Gene expression profile in meningioma patients at the single cell level
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE278452 | GEO | 2025/05/14
REPOSITORIES: GEO
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Project description:Genomic profiling of anaplastic meningioma can inform prognostic gene level alterations in lower-grade meningiomas, potentially reflecting evolution of anaplastic meningioma from lowergrade precursor tumours. Larger scale studies in paired primary and recurrent meningiomas are warranted to unravel the evolutionary path to anaplastic meningiomas and prognostic genomic alterations in detail
2021-05-04 | GSE173825 | 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
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: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.
2008-11-21 | GSE12530 | GEO
Project description:Genomic profiling of anaplastic meningioma identifies recurrent genetic alterations with relevance to lower-grade meningioma
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Project description:Comparative transcriptomic analysis of n=10 primary Meningioma WHO grade III
2020-11-05 | GSE149923 | GEO
Project description:Primary Meningioma WHO grade III
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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: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