Hyperplasia and Carcinomas in Pten-Deficient Mice and Reduced PTEN Protein in Human Bladder Cancer Patients
Bibliographic record
Abstract
PTEN is a tumor suppressor gene mutated in many human cancers. We used the Cre-loxP system to generate an urothelium-specific null mutation of Pten in mice [FabpCrePten(flox/flox) (FPten(flox/flox)) mice]. Histologic examination revealed that all FPten(flox/flox) mice exhibited urothelial hyperplasia in which component cells showed enlarged nuclei and increased cell size. With time, 10% of FPten(flox/flox) mice spontaneously developed pedicellate papillary transitional cell carcinomas (TCC). This type of tumor also arose in FPten(flox/flox) mice treated with the chemical carcinogen N-butyl-N-(4-hydroxybutyl) nitrosamine. FPten(flox/flox) urothelial cells were hyperproliferative and showed increased activation of the survival signaling molecules Akt and extracellular signal-regulated kinase. In humans, 53% of primary bladder cancer patients exhibited decreased or absent expression of PTEN protein in either the cytoplasm or nucleus of tumor cells. In early bladder cancers, PTEN expression was repressed in 42% of superficial papillary TCC but in only 8% of cases of carcinoma in situ (CIS). In advanced bladder cancers, PTEN protein was significantly reduced (particularly in the nucleus) in 94% of cases, and this decrease in PTEN correlated with disease stage and grade. Thus, PTEN deficiency may contribute to bladder cancer both by initiating superficial papillary TCC and by promoting the progression of CIS to advanced invasive and metastatic forms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".