Limits...
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.

Show MeSH

Related in: MedlinePlus

Correlation between promoter region methylation profiles and expression levels of genes downregulated in (a) Lu and (b) BaB subtypes. Unmethylated genes in the whole promoter region of 30 cell lines were excluded. Light red color was used for negative correlation and light green for positive correlation. Columns from right to left denote positions getting away from TSS. Each row is a downregulated gene in the subtype.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3794600&req=5

gkt643-F5: Correlation between promoter region methylation profiles and expression levels of genes downregulated in (a) Lu and (b) BaB subtypes. Unmethylated genes in the whole promoter region of 30 cell lines were excluded. Light red color was used for negative correlation and light green for positive correlation. Columns from right to left denote positions getting away from TSS. Each row is a downregulated gene in the subtype.

Mentions: We measured the methylation correlation for various genomic regions of downregulated genes in Lu and BaB subtype (Figures 5 and 6). As only two genes were detected as downregulated in BaA subtype, the correlation results for BaA subtype were not included. Interestingly, when methylation in promoter regions was considered, several genes showed a clear negative correlation at the proximal regions of TSSs. Figure 5 is heatmaps that visualize promoter region methylation and downstream gene expression (light red colors mean that two vectors (methylation profiles and expression levels) were highly negatively correlated and bright green were positively correlated), and the gene at each row is provided in Supplementary Tables S2 and S3. In both Lu and BaB subtypes, strong negative correlations were observed in promotor regions, and methylation in the promotor regions near TSS showed strongest negative correlations. However, there were significant differences in promotor methylation patterns in Lu and BaB subtypes. In Lu subtypes, weaker negative correlations were observed at genomic regions further away from TSS. On the contrary, in BaB subtypes, consistently strong negative correlations were observed in entire promotor regions. Supplementary Table S4 shows the difference of the correlation coefficient in each promoter region, measured by t-test. This result implies that the DNA methylation on the promoter region has stronger epigenetic inactivation in Basal-like subtypes and the methylation of this region may contribute to breast cancer progression.Figure 5.


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)

Correlation between promoter region methylation profiles and expression levels of genes downregulated in (a) Lu and (b) BaB subtypes. Unmethylated genes in the whole promoter region of 30 cell lines were excluded. Light red color was used for negative correlation and light green for positive correlation. Columns from right to left denote positions getting away from TSS. Each row is a downregulated gene in the subtype.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

gkt643-F5: Correlation between promoter region methylation profiles and expression levels of genes downregulated in (a) Lu and (b) BaB subtypes. Unmethylated genes in the whole promoter region of 30 cell lines were excluded. Light red color was used for negative correlation and light green for positive correlation. Columns from right to left denote positions getting away from TSS. Each row is a downregulated gene in the subtype.
Mentions: We measured the methylation correlation for various genomic regions of downregulated genes in Lu and BaB subtype (Figures 5 and 6). As only two genes were detected as downregulated in BaA subtype, the correlation results for BaA subtype were not included. Interestingly, when methylation in promoter regions was considered, several genes showed a clear negative correlation at the proximal regions of TSSs. Figure 5 is heatmaps that visualize promoter region methylation and downstream gene expression (light red colors mean that two vectors (methylation profiles and expression levels) were highly negatively correlated and bright green were positively correlated), and the gene at each row is provided in Supplementary Tables S2 and S3. In both Lu and BaB subtypes, strong negative correlations were observed in promotor regions, and methylation in the promotor regions near TSS showed strongest negative correlations. However, there were significant differences in promotor methylation patterns in Lu and BaB subtypes. In Lu subtypes, weaker negative correlations were observed at genomic regions further away from TSS. On the contrary, in BaB subtypes, consistently strong negative correlations were observed in entire promotor regions. Supplementary Table S4 shows the difference of the correlation coefficient in each promoter region, measured by t-test. This result implies that the DNA methylation on the promoter region has stronger epigenetic inactivation in Basal-like subtypes and the methylation of this region may contribute to breast cancer progression.Figure 5.

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