Dataset Information

Combined analysis of DNA methylation and cell cycle in cancer cells.

ABSTRACT: DNA methylation is a chemical modification of DNA involved in the regulation of gene expression by controlling the access to the DNA sequence. It is the most stable epigenetic mark and is widely studied for its role in major biological processes. Aberrant DNA methylation is observed in various pathologies, such as cancer. Therefore, there is a great interest in analyzing subtle changes in DNA methylation induced by biological processes or upon drug treatments. Here, we developed an improved methodology based on flow cytometry to measure variations of DNA methylation level in melanoma and leukemia cells. The accuracy of DNA methylation quantification was validated with LC-ESI mass spectrometry analysis. The new protocol was used to detect small variations of cytosine methylation occurring in individual cells during their cell cycle and those induced by the demethylating agent 5-aza-2'-deoxycytidine (5AzadC). Kinetic experiments confirmed that inheritance of DNA methylation occurs efficiently in S phase and revealed a short delay between DNA replication and completion of cytosine methylation. In addition, this study suggests that the uncoupling of 5AzadC effects on DNA demethylation and cell proliferation might be related to the duration of the DNA replication phase.

SUBMITTER: Desjobert C

PROVIDER: S-EPMC4622670 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Combined analysis of DNA methylation and cell cycle in cancer cells.

Desjobert Cécile C El Maï Mounir M Gérard-Hirne Tom T Guianvarc'h Dominique D Carrier Arnaud A Pottier Cyrielle C Arimondo Paola B PB Riond Joëlle J

Epigenetics 20150123 1

DNA methylation is a chemical modification of DNA involved in the regulation of gene expression by controlling the access to the DNA sequence. It is the most stable epigenetic mark and is widely studied for its role in major biological processes. Aberrant DNA methylation is observed in various pathologies, such as cancer. Therefore, there is a great interest in analyzing subtle changes in DNA methylation induced by biological processes or upon drug treatments. Here, we developed an improved meth ...[more]

PMID: 25531272

Similar Datasets

Project description:BackgroundDNA methylation is a stable epigenetic mark that is frequently altered in tumors. DNA methylation features are attractive biomarkers for disease states given the stability of DNA methylation in living cells and in biologic specimens typically available for analysis. Widespread accumulation of methylation in regulatory elements in some cancers (specifically the CpG island methylator phenotype, CIMP) can play an important role in tumorigenesis. High resolution assessment of CIMP for the entire genome, however, remains cost prohibitive and requires quantities of DNA not available for many tissue samples of interest. Genome-wide scans of methylation have been undertaken for large numbers of tumors, and higher resolution analyses for a limited number of cancer specimens. Methods for analyzing such large datasets and integrating findings from different studies continue to evolve. An approach for comparison of findings from a genome-wide assessment of the methylated component of tumor DNA and more widely applied methylation scans was developed.MethodsMethylomes for 76 primary endometrial cancer and 12 normal endometrial samples were generated using methylated fragment capture and second generation sequencing, MethylCap-seq. Publically available Infinium HumanMethylation 450 data from The Cancer Genome Atlas (TCGA) were compared to MethylCap-seq data.ResultsAnalysis of methylation in promoter CpG islands (CGIs) identified a subset of tumors with a methylator phenotype. We used a two-stage approach to develop a 13-region methylation signature associated with a "hypermethylator state." High level methylation for the 13-region methylation signatures was associated with mismatch repair deficiency, high mutation rate, and low somatic copy number alteration in the TCGA test set. In addition, the signature devised showed good agreement with previously described methylation clusters devised by TCGA.ConclusionWe identified a methylation signature for a "hypermethylator phenotype" in endometrial cancer and developed methods that may prove useful for identifying extreme methylation phenotypes in other cancers.

Dataset Information

Combined analysis of DNA methylation and cell cycle in cancer cells.

Publications

Combined analysis of DNA methylation and cell cycle in cancer cells.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets