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Integrated analysis of genome-wide DNA methylation and gene expression profiles in molecular subtypes of breast cancer.

Rhee JK, Kim K, Chae H, Evans J, Yan P, Zhang BT, Gray J, Spellman P, Huang TH, Nephew KP, Kim S - Nucleic Acids Res. (2013)

Bottom Line: As well-developed methods for the integrated analysis do not currently exist, we created a series of four different analysis methods.On the computational side, our goal is to develop methylome data analysis protocols for the integrated analysis of DNA methylation and gene expression data on the genome scale.On the cancer biology side, we present comprehensive genome-wide methylome analysis results for differentially methylated regions and their potential effect on gene expression in 30 breast cancer cell lines representing three molecular phenotypes, luminal, basal A and basal B.

View Article: PubMed Central - PubMed

Affiliation: Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-742, Korea, Bioinformatics Institute, Seoul National University, Seoul 151-744, Korea, School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN 55905, USA, The Ohio State University Comprehensive Cancer Center Nucleic Acid Shared Resource-Illumina Core, Columbus, OH 43210, USA, School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea, OHSU Knight Cancer Institute, Portland, OR 97239, USA, Department of Molecular Medicine/Institute of Biotechnology, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229-3900, USA, Medical Sciences, Indiana University School of Medicine, Bloomington, IN 47405, USA and Department of Cellular and Integrative Physiology, Indiana University School of Medicine, Indianapolis, IN 46202, USA.

ABSTRACT
Aberrant DNA methylation of CpG islands, CpG island shores and first exons is known to play a key role in the altered gene expression patterns in all human cancers. To date, a systematic study on the effect of DNA methylation on gene expression using high resolution data has not been reported. In this study, we conducted an integrated analysis of MethylCap-sequencing data and Affymetrix gene expression microarray data for 30 breast cancer cell lines representing different breast tumor phenotypes. As well-developed methods for the integrated analysis do not currently exist, we created a series of four different analysis methods. On the computational side, our goal is to develop methylome data analysis protocols for the integrated analysis of DNA methylation and gene expression data on the genome scale. On the cancer biology side, we present comprehensive genome-wide methylome analysis results for differentially methylated regions and their potential effect on gene expression in 30 breast cancer cell lines representing three molecular phenotypes, luminal, basal A and basal B. Our integrated analysis demonstrates that methylation status of different genomic regions may play a key role in establishing transcriptional patterns in molecular subtypes of human breast cancer.

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Related in: MedlinePlus

Methylation density of promoter regions in 30 breast cancer cell lines. Density was measured for each subtype. The methylation levels are on the x-axis, and the y-axis is probabilistic density. Unusual bulbs around 100 on the x-axis were because methylation levels over 100 were truncated to 100. Lu, luminal; BaA, basal A; BaB, basal B.
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gkt643-F2: Methylation density of promoter regions in 30 breast cancer cell lines. Density was measured for each subtype. The methylation levels are on the x-axis, and the y-axis is probabilistic density. Unusual bulbs around 100 on the x-axis were because methylation levels over 100 were truncated to 100. Lu, luminal; BaA, basal A; BaB, basal B.

Mentions: We measured and compared the methylation density of 2 kb promoter regions for all genes in 30 breast cancer cell lines. Figure 2 shows subtype-specific density plots of promoter regions, excluding unmethylated genes. Overall, the methylation density was similar in each subtype. We observe that the number of highly methylated (>50) promoter regions tended to be lower in basal B (BaB). The density of the regions whose methylation levels were over 50 was around 10% in Lu and basal A (BaA), but 4% in BaB.Figure 2.


Integrated analysis of genome-wide DNA methylation and gene expression profiles in molecular subtypes of breast cancer.

Rhee JK, Kim K, Chae H, Evans J, Yan P, Zhang BT, Gray J, Spellman P, Huang TH, Nephew KP, Kim S - Nucleic Acids Res. (2013)

Methylation density of promoter regions in 30 breast cancer cell lines. Density was measured for each subtype. The methylation levels are on the x-axis, and the y-axis is probabilistic density. Unusual bulbs around 100 on the x-axis were because methylation levels over 100 were truncated to 100. Lu, luminal; BaA, basal A; BaB, basal B.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3794600&req=5

gkt643-F2: Methylation density of promoter regions in 30 breast cancer cell lines. Density was measured for each subtype. The methylation levels are on the x-axis, and the y-axis is probabilistic density. Unusual bulbs around 100 on the x-axis were because methylation levels over 100 were truncated to 100. Lu, luminal; BaA, basal A; BaB, basal B.
Mentions: We measured and compared the methylation density of 2 kb promoter regions for all genes in 30 breast cancer cell lines. Figure 2 shows subtype-specific density plots of promoter regions, excluding unmethylated genes. Overall, the methylation density was similar in each subtype. We observe that the number of highly methylated (>50) promoter regions tended to be lower in basal B (BaB). The density of the regions whose methylation levels were over 50 was around 10% in Lu and basal A (BaA), but 4% in BaB.Figure 2.

Bottom Line: As well-developed methods for the integrated analysis do not currently exist, we created a series of four different analysis methods.On the computational side, our goal is to develop methylome data analysis protocols for the integrated analysis of DNA methylation and gene expression data on the genome scale.On the cancer biology side, we present comprehensive genome-wide methylome analysis results for differentially methylated regions and their potential effect on gene expression in 30 breast cancer cell lines representing three molecular phenotypes, luminal, basal A and basal B.

View Article: PubMed Central - PubMed

Affiliation: Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-742, Korea, Bioinformatics Institute, Seoul National University, Seoul 151-744, Korea, School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN 55905, USA, The Ohio State University Comprehensive Cancer Center Nucleic Acid Shared Resource-Illumina Core, Columbus, OH 43210, USA, School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea, OHSU Knight Cancer Institute, Portland, OR 97239, USA, Department of Molecular Medicine/Institute of Biotechnology, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229-3900, USA, Medical Sciences, Indiana University School of Medicine, Bloomington, IN 47405, USA and Department of Cellular and Integrative Physiology, Indiana University School of Medicine, Indianapolis, IN 46202, USA.

ABSTRACT
Aberrant DNA methylation of CpG islands, CpG island shores and first exons is known to play a key role in the altered gene expression patterns in all human cancers. To date, a systematic study on the effect of DNA methylation on gene expression using high resolution data has not been reported. In this study, we conducted an integrated analysis of MethylCap-sequencing data and Affymetrix gene expression microarray data for 30 breast cancer cell lines representing different breast tumor phenotypes. As well-developed methods for the integrated analysis do not currently exist, we created a series of four different analysis methods. On the computational side, our goal is to develop methylome data analysis protocols for the integrated analysis of DNA methylation and gene expression data on the genome scale. On the cancer biology side, we present comprehensive genome-wide methylome analysis results for differentially methylated regions and their potential effect on gene expression in 30 breast cancer cell lines representing three molecular phenotypes, luminal, basal A and basal B. Our integrated analysis demonstrates that methylation status of different genomic regions may play a key role in establishing transcriptional patterns in molecular subtypes of human breast cancer.

Show MeSH
Related in: MedlinePlus