Human primary meningioma vs. non-neoplastic adult meningeal tissue
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Oscar D'Urso
PROVIDER: E-GEOD-19720 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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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.
2011-01-02 | GSE19720 | 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:Meningiomas represent one of the most common and clinically heterogeneous brain tumor types that only modestly correlate with histopathologic features. While emerging molecular profiling efforts have linked specific genomic drivers to distinct clinical patterns, the proteomic landscape of meningiomas remains largely unexplored. We utilize mass spectrometry to profile a clinically well-annotated cohort (n=69) of meningiomas stratified to span all three World Health Organization (WHO) grades and various degrees of clinical aggressiveness. In total, we quantify 3042 unique proteins and compare the patterns across different clinical parameters. Unsupervised clustering analysis highlighted distinct proteomic (n=106 proteins, Welch’s t-test, P<0.01) and pathway-level (e.g. Notch and PI3K/AKT/mTOR) differences between convexity and skull base meningiomas. Supervised comparative analyses of different pathological grades revealed distinct patterns between benign (WHO Grade I) and atypical/malignant (WHO Grade II and III) meningiomas with classic oncogenes often enriched in higher grade lesions. Independent of WHO grade, clinically aggressive meningiomas, that rapidly recurred, also had distinctive protein patterns that converged on mRNA processing and impaired activation of the extracellular matrix naba matrisome complex. Larger sized meningiomas, and those with previous radiation exposure, also had distinct protein profiles. Collectively, we highlight distinct clinically-dependent proteomic patterns of meningiomas that may help better predict outcome and guide the development of more personalized and directed therapies.
2019-05-30 | PXD012923 | Pride
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 common brain tumours arising from meningeal tissue. Despite the majority of them displaying benign features, they can cause mild to severe morbidity. The current main therapeutic approach is complete tumour resection commonly with adjunct radiation therapy. However, tumour location can hamper complete resection and chemotherapies are ineffective. In this study we aim to elucidate dysregulated pathways in meningioma pathogenesis and identify novel molecular targets by deciphering the proteome and phosphoproteome of different grades of meningiomas. Tumour lysates were collected from grade I, II and III frozenmeningioma specimens and three normal healthy human meninges.
2019-01-02 | PXD007073 | Pride
Project description:Meningiomas are common brain tumours arising from meningeal tissue. Despite the majority of them displaying benign features, they can cause mild to severe morbidity. The current main therapeutic approach is complete tumour resection commonly with adjunct radiation therapy. However, tumour location can hamper complete resection and chemotherapies are ineffective. In this study we aim to elucidate dysregulated pathways in meningioma pathogenesis and identify novel molecular targets by deciphering the proteome and phosphoproteome of different grades of meningiomas. Tumour lysates were collected from grade I, II and III frozemeningioma specimens and three normal healthy human meninges.
2019-01-07 | PXD007044 | Pride
Project description:Meningiomas are common brain tumours arising from meningeal tissue. Despite the majority of them displaying benign features, they can cause mild to severe morbidity. The current main therapeutic approach is complete tumour resection commonly with adjunct radiation therapy. However, tumour location can hamper complete resection and chemotherapies are ineffective. In this study we aim to elucidate dysregulated pathways in meningioma pathogenesis and identify novel molecular targets by deciphering the proteome and phosphoproteome of different grades of meningiomas. Tumour lysates were collected from grade I, II and III frozenmeningioma specimens and three normal healthy human meninges.
2019-01-02 | PXD007125 | Pride
Project description:Meningiomas are frequent central nervous system tumors. Although most meningiomas are benign (WHO grade I) and curable by surgery, WHO grade II and III tumors remain therapeutically challenging due to frequent recurrence. Interestingly, relapse also occurs in some WHO grade I meningiomas. Hence, we investigated the transcriptional features defining aggressive (recurrent, malignantly progressing or WHO grade III) meningiomas in 144 cases. Meningiomas were categorized into non-recurrent (NR), recurrent (R), and tumors undergoing malignant progression (M) in addition to their WHO grade. Unsupervised transcriptomic analysis in 62 meningiomas revealed transcriptional profiles lining up according to WHO grade and clinical subgroup. Notably aggressive subgroups (R+M tumors and WHO grade III) shared a large set of differentially expressed genes (n=332; p<0.01, FC>1.25). In an independent multicenter validation set (n=82), differential expression of 10 genes between WHO grades was confirmed. Additionally, among WHO grade I tumors differential expression between NR and aggressive R+M tumors was af rmed for PTTG1, AURKB, ECT2, UBE2C and PRC1, while MN1 and LEPR discriminated between NR and R+M WHO grade II tumors. Univariate survival analysis revealed a significant association with progression-free survival for PTTG1, LEPR, MN1, ECT2, PRC1, COX10, UBE2C expression, while multivariate analysis identified a prediction for PTTG1 and LEPR mRNA expression independent of gender, WHO grade and extent of resection. Finally, stainings of PTTG1 and LEPR confirmed malignancy-associated protein expression changes. In conclusion, based on the so far largest study sample of WHO grade III and recurrent meningiomas we report a comprehensive transcriptional landscape and two prognostic markers. Comparative transcriptomic analysis of 62 low- and high-grade meningiomas
2016-02-15 | E-GEOD-74385 | 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 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.
2008-06-12 | E-GEOD-4039 | biostudies-arrayexpress