Oncogenes as Regulators of Tissue Factor Expression in Cancer: Implications for Tumor Angiogenesis and Anti-Cancer Therapy
Bibliographic record
Abstract
Up-regulation of tissue factor (TF) is often observed in cancer. TF is a cell-associated receptor for coagulation factor VII/VIIa, an interaction known to activate the coagulation cascade. At the same time, TF is also known as a mediator of intracellular signaling events that can alter gene expression patterns and cell behavior. Both aspects of TF activity are of possible relevance to tumor growth, metastasis, and angiogenesis, including up-regulation of vascular endothelial growth factor (VEGF). TF up-regulation is often observed on the surfaces of tumor-associated endothelial cells, inflammatory cells, and particularly on cancer cells themselves. In the last case, high TF levels may be associated with poor prognosis and parallel clinical (and genetic) tumor progression. We have proposed elsewhere that TF may be a target of oncogenic events in cancer. Here we discuss our observations suggesting that oncogene-targeting agents may down-regulate TF expression. Such is the effect of treatment with the neutralizing monoclonal antibody (C225) raised against the epidermal growth factor receptor (EGFR) in EGFR-dependent squamous cell carcinoma cells (A431). This two- to threefold TF down-regulation by C225 treatment is paralleled by a decrease in expression of VEGF. It is conceivable that TF participates in signals that regulate VEGF and angiogenesis triggered by activated oncogenic pathways. Therefore, direct targeting of TF in cancer should be considered in combination with other treatment modalities such as oncogene-directed therapies, antiangiogenic agents (e.g., VEGF antagonists), and anti-cancer chemotherapy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".