Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0017636 (glioblastoma)
 18,345 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The subclassification of glioblastoma (GBM) into clinically relevant subtypes using microRNA (miRNA)- and messenger RNA (mRNA)-based integrated analysis has been attempted. Because miRNAs regulate multiple gene-signaling pathways, understanding miRNA-mRNA interactions is a prerequisite for understanding glioma biology. However, such associations have not been thoroughly examined using high-throughput integrated analysis. To identify significant miRNA-mRNA correlations, we selected and quantified signature miRNAs and mRNAs in 82 gliomas (grade II: 14, III: 16, IV: 52) using real-time reverse-transcriptase polymerase chain reaction. Quantitative expression data were integrated into a single analysis platform that evaluated the expression relationship between miRNAs and mRNAs. The 21 miRNAs include miR-15b, -21, -34a, -105, -124a, -128a, -135b, -184, -196a-b, -200a-c, -203, -302a-d, -363, -367, and -504. In addition, we examined 23 genes, including proneural markers (DLL3, BCAN, and OLIG2), mesenchymal markers (YKL-40, CD44, and Vimentin), cancer stem cell-related markers, and receptor tyrosine kinase genes. Primary GBM was characterized exclusively by upregulation of mesenchymal markers, whereas secondary GBM was characterized by significant downregulation of mesenchymal markers, miR-21, and -34a, and by upregulation of proneural markers and miR-504. Statistical analysis showed that expression of miR-128a, -504, -124a, and -184 each negatively correlated with the expression of mesenchymal markers in GBM. Our functional analysis of miR-128a and -504 as inhibitors demonstrated that suppression of miR-128a and -504 increased the expression of mesenchymal markers in glioblastoma cell lines. Mesenchymal signaling in GBM may be negatively regulated by miR-128a and -504.
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PMID:Associations between microRNA expression and mesenchymal marker gene expression in glioblastoma. 2284 9

Glioblastoma (GBM), the most malignant of the brain tumors is classified on the basis of molecular signature genes using TCGA data into four subtypes- classical, mesenchymal, proneural and neural. The mesenchymal phenotype is associated with greater aggressiveness and low survival in contrast to GBMs enriched with proneural genes. The proinflammatory cytokines secreted in the microenvironment of gliomas play a key role in tumor progression. The study focused on the role of Oncostatin-M (OSM), an IL-6 family cytokine in inducing mesenchymal properties in GBM. Analysis of TCGA and REMBRANDT data revealed that expression of OSMR but not IL-6R or LIFR is upregulated in GBM and has negative correlation with survival. Amongst the GBM subtypes, OSMR level was in the order of mesenchymal > classical > neural > proneural. TCGA data and RT-PCR analysis in primary cultures of low and high grade gliomas showed a positive correlation between OSMR and mesenchymal signature genes-YKL40/CHI3L1, fibronectin and vimentin and a negative correlation with proneural signature genes-DLL3, Olig2 and BCAN. OSM enhanced transcript and protein level of fibronectin and YKL-40 and reduced the expression of Olig2 and DLL3 in GBM cells. OSM-regulated mesenchymal phenotype was associated with enhanced MMP-9 activity, increased cell migration and invasion. Importantly, OSM induced mesenchymal markers and reduced proneural genes even in primary cultures of grade-III glioma cells. We conclude that OSM-mediated signaling contributes to aggressive nature associated with mesenchymal features via STAT3 signaling in glioma cells. The data suggest that OSMR can be explored as potential target for therapeutic intervention.
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PMID:Oncostatin-M differentially regulates mesenchymal and proneural signature genes in gliomas via STAT3 signaling. 2574 42

