TNF and IFNγ-induced cell death requires IRF1 and ELAVL1 to promote CASP8 expression
Bibliographic record
Abstract
TNFα and IFNγ (TNF/IFNγ) synergistically induce caspase-8 activation and cancer cell death. However, the mechanism of IFNγ in promoting TNF-initiated caspase-8 activation in cancer cells is poorly understood. Here, we found that in addition to CASP8, CYLD is transcriptionally upregulated by IFNγ-induced transcription factor IRF1. IRF1-mediated CASP8 and CYLD upregulation additively mediates TNF/IFNγ-induced cancer cell death. Clinically, the expression levels of TNF, IFNγ, CYLD, and CASP8 in melanoma tumors are increased in patients responsive to immune checkpoint blockade (ICB) therapy after anti-PD-1 treatment. Accordingly, our genetic screen revealed that ELAVL1 (HuR) is required for TNF/IFNγ-induced caspase-8 activation. Mechanistically, ELAVL1 binds CASP8 mRNA and extends its stability to sustain caspase-8 expression both in IFNγ-stimulated and in basal conditions. Consequently, ELAVL1 determines death receptors-initiated caspase-8-dependent cell death triggered from stimuli including TNF and TRAIL by regulating basal/stimulated caspase-8 levels. As caspase-8 is a master regulator in cell death and inflammation, these results provide valuable clues for tumor immunotherapy and inflammatory diseases.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".