c-Met activation in medulloblastoma induces tissue factor expression and activity: effects on cell migration
Bibliographic record
Abstract
Met, the receptor for hepatocyte growth factor (HGF), is a receptor tyrosine kinase that has recently emerged as an important contributor to human neoplasia. In physiological and pathological conditions, Met triggers various cellular functions related to cell proliferation, cell migration and the inhibition of apoptosis, and also regulates a genetic program leading to coagulation. Since medulloblastomas (MBs) express high levels of tissue factor (TF), the main initiator of blood coagulation, we therefore examined the link between Met and TF expression in these pediatric tumors. We observed that stimulation of the MB cell line DAOY with HGF led to a marked increase of TF expression and procoagulant activity, in agreement with analysis of clinical MB tumor specimens, in which tumors expressing high levels of Met also showed high levels of TF. The HGF-dependent increase in TF expression and activity required Src family kinases and led to the translocation of TF to actin-rich structures at the cell periphery, suggesting a role of the protein in cell migration. Accordingly, addition of physiological concentrations of the TF activator factor VIIa (FVII) to HGF-stimulated DAOY cells promoted a marked increase in the migratory potential of these cells. Overall, these results suggest that HGF-induced activation of the Met receptor results in TF expression by MB cells and that this event probably contribute to tumor proliferation by enabling the formation of a provisional fibrin matrix. In addition, TF-mediated non-hemostatic functions, such as migration toward FVIIa, may also play a central role in MB aggressiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".