Pituitary Tumor Transforming Gene Overexpression Facilitates Pituitary Tumor Development
Bibliographic record
Abstract
Intrinsic and extrinsic stimuli result in profound pituitary growth changes ranging from hypoplasia to hyperplasia. Pituitary tumor transforming gene (PTTG) abundance correlates with pituitary trophic status. Mice with Pttg inactivation exhibit pituitary hypoplasia, whereas targeted pituitary PTTG overexpression driven by alpha-subunit glycoprotein (alphaGSU) promoter results in focal pituitary hyperplasia. To test the impact of pituitary hyperplasia on tumor development, we crossbred alphaGSU.PTTG with Rb+/- mice, which develop pituitary tumors with high penetrance. Pituitary glands of resulting bitransgenic alphaGSU.PTTGxRb+/- mice were compared with monotransgenic alphaGSU.PTTG, Rb+/-, and wild-type mice. Confocal microscopy showed that PTTG-overexpressing cells have enlarged nuclei and marked redistribution of chromatin, and electron microscopy of alphaGSU.PTTG pituitaries showed enlarged gonadotrophs with prominent Golgi complexes and numerous secretory granules. These morphological findings were even more remarkable in alphaGSU.PTTGxRb+/- pituitaries. Mice from all four genotypes were sequentially imaged by magnetic resonance imaging to evaluate pituitary volume, and glands from alphaGSU.PTTGxRb+/- mice were the largest as early as 2 months of age (P = 0.0003). Cumulative incidence of pituitary tumors visualized by magnetic resonance imaging did not differ between Rb+/- and alphaGSU.PTTGxRb+/- mice. However, anterior lobe tumors determined after necropsy were 3.5 times more frequent in alphaGSU.PTTGxRb+/- than in Rb+/- mice (P = 0.0036), whereas the frequency of intermediate lobe tumors was similar. In summary, alphaGSU.PTTGxRb+/- pituitary glands exhibit enhanced cellular activity, increased volume, and higher prevalence of anterior pituitary tumors, indicating that changes in pituitary PTTG content directly relate to both pituitary trophic status and tumorigenic potential.
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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) | 0.002 | 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".