Glioblastomas in adults are highly heterogeneous tumors that can develop throughout the brain. To date no predictive-location marker has been identified. We previously derived two glioblastoma cell lines from cortical and periventricular locations and demonstrated distinct transcriptomic profiles. Based on these preliminary results, the aim of this study was to correlate glioblastoma locations with the expression of ten selected genes (VEGFC, FLT4, MET, HGF, CHI3L1, PROM1, NOTCH1, DLL3, PDGFRA, BCAN). Fifty nine patients with newly diagnosed glioblastomas were retrospectively included. Tumors were classified into cortical and periventricular locations, which were subsequently segregated according to cerebral lobes involved: cortical fronto-parietal (C-FP), cortical temporal (C-T), periventricular fronto-parietal (PV-FP), periventricular temporal (PV-T), and periventricular occipital (PV-O). Gene expression levels were determined using RT-qPCR. Compared to cortical glioblastomas, periventricular glioblastomas were characterized by a higher expression of two mesenchymal genes, VEGFC (p = 0.001) and HGF (p = 0.001). Among cortical locations, gene expressions were homogeneous. In contrast, periventricular locations exhibited distinct expression profiles. PV-T tumors were associated with higher expression of two proneural and cancer stem cell genes, NOTCH1 (p = 0.028) and PROM1 (p = 0.033) while PV-FP tumors were characterized by high expression of a mesenchymal gene, CHI3L1 (p = 0.006). Protein expression of NOTCH1 was correlated with RNA expression levels. PV-O glioblastomas were associated with lower expression of VEGFC (p = 0.032) than other periventricular locations, whereas MET overexpression remained exceptional. These data suggest a differential gene expression profile according to initial glioblastoma location.
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PMID:Molecular heterogeneity of glioblastomas: does location matter? 2663 6

Gliomas are lethal brain tumors that resist standard therapeutic approaches. Immunotherapy is a promising alternative strategy mostly developed in the context of glioblastoma. However, there is a need for implementing immunotherapy for grade II/III gliomas, as these are the most common CNS tumors in young adults with a high propensity for recurrence, making them lethal despite current treatments. We recently identified HLA-A2-restricted tumor-associated antigens by peptide elution from glioblastoma and formulated a multipeptide vaccine (IMA950) evaluated in phase I/II clinical trials with promising results. Here, we investigated expression of the IMA950 antigens in patients with grade II/III astrocytoma, oligodendroglioma or ependymoma, at the mRNA, protein and peptide levels. We report that the BCAN, CSPG4, IGF2BP3, PTPRZ1 and TNC proteins are significantly over-expressed at the mRNA (n = 159) and protein (n = 36) levels in grade II/III glioma patients as compared to non-tumor samples (IGF2BP3 being absent from oligodendroglioma). Most importantly, we detected spontaneous antigen-specific T cell responses to one or more of the IMA950 antigens in 100% and 71% of grade II and grade III patients, respectively (27 patients tested). These patients displayed T cell responses of better quality (higher frequency, broader epitope targeting) than patients with glioblastoma. Detection of spontaneous T cell responses to the IMA950 antigens shows that these antigens are relevant for tumor targeting, which will be best achieved by combination with CD4 epitopes such as the IDH1R132H peptide. Altogether, we provide the rationale for using a selective set of IMA950 peptides for vaccination of patients with grade II/III glioma.
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PMID:Antigenic expression and spontaneous immune responses support the use of a selected peptide set from the IMA950 glioblastoma vaccine for immunotherapy of grade II and III glioma. 2930 20

This study aims to discover genes with prognostic potential for glioblastoma (GBM) patients' survival in a patient group that has gone through standard of care treatments including surgeries and chemotherapies, using tumor gene expression at initial diagnosis before treatment. The Cancer Genome Atlas (TCGA) GBM gene expression data are used as inputs to build a deep multilayer perceptron network to predict patient survival risk using partial likelihood as loss function. Genes that are important to the model are identified by the input permutation method. Univariate and multivariate Cox survival models are used to assess the predictive value of deep learned features in addition to clinical, mutation, and methylation factors. The prediction performance of the deep learning method was compared to other machine learning methods including the ridge, adaptive Lasso, and elastic net Cox regression models. Twenty-seven deep-learned features are extracted through deep learning to predict overall survival. The top 10 ranked genes with the highest impact on these features are related to glioblastoma stem cells, stem cell niche environment, and treatment resistance mechanisms, including POSTN, TNR, BCAN, GAD1, TMSB15B, SCG3, PLA2G2A, NNMT, CHI3L1 and ELAVL4.
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PMID:Prognostic Gene Discovery in Glioblastoma Patients using Deep Learning. 3062 92