Tumorigenesis facilitated by <i>Pten</i> deficiency in the skin: Evidence of <i>p53‐Pten</i> complex formation on the initiation phase
Post-publication record
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Bibliographic record
Abstract
Pten, a tumor suppressor gene, is mutated in various human cancers and in hereditary cancer syndromes, such as Cowden disease. We have previously developed a knockout mouse in which Pten is specifically disrupted in the skin, resulting in hyperproliferation and spontaneous tumorigenesis of the skin keratinocytes. In this study, we further clarified the effects of Pten deficiency in tumorigenesis, by using a two-step model in intact skin of Pten knockout mouse. Although the conventional protocol requires serial exposures to DMBA and TPA, mice deficient for Pten developed skin papilloma within 6 weeks after a single exposure to DMBA, indicating that loss of Pten has a tumor-promoting effect. Serial exposure to DMBA-TPA ointments produced 10-fold more papillomas in the skin of knockout mice than in the wild-type counterpart, suggesting an increased rate of initiation. Therefore, we precisely examined the effect of DMBA. This treatment was highly apoptotic in wild-type mice, whereas the number of apoptotic cells was diminished in Pten-deficient skin. Moreover, primary keratinocytes isolated from Pten-deficient mice were also resistant to the apoptotic effect of DMBA. The status of p53, Pten proteins and downstream targets of p53, such as p21, 14-3-3, and Reprimo, were also examined, and we found that accumulation of p53 protein and up-regulation of p53 targets were delayed in Pten-knockout skin. These observations suggest that Pten is involved in rapid recruitment of p53 in the tumor initiation phase.
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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 | 0.001 |
| 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".