Tumor cells expressing tissue factor influence the migration of smooth muscle cells in a catalytic activity-dependent way
Bibliographic record
Abstract
The expression of tissue factor (TF) in tumors reportedly exacerbates the aggressiveness of several types of cancers. The shedding of TF-containing membrane particles is believed to influence the ability of tumors to expand and metastasize, and these microparticles may also be harmful in the onset of disseminated intravascular coagulation in specific cancers. Furthermore, the intracellular signaling that is elicited after the formation of the TF / coagulation factor VIIa complex at the cell membrane modulates the activity of adhesion molecules and mitogen-activated protein (MAP) kinases. To evaluate whether TF overexpression in tumor cells modulates its shedding and neighboring stromal cells by its catalytic or intracellular activity, TF-GFP (green fluorescent protein) and a tailless form (TFDeltaC-GFP) were stably expressed in the rat Morris hepatoma and human HT1080 fibrosarcoma cell lines. Both TF proteins were efficiently produced by tumor cells and functionally active, and their clotting activity could be blocked by the active site-inhibited factor VIIa (ASIS). TF-expressing tumorigenic cells produced a soluble factor that increased the migration of arterial smooth muscle cells in vitro. This effect was abrogated by ASIS and the PAR-1 receptor antagonist ATAP-2, showing that it is dependent on the proteolytic activity of the TF ligand factor VIIa and the thrombin-activated cell membrane receptor. We propose that TF-containing microparticles that are released in the culture medium by tumor cells influence the migratory behavior of neighboring stromal cells, thus aiding the cancer cell's tumorigenic potential.
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".