Enhancer landscape of meningioma
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA586777 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Enhancer landscape of meningioma [RNA-seq]
| PRJNA586821 | ENA
Project description:Enhancer landscape of meningioma [ChIP-seq]
| PRJNA586822 | ENA
Project description:Meningiomas are the most common primary intracranial tumor. However, surgical resection and radiation frequently fail to eliminate high grade tumors, leading to significant morbidity and mortality. Predicting which tumors will recur rapidly is critical to effective treatment strategies. To address the prognostic challenges and dearth of therapeutic targets, we interrogated the enhancer landscape of a diverse cohort of meningiomas. Enhancers robustly stratified meningiomas into three biologically distinct groups and identified a subset of tumors with a poor prognosis, independent of histological grading. Integrating enhancer networks with transcriptional profiles revealed unique lineage transcriptional regulators associated with each subgroup. A strong hormonal epidemiologic association is well-characterized in meningiomas, but mechanistic insight remains lacking. We identified differential hormonal regulators that stratified between subgroups, and implicated progesterone receptor in maintaining the super enhancer network of a subset of tumors. Super enhancers marked critical and druggable dependencies across a panel of meningioma models.
2021-01-01 | GSE139650 | GEO
Project description:Meningiomas are the most common primary intracranial tumor. However, surgical resection and radiation frequently fail to eliminate high grade tumors, leading to significant morbidity and mortality. Predicting which tumors will recur rapidly is critical to effective treatment strategies. To address the prognostic challenges and dearth of therapeutic targets, we interrogated the enhancer landscape of a diverse cohort of meningiomas. Enhancers robustly stratified meningiomas into three biologically distinct groups and identified a subset of tumors with a poor prognosis, independent of histological grading. Integrating enhancer networks with transcriptional profiles revealed unique lineage transcriptional regulators associated with each subgroup. A strong hormonal epidemiologic association is well-characterized in meningiomas, but mechanistic insight remains lacking. We identified differential hormonal regulators that stratified between subgroups, and implicated progesterone receptor in maintaining the super enhancer network of a subset of tumors. Super enhancers marked critical and druggable dependencies across a panel of meningioma models.
2021-01-01 | GSE139651 | GEO
Project description:This SuperSeries is composed of the following subset Series: GSE40684: Foxp3 exploits a preexistent enhancer landscape for regulatory T cell lineage specification [ChIP-Seq] GSE40685: Foxp3 exploits a preexistent enhancer landscape for regulatory T cell lineage specification [Expression] Refer to individual Series
2012-09-28 | E-GEOD-40686 | biostudies-arrayexpress
Project description:Recent studies suggest a hierarchical model in which lineage-determining factors act in a collaborative manner to select and prime cell-specific enhancers, thereby enabling signal-dependent transcription factors to bind and function in a cell type-specific manner. Consistent with this model, TLR4 signaling primarily regulates macrophage gene expression through a pre-existing enhancer landscape. However, TLR4 signaling also induces priming of ~3000 enhancer-like regions de novo, enabling visualization of intermediates in enhancer selection and activation. Unexpectedly, we find that enhancer transcription precedes local mono- and di-methylation of histone H3 lysine 4 (H3K4me1/2). H3K4 methylation at de novo enhancers is primarily dependent on the histone methyltransferases Mll1, Mll2/4 and Mll3, and is significantly reduced by inhibition of RNA polymerase II elongation. Collectively, these findings suggest an essential role of enhancer transcription in H3K4me1/2 deposition at de novo enhancers that is independent of potential functions of the resulting eRNA transcripts. ChIP-Seq and Gro-Seq profiling was performed in thioglycollate-elicited peritoneal macrophages, PU.1-/- and PUER cells treated as indicated.
2013-08-09 | E-GEOD-48759 | biostudies-arrayexpress
Project description:Enhancer and super-enhancer landscape in ADPKD
| PRJNA836927 | ENA
Project description:enhancer and super-enhancer landscape in ADPKD
| PRJNA781663 | ENA
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