Affymetrix SNP array data for meningioma samples
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE287920 | GEO | 2025/01/31
REPOSITORIES: GEO
Similar Datasets
Project description:We analyzed 1042 meningiomas with whole-genome microarray including 12 meningiomas with multifocal sampling to characterize how CNV thresholds influence their molecular classification and outcome.
2025-01-31 | GSE287929 | GEO
Project description:Cytogenetic profiles of 50 meningiomas using high-density GeneChip Mapping 500K set and Genome-Wide Human SNP 6.0 Array in the tumor tissues and in the peripheral blood of the same patients. A total of two hundred 500k arrays (100 tumor samples and 100 blood samples) and 14 SNP6.0 arrays (7 tumour samples and 7 peripheral blood samples) were studied to explore the most common recurrent chromosomal abnormalities (gains and losses) in meningiomas. Our results confirm that del(22q) (52%) and del(1p) (16%) (common deleted regions: 22q11.21-22q13.3. and 1p31.2-p36.33) are the most frequent abnormalities. Additionally, recurrent monosomy 14 (8%), del(6p) (10%), del(7p) (10%) and del(19p) (6%) were also observed, while copy number variation (CNV) patterns consistent with recurrent chromosome gains, gene amplification was absent or rare. Based on their overall SNP profiles meningiomas could be classified into: i) diploid cases, ii) meningiomas with a single chromosome change (e.g. monosomy 22/del(22q) and iii) tumours with ≥2 altered chromosomes.
2012-11-30 | GSE42624 | GEO
Project description:Cytogenetic profiles of 50 meningiomas using high-density GeneChip Mapping 500K set and Genome-Wide Human SNP 6.0 Array in the tumor tissues and in the peripheral blood of the same patients. A total of two hundred 500k arrays (100 tumor samples and 100 blood samples) and 14 SNP6.0 arrays (7 tumour samples and 7 peripheral blood samples) were studied to explore the most common recurrent chromosomal abnormalities (gains and losses) in meningiomas. Our results confirm that del(22q) (52%) and del(1p) (16%) (common deleted regions: 22q11.21-22q13.3. and 1p31.2-p36.33) are the most frequent abnormalities. Additionally, recurrent monosomy 14 (8%), del(6p) (10%), del(7p) (10%) and del(19p) (6%) were also observed, while copy number variation (CNV) patterns consistent with recurrent chromosome gains, gene amplification was absent or rare. Based on their overall SNP profiles meningiomas could be classified into: i) diploid cases, ii) meningiomas with a single chromosome change (e.g. monosomy 22/del(22q) and iii) tumours with M-bM-^IM-%2 altered chromosomes. 500K SNP mapping set array and Genome-Wide Human SNP 6.0 Array were used to profile 50 meningiomas with matched blood DNA samples. Loss of heterozygosity (LOH) and copy number abnormality (CNA) profiles were derived from each tumour-blood pair. In seven tumors, both types of arrays were assessed.
2012-11-30 | E-GEOD-42624 | biostudies-arrayexpress
Project description:Purpose: Clustering of meningiomas using DNA methylation and RNA-sequencing data yields groups which predict clinical behavior than the current clinical standard of histopathologic grading. Both techniques segregate many common genomic features seen in meningiomas similarly, raising the question of whether they were identifying the same underyling groups. Methods: DNA methylation profiling (EPIC 850K array) and RNA-sequencing of 110 primary meningiomas was performed. Unsupervised clustering was performed using each type of data, followed by computational modeling which explored correlations of promoter methylation and copy number variation (CNV) with gene expression. Results: We performed unsupervised clustering of the DNA methylation data and gound three clusters to be optimal. These clusters aligned closely with our prior transcriptional classification (Patel et al. PNAS 2019). These groups also closely aligned with groups defined by large-scale cytogenetic changes and merlin expression/genomic instability, suggesting the presence of three biologic groups of meningioma that can be identified by multiple methods. Computational modeling demonstrated that both promoter methylation and CNV correlated closely with gene expression differences seen in meningioma and our biologic groups, although cytogenetic changes (particularly chr 1p loss) was predominant in the clinically aggressive tumors. Conclusions: Our analysis suggests the presence of three biologic groups of meningioma (MenG A, B, and C) which can be accurately identified through DNA methylation, RNA-seq, and cytogenetic profiling.
