98344a30-5717-4857-8bdd-14f0babe88ad - samples
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ABSTRACT:
PROVIDER: EGAD00001007677 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:RNA-sequencing of meningiomas for integrative molecular classification.
| EGAD00001007494 | EGA
Project description:Integrative Molecular Classification of Meningiomas
| EGAS00001004982 | EGA
Project description:Spinal meningiomas account for 1.2-12% of all meningiomas and 25-45% of all spinal tumours. About 20% of intracranial, but also 4.6% of spinal meningiomas recur and require additional treatment. The classification of intracranial meningiomas has evolved considerably in recent years and uses genetic [1,2,4] as well as epigenetic parameters [3,5] in order to more precisely predict the patients’ prognosis and to lay the ground for therapeutic regiments that are adapted to the aggressiveness of a patient’s tumor. On the other hand, spinal meningiomas are missing in many of the large cohorts that were gathered for the molecular profiling of meningiomas. Also, they have never been thoroughly analyzed separately, and their classification still relies on histopathological findings solely. We analysed 65 tumour samples from 63 patients, who had histologically proven spinal meningioma to perform genetic and epigenetic profiling. Clinical features are described in Supplementary Table 1, online resource. T-distributed Stochastic Neighbor Embedding (t-SNE) analysis of genome-wide DNA methylation data shows that most spinal meningiomas separate from cranial meningiomas (Fig. 1A&B) and form two distinct clusters.
2022-10-12 | GSE212449 | GEO
Project description:Meningiomas are typically considered a benign tumor that can be cured by complete surgical resection; however, a percentage of patients have recurrent disease, even after apparently complete resections. These patients require additional surgeries, radiation therapy, chemotherapy, or a combination of all three. The ability to recognize these patients prior to recurrence would promote earlier use of adjuvant therapy, thus improving overall patient outcome. Unfortunately, identification of meningiomas with this more aggressive phenotype is difficult, and standard histopathological techniques rarely suffice. The identification of genetic and molecular parameters that can help to define these more aggressive tumors would improve prognostication and treatment planning for patients with meningiomas. 1. Establish gene profiles for benign (grade 1) and aggressive (grades 2 and 3) meningiomas. 2. Determine if there are particular expression profiles that can help differentiate between benign and aggressive meningiomas. 3. Determine if there is/are specific gene(s) whose expression is/are altered in benign vs aggressive tumors. 4. Determine if there is a correlation between specific genetic abnormalities in these tumors (as analyzed by fluorescent in situ hybridization; FISH) and gene expression profiles. Our overall hypothesis is that there are molecular and biochemical changes that can be used to identify meningiomas that will have a more aggressive clinical course. Specific Aims 1 and 2: RNA from flash frozen or RNA-later preserved tissue (from all three grades of meningiomas) has been used for RNA isolation using standard protocols. RNA quantity has been determined using a RiboGreen RNA quantitation Kit (Molecular Probes), and RNA quality has been demonstrated using standard formaldehyde gels. These samples will be sent to the NINDS/NIMH microarray consortium for Affymetrix microarray analyses. Data analysis will be done using GeneSpring software (Silicon Genetics, Inc.) with assistance from consortium personnel. Specific Aim 3: Differentially expressed genes identified through microarray analyses will be analyzed using quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Real time qRT-PCR is a standard technique used in our laboratory for gene expression analysis. Specific Aim 4: FISH analyses of paraffin-embedded tissue has been completed for 77 tumors. We have frozen tissue from a number of these patients. RNA from these samples will be used for microarray analyses (Specific Aims 1-3). The results of Speicifc Aims 1 and 2 will affect how we perform our correlation analyses. This will be done with the assistance of contracted statistical personnel
2008-06-13 | E-GEOD-4780 | biostudies-arrayexpress
Project description:Single-cell RNA sequencing of meningiomas
| PRJNA761582 | ENA
Project description:Single-cell RNA sequencing of meningiomas
| PRJNA881320 | ENA
Project description:Meningiomas account for roughly one-third of all primary brain tumors. Although typically benign, about 20% of meningiomas are aggressive, and despite the rigor of the current histopathological classification system, there remains considerable uncertainty in predicting tumor behavior. Here we analyzed 160 tumors from all three WHO grades (I-III) using clinical, gene expression and sequencing data. Unsupervised clustering analysis identified three molecular groups that reliably predicted clinical severity. These groups did not directly correlate with the WHO grading system, which would classify more than half of the tumors in the most aggressive molecular group as benign. Transcriptional and biochemical analyses revealed that aggressive meningiomas involve loss of the repressor function of the DREAM complex, resulting in cell cycle activation, and only tumors in this group tend to recur after full resection. These findings should improve our ability to predict recurrence and develop targeted treatments for these clinically challenging tumors.
2019-11-13 | GSE136661 | GEO
Project description:We generated a comprehensive dataset utilizing the cuprizone model. This dataset encompasses bulk RNA-seq, single-nucleus RNA-seq (snRNA-seq), and spatial transcriptomics, with the aim of investigating the molecular changes linked to the cuprizone-induced demyelination phenotype. Integration of these omics dataset allowed us to investigate the changes occurring at both the single-cell and spatial levels, thereby enabling a deeper understanding of the cellular dynamics and molecular interactions associated with the cuprizone-induced demyelination phenotype.
2024-11-06 | GSE255370 | GEO
Project description:We generated a comprehensive dataset utilizing the cuprizone model. This dataset encompasses bulk RNA-seq, single-nucleus RNA-seq (snRNA-seq), and spatial transcriptomics, with the aim of investigating the molecular changes linked to the cuprizone-induced demyelination phenotype. Integration of these omics dataset allowed us to investigate the changes occurring at both the single-cell and spatial levels, thereby enabling a deeper understanding of the cellular dynamics and molecular interactions associated with the cuprizone-induced demyelination phenotype.
2024-11-06 | GSE255369 | GEO
Project description:We generated a comprehensive dataset utilizing the cuprizone model. This dataset encompasses bulk RNA-seq, single-nucleus RNA-seq (snRNA-seq), and spatial transcriptomics, with the aim of investigating the molecular changes linked to the cuprizone-induced demyelination phenotype. Integration of these omics dataset allowed us to investigate the changes occurring at both the single-cell and spatial levels, thereby enabling a deeper understanding of the cellular dynamics and molecular interactions associated with the cuprizone-induced demyelination phenotype.
2024-11-06 | GSE255368 | GEO