Glioma Tumor Stem-Like Cells Promote Tumor Angiogenesis and Vasculogenesis via Vascular Endothelial Growth Factor and Stromal-Derived Factor 1
Bibliographic record
Abstract
Cancer stem cells (CSC) are predicted to be critical drivers of tumor progression due to their self-renewal capacity and limitless proliferative potential. An emerging area of research suggests that CSC may also support tumor progression by promoting tumor angiogenesis. To investigate how CSC contribute to tumor vascular development, we used an approach comparing tumor xenografts of the C6 glioma cell line containing either a low or a high fraction of CSC. Compared with CSC-low tumors, CSC-high tumors exhibited increased microvessel density and blood perfusion and induced increased mobilization and tumor recruitment of bone marrow-derived endothelial progenitor cells (EPC). CSC-high C6 cell cultures also induced higher levels of endothelial cell proliferation and tubule organization in vitro compared with CSC-low cultures. CSC-high cultures and tumors expressed increased levels of the proangiogenic factors vascular endothelial growth factor and stromal-derived factor 1, and when signaling by either factor was blocked, all aspects of angiogenesis observed in CSC-high cultures and tumors, including microvessel density, perfusion, EPC mobilization/recruitment, and stimulation of endothelial cell activity, were reduced to levels comparable with those observed in CSC-low cultures/tumors. These results suggest that CSC contribute to tumor angiogenesis by promoting both local endothelial cell activity and systemic angiogenic processes involving bone marrow-derived EPC in a vascular endothelial growth factor-dependent and stromal-derived factor 1-dependent manner.
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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) | 0.001 | 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".