Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

RNA sequencing of meningiomas

ABSTRACT: RNA sequencing of meningiomas

PROVIDER: PRJNA761580 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR15808800.fastq.gz	Fastqsanger.gz
	SRR15808900.fastq.gz	Fastqsanger.gz
	SRR15808801.fastq.gz	Fastqsanger.gz
	SRR15808901.fastq.gz	Fastqsanger.gz
	SRR15808802.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 185

Similar Datasets

RNA sequencing of meningiomas

Project description:We report RNA sequencing of 185 meningiomas, which are used to interrogate the biology underlying DNA methylation groups of meningioma.

2022-01-10 | GSE183653 | GEO

Single-cell RNA sequencing of meningiomas

Project description:Single-cell RNA sequencing of meningiomas

| PRJNA881320 | ENA

Single-cell RNA sequencing of meningiomas

Project description:Single-cell RNA sequencing of meningiomas

| PRJNA761582 | ENA

Single-cell RNA sequencing of patient-derived xenograft meningiomas

Project description:We report single-cell RNA sequencing of 23 PDX meningiomas that underwent radiation therapy, were treated with a Notch3 neutralizing antibody, or overexpressed Notch3.

2024-05-03 | GSE239795 | GEO

Single-cell RNA sequencing of canine meningiomas

Project description:Single-cell RNA sequencing of canine meningiomas

| PRJNA932291 | ENA

Single-cell RNA sequencing of patient-derived xenograft meningiomas

Project description:Single-cell RNA sequencing of patient-derived xenograft meningiomas

| PRJNA1000978 | ENA

"Immunogenic subtype" characterised by macrophages infiltration predominantly comprise meningiomas in neurofibromatosis type 2 patients

Project description:Aims: Whereas recent molecular analysis has revealed that sporadic meningioma has various genetic, epigenetic, and transcriptomic profiles, those of meningioma in NF2 patients are not fully elucidated. This study probed meningiomas' clinical, histological, and molecular characteristics in NF2 patients. Methods: A long-term retrospective follow-up (13.5 ± 5.5 years) study involving 159 meningiomas in NF2 patients was performed. We assessed their characteristics by performing immunohistochemistry (IHC), bulk-RNA sequencing, and copy number analysis. All variables of meningiomas in NF2 patients were compared with those of 189 sporadic NF2-altered meningiomas. Results: Most meningiomas in NF2 patients were stable, and the mean annual growth rate was 1.0 ± 1.8 cm3/yr. Twenty-eight meningiomas (17.6%) in 25 patients (43.1%) were resected during the follow-up period. WHO grade 1 meningiomas in NF2 patients` were more frequent than in sporadic NF2-altered meningiomas (92.9% vs 80.9%). Transcriptomic analysis for NF2 patients`/sporadic NF2-altered WHO grade 1 meningiomas (n = 14 versus 15, respectively) showed that tumours in NF2 patients had still higher immune response and immune cell infiltration than sporadic NF2-altered meningiomas. Furthermore, RNA-seq/IHC-derived immunophenotyping corroborated this higher immune response by identifying myeloid cell infiltration, especially macrophages. Conclusions: Clinical, histological, and transcriptomic analyses for meningiomas in NF2 patients demonstrated that meningiomas in NF2 patients showed less aggressive behaviour than sporadic NF2-altered meningiomas and elicited marked immune response by identifying myeloid cell infiltration, especially macrophages.

2023-08-08 | GSE232528 | GEO

Genomic landscape of meningiomas

Project description:This SuperSeries is composed of the following subset Series: GSE16581: Genomic landscape of meningiomas: gene expression GSE16583: Genomic landscape of meningiomas: genotyping Refer to individual Series

2010-12-01 | E-GEOD-16584 | biostudies-arrayexpress

Expression data from meningiomas and normal meninges

Project description:Correlate the gene expression profiles with the most relevant patterns of chromosome abnormalities (cytogenetic subgroups of meningiomas) and the gene expression profiles could help to explain the differences in clinical behaviour of meningiomas. The impact of tumor cytogenetics on the gene expression analyzed in meningiomas. Here, we analyzed the gene expression profiles (GEP) of 47 tumors and correlated them with the most clinical relevant cytogenetic subgroups of meningiomas, including diploid tumors, isolated -22/22q-, del(1p36) alone and complex karyotypes associated with del(1p36) and/or -14q. In addition, 4 samples containing purified RNA extracted from normal meningeal cells (BioChain Institute, Hayward, CA and US Biological, Swampscott, MA, USA) were also processed.

2013-01-05 | E-GEOD-43290 | biostudies-arrayexpress

Genomic landscape of meningiomas: genotyping

Project description:Meningiomas are one of the most common adult brain tumors. For most patients, surgical excision is curative. However, up to 20% recur. Currently, the molecular determinants predicting recurrence and malignant transformation are lacking. We performed global genetic and genomic analysis of 85 meningioma samples of various grades. Copy number alterations were assessed by 100K SNP arrays and correlated with gene expression, proliferation indices, and clinical outcome. In addition to chromosome 22q loss, which was detected in the majority of clinical samples, chromosome 18q and 6q loss significantly predicted recurrence and was associated with anaplastic histology. Five "classes" of meningiomas were detected by gene expression analysis that correlated with copy number alterations, recurrence risk, and malignant histology. These classes more accurately predicted tumor recurrence than Ki-67 index, the gold standard for determining risk of recurrence, and highlight substantial expression heterogeneity between meningiomas. These data offer the most complete description of the genomic landscape of meningiomas and provide a set of tools that could be used to more accurately stratify meningioma patients into prognostic risk groups.

2011-10-12 | GSE16583 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data