Metabolomic and lipidomic profiling reveals distinct subtypes of glioblastoma IDH-wildtype and shared metabolic features with astrocytoma IDH-mutant grade 4

Clicks: 12
ID: 327537
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #122 of 144 articles by views in Neuro-Oncology Advances

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 144 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Background Glioblastoma IDH-wildtype (GBM) is a metabolically diverse and aggressive brain tumor. While transcriptomic subtypes are well-defined, they lack a direct understanding of the tumor’s metabolism. We used integrated, untargeted metabolomics and lipidomics to define the functional metabolic landscape of GBM and astrocytoma, IDH-mutant, Grade 4, and to identify intrinsic metabolic subtypes with specific vulnerabilities. Methods Brain tissue from GBM patients (n = 38), astrocytoma IDH-mutant, Grade 4 (n = 5), and non-neoplastic controls (n = 20) underwent untargeted metabolomic and lipidomic profiling. Data were analyzed using Partial Least Squares Discriminant Analysis (PLS-DA), Principal Component Analysis (PCA), and K-means clustering. Quantitative pathway enrichment analyses were performed based on established databases and Z-scores are reported for effect sizes. A multinomial logistic regression classifier was trained to assess diagnostic utility. Results GBM and IDH-mutant astrocytoma, Grade 4 exhibited distinct metabolomic profiles, driven by 2-hydroxyglutarate accumulation in IDH-mutants. Machine learning classification trained on the metabolomic profiles achieved 95% diagnostic accuracy. Within GBM, K-means clustering of the metabolomics dataset revealed three functionally distinct subtypes. Cluster 1 was defined by fatty acid turnover. Cluster 2 was characterized by the robust accumulation of triglycerides, alongside a significantly reduced hexosylceramide-to-ceramide ratio. Cluster 3 was distinguished by upregulated one-carbon and folate metabolism, a significant depletion of acylcarnitines, and susceptibility to ferroptotic cell death. Conclusions Metabolomic and lipidomic profiling holds promise as an accurate diagnostic tool, while also enabling a deeper metabolic understanding of high-grade gliomas. This approach resulted in the identification of three distinct metabolic subtypes of GBM, each possessing unique therapeutic vulnerabilities.
Reference Key
openalex_W7207615094 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors John Paul Aboubechara, Yin Liu, Oliver Fiehn, Muhammad Sulman, Alexander Del Bosque, Diego Cantillo, Lina A Dahabiyeh, Ruben Fragoso, Jonathan W. Riess, Rawad Hodeify, Orin Bloch, Vihar Patel, Orwa Aboud
Are you one of these authors? Claim your profile
Journal Neuro-Oncology Advances
Year 2026
DOI
10.1093/noajnl/vdag199
URL
https://doi.org/10.1093/noajnl/vdag199
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.