Differential involvement of the Fas receptor/ligand system in p53-dependent apoptosis in human prostate cancer cells
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to characterize the involvement of the Fas receptor/ligand system in p53-dependent apoptosis in human prostate cancer cells. METHODS: The effects of adenovirus-mediated p53 gene transfer (Ad5CMV-p53) into human prostate cancer LNCaP, DU145, and PC3 cells on their growth, apoptosis and Fas receptor/ligand expression were examined by the MTT assay, DNA fragmentation assay, and Northern blot analysis, respectively. The sensitivity of these cells to an agonistic anti-Fas receptor antibody (CH11) and the effects of an antagonistic anti-Fas ligand antibody (4H9) on Ad5CMV-p53-induced apoptosis were analyzed by the MTT assay and DNA fragmentation assay. RESULTS: Ad5CMV-p53 treatment resulted in substantial growth inhibition, induction of apoptosis and up-regulation of Fas receptor as well as Fas ligand mRNA expression in LNCaP, DU145 and PC3 cells. Despite the abundant expression of Fas receptor in all of these cells, CH11 induced apoptosis only in PC3 cells. Furthermore, 4H9 partially blocked the apoptosis induced by Ad5CMV-p53 in PC3 cells, but not in LNCaP and DU145 cells. CONCLUSIONS: The Fas receptor/ligand system is differentially involved in p53-dependent apoptosis in prostate cancer cells; therefore, reintroduction of wild-type p53 into prostate cancer cells may induce apoptosis through Fas receptor/ligand interaction as well as through an alternative pathway.
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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) | 0.001 | 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".