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

Jaesik Jeong, Lang Li, Yunlong Liu, Kenneth Nephew, Tim Hui Ming Huang, Changyu Shen

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number55
JournalBMC Medical Genomics
Volume3
DOIs
StatePublished - 2010

Fingerprint

Estrogen Receptor Modulators
Transcriptome
Methylation
DNA Methylation
Breast Neoplasms
Gene Expression
Genes
Cell Line
Estrogen Receptor alpha
Gene Expression Regulation
Therapeutics
Drug Resistance
Genetic Promoter Regions
Transcription Factors

ASJC Scopus subject areas

  • Genetics(clinical)
  • Genetics

Cite this

An empirical Bayes model for gene expression and methylation profiles in antiestrogen resistant breast cancer. / Jeong, Jaesik; Li, Lang; Liu, Yunlong; Nephew, Kenneth; Huang, Tim Hui Ming; Shen, Changyu.

In: BMC Medical Genomics, Vol. 3, 55, 2010.

Research output: Contribution to journalArticle

@article{80d4828c172d435991c1694f455bdd1b,
title = "An empirical Bayes model for gene expression and methylation profiles in antiestrogen resistant breast cancer",
abstract = "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.",
author = "Jaesik Jeong and Lang Li and Yunlong Liu and Kenneth Nephew and Huang, {Tim Hui Ming} and Changyu Shen",
year = "2010",
doi = "10.1186/1755-8794-3-55",
language = "English",
volume = "3",
journal = "BMC Medical Genomics",
issn = "1755-8794",
publisher = "BioMed Central",

}

TY - JOUR

T1 - An empirical Bayes model for gene expression and methylation profiles in antiestrogen resistant breast cancer

AU - Jeong, Jaesik

AU - Li, Lang

AU - Liu, Yunlong

AU - Nephew, Kenneth

AU - Huang, Tim Hui Ming

AU - Shen, Changyu

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=78649330451&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78649330451&partnerID=8YFLogxK

U2 - 10.1186/1755-8794-3-55

DO - 10.1186/1755-8794-3-55

M3 - Article

C2 - 21108837

AN - SCOPUS:78649330451

VL - 3

JO - BMC Medical Genomics

JF - BMC Medical Genomics

SN - 1755-8794

M1 - 55

ER -