Characterisation of fibronectin-mediated FAK signalling pathways in lung cancer cell migration and invasion
Bibliographic record
Abstract
BACKGROUND: Focal adhesion kinase (FAK) is overexpressed in a variety of cancers, such as breast, colon, prostate, ovary, and lung cancers. However, the mechanism by which extracellular matrix fibronectin stimulates lung cancer cell migration and invasion through FAK remains to be investigated. METHODS: The signalling pathways in fibronectin-mediated lung cancer cell migration and invasion were examined using western blotting. The metastasis function was detected by wound healing, migration and invasion assays. Further, RNA interference and kinase inhibitors were also used to study the downstream signals. RESULTS: In this study, we examined the FAK signalling pathways in relation to calpain-2 and RhoA in fibronectin-mediated lung cancer cell migration and invasion. We found that A549 lung epithelial cells stimulated by fibronectin showed increased phosphorylation of FAK and its downstream targets, Src, ERK1/2, phosphatidylinositol 3'-kinase (PI3K), and Akt. Consistent with this observation, depletion of FAK by siRNA resulted in the inhibition of Src, ERK1/2, PI3K, and Akt activity. In addition, the Src inhibitor, PP2, blocked the phosphorylation of FAK, ERK1/2, PI3K, and Akt. Conversely, inhibition of MEK1/2 using PD98059 reduced the expression of matrix metalloproteinase-9 (MMP9) and calpain-2. The PI3K inhibitor, LY294002, further blocked the expression of MMP9 and RhoA. Inhibition of both MEK1/2 and PI3K caused reduced cell migration and invasion. CONCLUSION: Our data suggest that fibronectin-mediated activation of FAK that leads to lung cancer metastasis could occur through ERK or PI3K/Akt regulation of MMP9/calpain-2 or MMP9/RhoA activity, respectively.
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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) | 0.002 | 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".