Suppression of IRF4 by IRF1, 3, and 7 in Noxa Expression Is a Necessary Event for IFN-γ–Mediated Tumor Elimination
Bibliographic record
Abstract
IFN-γ plays a critical role in tumor immunosurveillance by affecting either immune cells or tumor cells; however, IFN-mediated effects on tumor elimination are largely unknown. In this study, we showed that IFN regulatory factors (IRF) modulated by IFNs up- and downregulated Noxa expression, a prodeath BH3 protein, in various cancer cells. Inhibition of Noxa expression using short hairpin RNA in tumor cells leads to resistance against lipopolysaccharide (LPS)-induced tumor elimination, in which IFN-γ is known as a critical effecter in mice. Chromatin immunoprecipitation analysis in both CT26 cells and SP2/0 cells, sensitive and resistant to LPS-induced tumor elimination, respectively, revealed that the responsiveness of IRF1, 3, 4, and 7 in the Noxa promoter region in response to IFN-γ might be crucial in LPS-induced tumor elimination. IRF1, 3, and 7 were upregulated by IFN-γ and activated Noxa expression, leading to the death of Noxa wild-type baby mouse kidney (BMK) cells but not of Noxa-deficient BMK cells. In contrast, IRF4 acts as a repressor for Noxa expression and inhibits cell death induced by IRF1, 3, or 7. Therefore, although IFN-γ alone are not able to induce cell death in tumor cells in vitro, Noxa induction by IFN-γ, which is regulated by the balance between its activators (IRF1, 3, and 7) and its repressor (IRF4), is crucial to increasing the susceptibility of tumor cells to immune cell-mediated cytotoxicity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".