ArticleBiomedicines2023
Profile Characterization of Biogenic Amines in Glioblastoma Patients Undergoing Standard-of-Care Treatment.
Article in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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
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Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- Machine Learning-Driven Metabolomic Biomarker Discovery in Glioblastoma: Advances, Challenges, and Future Directions.International journal of molecular sciences · 2026Review
- Glioblastoma metabolomics: uncovering biomarkers for diagnosis, prognosis and targeted therapy.Journal of experimental & clinical cancer research : CR · 2025Review
- Analyses of Saliva Metabolome Reveal Patterns of Metabolites That Differentiate SARS-CoV-2 Infection and COVID-19 Disease Severity.Metabolites · 2025Article
- MetaWise: Combined Feature Selection and Weighting Method to Link the Serum Metabolome to Treatment Response and Survival in Glioblastoma.International journal of molecular sciences · 2024Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 3 countries.
Funding
Abstract
introductionBiogenic amines play important roles throughout cellular metabolism. This study explores a role of biogenic amines in glioblastoma pathogenesis. Here, we characterize the plasma levels of biogenic amines in glioblastoma patients undergoing standard-of-care treatment.
methodsWe examined 138 plasma samples from 36 patients with isocitrate dehydrogenase (IDH) wild-type glioblastoma at multiple stages of treatment. Untargeted gas chromatography-time of flight mass spectrometry (GC-TOF MS) was used to measure metabolite levels. Machine learning approaches were then used to develop a predictive tool based on these datasets.
resultsSurgery was associated with increased levels of 12 metabolites and decreased levels of 11 metabolites. Chemoradiation was associated with increased levels of three metabolites and decreased levels of three other metabolites. Ensemble learning models, specifically random forest (RF) and AdaBoost (AB), accurately classified treatment phases with high accuracy (RF: 0.81 ± 0.04, AB: 0.78 ± 0.05). The metabolites sorbitol and N-methylisoleucine were identified as important predictive features and confirmed via SHAP.
conclusionTo our knowledge, this is the first study to describe plasma biogenic amine signatures throughout the treatment of patients with glioblastoma. A larger study is needed to confirm these results with hopes of developing a diagnostic algorithm.
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Registered trials
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