Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

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

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

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

Myelodysplastic Syndromes (MDS) and Acute Myeloid Leukaemia (AML)

Project description:A 10K cDNA microarray was used to compare neutrophil RNA from 21 MDS patient and 2 AML patients to pooled neutrophil RNA from 7 healthy controls.

2005-01-09 | E-GEOD-971 | biostudies-arrayexpress

Myelodysplastic Syndromes (MDS) and Acute Myeloid Leukaemia (AML)

Project description:A 10K cDNA microarray was used to compare neutrophil RNA from 21 MDS patient and 2 AML patients to pooled neutrophil RNA from 7 healthy controls. Keywords: repeat sample

2005-01-10 | GSE971 | GEO

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

2009-06-09 | GSE15061 | GEO

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)

Project description:We recently identified recurrent mutations of cohesin complex in myeloid neoplasms through whole-exome sequencing analysis. In this study, we performed SNP array analysis to detect abnormal copy number of the cohesin genes. Copy number analysis by Affymetrix 50K or 250K SNP arrays was performed for 93 AML and 70 MDS tumor samples.

2013-07-16 | E-GEOD-47682 | biostudies-arrayexpress

Affymetrix SNP array data for myelodysplastic syndromes (MDS) samples

Project description:The progressive mechanism of myelodysplastic syndrome (MDS) remains unknown. We report that ROBO1 and ROBO2 are identified as novel progression-related somatic mutations using whole-exome and targeted sequencing in six of 16 (37.5%) paired MDS patients undergoing disease progression. To investigated the effect of ROBO1 or ROBO2 on ROBO1/2 CN number and LOH, we employed a Cytosan 750K chip to analyze the copy-number variations (CNVs) and loss of heterogeneity (LOH) in MDS patients with ROBO1&2 mutations. Copy number and LOH analysis of Affymetrix CytoScan 750K array was performed for 14 MDS patients with ROBO1 or ROBO2 mutations

2015-09-15 | E-GEOD-73005 | biostudies-arrayexpress

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

2009-11-01 | GSE18366 | GEO

Aberrant alternative splicing profiling for myelodysplastic syndrome (MDS) patients

Project description:Transcriptome analysis of total RNA from bone marrow (BM) mononuclear cells of MDS patients and normal dornors Global gene expression and alternative splicing profiling among patients with myelodysplastic syndrome (MDS) compared with normal donors

2018-12-05 | GSE107400 | GEO