2021-11-30 | GSE189521 | GEO
Project description:Purpose: Clustering of meningiomas using DNA methylation and RNA-sequencing data yields groups which predict clinical behavior than the current clinical standard of histopathologic grading. Both techniques segregate many common genomic features seen in meningiomas similarly, raising the question of whether they were identifying the same underyling groups. Methods: DNA methylation profiling (EPIC 850K array) and RNA-sequencing of 110 primary meningiomas was performed. Unsupervised clustering was performed using each type of data, followed by computational modeling which explored correlations of promoter methylation and copy number variation (CNV) with gene expression. Results: We performed unsupervised clustering of the DNA methylation data and gound three clusters to be optimal. These clusters aligned closely with our prior transcriptional classification (Patel et al. PNAS 2019). These groups also closely aligned with groups defined by large-scale cytogenetic changes and merlin expression/genomic instability, suggesting the presence of three biologic groups of meningioma that can be identified by multiple methods. Computational modeling demonstrated that both promoter methylation and CNV correlated closely with gene expression differences seen in meningioma and our biologic groups, although cytogenetic changes (particularly chr 1p loss) was predominant in the clinically aggressive tumors. Conclusions: Our anaylsis suggests the presence of three biologic groups of meningioma (MenG A, B, and C) which can be accurately identified through DNA methylation, RNA-seq, and cytogenetic profiling.
2021-11-30 | GSE189672 | GEO
Project description:Variability of meningioma growth, even within benign subgroup, makes some remain unaltered, while others grow fast despite an appended radiotherapy. Biomarkers that differentiate between less and more aggressive meningiomas would therefore have a significant clinical benefit in prediction of their biological behaviour. The aim of the study was to identify new candidate miRNAs enabling better and proper prediction of meningioma recurrence. We used miRNA 4.0 array (Applied Biosystems, Foster City, CA, USA) to detect miRNA profiles in 44 primary meningioma patients (19 with subsequent recurrence) and 5 healthy controls. Moreover, miRNA profiles were analyzed in 15 secondary meningiomas.
2019-02-15 | GSE126563 | GEO
Project description:Meningiomas are common brain tumors that are classified into three World Health Organization grades (Grade I: benign, Grade II: atypical and Grade III: malignant) and are molecularly ill-defined tumors. The purpose of this study was identify microRNA (miRNA) molecular signatures unique to the different grades of meningiomas correlating them to prognosis. We have used a miRNA expression microarray to show that meningiomas of all three grades fall into two main molecular groups designated “benign” and “malignant” meningiomas. While all typical meningiomas fall into the benign group and all anaplastic meningiomas fall into the malignant group, atypical meningiomas distribute into either one of these groups. We have identified a miRNA signature that distinguishes benign meningiomas from malignant meningiomas. We studied the gene expression profiles of 340 mammalian miRNAs in 37 primary meningioma tumors by means of DNA microarrays.
2011-01-02 | E-GEOD-19720 | biostudies-arrayexpress
Project description:Genomic profiling of anaplastic meningioma can inform prognostic gene level alterations in lower-grade meningiomas, potentially reflecting evolution of anaplastic meningioma from lowergrade precursor tumours. Larger scale studies in paired primary and recurrent meningiomas are warranted to unravel the evolutionary path to anaplastic meningiomas and prognostic genomic alterations in detail
2021-05-04 | GSE173825 | GEO
Project description:Meningiomas are common brain tumors that are classified into three World Health Organization grades (Grade I: benign, Grade II: atypical and Grade III: malignant) and are molecularly ill-defined tumors. The purpose of this study was identify microRNA (miRNA) molecular signatures unique to the different grades of meningiomas correlating them to prognosis. We have used a miRNA expression microarray to show that meningiomas of all three grades fall into two main molecular groups designated “benign” and “malignant” meningiomas. While all typical meningiomas fall into the benign group and all anaplastic meningiomas fall into the malignant group, atypical meningiomas distribute into either one of these groups. We have identified a miRNA signature that distinguishes benign meningiomas from malignant meningiomas.
2011-01-02 | GSE19720 | GEO
Project description:Comparison of the gene expression profiles with meningiomas of different grading.
2014-03-06 | GSE55609 | GEO