Cell-cell and cell-matrix adhesion in survival and metastasis: Stat3 versus Akt
Bibliographic record
Abstract
Both cell-cell and cell-matrix adhesion are important for epithelial cell differentiation and function. Classical cadherins mediate cell to cell interactions and are potent activators of the signal transducer and activator of transcription (Stat3), thereby offering survival signaling. While the epithelial (E)-cadherin is required for cells to remain tightly associated within differentiated epithelial tissues, cadherin-11 promotes invasion and metastasis, preferentially to the bone. Cell adhesion to the extracellular matrix is mediated through the integrin receptors that bind to the focal adhesion kinase (FAK)/Src complex, thus activating downstream effectors such as Ras/Erk1/2 and PI3k/Akt, but not Stat3. Therefore, at high densities of cultured cells or in epithelial tissues, co-ordinate activation of the complementary cadherin/Stat3 and integrin/FAK pathways can greatly enhance survival and growth of tumor cells. In neoplastically transformed cells on the other hand, a variety of oncogenes including activated Src or receptor tyrosine kinases, activate both pathways. Still, most single-agent therapies directed against these signaling pathways have proven disappointing in the clinic. Combined targeting of the Src/FAK and Stat3 pathways with inhibitory drugs would be expected to have greater efficacy in inhibiting tumor cell survival, and enhancing sensitivity to conventional cytotoxic drugs for treatment of metastatic disease.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".