all genes) and local (individual gene) views of the interplay. It provides important insight on future effort to develop therapeutic agent to re-sensitize breast cancer cells to treatment. BioMed Central 2010-11-25 /pmc/articles/PMC3003621/ /pubmed/21108837 http://dx.doi.org/10.1186/1755-8794-3-55 Text en Copyright ©2010 Jeong et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<url>http://creativecommons.org/licenses/by/2.0</url>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Jeong, Jaesik
Li, Lang
Liu, Yunlong
Nephew, Kenneth P
Huang, Tim Hui-Ming
Shen, Changyu
An empirical Bayes model for gene expression and methylation profiles in antiestrogen resistant breast cancer
title An empirical Bayes model for gene expression and methylation profiles in antiestrogen resistant breast cancer
title_full An empirical Bayes model for gene expression and methylation profiles in antiestrogen resistant breast cancer
title_fullStr An empirical Bayes model for gene expression and methylation profiles in antiestrogen resistant breast cancer
title_full_unstemmed An empirical Bayes model for gene expression and methylation profiles in antiestrogen resistant breast cancer
title_short An empirical Bayes model for gene expression and methylation profiles in antiestrogen resistant breast cancer
title_sort empirical bayes model for gene expression and methylation profiles in antiestrogen resistant breast cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3003621/
https://www.ncbi.nlm.nih.gov/pubmed/21108837
http://dx.doi.org/10.1186/1755-8794-3-55
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