Diverse Roles of Tissue Factor–Expressing Cell Subsets in Tumor Progression
Bibliographic record
Abstract
Oncogenic upregulation of tissue factor (TF) and release of TF-containing microvesicles play an important role in cancer-related coagulopathy (Trousseau's syndrome), angiogenesis, and disease progression. In addition, certain types of host cells (stromal cells, inflammatory cells, activated endothelium) may also express TF. Although the relative contribution of host-related versus tumor-related TF to tumor progression is not known, our recent studies indicate that the role of both sources of TF in tumor formation is complex and context-dependent. Disruption of TF expression/activity in cancer cells leads to tumor growth inhibition in immunodeficient mice, even in cases where TF overexpression is driven by potent oncogenes ( K-RAS or EGFR). Interestingly, TF expression in vivo appears to be influenced by many factors, including the level of oncogenic transformation, tumor microenvironment, and differentiation from cancer stem-like cells. We postulate that activation of TF signaling and coagulation may deliver growth-promoting stimuli (e.g., fibrin, thrombin, platelets) to dormant cancer stem cells (CSCs). Functionally, these influences may be tantamount to formation of a provisional (TF-dependent) cancer stem cell niche. As such, these changes may contribute to the involvement of CSCs in tumor growth, angiogenesis, and metastasis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".