Osteoprotegerin (OPG) protects ovarian cancer cells from TRAIL-induced apoptosis but does not contribute to malignant ascites-mediated attenuation of TRAIL-induced apoptosis
Bibliographic record
Abstract
BACKGROUND: Resistance to apoptosis is a major problem in ovarian cancer and correlates with poor prognosis. Osteoprotegerin (OPG) is a secreted factor in malignant ascites and acts as a decoy receptor for receptor activator of NF-κB ligand (RANKL) and tumor necrosis factor-related apoptosis-inducing ligand (TRAIL). TRAIL promotes apoptosis in ovarian cancer cells. Ovarian cancer ascites attenuate TRAIL-induced apoptosis raising the possibility that OPG contained in ascites may abrogate the anti-tumor activity of TRAIL. METHODS: Determination of OPG levels in ascites was measured by ELISA. Effect of OPG on TRAIL-induced cell death was determined by XTT and colony forming assays in ovarian cancer cell lines and primary tumor cells. Apoptosis was assessed by ELISA. RESULTS: We found that recombinant OPG and malignant ascites attenuates TRAIL-induced cell death and apoptosis in a dose-dependent manner in ovarian cancer cell lines and primary ovarian tumor cells. OPG is present at high levels in the ascites of patients with ovarian cancer. We found a positive correlation between the levels of OPG in ascites and the ability of the ascites to attenuate TRAIL-induced cell death. The anti-apoptotic effect of ascites was not reversed by co-incubation with an OPG blocking antibody. CONCLUSIONS: OPG and malignant ascites protect ovarian cancer cells from TRAIL-induced apoptosis. Although malignant ascites contain high levels of OPG, OPG is not a critical component that contributes to ascites-mediated attenuation of TRAIL-induced apoptosis.
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".