Stat1 stimulates cap-independent mRNA translation to inhibit cell proliferation and promote survival in response to antitumor drugs
Bibliographic record
Abstract
The signal transducer and activator of transcription 1 (Stat1) functions as a tumor suppressor via immune regulatory and cell-autonomous pathways. Herein, we report a previously unidentified cell-autonomous Stat1 function, which is its ability to exhibit both antiproliferative and prosurvival properties by facilitating translation of mRNAs encoding for the cyclin-dependent kinase inhibitor p27(Kip1) and antiapoptotic proteins X-linked inhibitor of apoptosis and B-cell lymphoma xl. Translation of the select mRNAs requires the transcriptional function of Stat1, resulting in the up-regulation of the p110γ subunit of phosphoinositide 3-kinase (PI3K) class IB and increased expression of the translational repressor translation initiation factor 4E (eIF4E)-binding protein 1 (4EBP1). Increased PI3Kγ signaling promotes the degradation of the eIF4A inhibitor programmed cell death protein 4, which favors the cap-independent translation of the select mRNAs under conditions of general inhibition of protein synthesis by up-regulated eIF4E-binding protein 1. As such, Stat1 inhibits cell proliferation but also renders cells increasingly resistant to antiproliferative effects of pharmacological inhibitors of PI3K and/or mammalian target of rapamycin. Stat1 also protects Ras-transformed cells from the genotoxic effects of doxorubicin in culture and immune-deficient mice. Our findings demonstrate an important role of mRNA translation in the cell-autonomous Stat1 functions, with implications in tumor growth and treatment with chemotherapeutic drugs.
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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) | 0.003 | 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".