Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Genome-wide methylome profiling of normal breast and breast tumors

ABSTRACT: To improve our understanding of the effect of differential methylation on gene expression between normal and tumor breast cells, we employed high-throughput sequencing technology to determine and compare CpG methylation of normal breast and four breast tumor genomes at single-base resolution. By comparing the methylation profiles between normal and tumor, we identified large hypomethylated zones in associated with large tissue-specific genes and gene deserts. We identified small hypomethylated regions in the methylomes and termed these sites as unmethylated islands (UMI). The UMI are highly correlated with positive regulatory chromatin marks and exhibit differential methylation mainly at the island shores. Our analysis showed there is a complex relationship between genome-wide promoter differential methylation and gene expression. Four other data sets from this study were also deposited at ArrayExpress under accession numbers E-MTAB-1935, E-MTAB-1952, E-MTAB-1958 and E-MTAB-1961.

INSTRUMENT(S): Illumina Genome Analyzer IIx, Illumina HiSeq 2000

ORGANISM(S): Homo sapiens

SUBMITTER: Ming-Ta Hsu

PROVIDER: E-MTAB-2014 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Hierarchical clustering of breast cancer methylomes revealed differentially methylated and expressed breast cancer genes.

Lin I-Hsuan IH Chen Dow-Tien DT Chang Yi-Feng YF Lee Yu-Ling YL Su Chia-Hsin CH Cheng Ching C Tsai Yi-Chien YC Ng Swee-Chuan SC Chen Hsiao-Tan HT Lee Mei-Chen MC Chen Hong-Wei HW Suen Shih-Hui SH Chen Yu-Cheng YC Liu Tze-Tze TT Chang Chuan-Hsiung CH Hsu Ming-Ta MT

PloS one 20150223 2

Oncogenic transformation of normal cells often involves epigenetic alterations, including histone modification and DNA methylation. We conducted whole-genome bisulfite sequencing to determine the DNA methylomes of normal breast, fibroadenoma, invasive ductal carcinomas and MCF7. The emergence, disappearance, expansion and contraction of kilobase-sized hypomethylated regions (HMRs) and the hypomethylation of the megabase-sized partially methylated domains (PMDs) are the major forms of methylation ...[more]

PMID: 25706888

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Project description:A fundamental challenge in the post-genome era is to understand and annotate the consequences of genetic variation, particularly within the context of human tissues. We describe a set of integrated experiments designed to investigate the effects of common genetic variability on DNA methylation, mRNA expression and microRNA (miRNA) expression in four distinct human brain regions. We show that brain tissues may be readily distinguished based on methylation status or expression profile. We find an abundance of genetic cis regulation mRNA expression and show for the first time abundant quantitative trait loci for DNA CpG methylation. We observe that the largest magnitude effects occur across distinct brain regions. We believe these data, which we have made publicly available, will be useful in understanding the biological effects of genetic variation. Authorized Access data: Mapping of GEO sample accessions to dbGaP subject/sample IDs is available through dbGaP Authorized Access, see http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000249.v1.p1 Because of our interest in genomic regulation of expression and neurological disorders we embarked upon a series of experiments to provide a brain region-specific contextual framework for genetic and epigenetic regulation of gene expression. We obtained frozen brain tissue from the cerebellum, frontal cortex, pons and temporal cortex from 150 subjects (total 600 tissue samples). We undertook four separate assays across this series; first, genome-wide SNP genotyping; second, assay of >27,000 CpG methylation sites in each of the four brain regions; third, mRNA expression profiling of >22,000 transcripts in all four brain regions; and, fourth, miRNA expression profiling of 735 miRNA transcripts. Here we discuss the results of these experiments, particularly in the context of integrated datasets to define expression and CpG methylation quantitative trait loci (eQTL and methQTL) and detailing differences and similarities across brain regions.

Dataset Information

Genome-wide methylome profiling of normal breast and breast tumors

Publications

Hierarchical clustering of breast cancer methylomes revealed differentially methylated and expressed breast cancer genes.

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