Mesenchymal Stem Cells and Fibroblasts Contribute to Microvascular Proliferation in Glioblastoma and are Correlated with Immunosuppression and Poor Outcome
Bibliographic record
Abstract
Microvascular proliferation (MVP) is a disease-defining hallmark of glioblastoma and other World Health Organization grade 4 gliomas. MVP also serves as a poor prognostic marker in various solid tumors. Despite its clinical significance, the mechanisms and biological consequences of MVP are controversial and remain unclear. In this study, we performed single-cell RNA sequencing on paired CD45-CD105+ vascular/perivascular stromal cells (PVSC) and CD45+CD105± immune cells from 16 primary glioma patient samples, both with and without MVP. This analysis revealed the presence of developmentally related mesenchymal stem cells alongside cancer-associated fibroblasts, pericytes, fibromyocytes, and smooth muscle cells within the CD45-CD105+ compartment. RNA velocity analysis identified PDGFRB as a putative driver gene guiding mesenchymal stem cells toward more mature PVSCs in the context of MVP. Signaling network analysis and digital spatial profiling uncovered interactions between PDGFRB+ PVSCs and immunosuppressive myeloid cell subsets enriched in the perivascular niche, suggesting targetable receptor-ligand interactions. Additionally, a gene signature of MVP-associated PVSCs from gliomas predicted worse prognosis in multiple other solid tumors. This study provides a transcriptomic cell atlas of PVSCs and immune cells in glioma, helping to refine the biological model of MVP which has traditionally focused on endothelial cells.
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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 | 0.001 | 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) | 0.001 | 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".