Browse
Submit Data
Databases
API
Help

Dataset Information

25 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Mesenchymal and Proneural Subtypes of Glioblastoma Disclose Branching Based on GSC Associated Signature.

ABSTRACT: Glioblastomas (GBM)-the most common, therapy-resistant, and lethal tumors driven by populations of glioma stem cells (GSCs) are still on the list of the most complicated pathologies. Thus, deeper understanding and characterization of GSCs is indispensable to find suitable targets and develop more effective therapies. In the present study, we applied native glioblastoma cells and GSCs sequencing, screened for GSC-specific targets and checked if the signature is related to GBM patient pathological, clinical data as well as molecular subtypes applying TCGA cohort. Data analysis revealed that tumors of proneural and mesenchymal subtypes are branching in separate clusters based on screened gene expression. Samples of the same subtype revealed significantly different patient survival prognosis as well as recurrence chance between the clusters. Recently, different subpopulations of mesenchymal GSC demonstrating different properties were shown, which indicates possible internal heterogeneity of GBM subtypes as well. Current findings also revealed branching of molecular GBM subtypes that were significantly linked to patient outcome and that might be decided by distinct GSC subpopulations.

SUBMITTER: Steponaitis G

PROVIDER: S-EPMC8124327 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Altered cytoskeletal status in the transition from proneural to mesenchymal glioblastoma subtypes.

Project description:Glioblastoma is a highly aggressive brain tumor with poor patient prognosis. Treatment outcomes remain limited, partly due to intratumoral heterogeneity and the invasive nature of the tumors. Glioblastoma cells invade and spread into the surrounding brain tissue, and even between hemispheres, thus hampering complete surgical resection. This invasive motility can arise through altered properties of the cytoskeleton. We hypothesize that cytoskeletal organization and dynamics can provide important clues to the different malignant states of glioblastoma. In this study, we investigated cytoskeletal organization in glioblastoma cells with different subtype expression profiles, and cytoskeletal dynamics upon subtype transitions. Analysis of the morphological, migratory, and invasive properties of glioblastoma cells identified cytoskeletal components as phenotypic markers that can serve as diagnostic or prognostic tools. We also show that the cytoskeletal function and malignant properties of glioblastoma cells shift during subtype transitions induced by altered expression of the neurodevelopmental transcription factor SOX2. The potential of SOX2 re-expression to reverse the mesenchymal subtype into a more proneural subtype might open up strategies for novel glioblastoma treatments.

| S-EPMC9197936 | biostudies-literature

Coexpression analysis of CD133 and CD44 identifies proneural and mesenchymal subtypes of glioblastoma multiforme.

Project description:Accumulating evidence suggests that the stem cell markers CD133 and CD44 indicate molecular subtype in Glioblastoma Multiforme (GBM). Gene coexpression analysis of The Cancer Genome Atlas GBM dataset was undertaken to compare markers of the Glioblastoma Stem-Progenitor Cell (GSPC) phenotype. Pearson correlation identified genes coexpressed with stem cell markers, which were then used to build a gene signature that classifies patients based on a CD133 coexpression module signature (CD133-M) or CD44-M subtype. CD133-M tumors were enriched for the Proneural (PN) GBM subtype compared to Mesenchymal (MES) subtype for CD44-M tumors. Gene set enrichment identified DNA replication/cell cycle genes in the CD133-M and invasion/migration in CD44-M, while functional experiments showed enhanced cellular growth in CD133 expressing cells and enhanced invasion in cells expressing CD44. As with the 4 major molecular subtypes of GBM, there was no long-term survival difference between CD44-M and CD133-M patients, although CD44-M patients responded better to temozolomide while CD133-M patients benefited from radiotherapy. The use of a targeted coexpression approach to predict functional properties of surface marker expressing cells is novel, and in the context of GBM, supports accumulating evidence that CD133 and CD44 protein marker expression correlates with molecular subtype.

| S-EPMC4467436 | biostudies-literature

Angiogenic Gene Signature Derived from Subtype Specific Cell Models Segregate Proneural and Mesenchymal Glioblastoma.

