Distinct spatial N‐glycan profiles reveal glioblastoma‐specific signatures
Bibliographic record
Abstract
This study explored the complex interactions between glycosylation patterns, tumour biology, and therapeutic responses to temozolomide (TMZ) in human malignant glioma, specifically CNS WHO grade 3 oligodendroglioma (ODG) and glioblastoma (GB). Using spatial imaging of N-glycans in formalin-fixed paraffin-embedded (FFPE) tissue sections via MALDI-MSI, we analysed the N-glycome in primary and recurrent GB tissues and orthotopic xenografts of patient-derived brain tumour-initiating cells (BTIC) sensitive or resistant to TMZ. We identified unique N-glycosylation profiles, with nontumor brain (NTB) and ODG showing higher levels of bisecting and tri-antennary structures, while GB exhibited more tetra-antennary and sialylated N-glycans. Distinctive sialylation patterns were observed, with specific α2,6 and α2,3 isomeric linkages significantly altered in GB. Moreover, comparative analysis of primary and recurrent GB tissues revealed elevated high mannose N-glycans in primary GB and fucosylated bi- and tri-antennary N-glycans in recurrent GB tissues. Next, in the orthotopic xenografts of TMZ-sensitive and TMZ-resistant patient brain tumour initiating cells (BTIC), we identified potential N-glycan markers for TMZ treatment response and resistance. Finally, we found significantly altered expression of genes involved in N-glycan biosynthesis in malignant glioma, highlighting the crucial role of N-glycans in glioma and therapy resistance. This study lays the foundation for developing glycosylation-based diagnostic biomarkers and targeted therapies, potentially improving clinical outcomes for GB patients. © 2025 The Pathological Society of Great Britain and Ireland.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".