The secreted protein acidic and rich in cysteine is a critical mediator of cell death program induced by WIN/TRAIL combined treatment in osteosarcoma cells
Bibliographic record
Abstract
Secreted protein acidic and rich in cysteine (SPARC) is a multi-functional protein which modulates cell-cell and cell-matrix interactions. In cancer cells, SPARC behaves as a tumor promoter in a number of tumors, but it can also act as a tumor suppressor factor. Our previous results showed that the synthetic cannabinoid WIN55,212-2 (WIN), a potent cannabinoid receptor agonist, is able to sensitize osteosarcoma MG63 cells to TNF-related apoptosis-inducing ligand (TRAIL)-induced apoptosis which is accompanied with endoplasmic reticulum (ER)-stress induction and the increase in autophagic markers. In the present investigation, we studied the role of SPARC in WIN/TRAIL-induced apoptosis demonstrating that WIN increased the level of SPARC protein and mRNA in a time-dependent manner. This event was functional to WIN/TRAIL-dependent apoptosis as demonstrated by RNA interfering analysis which indicated that SPARC-silenced cells were less sensitive to cytotoxic effects induced by the combined treatment. Our experiments also demonstrate that SPARC interacts with caspase-8 thus probably favoring its translocation to plasma membrane and the activation of extrinsic apoptotic pathway. In conclusion, to the best of our knowledge, our results are the first to show that WIN-dependent increase in the level of SPARC plays a critical role in sensitizing osteosarcoma cells to TRAIL action.
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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".