Meningioma transcriptomic landscape demonstrates novel subtypes with regional associated biology and patient outcome
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1059263 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR27388500_1.fastq.gz | Fastqsanger.gz | |||
| SRR27388500_2.fastq.gz | Fastqsanger.gz | |||
| SRR27388600_1.fastq.gz | Fastqsanger.gz | |||
| SRR27388600_2.fastq.gz | Fastqsanger.gz | |||
| SRR27388700_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 558 |
Similar Datasets
Project description:Meningiomas are the most common primary intracranial tumors in humans. While most of these tumors are benign, some are malignant, rapidly recur after multimodal treatment with surgery and radiotherapy, and can ultimately be fatal. The current WHO grade system does not always identify high risk meningiomas, therefore better characterizations of the biology of aggressive tumors are needed. In order to address these challenges, we combined 13 bulk RNA-Seq datasets, corrected for batch effects, and applied Uniform Manifold Approximation and Projection (UMAP) to create a reference landscape of ~1300 meningioma tumors. Our analyses revealed multiple distinct meningioma subtypes with specific biological signatures. Clinical metadata, mutations, copy number alterations, and gene-fusion data effectively correlated with regions of the UMAP. Notably, regional distribution of time to recurrence identified major clusters as well as intra-cluster differences of meningiomas with varying patient outcomes. The most aggressive subtype, characterized by an enrichment of higher WHO grades, frequent tumor recurrences, and shorter time to recurrence, exhibited elevated proliferation rates and RNA expression resembling muscle development. To facilitate clinical applications, we developed a cross-validated nearest-neighbors-based algorithm that accurately maps new patients onto this UMAP landscape. Our study highlights the utility of transcriptomic analysis in discerning meningioma heterogeneity as well as successful combination of multiple datasets from various sources. We provide a valuable tool for understanding the disease, predicting tumor biology and patient prognosis. This resource is accessible via the open source, interactive online tool Oncoscape, where the scientific community can explore the landscape and mine clinical and genomic metadata.
2024-04-26 | GSE252291 | GEO
Project description:Meningiomas, although mostly benign, can be recurrent and fatal. World Health Organization (WHO) grading of the tumor does not always identify high-risk meningioma, and better characterizations of their aggressive biology are needed. To approach this problem, we combined 13 bulk RNA sequencing (RNA-seq) datasets to create a dimension-reduced reference landscape of 1,298 meningiomas. The clinical and genomic metadata effectively correlated with landscape regions, which led to the identification of meningioma subtypes with specific biological signatures. The time to recurrence also correlated with the map location. Further, we developed an algorithm that maps new patients onto this landscape, where the nearest neighbors predict outcome. This study highlights the utility of combining bulk transcriptomic datasets to visualize the complexity of tumor populations. Further, we provide an interactive tool for understanding the disease and predicting patient outcomes. This resource is accessible via the online tool Oncoscape, where the scientific community can explore the meningioma landscape.
| S-EPMC11228955 | biostudies-literature
Project description:Although meningiomas are one of the most frequent primary intracranial tumors, there are only a few studies dealing with gene regulation processes in meningiomas. MiRNAs are key regulators of gene expression and regulation and miRNA profiles offer themselves as biomarkers for cancer development and progression. To investigate the role of miRNAs during meningioma growth and progression, we compared expression of 1205 miRNAs in 55 meningioma samples of different tumor grades and histological subtypes. We were able to classify histological subtypes in WHO grade I meningiomas with up to 97% accuracy (meningothelial versus fibroblastic) and different WHO grades with up to 93% accuracy (WHO I versus WHO III). We found significant downregulation of miRNAs on chromosome 1p36 and within two large miRNA clusters on 14q32 in high grade meningiomas, two regions that are yet associated with meningioma progression. We also identified several miRNAs associated with epithelial to mesenchymal transition differentially expressed in meningothelial meningioma compared to fibroblastic meningioma. Combined, our data show that meningiomas of different WHO grades and histological subtypes show a specific miRNA expression profile. Some individual miRNAs can also serve as potential biomarkers for meningioma progression.
2014-12-31 | GSE50641 | GEO
Project description:Comparison of the gene expression profiles with meningiomas of different grading. 24 primary meningioma cultures from surgical specimen were maintained to primary meningioma cultures.
2014-03-06 | E-GEOD-55609 | biostudies-arrayexpress
Project description:We performed expression profiling of 24 meningioma and two dura controls analyzing 55000 transcripts including 18300 known genes. We compared expression in meningioma vs. dura, expression of low grade (WHO I) vs. higher-grade (WHO II and WHO IIII) tumors and expression of meningothelial and syncytial meningioma vs. fibroblastic meningioma. Gene expression was analysed in 24 meningioma including eight of each WHO grade and two dura controls analyzing 55000 transcripts including 18300 known genes.
