IFN Regulatory Factor 8 Sensitizes Soft Tissue Sarcoma Cells to Death Receptor–Initiated Apoptosis via Repression of FLICE-like Protein Expression
Bibliographic record
Abstract
IFN regulatory factor 8 (IRF8) has been shown to suppress tumor development at least partly through regulating apoptosis of tumor cells; however, the molecular mechanisms underlying IRF8 regulation of apoptosis are still not fully understood. Here, we showed that disrupting IRF8 function resulted in inhibition of cytochrome c release, caspase-9 and caspase-3 activation, and poly(ADP-ribose) polymerase cleavage in soft tissue sarcoma (STS) cells. Inhibition of the mitochondrion-dependent apoptosis signaling cascade is apparently due to blockage of caspase-8 and Bid activation. Analysis of signaling events upstream of caspase-8 revealed that disrupting IRF8 function dramatically increases FLIP mRNA stability, resulting in increased IRF8 protein level. Furthermore, primary myeloid cells isolated from IRF8-null mice also exhibited increased FLIP protein level, suggesting that IRF8 might be a general repressor of FLIP. Nuclear IRF8 protein was absent in 92% (55 of 60) of human STS specimens, and 99% (59 of 60) of human STS specimens exhibited FLIP expression, suggesting that the nuclear IRF8 protein level is inversely correlated with FLIP level in vivo. Silencing FLIP expression significantly increased human sarcoma cells to both FasL-induced and tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)-induced apoptosis, and ectopic expression of IRF8 also significantly increased the sensitivity of these human sarcoma cells to FasL- and TRAIL-induced apoptosis. Taken together, our data suggest that IRF8 mediates FLIP expression level to regulate apoptosis and targeting IRF8 expression is a potentially effective therapeutic strategy to sensitize apoptosis-resistant human STS to apoptosis, thereby possibly overcoming chemoresistance of STS, currently a major obstacle in human STS therapy.
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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".