The PTTG1-Binding Factor (PBF/PTTG1IP) Regulates p53 Activity in Thyroid Cells
Bibliographic record
Abstract
The PTTG1-binding factor (PBF/PTTG1IP) has an emerging repertoire of roles, especially in thyroid biology, and functions as a protooncogene. High PBF expression is independently associated with poor prognosis and lower disease-specific survival in human thyroid cancer. However, the precise role of PBF in thyroid tumorigenesis is unclear. Here, we present extensive evidence demonstrating that PBF is a novel regulator of p53, a tumor suppressor protein with a key role in maintaining genetic stability, which is infrequently mutated in differentiated thyroid cancer. By coimmunoprecipitation and proximity-ligation assays, we show that PBF binds specifically to p53 in thyroid cells and significantly represses transactivation of responsive promoters. Further, we identify that PBF decreases p53 stability by enhancing ubiquitination, which appears dependent on the E3 ligase activity of Mdm2. Impaired p53 function was evident in a transgenic mouse model with thyroid-specific PBF overexpression (transgenic PBF mice), which had significantly increased genetic instability as indicated by fluorescent inter simple sequence repeat-PCR analysis. Consistent with this, approximately 40% of all DNA repair genes examined were repressed in transgenic PBF primary cultures, including genes with critical roles in maintaining genomic integrity such as Mgmt, Rad51, and Xrcc3. Our data also revealed that PBF induction resulted in up-regulation of the E2 enzyme Rad6 in murine thyrocytes and was associated with Rad6 expression in human thyroid tumors. Overall, this work provides novel insights into the role of the protooncogene PBF as a negative regulator of p53 function in thyroid tumorigenesis, in which PBF is generally overexpressed and p53 mutations are rare compared with other tumor types.
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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".