Pituitary tumor-transforming gene in endocrine and other neoplasms: a review and update
Bibliographic record
Abstract
Pituitary tumor-transforming gene (PTTG) was only recently discovered. Its overexpression occurs in a wide variety of endocrine and non-endocrine tumors, including ones of pituitary, thyroid, ovary, breast, prostate, lung, esophagus, colon, and the central nervous system. It affects tumor invasiveness and recurrence in several systems, functions as a securin during cell cycle progression, and inhibits premature sister chromatid separation. PTTG is involved in multiple cellular pathways, including proliferation, DNA repair, transformation, angiogenesis induction, invasion, and the induction of genetic instability. In thyroid carcinomas, PTTG expression is a marker of invasiveness. PTTG is overexpressed in most pituitary adenomas, where it appears to correlate with recurrence and angiogenesis. Increasing evidence also points to the role of PTTG in endocrine organ development. For example, PTTG knockout mice show defective pancreatic beta-cell proliferation. Herein, we review the current knowledge regarding PTTG-mediated pathways based on evidence from in vivo and in vitro studies as well as knockout mice models. We also summarize the issue of PTTG expression and its correlation with clinicopathologic parameters in patients with neoplasms, particularly of endocrine organs. In addition, we discuss in vitro and in vivo therapeutic models targeting PTTG overexpression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".