The proto‐oncogene PBF binds p53 and is associated with prognostic features in colorectal cancer
Bibliographic record
Abstract
The PTTG1-binding factor (PBF) is a transforming gene capable of eliciting tumor formation in xenograft models. However, the precise role of PBF in tumorigenesis and its prognostic value as a cancer biomarker remain largely uncharacterised, particularly in malignancies outside the thyroid. Here, we provide the first evidence that PBF represents a promising prognostic marker in colorectal cancer. Examination of a total of 39 patients demonstrated higher PBF expression at both the mRNA (P = 0.009) and protein (P < 0.0001) level in colorectal tumors compared to matched normal tissue. Critically, PBF was most abundant in colorectal tumors associated with Extramural Vascular Invasion (EMVI), increased genetic instability (GI) and somatic TP53 mutations, all features linked with recurrence and poorer patient survival. We further demonstrate by glutathione-S-transferase (GST) pull-down and coimmunoprecipitation that PBF binds to the tumor suppressor protein p53, as well as to p53 mutants (Δ126-132, M133K, V197E, G245D, I255F and R273C) identified in the colorectal tumors. Importantly, overexpression of PBF in colorectal HCT116 cells interfered with the transcriptional activity of p53-responsive genes such as mdm2, p21 and sfn. Diminished p53 stability (> 90%; P < 0.01) was also evident with a concurrent increase in ubiquitinated p53. Human colorectal tumors with wild-type TP53 and high PBF expression also had low p53 protein levels (P < 0.05), further emphasizing a putative interaction between these genes in vivo. Overall, these results demonstrate an emerging role for PBF in colorectal tumorigenesis through regulating p53 activity, with implications for PBF as a prognostic indicator for invasive tumors.
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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".