Neural Stem Cell Targeting of Glioma Is Dependent on Phosphoinositide 3-Kinase Signaling
Bibliographic record
Abstract
The utility of neural stem cells (NSCs) has extended beyond regenerative medicine to targeted gene delivery, as NSCs possess an inherent tropism to solid tumors, including invasive gliomas. However, for optimal clinical implementation, an understanding of the molecular events that regulate NSC tumor tropism is needed to ensure their safety and to maximize therapeutic efficacy. We show that human NSC lines responded to multiple tumor-derived growth factors and that hepatocyte growth factor (HGF) induced the strongest chemotactic response. Gliomatropism was critically dependent on c-Met signaling, as short hairpin RNA-mediated ablation of c-Met significantly attenuated the response. Furthermore, inhibition of Ras-phosphoinositide 3-kinase (PI3K) signaling impaired the migration of human neural stem cells (hNSCs) toward HGF and other growth factors. Migration toward tumor cells is a highly regulated process, in which multiple growth factor signals converge on Ras-PI3K, causing direct modification of the cytoskeleton. The signaling pathways that regulate hNSC migration are similar to those that promote unregulated glioma invasion, suggesting shared cellular mechanisms and responses. Disclosure of potential conflicts of interest is found at the end of this article.
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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".