Project description:Intertumoral molecular heterogeneity in glioblastoma identifies four major subtypes based on expression of molecular markers. Among them, the two clinically interrelated subtypes, proneural and mesenchymal, are the most aggressive with proneural liable for conversion to mesenchymal upon therapy. Using two patient-derived novel primary cell culture models (MTA10 and KW10), we developed a minimal but unique four-gene signature comprising genes vascular endothelial growth factor A (VEGF-A), vascular endothelial growth factor B (VEGF-B) and angiopoietin 1 (ANG1), angiopoietin 2 (ANG2) that effectively segregated the proneural (MTA10) and mesenchymal (KW10) glioblastoma subtypes. The cell culture preclassified as mesenchymal showed elevated expression of genes VEGF-A, VEGF-B and ANG1, ANG2 as compared to the other cell culture model that mimicked the proneural subtype. The differentially expressed genes in these two cell culture models were confirmed by us using TCGA and Verhaak databases and we refer to it as a minimal multigene signature (MMS). We validated this MMS on human glioblastoma tissue sections with the use of immunohistochemistry on preclassified (YKL-40 high or mesenchymal glioblastoma and OLIG2 high or proneural glioblastoma) tumor samples (n = 30). MMS segregated mesenchymal and proneural subtypes with 83% efficiency using a simple histopathology scoring approach (p = 0.008 for ANG2 and p = 0.01 for ANG1). Furthermore, MMS expression negatively correlated with patient survival. Importantly, MMS staining demonstrated spatiotemporal heterogeneity within each subclass, adding further complexity to subtype identification in glioblastoma. In conclusion, we report a novel and simple sequencing-independent histopathology-based biomarker signature comprising genes VEGF-A, VEGF-B and ANG1, ANG2 for subtyping of proneural and mesenchymal glioblastoma.

| S-EPMC5504164 | biostudies-literature

CXCR4 expression is associated with proneural-to-mesenchymal transition in glioblastoma.

Project description:Glioblastoma (GBM) is the most common primary intracranial malignant tumor and consists of three molecular subtypes: proneural (PN), mesenchymal (MES) and classical (CL). Transition between PN to MES subtypes (PMT) is the glioma analog of the epithelial-mesenchymal transition (EMT) in carcinomas and is associated with resistance to therapy. CXCR4 signaling increases the expression of MES genes in glioma cell lines and promotes EMT in other cancers. RNA sequencing (RNAseq) data of PN GBMs in The Cancer Genome Atlas (TCGA) and secondary high-grade gliomas (HGGs) from an internal cohort were examined for correlation between CXCR4 expression and survival as well as expression of MES markers. Publicly available single-cell RNA sequencing (scRNAseq) data was analyzed for cell type specific CXCR4 expression. These results were validated in a genetic mouse model of PN GBM. Higher CXCR4 expression was associated with significantly reduced survival and increased expression of MES markers in TCGA and internal cohorts. CXCR4 was expressed in immune and tumor cells based on scRNAseq analysis. Higher CXCR4 expression within tumor cells on scRNAseq was associated with increased MES phenotype, suggesting a cell-autonomous effect. In a genetically engineered mouse model, tumors induced with CXCR4 exhibited a mesenchymal phenotype and shortened survival. These results suggest that CXCR4 signaling promotes PMT and shortens survival in GBM and highlights its inhibition as a potential therapeutic strategy.

| S-EPMC10071545 | biostudies-literature

ME2 Promotes Proneural-Mesenchymal Transition and Lipogenesis in Glioblastoma.

Project description:Malic enzyme 2 (ME2) catalyzes the formation of pyruvate from malic acid and is abnormally expressed in some tumors. However, the exact effects of ME2 on proneural-mesenchymal transition (PMT) and lipogenesis in glioblastoma multiforme (GBM) remain unexplored. Here, we found that ME2 expression was significantly higher in GBM than in normal brain tissues and negatively correlated with overall survival of patients with GBM. Furthermore, we demonstrated that ME2 was positively correlated with mesenchymal features in GBM and promoted proliferation, migration, and invasion of glioma cells. Moreover, ME2 upregulated the expression of mesenchymal markers (N-cadherin, vimentin, YKL40, and MET), whereas it inhibited the expression of proneural maker OLIG2, indicating that ME2 might promote PMT in GBM. We also found that ME2 inhibited the production of mitochondrial reactive oxygen species and AMPK phosphorylation, resulting in SREBP-1 maturation and nuclear localization and enhancing the ACSS2 lipogenesis pathway. Taken together, these results suggest that ME2 promotes PMT and is linked with reprogramming of lipogenesis via AMPK-SREBP-1-ACSS2 signaling in GBM. Therefore, ME2 has potential as a new classification marker in GBM and could provide a new approach to glioma treatment.

| S-EPMC8351415 | biostudies-literature

A Voxel-Based Radiographic Analysis Reveals the Biological Character of Proneural-Mesenchymal Transition in Glioblastoma.

Project description:Introduction: Proneural and mesenchymal subtypes are the most distinct demarcated categories in classification scheme, and there is often a shift from proneural type to mesenchymal subtype in the progression of glioblastoma (GBM). The molecular characters are determined by specific genomic methods, however, the application of radiography in clinical practice remains to be further studied. Here, we studied the topography features of GBM in proneural subtype, and further demonstrated the survival characteristics and proneural-mesenchymal transition (PMT) progression of samples by combining with the imaging variables. Methods: Data were acquired from The Cancer Imaging Archive (TCIA, http://cancerimagingarchive.net). The radiography image, clinical variables and transcriptome subtype from 223 samples were used in this study. Proneural and mesenchymal subtype on GBM topography based on overlay and Voxel-based lesion-symptom mapping (VLSM) analysis were revealed. Besides, we carried out the comparison of survival analysis and PMT progression in and outside the VLSM-determined area. Results: The overlay of total GBM and separated image of proneural and mesenchymal subtype revealed a correlation of the two subtypes. By VLSM analysis, proneural subtype was confirmed to be related to left inferior temporal medulla, and no significant voxel was found for mesenchymal subtype. The subsequent comparison between samples in and outside the VLSM-determined area showed difference in overall survival (OS) time, tumor purity, epithelial-mesenchymal transition (EMT) score and clinical variables. Conclusions: PMT progression was determined by radiography approach. GBM samples in the VLSM-determined area tended to harbor the signature of proneural subtype. This study provides a valuable VLSM-determined area related to the predilection site, prognosis and PMT progression by the association between GBM topography and molecular characters.

