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Gene expression profiling of pediatric myelodysplastic syndrome (MDS) characterizes disease subtype and time to progression into acute myeloid leukemia (AML)

ABSTRACT: Identification of relevant subgroups in childhood MDS patients by gene expression analysis and gene involve in progression into AML

ORGANISM(S): Homo sapiens

PROVIDER: GSE29326 | GEO | 2012/02/29

SECONDARY ACCESSION(S): PRJNA140121

REPOSITORIES: GEO

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Gene expression profiling of pediatric myelodysplastic syndrome (MDS) characterizes disease subtype and time to progression into acute myeloid leukemia (AML)

Project description:Identification of relevant subgroups in childhood MDS patients by gene expression analysis and gene involve in progression into AML Class comparison between different subgroups of pediatric MDS (class risk, classification subtype)

2012-02-29 | E-GEOD-29326 | biostudies-arrayexpress

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Gene array prediction of AML transformation in MDS

Project description:Microarray-based classifiers and prognosis models identify subgroups with distinct clinical outcomes and high risk of AML transformation of myelodysplastic syndrome (MDS) An array-based Diagnostic Classifier (DC) model, developed for and evaluated during the MILE study, correctly identified ~50% of the unfractionated MDS specimens submitted to the study; predictions for the other samples were split between “none-of-the-targets” classes and AML signatures, but this distinction also reflected clinical outcome in terms of time to AML transformation. Furthermore, an improved Prognostic Classifier (PC) model was developed that correlated with both time to AML transformation and overall survival. Keywords: Microarray-based gene expression profiling aimed at prediction of AML transformation in MDS

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Differential Gene Expression Profiles in CD34+ Myelodysplastic Syndrome Marrow cells to Disease Subtype and Progression

Project description:Microarray analysis with 40,000 cDNA gene chip arrays determined differential gene expression profiles (GEPs) in CD34+ marrow cells from myelodysplastic syndrome (MDS) patients compared to normal individuals. Using focused bioinformatics analyses, we found 1175 genes significantly differentially expressed by MDS vs Normal, requiring a minimum of 39 genes to separately classify these patients. Major GEP differences were demonstrated between Normal and MDS patients and between several MDS subgroups: (1) those whose disease remained stable (sMDS) and those who subsequently transformed (tMDS) to acute myeloid leukemia (AML); (2) between del(5q) and other MDS patients. A 6-gene ‘poor risk’ signature was defined which was associated with AML transformation and provided additive prognostic information for IPSS Intermediate-1 patients. Over-expression of genes generating ribosomal proteins and for other signaling pathways was demonstrated in the tMDS patients. Comparison of del(5q) to the remaining MDS patients showed 1924 differentially expressed genes, with under-expression of 1014 genes, 11 of which were within the 5q31-32 Commonly Deleted Region. These data demonstrated (1) GEPs distinguishing MDS patients from normal and between those with differing clinical outcomes (tMDS vs sMDS) and cytogenetics [eg, del(5q)] ; and (2) molecular criteria refining prognostic categorization and associated biologic processes in MDS. Gene expression profiles from CD34+ cells of 35 MDS subjects and 6 Normals were compared.

2009-10-31 | E-GEOD-18366 | biostudies-arrayexpress

Affymetrix SNP array data for acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS)

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Affymetrix SNP array data for myelodysplastic syndromes (MDS) samples

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Differential Gene Expression Profiles in CD34+ Myelodysplastic Syndrome Marrow cells to Disease Subtype and Progression

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Whole genome SNP array data from myelodysplastic syndromes (MDS) patients

Project description:With the whole genome SNP array information obtained from tumor and matched normal control, we could evaluate the acquired copy number variations (CNVs) and uniparental disomies (UPDs) . Seven MDS patients in a whole genome sequencing project were included in this experiment. To detect acquired CNVs and UPDs in MDS patients, we genotyped both CD34+ cell from bone marrow and matched skin tissue.

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