Literature DB >> 15538567

Distinction of acute lymphoblastic leukemia from acute myeloid leukemia through microarray-based DNA methylation analysis.

Christian Scholz1, Inko Nimmrich, Matthias Burger, Evelyne Becker, Bernd Dörken, Wolf-Dieter Ludwig, Sabine Maier.   

Abstract

Patterns of DNA methylation are substantially altered in malignancies compared to normal tissue, with both genome-wide hypomethylation and regional increase of cytosine methylation at dinucleotides of cytosine and guanine, i.e., CpG dinucleotides. While genome-wide hypomethylation renders chromosomes instable, hypermethylation of CpGs in promoter regions is generally associated with transcriptional silencing, e.g., of tumor suppressor genes. To investigate whether disease-specific methylation profiles exist for different entities of acute leukemia, a microarray-based DNA methylation analysis simultaneously assessing 249 CpG dinucleotides originating from 57 genes was employed. Hereby, samples from precursor B-cell acute lymphoblastic leukemia (ALL) could be distinguished from cases of acute myeloid leukemia by virtue of N33, EGR4, CDC2, CCND2, or MOS hypermethylation in ALL.

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Year:  2004        PMID: 15538567     DOI: 10.1007/s00277-004-0969-1

Source DB:  PubMed          Journal:  Ann Hematol        ISSN: 0939-5555            Impact factor:   3.673


  11 in total

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Review 3.  TUSC3: functional duality of a cancer gene.

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4.  Quantitative detection of TUSC3 promoter methylation -a potential biomarker for prognosis in lung cancer.

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5.  Characterizing DNA methylation patterns in pancreatic cancer genome.

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6.  A seven-gene CpG-island methylation panel predicts breast cancer progression.

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7.  Next generation sequencing: advances in characterizing the methylome.

Authors:  Kristen H Taylor; Huidong Shi; Charles W Caldwell
Journal:  Genes (Basel)       Date:  2010-07-01       Impact factor: 4.096

8.  A comprehensive microarray-based DNA methylation study of 367 hematological neoplasms.

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Journal:  PLoS One       Date:  2009-09-11       Impact factor: 3.240

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10.  Identification of a small optimal subset of CpG sites as bio-markers from high-throughput DNA methylation profiles.

Authors:  Hailong Meng; Edward L Murrelle; Guoya Li
Journal:  BMC Bioinformatics       Date:  2008-10-27       Impact factor: 3.169

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