Notch3 Functions as a Tumor Suppressor by Controlling Cellular Senescence
Bibliographic record
Abstract
Notch signaling regulates a broad spectrum of cell fate decisions and differentiation. Both oncogenic and tumor suppressor functions have been shown for Notch signaling. However, little is known about the underlying mechanisms of its tumor suppressor function. Here, we report that expression of Notch3, a member of Notch family transmembrane receptors, was elevated in human cells during senescence activated by various senescence-inducing stimuli. This upregulation of Notch3 was required for the induction of p21 expression in senescent cells. Downregulation of Notch3 led to a delayed onset of senescence and extended replicative lifespan, whereas adventitious expression of Notch3 was sufficient to activate senescence and p21 expression. The ability of Notch3 to induce senescence and p21 expression was dependent on the canonical Notch singling. Deletion of p21 in cells significantly attenuated Notch3-induced senescence. Furthermore, a significant decrease in Notch3 expression was observed in human tumor cell lines as well as primary human breast cancer and melanoma samples compared with normal tissues. Restoration of Notch3 expression in human tumor cells resulted in inhibition of cell proliferation and activation of senescence. Collectively, our results reveal a novel function of Notch3 in senescence regulation and tumor suppression.
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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) | 0.001 | 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 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".