Dataset Information

Methylation target array for rapid analysis of CpG island hypermethylation in multiple tissue genomes.

ABSTRACT: Hypermethylation of multiple CpG islands is a common event in cancer. To assess the prognostic values of this epigenetic alteration, we developed Methylation Target Array (MTA), derived from the concept of tissue microarray, for simultaneous analysis of DNA hypermethylation in hundreds of tissue genomes. In MTA, linker-ligated CpG island fragments were digested with methylation-sensitive endonucleases and amplified with flanking primers. A panel of 468 MTA amplicons, which represented the whole repertoire of methylated CpG islands in 93 breast tumors, 20 normal breast tissues, and 4 breast cancer cell lines, were arrayed on nylon membrane for probe hybridization. Positive hybridization signals detected in tumor amplicons, but not in normal amplicons, were indicative of aberrant hypermethylation in tumor samples. This is attributed to aberrant sites that were protected from methylation-sensitive restriction and were amplified by PCR in tumor samples, while the same sites were restricted and could not be amplified in normal samples. Hypermethylation frequencies of the 10 genes tested in breast tumors and cancer cell lines were 60% for GPC3, 58% for RASSF1A, 32% for 3OST3B, 30% for HOXA5, 28% for uPA, 25% for WT1, 23% for BRCA1, 9% for DAPK1, and 0% for KL. Furthermore, hypermethylation of 5 to 7 loci of these genes was significantly correlated with hormone receptor status, clinical stages, and ages at diagnosis of the patients analyzed. This novel approach thus provides an additional avenue for assessing clinicopathological consequences of DNA hypermethylation in breast cancer.

SUBMITTER: Chen CM

PROVIDER: S-EPMC1868173 | biostudies-literature | 2003 Jul

REPOSITORIES: biostudies-literature

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Methylation target array for rapid analysis of CpG island hypermethylation in multiple tissue genomes.

Chen Chuan-Mu CM Chen Hsiao-Ling HL Hsiau Timothy H-C TH Hsiau Andrew H-A AH Shi Huidong H Brock Graham J R GJ Wei Susan H SH Caldwell Charles W CW Yan Pearlly S PS Huang Tim Hui-Ming TH

The American journal of pathology 20030701 1

Hypermethylation of multiple CpG islands is a common event in cancer. To assess the prognostic values of this epigenetic alteration, we developed Methylation Target Array (MTA), derived from the concept of tissue microarray, for simultaneous analysis of DNA hypermethylation in hundreds of tissue genomes. In MTA, linker-ligated CpG island fragments were digested with methylation-sensitive endonucleases and amplified with flanking primers. A panel of 468 MTA amplicons, which represented the whole ...[more]

PMID: 12819009

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Project description:BACKGROUND: The computational prediction of DNA methylation has become an important topic in the recent years due to its role in the epigenetic control of normal and cancer-related processes. While previous prediction approaches focused merely on differences between methylated and unmethylated DNA sequences, recent experimental results have shown the presence of much more complex patterns of methylation across tissues and time in the human genome. These patterns are only partially described by a binary model of DNA methylation. In this work we propose a novel approach, based on profile analysis of tissue-specific methylation that uncovers significant differences in the sequences of CpG islands (CGIs) that predispose them to a tissue- specific methylation pattern. RESULTS: We defined CGI methylation profiles that separate not only between constitutively methylated and unmethylated CGIs, but also identify CGIs showing a differential degree of methylation across tissues and cell-types or a lack of methylation exclusively in sperm. These profiles are clearly distinguished by a number of CGI attributes including their evolutionary conservation, their significance, as well as the evolutionary evidence of prior methylation. Additionally, we assess profile functionality with respect to the different compartments of protein coding genes and their possible use in the prediction of DNA methylation. CONCLUSION: Our approach provides new insights into the biological features that determine if a CGI has a functional role in the epigenetic control of gene expression and the features associated with CGI methylation susceptibility. Moreover, we show that the ability to predict CGI methylation is based primarily on the quality of the biological information used and the relationships uncovered between different sources of knowledge. The strategy presented here is able to predict, besides the constitutively methylated and unmethylated classes, two more tissue specific methylation classes conserving the accuracy provided by leading binary methylation classification methods.

Dataset Information

Methylation target array for rapid analysis of CpG island hypermethylation in multiple tissue genomes.

Publications

Methylation target array for rapid analysis of CpG island hypermethylation in multiple tissue genomes.

Similar Datasets

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