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GSE19429
Homo sapiens
200 Downloadable Samples
Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
In order to gain insight into the molecular pathogenesis of the myelodysplastic syndromes (MDS), we performed global gene expression profiling and pathway analysis on the hematopoietic stem cells (HSC) of 183 MDS patients as compared with the HSC of 17 healthy controls. The most significantly deregulated pathways in MDS include interferon signaling, thrombopoietin signaling and the Wnt pathway. Among the most significantly deregulated gene pathways in early MDS are immunodeficiency, apoptosis and chemokine signaling, whereas advanced MDS is characterized by deregulation of DNA damage response and checkpoint pathways. We have identified distinct gene expression profiles and deregulated gene pathways in patients with del(5q), trisomy 8 or 7/del(7q). Patients with trisomy 8 are characterized by deregulation of pathways involved in the immune response, patients with 7/del(7q) by pathways involved in cell survival, whilst patients with del(5q) show deregulation of integrin signaling and cell cycle regulation pathways. This is the first study to determine deregulated gene pathways and ontology groups in the HSC of a large group of MDS patients. The deregulated pathways identified are likely to be critical to the MDS HSC phenotype and give new insights into the molecular pathogenesis of this disorder thereby providing new targets for therapeutic intervention.
Publication Title
Deregulated gene expression pathways in myelodysplastic syndrome hematopoietic stem cells.
Sample Metadata Fields
Specimen part, Disease
View Samples- Homo sapiens
- 168 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
We aimed to determine the impact of the common mutations on the transcriptome in myelodysplastic syndromes (MDS). We linked genomic data with gene expression microarray data and we deconvoluted the expression of genes into contributions stemming from each genetic and cytogenetic alteration, providing insights into how driver mutations interfere with the transcriptomic state. We modelled the influence of mutations and expression changes on diagnostic clinical variables as well as survival.
Publication Title
Combining gene mutation with gene expression data improves outcome prediction in myelodysplastic syndromes.
Sample Metadata Fields
Specimen part, Disease
View Samples