2008-11-20 | E-GEOD-12530 | biostudies-arrayexpress
Project description:Background: Meningiomas account for about 27% of primary brain tumors, making them one of the most common brain tumor. They are most common in people between the ages of 40 and 70 and are more common in women than in men. Most meningiomas (90%) are categorized as benign tumors, with the remaining 10% being atypical or malignant. Multiple classifications exist today, but the most commonly used is the World Health Organization’s (WHO) which classifies meningiomas into three histological grades: grade I (benign), grade II (atypical), and grade III (anaplastic) in accordance with the clinical prognosis. Most of these subtypes behave similarly, however anaplastics are the most aggressive. The ability to distinguish benign from atypical and anaplastic tumors is important because of its impact on treatment decisions. A molecular based classification system has the likelihood of being a better prognostic indicator and is useful for identifying alterations in pathways and networks that drive tumor progression and growth. The information obtained can potentially be translated into more effective and less toxic targeted therapies. We tested a method for genome wide expression profiling of formalin-fixed, paraffin-embedded tissues. We applied the method to the analysis of the clinical outcome of meningioma tumor. Materials and Methods: The training set consisted of tissue samples from 63 patients who were consecutively treated with surgery for meningioma between 1990 and 2005. For each patient data on clinical outcomes and formalin-fixed, paraffin-embedded blocks of tumor were available. The validation set included tissue samples from 189 patients with meningioma who consecutively underwent surgery between 1992 and 2006. We used a custom 60-mer amino modified oligo- array, containing 912 probes, a lot of which specific for genes commonly altered in cancer. Functional annotation was performed by means of gene set enrichment analysis (GSEA, www. broad.mit.edu/gsea/). Survival analyses were performed with the use of the log-rank test and Cox regression modeling. All analyses were performed with the use of GenePattern. Results: We investigated whether gene-expression profiles of meningioma tumors were associated with the clinical outcome. Using a standard leaveone- out cross-validation procedure, we found the meningioma signature to be significantly correlated with survival (P = 0.0001). The survival correlated signature contained 219 genes and was tested in the validation set. Conclusion: These results support the validity of the survival signature and highlight the potential role of tumoral meningioma tissue in predicting the outcome for patients with meningioma tumors.
2011-05-11 | GSE16156 | GEO
Project description:Background: Meningiomas account for about 27% of primary brain tumors, making them one of the most common brain tumor. They are most common in people between the ages of 40 and 70 and are more common in women than in men. Most meningiomas (90%) are categorized as benign tumors, with the remaining 10% being atypical or malignant. Multiple classifications exist today, but the most commonly used is the World Health Organization’s (WHO) which classifies meningiomas into three histological grades: grade I (benign), grade II (atypical), and grade III (anaplastic) in accordance with the clinical prognosis. Most of these subtypes behave similarly, however anaplastics are the most aggressive. The ability to distinguish benign from atypical and anaplastic tumors is important because of its impact on treatment decisions. A molecular based classification system has the likelihood of being a better prognostic indicator and is useful for identifying alterations in pathways and networks that drive tumor progression and growth. The information obtained can potentially be translated into more effective and less toxic targeted therapies. We tested a method for genome wide expression profiling of formalin-fixed, paraffin-embedded tissues. We applied the method to the analysis of the clinical outcome of meningioma tumor. Materials and Methods: The training set consisted of tissue samples from 63 patients who were consecutively treated with surgery for meningioma between 1990 and 2005. For each patient data on clinical outcomes and formalin-fixed, paraffin-embedded blocks of tumor were available. The validation set included tissue samples from 189 patients with meningioma who consecutively underwent surgery between 1992 and 2006. We used a custom 60-mer amino modified oligo- array, containing 912 probes, a lot of which specific for genes commonly altered in cancer. Functional annotation was performed by means of gene set enrichment analysis (GSEA, www. broad.mit.edu/gsea/). Survival analyses were performed with the use of the log-rank test and Cox regression modeling. All analyses were performed with the use of GenePattern. Results: We investigated whether gene-expression profiles of meningioma tumors were associated with the clinical outcome. Using a standard leaveone- out cross-validation procedure, we found the meningioma signature to be significantly correlated with survival (P = 0.0001). The survival correlated signature contained 219 genes and was tested in the validation set. Conclusion: These results support the validity of the survival signature and highlight the potential role of tumoral meningioma tissue in predicting the outcome for patients with meningioma tumors.
2011-05-11 | GSE16153 | GEO
Project description:Methylation analysis of meningioma cases Genomic DNA isolated directly from human meningioma samples
2024-08-21 | GSE270371 | GEO
Project description:Methylation analysis of meningioma samples. Genomic DNA isolated directly from fresh-frozen human meningioma specimens.
2021-07-14 | GSE180061 | GEO
Project description:Methylation analysis of meningioma samples. Genomic DNA isolated directly from fresh-frozen human meningioma specimens.
2022-03-30 | GSE168726 | GEO