| Title: | From biomarker discovery to clinical applications of metabolomics in glioblastoma |
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| Authors: | ID Šamec, Neja (Author)
ID Krapež, Gloria (Author)
ID Skubic, Cene (Author)
ID Jovchevska, Ivana (Author)
ID Videtič Paska, Alja (Author) |
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| Files: |  PDF - Presentation file, download (2,23 MB)
MD5: 78363708F949437867BA396ADFE64E68
 URL - Source URL, visit https://www.mdpi.com/2218-1989/15/5/295
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| Language: | English |
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| Typology: | 1.02 - Review Article |
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| Organization: |  UKC LJ - Ljubljana University Medical Centre
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| Abstract: | Background/Objectives: In recent years, interest in studying changes in cancer metabolites has resulted in significant advances in the metabolomics field. Glioblastoma remains the most aggressive and lethal brain malignancy, which presents with notable metabolic reprogramming. Methods: We performed literature research from the PubMed database and considered research articles focused on the key metabolic pathways altered in glioblastoma (e.g., glycolysis, lipid metabolism, TCA cycle), the role of oncometabolites and metabolic plasticity, and the differential expression of metabolites in glioblastoma. Currently used metabolomics approaches can be either targeted, focusing on specific metabolites and pathways, or untargeted, which involves data-driven exploration of the metabolome and also results in the identification of new metabolites. Data processing and analysis is of great importance and can be improved with the integration of machine learning approaches for metabolite identification. Results: Changes in α/β-glucose, lactate, choline, and 2-hydroxyglutarate were detected in glioblastoma compared with non-tumor tissues. Different metabolites such as fumarate, tyrosine, and leucine, as well as citric acid, isocitric acid, shikimate, and GABA were detected in blood and CSF, respectively. Conclusions: Although promising new technological and bioinformatic approaches help us understand glioblastoma better, challenges associated with biomarker availability, tumor heterogeneity, interpatient variability, standardization, and reproducibility still remain. Metabolomics research, either alone or combined with genomics or proteomics (i.e., multiomics) in glioblastoma, can lead to biomarker identification, tracking of metabolic therapy response, discovery of novel metabolites and pathways, and identification of potential therapeutic targets. |
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| Keywords: | Omics, metabolome, multiomic integration, tumor metabolic reprogramming, biomarker, diagnosis, therapy, precision oncology |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Year of publishing: | 2025 |
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| Number of pages: | str. 1-24 |
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| Numbering: | Vol. 15, no. 5, [article no.] 295 |
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| PID: | 20.500.12556/DiRROS-29145  |
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| UDC: | 577.2 |
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| ISSN on article: | 2218-1989 |
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| DOI: | 10.3390/metabo15050295 |
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| COBISS.SI-ID: | 235300099 |
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| Note: | Nasl. z nasl. zaslona;
Opis vira z dne 9. 5. 2025;
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| Publication date in DiRROS: | 22.04.2026 |
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| Views: | 163 |
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| Downloads: | 142 |
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