Unknown,Transcriptomics,Genomics,Proteomics

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Discovery of novel hypermethylated gene loci in prostate cancer using genome wide coverage CpG island microarrays


ABSTRACT: In order to identify methylation changes in prostate cancer, we performed a genome-wide analysis of DNA methylation using Agilent human CpG island arrays. We then chose specific genes to validate methylation both in the same cases as were hybridized to the array (using quantitative EpiTYPER analysis) and in an independent series of prostate cancer samples (using MethyLight quantitative methylation specific PCR). We specifically chose low grade (Gleason score 6 cases) and high grade (Gleason score 8 cases) to discover methylated genes/loci that may be involved in the progression to a higher grade of prostate cancer. We collected 20 specimens consisting of 10 Gleason 6 and 10 Gleason 8 prostate cancers, and compared these to a reference lymphocyte pool (6 age matched, healthy men) to determine cancer associated methylation changes as well as disease progression associated methylation changes. We performed the differential methylation hybridization procedure as described by Yan et al. (Methods, 2002) on each case to enrich for methylated DNA. Each specimen in the reference pool underwent the same enrichment with amplicons being pooled at the end of the procedure. Each prostate cancer case was subsequently co-hybridized to the microarray with the reference pool.

ORGANISM(S): Homo sapiens

SUBMITTER: Ken Kron 

PROVIDER: E-GEOD-15298 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications


<h4>Background</h4>Promoter and 5' end methylation regulation of tumour suppressor genes is a common feature of many cancers. Such occurrences often lead to the silencing of these key genes and thus they may contribute to the development of cancer, including prostate cancer.<h4>Methodology/principal findings</h4>In order to identify methylation changes in prostate cancer, we performed a genome-wide analysis of DNA methylation using Agilent human CpG island arrays. Using computational and gene-sp  ...[more]

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