ArticleMetabolites2020
Analysis and Simulation of Glioblastoma Cell Lines-Derived Extracellular Vesicles Metabolome.
Article in Metabolites, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Evidence of cancer: a systematic review of metabolomics in extracellular vesicles for cancer biomarker detection.Metabolomics : Official journal of the Metabolomic Society · 2025Pooled it
- Significance of GSH and HRedox report : communications in free radical research · 2026Review
- Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.Journal of nanobiotechnology · 2026Review
- From Biomarker Discovery to Clinical Applications of Metabolomics in Glioblastoma.Metabolites · 2025Review
- Article
- Extracellular Vesicle Preparation and Analysis: A State-of-the-Art Review.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Article
- Metabolic features of tumor-derived extracellular vesicles: challenges and opportunities.Extracellular vesicles and circulating nucleic acids · 2024Review
- Emerging role of extracellular vesicles in multiple sclerosis: From cellular surrogates to pathogenic mediators and beyond.Journal of neuroimmunology · 2023Review
- Multiparametric Biosensors for Characterizing Extracellular Vesicle Subpopulations.ACS pharmacology & translational science · 2023Article
- Isolation and characterization of extracellular vesicles and future directions in diagnosis and therapy.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology · 2023Review
- Sodium Propionate Contributes to Tumor Cell Growth Inhibition through PPAR-γ Signaling.Cancers · 2022Article
- Preclinical and Clinical Applications of Metabolomics and Proteomics in Glioblastoma Research.International journal of molecular sciences · 2022Review
- GSK343, an Inhibitor of Enhancer of Zeste Homolog 2, Reduces Glioblastoma Progression through Inflammatory Process Modulation: Focus on Canonical and Non-Canonical NF-κB/IκBα Pathways.International journal of molecular sciences · 2022Article
- The Potential of Metabolomics in Biomedical Applications.Metabolites · 2022Review
- Definition of an Inflammatory Biomarker Signature in Plasma-Derived Extracellular Vesicles of Glioblastoma Patients.Biomedicines · 2022Article
- Metabolomics of Extracellular Vesicles: A Future Promise of Multiple Clinical Applications.International journal of nanomedicine · 2022Review
- Immunomodulatory effects of extracellular vesicles in glioblastoma.Frontiers in cell and developmental biology · 2022Review
- Glioblastoma Metabolomics-In Vitro Studies.Metabolites · 2021Review
- Correlation of Metabolic Profiles of Plasma and Cerebrospinal Fluid of High-Grade Glioma Patients.Metabolites · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glioblastoma (GBM) is one of the most aggressive cancers of the central nervous system. Despite current advances in non-invasive imaging and the advent of novel therapeutic modalities, patient survival remains very low. There is a critical need for the development of effective biomarkers for GBM diagnosis and therapeutic monitoring. Extracellular vesicles (EVs) produced by GBM tumors have been shown to play an important role in cellular communication and modulation of the tumor microenvironment. As GBM-derived EVs contain specific "molecular signatures" of their parental cells and are able to transmigrate across the blood-brain barrier into biofluids such as the blood and cerebrospinal fluid (CSF), they are considered as a valuable source of potential diagnostic biomarkers. Given the relatively harsh extracellular environment of blood and CSF, EVs have to endure and adapt to different conditions. The ability of EVs to adjust and function depends on their lipid bilayer, metabolic content and enzymes and transport proteins. The knowledge of EVs metabolic characteristics and adaptability is essential for their utilization as diagnostic and therapeutic tools. The main aim of this study was to determine the metabolome of small EVs or exosomes derived from different GBM cells and compare to the metabolic profile of their parental cells using NMR spectroscopy. In addition, a possible flux of metabolic processes in GBM-derived EVs was simulated using constraint-based modeling from published proteomics information. Our results showed a clear difference between the metabolic profiles of GBM cells, EVs and media. Machine learning analysis of EV metabolomics, as well as flux simulation, supports the notion of active metabolism within EVs, including enzymatic reactions and the transfer of metabolites through the EV membrane. These results are discussed in the context of novel GBM diagnostics and therapeutic monitoring.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.