ArticleInternational journal of molecular sciences2022
Glioblastoma Extracellular Vesicle-Specific Peptides Inhibit EV-Induced Neuronal Cytotoxicity.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed, 12 citations in OpenAlex.
- Differential Effects of Extracellular Vesicles from Two Different Glioblastomas on Normal Human Brain Cells.Neurology international · 2024Article
- Immunopathology of Extracellular Vesicles in Macrophage and Glioma Cross-Talk.Journal of clinical medicine · 2023Review
- Engineered EVs designed to target diseases of the CNS.Journal of controlled release : official journal of the Controlled Release Society · 2023Review
- The human neurosecretome: extracellular vesicles and particles (EVPs) of the brain for intercellular communication, therapy, and liquid-biopsy applications.Frontiers in molecular biosciences · 2023Review
- The translational paradigm of nanobiomaterials: Biological chemistry to modern applications.Materials today. Bio · 2022Review
- Phenotype and Neuronal Cytotoxic Function of Glioblastoma Extracellular Vesicles.Biomedicines · 2022Article
- Exosomal Micro-RNAs as Intercellular Communicators in Idiopathic Pulmonary Fibrosis.International journal of molecular sciences · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 1 institution in 1 country.
Funding
Abstract
WHO Grade 4 IDH-wild type astrocytoma (GBM) is the deadliest brain tumor with a poor prognosis. Meningioma (MMA) is a more common "benign" central nervous system tumor but with significant recurrence rates. There is an urgent need for brain tumor biomarkers for early diagnosis and effective treatment options. Extracellular vesicles (EVs) are tiny membrane-enclosed vesicles that play essential functions in cell-to-cell communications among tumor cells. We aimed to identify epitopes of brain tumor EVs by phage peptide libraries. EVs from GBM plasma, MMA plasma, or brain tumor cell lines were used to screen phage-displayed random peptide libraries to identify high-affinity peptides. We purified EVs from three GBM plasma pools (23 patients), one MMA pool (10 patients), and four brain tumor cell lines. We identified a total of 21 high-affinity phage peptides (12 unique) specific to brain tumor EVs. The peptides shared high sequence homologies among those selected by the same EVs. Dose-response ELISA demonstrated that phage peptides were specific to brain tumor EVs compared to controls. Peptide affinity purification identified unique brain tumor EV subpopulations. Significantly, GBM EV peptides inhibit brain tumor EV-induced complement-dependent cytotoxicity (necrosis) in neurons. We conclude that phage display technology could identify specific peptides to isolate and characterize tumor EVs.
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.