Caspase-1α Is Down-regulated in Human Ovarian Cancer Cells and the Overexpression of Caspase-1α Induces Apoptosis
Bibliographic record
Abstract
Caspase-1 plays a key role in the processing of cytokines and in the apoptosis of neurons and macrophages. Whether it also causes apoptosis of cancer cells has been unclear. In this study, we screened an array of apoptosis-related proteins in ovarian carcinoma cell lines and their tissue of origin, ovarian surface epithelium (OSE). Caspase-1alpha protein was abundant in OSE and in nontumorigenic OSE with extended but limited life spans (immortalized OSE), but was reduced in the cancer lines A2780 and OVCAR10. By Western blot and immunofluorescence, caspase-1alpha levels were greatly reduced in six of eight ovarian carcinoma lines compared with OSE. By real-time reverse transcription-PCR, steady-state transcripts of the CASP1 gene were proportional to protein levels. Caspase-1alpha overexpression caused significant apoptosis, but overexpression of a caspase-1alpha mutant without catalytic activity did not, confirming that the effect was caspase-1alpha-specific. Immunofluorescence of caspase-1alpha and terminal nucleotidyl transferase-mediated dUTP-X nick end labeling colocalization clearly established a link between apoptosis and caspase-1alpha expression. Caspase-9 and caspase-3 were activated in caspase-1alpha overexpressing A2780 cells, suggesting involvement of an intrinsic apoptotic pathway. Caspase-1alpha overexpression did not change the apoptotic effect of cisplatin in A2780 and OVCAR10 cells, suggesting that this agent activates a different pathway. Immunohistochemically, caspase-1 was lower in ovarian serous carcinomas than in OSE. Our study indicates, for the first time, that caspase-1alpha is proapoptotic in ovarian cancer cells, and raises the possibility that its down-regulation is one of the mechanisms which increase resistance to apoptosis in cancer 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 | 0.001 |
| 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".