Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA92371 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Genetic and epigenetic processes result in gene expression changes through alteration of the chromatin structure. The relative position of genes on chromosomes has therefore important functional implications and can be exploited to model microarray datasets. Gliomas are the most frequent primary brain tumours in adults and their prognosis is related to histology and grade. In oligodendrogliomas, allelic loss of 1p/19q and hypermethylation of MGMT promoter is associated with longer survival and chemosensitivity. In this work we used oligonucleotide microarray to study a group of 30 gliomas with various oligodendroglial and astrocytic components. We used an original approach combining a wavelet model of inter-probe genomic distance (CHROMOWAVE) and unsupervised method of analysis (Singular Value Decomposition) in order to discover new prognostic chromosomal patterns of gene expression. We identified a major pattern of variation that strongly correlated with survival (p= 0.007) and could be visualized as a genome-wide chromosomal pattern including widespread gene expression changes on 1p, 19q, 4, 18, 13 and 9q and multiple smaller clusters scattered along chromosomes. Gene expression changes on chromosomes 1p, 19q and 9q were significantly correlated with the allelic loss of these regions as measured by FISH. Differential expression of genes implicated in drug resistance was also a feature of this chromosomal pattern and in particular low expression of MGMT was correlated with favourable prognosis (p<0.0001). Remarkably, unsupervised analysis of the expression of individual genes and not of their chromosomal ensemble produced a pattern that could not be associated with prognosis, emphasizing the determinant role of the wavelet mathematical modelling. Experiment Overall Design: Unsupervised analysis using wavelet models of 30 diffuse gliomas
2008-06-12 | E-GEOD-2817 | biostudies-arrayexpress
Project description:Genetic and epigenetic processes result in gene expression changes through alteration of the chromatin structure. The relative position of genes on chromosomes has therefore important functional implications and can be exploited to model microarray datasets. Gliomas are the most frequent primary brain tumours in adults and their prognosis is related to histology and grade. In oligodendrogliomas, allelic loss of 1p/19q and hypermethylation of MGMT promoter is associated with longer survival and chemosensitivity. In this work we used oligonucleotide microarray to study a group of 30 gliomas with various oligodendroglial and astrocytic components. We used an original approach combining a wavelet model of inter-probe genomic distance (CHROMOWAVE) and unsupervised method of analysis (Singular Value Decomposition) in order to discover new prognostic chromosomal patterns of gene expression. We identified a major pattern of variation that strongly correlated with survival (p= 0.007) and could be visualized as a genome-wide chromosomal pattern including widespread gene expression changes on 1p, 19q, 4, 18, 13 and 9q and multiple smaller clusters scattered along chromosomes. Gene expression changes on chromosomes 1p, 19q and 9q were significantly correlated with the allelic loss of these regions as measured by FISH. Differential expression of genes implicated in drug resistance was also a feature of this chromosomal pattern and in particular low expression of MGMT was correlated with favourable prognosis (p<0.0001). Remarkably, unsupervised analysis of the expression of individual genes and not of their chromosomal ensemble produced a pattern that could not be associated with prognosis, emphasizing the determinant role of the wavelet mathematical modelling. Keywords: wavelet, glioma, unsupervised
2005-12-15 | GSE2817 | GEO
Project description:caArray_nelso-00262: Gene expression profiling of gliomas strongly predicts survival
| PRJNA325501 | ENA
Project description:Migrated from 1.6 id: 1015897590491013 GEDP id: 760 In current clinical practice, histology-based grading of diffuse infiltrative gliomas is the best predictor of patient survival time. Yet histology provides little insight into the underlying biology of gliomas and is limited in its ability to identify and guide new molecularly targeted therapies. We have performed large-scale gene expression analysis using the Affymetrix HG U133 oligonucleotide arrays on 85 diffuse infiltrating gliomas of all histologic types to assess whether a gene expression-based, histology-independent classifier is predictive of survival and to determine whether gene expression signatures provide insight into the biology of gliomas. We found that gene expression-based grouping of tumors is a more powerful survival predictor than histologic grade or age. The poor prognosis samples could be grouped into three different poor prognosis groups, each with distinct molecular signatures. We further describe a list of 