Osteoprotegerin (OPG) activates integrin, focal adhesion kinase (FAK), and Akt signaling in ovarian cancer cells to attenuate TRAIL-induced apoptosis
Bibliographic record
Abstract
BACKGROUND: Resistance to apoptosis is a major problem in ovarian cancer (OC) and correlates with poor prognosis. Osteoprotegerin (OPG) is a soluble secreted factor that acts as a decoy receptor for receptor activator of NF-κB ligand (RANKL) and tumor necrosis factor-related apoptosis-inducing ligand (TRAIL). OPG has been reported to attenuate TRAIL-induced apoptosis in a variety of cancer cells, including OC cells. OPG-mediated protection against TRAIL has been attributed to its decoy receptor function. However, OPG activates integrin/focal adhesion kinase (FAK) signaling in endothelial cells. In OC cells, activation of integrin/FAK signaling inhibits TRAIL-induced apoptosis. Based on these observations, we hypothesized that OPG could attenuate TRAIL-induced apoptosis in OC cells through integrin/FAK signaling. METHODS: In vitro experiments including immunoblots, colony formation assays, and apoptosis measurements were used to assess the effect of OPG on TRAIL-induced apoptosis. RESULTS: Exogenous OPG protected from TRAIL-induced apoptosis in a TRAIL binding-independent manner and OPG protection was αvβ3 and αvβ5 integrin/FAK signaling-dependent. Moreover, OPG-mediated activation of integrin/FAK signaling resulted in the activation of Akt. Inhibition of both integrin/FAK and Akt signaling significantly inhibited OPG-mediated attenuation of TRAIL-induced apoptosis. Although OPG also stimulated ERK1/2 phosphorylation, inhibition of ERK1/2 signaling did not significantly altered OPG protection. CONCLUSIONS: Our studies provide evidence, for the first time, that OPG can attenuate TRAIL-induced apoptosis in a TRAIL binding-independent manner through the activation of integrin/FAK/Akt signaling in OC cells.
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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 | 0.001 |
| 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".