Stat1 Phosphorylation Determines Ras Oncogenicity by Regulating p27Kip1
Bibliographic record
Abstract
Inactivation of p27 Kip1 is implicated in tumorigenesis and has both prognostic and treatment-predictive values for many types of human cancer. The transcription factor Stat1 is essential for innate immunity and tumor immunosurveillance through its ability to act downstream of interferons. Herein, we demonstrate that Stat1 functions as a suppressor of Ras transformation independently of an interferon response. Inhibition of Ras transformation and tumorigenesis requires the phosphorylation of Stat1 at tyrosine 701 but is independent of Stat1 phosphorylation at serine 727. Stat1 induces p27 Kip1 expression in Ras transformed cells at the transcriptional level through mechanisms that depend on Stat1 phosphorylation at tyrosine 701 and activation of Stat3. The tumor suppressor properties of Stat1 in Ras transformation are reversed by the inactivation of p27 Kip1. Our work reveals a novel functional link between Stat1 and p27 Kip1, which act in coordination to suppress the oncogenic properties of activated Ras. It also supports the notion that evaluation of Stat1 phosphorylation in human tumors may prove a reliable prognostic factor for patient outcome and a predictor of treatment response to anticancer therapies aimed at activating Stat1 and its downstream effectors.
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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) | 0.002 | 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".