44 genes whose expression patterns reliably classify gliomas into previously unrecognized biological and prognostic groups: these genes are outstanding candidates for use in histology-independent classification of high-grade gliomas. The ability of the large scale and 44 gene set expression signatures to group tumors into strong survival groups was validated with an additional external and independent data set from another institution composed of 50 additional gliomas. This demonstrates that large-scale gene expression analysis and subset analysis of gliomas reveals unrecognized heterogeneity of tumors and is efficient at selecting prognosis-related gene expression differences which are able to be applied across institutions. nelso-00262 Assay Type: Gene Expression Provider: Affymetrix Array Designs: HG-U133A, HG-U133B Organism: Homo sapiens (ncbitax) Material Types: total RNA, synthetic_RNA, organism_part, whole_organism Disease States: Glioma, Glioblastoma, Oligodendroglial Tumor, astrocytomas
2016-06-14 | E-GEOD-83294 | biostudies-arrayexpress
Project description:Vimentin DNA Methylation Predicts Survival in Breast Cancer
| PRJNA152475 | ENA
Project description:Migrated from 1.6 id: 1015897590491013 GEDP id: 760 In current clinical practice, histology-based grading of diffuse infiltrative gliomas is the best predictor of patient survival time. Yet histology provides little insight into the underlying biology of gliomas and is limited in its ability to identify and guide new molecularly targeted therapies. We have performed large-scale gene expression analysis using the Affymetrix HG U133 oligonucleotide arrays on 85 diffuse infiltrating gliomas of all histologic types to assess whether a gene expression-based, histology-independent classifier is predictive of survival and to determine whether gene expression signatures provide insight into the biology of gliomas. We found that gene expression-based grouping of tumors is a more powerful survival predictor than histologic grade or age. The poor prognosis samples could be grouped into three different poor prognosis groups, each with distinct molecular signatures. We further describe a list of 44 genes whose expression patterns reliably classify gliomas into previously unrecognized biological and prognostic groups: these genes are outstanding candidates for use in histology-independent classification of high-grade gliomas. The ability of the large scale and 44 gene set expression signatures to group tumors into strong survival groups was validated with an additional external and independent data set from another institution composed of 50 additional gliomas. This demonstrates that large-scale gene expression analysis and subset analysis of gliomas reveals unrecognized heterogeneity of tumors and is efficient at selecting prognosis-related gene expression differences which are able to be applied across institutions.
2016-06-14 | GSE83294 | GEO
Project description:Glial cell-derived brain tumors (gliomas) are devastating diseases without effective curative therapies. Gliomas are classified according to various schemes including the cancer-initiating cell type, World Health Organization tumor grades, and genetic markers such as Isocitrate dehydrogenase (IDH) mutations and chromosomal 1p/19q codeletion status. Genomics, transcriptomics and methylomics approaches are emerging as powerful means to refine classification. However, various aspects of cancer biology are solely reflected on the proteomelevel. Here, we employ mass spectrometry (MS) to characterize the proteomes and phospho-proteomes of IDHmut glioma entities with and without 1p/19q codeletion, IDHwt gliomas and control tissue from a total of 42 patients. Our data-dependent acquisition MS workflow on average quantifies more than 5000 proteins and 3000 phospho-sites per sample of formalin-fixed paraffin-embedded (FFPE) brain sections. The tumor proteomes reflected genetic alterations such as the loss of chromosome 1p/19q proteins, the loss of ATRX in non-codeletion IDHmut glioma, and the frequent loss and amplification of chromosomes 10 and 7, respectively,in IDH-wild type glioma. Nevertheless, 1p/19q codeletion status was not the major determinant of IDHmut glioma proteomes. Instead, the proteome profiles suggest an alternative classification of IDHmut gliomas that correlates with the loss of mitochondrial DNA-encoded proteins, the abundance of respiratory chain proteins, a distinct tumor suppressor and oncoprotein profile, an extracellular matrix signature, and alterations in the phospho-proteome. Moreover, the glioma association of numerous proteins implicated in other cancers by this dataset provides a resource for further mechanistic investigation of glioma genesis and progression.
2022-11-09 | PXD024427 | Pride
Project description:A Methylation-Based Signature Predicts Survival In Hepatocellular Carcinoma Patient
| PRJNA244008 | ENA
Project description:Genomic aberrations predicts survival in clear cell renal cell carcinoma
| PRJNA143551 | ENA