Transforming Growth Factor-β Regulator SnoN Modulates Mammary Gland Branching Morphogenesis, Postlactational Involution, and Mammary Tumorigenesis
Bibliographic record
Abstract
SnoN is an important negative regulator of transforming growth factor-beta (TGF-beta) signaling that was originally identified as a transforming oncogene in chicken embryonic fibroblasts. Both pro-oncogenic and antioncogenic activities of SnoN have been reported, but its function in normal epithelial cells has not been defined. In the mouse mammary gland, SnoN is expressed at relatively low levels, but it is transiently upregulated at late gestation before being downregulated during lactation and early involution. To assess the effects of elevated levels of SnoN, we generated transgenic mice expressing a SnoN fragment under the control of the mouse mammary tumor virus promoter. In this model system, SnoN elevation increased side-branching and lobular-alveolar proliferation in virgin glands, while accelerating involution in postlactation glands. Increased proliferation stimulated by SnoN was insufficient to induce mammary tumorigenesis. In contrast, elevated levels of SnoN cooperated with polyoma middle T antigen to accelerate the formation of aggressive multifocal adenocarcinomas and to increase the formation of pulmonary metastases. Our studies define functions of SnoN in mammary epithelial cell proliferation and involution, and provide the first in vivo evidence of a pro-oncogenic role for SnoN in mammalian tumorigenesis.
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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) | 0.001 | 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 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".