Genomic landscape of meningiomas: gene expression
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE16581 | GEO | 2010/11/30
SECONDARY ACCESSION(S): PRJNA122763
REPOSITORIES: GEO
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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
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. Tumor biopsies from 43 female and 25 male subjects with sporadic meningioma were identified from the UCLA Neuro-oncology Program Tissue Bank through institutional review board approved protocols. 43 tumors were designated "benign" WHO I, 19 tumors were "atypical" WHO II, and 6 were "anaplastic" WHO III. Gene expression analysis was performed on the 68 tumor biopsies.
2010-11-30 | E-GEOD-16581 | biostudies-arrayexpress
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. Tumor biopsies from 53 female and 32 male subjects with sporadic meningioma were identified from the UCLA Neuro-oncology Program Tissue Bank through institutional review board approved protocols. 57 tumors were designated "benign" WHO I, 20 tumors were "atypical" WHO II, and 8 were "anaplastic" WHO III. Affymetrix SNP arrays were performed according to the manufacturer's instructions on DNA extracted from flash frozen meningioma tumors.
2011-10-11 | E-GEOD-16583 | biostudies-arrayexpress
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: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:Meningiomas, named for their cell of origin, are the most common intracranial tumors in adults, representing 39% of all primary adult central nervous system tumors. These tumors originate in the meninges, which are the outer three layers of tissue between the skull and the brain that cover and protect the brain just under the skull. Most meningioma tumors (85-90 percent) are categorized as benign, with the remaining 10-15 percent being atypical meningioma or malignant meningioma (cancerous). The word “benign” can be misleading for meningiomas. Depending on location and growth rate, a benign meningioma brain tumor may impinge on vital nerves or compress the brain, causing disability. They may even become life threatening. We describe transcriptional signatures of four most common groups of benign meningiomas. Each subgroup of meningiomas displayed a unique gene expression program identifying signaling pathways potentially implicated in the tumorigenesis. These findings will improve our understanding of meningioma tumorigenesis. Objective: To define gene expression signatures of the most common subtypes of meningiomas to better understand cellular processes and signaling pathways specific for each tumor genotype.
2023-03-10 | GSE221429 | GEO
Project description:Meningiomas are the most common primary brain tumor. Though typically benign with a low mutational burden, histopathologic analysis has poor predictive value for malignant behavior and there are no proven chemotherapies. Although DNA methylation patterns distinguish subgroups of meningiomas and have higher predictive value for tumor behavior than histologic classification, little is known about differences in DNA methylation between meningiomas and surrounding normal dura tissue. Using multimodal studies of meningioma/dura pairs, we identified 4 distinct DNA methylation patterns. Diffuse DNA hypomethylation of malignant meningiomas readily facilitated their identification from lower grade tumors by unsupervised clustering. All clusters and 12/12 meningioma-dura pairs exhibited hypomethylation of the gene promoters of a module associated with the craniofacial patterning transcription factor FOXC1 and its upstream lncRNA FOXCUT. Furthermore, we identified an epigenetic continuum of increasing hypermethylation of polycomb repressive complex target promoters with increased histopathologic grade suggesting progressive epigenetic dysregulation is associated with increasing tumor aggressiveness. These findings are a starting point for future investigations of the role of epigenetic dysregulation of FOXC1 and cranial patterning genes in early stages of meningioma formation as well as studies of the utility of polycomb inhibitors for treatment of aggressive meningiomas.
2023-06-22 | GSE176457 | GEO
Project description:Meningiomas are the most common primary brain tumor. Though typically benign with a low mutational burden, histopathologic analysis has poor predictive value for malignant behavior and there are no proven chemotherapies. Although DNA methylation patterns distinguish subgroups of meningiomas and have higher predictive value for tumor behavior than histologic classification, little is known about differences in DNA methylation between meningiomas and surrounding normal dura tissue. Using multimodal studies of meningioma/dura pairs, we identified 4 distinct DNA methylation patterns. Diffuse DNA hypomethylation of malignant meningiomas readily facilitated their identification from lower grade tumors by unsupervised clustering. All clusters and 12/12 meningioma-dura pairs exhibited hypomethylation of the gene promoters of a module associated with the craniofacial patterning transcription factor FOXC1 and its upstream lncRNA FOXCUT. Furthermore, we identified an epigenetic continuum of increasing hypermethylation of polycomb repressive complex target promoters with increased histopathologic grade suggesting progressive epigenetic dysregulation is associated with increasing tumor aggressiveness. These findings are a starting point for future investigations of the role of epigenetic dysregulation of FOXC1 and cranial patterning genes in early stages of meningioma formation as well as studies of the utility of polycomb inhibitors for treatment of aggressive meningiomas.
2023-06-22 | GSE178139 | GEO
Project description:Meningiomas are among the most common brain tumors that arise from the leptomeningeal cover of the brain and spinal cord and account for around 37% of all central nervous system tumors. According to the World Health Organization, meningiomas are classified into three histological subtypes: benign, atypical, and anaplastic. Sometimes, meningiomas with a histological diagnosis of benign tumors show clinical characteristics and behavior of aggressive tumors. In this study, we examined the metabolomic and lipidomic profiles of meningioma tumors, focusing on comparing low-grade and high-grade tumors and identifying potential markers that can discriminate between benign and malignant tumors. High-resolution mass spectrometry coupled to liquid chromatography was used for untargeted metabolomics and lipidomics analyses of 85 tumor biopsy samples with different meningioma grades. We then applied feature selection and machine learning techniques to find the features with the highest information to aid in the diagnosis of meningioma grades. Three biomarkers were identified to differentiate low- and high-grade meningioma brain tumors. The use of mass-spectrometry-based metabolomics and lipidomics combined with machine learning analyses to prospect and characterize biomarkers associated with meningioma grades may pave the way for elucidating potential therapeutic and prognostic targets.
2024-06-12 | MTBLS4938 | MetaboLights