A Novel Interaction between Procaspase 8 and SPARC Enhances Apoptosis and Potentiates Chemotherapy Sensitivity in Colorectal Cancers
Bibliographic record
Abstract
Chemotherapy resistance accounts for the high mortality rates in patients with advanced cancers. We previously used a genomics approach to determine novel genes associated with this phenomenon and identified secreted protein acidic and rich in cysteine (SPARC) as a chemosensitizer capable of reversing therapy resistance in colorectal cancer cells by enhancing apoptosis in vitro and tumor regression in vivo. Here, we examined the mechanisms by which SPARC enhances apoptosis in the presence of chemotherapy. We show that SPARC potentiates apoptosis by augmenting the signaling cascade in a caspase-8-dependent manner, because apoptosis can be abolished by caspase 8 small interfering RNA in the presence of SPARC. This occurs independently of death receptor activation and leads to downstream involvement of Bid and subsequent apoptosis. Interestingly, this results from an interaction between SPARC and the N terminus of the procaspase-8 DED-containing domain. These exciting findings provide an initial map of the apoptosis signaling events mediated by SPARC and how this can ultimately result in the reversal of chemotherapy resistance and enhanced tumor regression. This signaling cascade can be exploited therapeutically and may have potential clinical implications for patients with advanced and therapy-refractory cancers.
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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".