Role of Raf-1 and FAK in cell density-dependent regulation of integrin-dependent activation of MAP kinase
Bibliographic record
Abstract
MAP kinase can be activated by integrin-dependent adhesion in a FAK-dependent manner. Cell-cell contact inhibition is continuously active in controlling cell growth and the loss of cell-cell contact inhibition is correlated with the malignant characteristics of cancer cells. In this study we showed that cell adhesion to fibronectin for 1 h activated MAP kinase phosphorylation. However, when non-tumorigenic HSG cells, MCF-10A cells, or 293 cells were plated on fibronectin-coated substrates for 1 h at high cell density (which favors cell-cell contact), MAP kinase phosphorylation was not enhanced. Tumorigenic breast cancer cells, BT474, Cama, MCF-7, MDA-MB-231 and SKBR3, did not show inhibition of MAP kinase phosphorylation but rather enhanced MAP kinase phosphorylation when cultured at high density on fibronectin-coated substrates. Adhesion of HSG cells to fibronectin also increased FAK phosphorylation and this FAK phosphorylation was partially inhibited when cells were cultured at high density. Expression of Raf-1 catalytic domain-GFP in HSG cells could overcome the cell density-dependent inhibition of MAP kinase phosphorylation and FAK phosphorylation. The expression of Raf-1-catalytic domain-GFP also upregulated the expression of alphav integrin and promoted cell-cell adhesion in HSG cells. These results suggest that the active form of Raf-1 may interrupt cell-cell contact inhibition by promoting alphav integrin expression, which has been implicated in cell aggregation.
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".