Regulation of transendothelial migration of colon cancer cells by E‐selectin‐mediated activation of MAP kinases in endothelial cells
Bibliographic record
Abstract
Cancer cells invasive properties depend on their intrinsic motile potential and their ability to breach the endothelial barrier. In the present work, we investigated mechanisms by which adhesion of colon cancer cells to E‐selectin expressed by endothelial cells regulates the endothelial barrier function and modulates cancer cells transmigration. We found that stimulation of E‐selectin by the adhesion of HT‐29 cells, in a static or a dynamic fashion, results in increased activity of ERK and p38 MAP kinases. In turn, activation of p38 and ERK enhanced transendothelial permeability and migration of HT‐29 cells. We also obtained evidence suggesting that p38‐mediated increase in transendothelial permeability and cancer cells migration depends on a myosin light chain phosphorylation‐mediated formation of stress fibers. On the other hand, the activation of ERK by E‐selectin modulated the opening of interendothelial spaces by initiating the activation of Src kinase activities and the dissociation of the VE‐cadherin/b‐catenin complex. Thus, we conclude that activation of E‐selectin by adhering cancer cells is an important process that regulates the extravasation of colon cancer cells by initiating p38‐ and ERK‐dependent mechanisms that both contribute to the regulation of the integrity of the endothelial layer.
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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".