| S-EPMC8010193 | biostudies-literature

USP10 deubiquitinates RUNX1 and promotes proneural-to-mesenchymal transition in glioblastoma.

Project description:The mesenchymal (MES) subtype of glioblastoma (GBM) is a highly aggressive, malignant and proliferative cancer that is resistant to chemotherapy. Runt-related transcription factor 1 (RUNX1) was shown to support MES GBM, however, its underlying mechanisms are unclear. Here, we identified USP10 as a deubiquitinating enzyme that regulates RUNX1 stabilization and is mainly expressed in MES GBM. Overexpression of USP10 upregulated RUNX1 and induced proneural-to-mesenchymal transition (PMT), thus maintaining MES properties in GBM. Conversely, USP10 knockdown inhibited RUNX1 and resulted in the loss of MES properties. USP10 was shown to interact with RUNX1, with RUNX1 being stabilized upon deubiquitylation. Moreover, we found that USP10 inhibitor Spautin-1 induced RUNX1 degradation and inhibited MES properties in vitro and in vivo. Furthermore, USP10 was strongly correlated with RUNX1 expression in samples of different subtypes of human GBM and had prognostic value for GBM patients. We identified USP10 as a key deubiquitinase for RUNX1 protein stabilization. USP10 maintains MES properties of GBM, and promotes PMT of GBM cells. Our study indicates that the USP10/RUNX1 axis may be a potential target for novel GBM treatments.

| S-EPMC10033651 | biostudies-literature

Proneural-mesenchymal antagonism dominates the patterns of phenotypic heterogeneity in glioblastoma

Project description: Not available

| S-EPMC10905000 | biostudies-literature

Monocyte depletion enhances neutrophil influx and proneural to mesenchymal transition in glioblastoma.

Project description:Myeloid cells comprise the majority of immune cells in tumors, contributing to tumor growth and therapeutic resistance. Incomplete understanding of myeloid cells response to tumor driver mutation and therapeutic intervention impedes effective therapeutic design. Here, by leveraging CRISPR/Cas9-based genome editing, we generate a mouse model that is deficient of all monocyte chemoattractant proteins. Using this strain, we effectively abolish monocyte infiltration in genetically engineered murine models of de novo glioblastoma (GBM) and hepatocellular carcinoma (HCC), which show differential enrichment patterns for monocytes and neutrophils. Eliminating monocyte chemoattraction in monocyte enriched PDGFB-driven GBM invokes a compensatory neutrophil influx, while having no effect on Nf1-silenced GBM model. Single-cell RNA sequencing reveals that intratumoral neutrophils promote proneural-to-mesenchymal transition and increase hypoxia in PDGFB-driven GBM. We further demonstrate neutrophil-derived TNF-a directly drives mesenchymal transition in PDGFB-driven primary GBM cells. Genetic or pharmacological inhibiting neutrophils in HCC or monocyte-deficient PDGFB-driven and Nf1-silenced GBM models extend the survival of tumor-bearing mice. Our findings demonstrate tumor-type and genotype dependent infiltration and function of monocytes and neutrophils and highlight the importance of targeting them simultaneously for cancer treatments.

| S-EPMC10070461 | biostudies-literature

Most human non-GCIMP glioblastoma subtypes evolve from a common proneural-like precursor glioma.

Project description:To understand the relationships between the non-GCIMP glioblastoma (GBM) subgroups, we performed mathematical modeling to predict the temporal sequence of driver events during tumorigenesis. The most common order of evolutionary events is 1) chromosome (chr) 7 gain and chr10 loss, followed by 2) CDKN2A loss and/or TP53 mutation, and 3) alterations canonical for specific subtypes. We then developed a computational methodology to identify drivers of broad copy number changes, identifying PDGFA (chr7) and PTEN (chr10) as driving initial nondisjunction events. These predictions were validated using mouse modeling, showing that PDGFA is sufficient to induce proneural-like gliomas and that additional NF1 loss converts proneural to the mesenchymal subtype. Our findings suggest that most non-GCIMP mesenchymal GBMs arise as, and evolve from, a proneural-like precursor.

| S-EPMC4143139 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data