BTF3 sustains cancer stem-like phenotype of prostate cancer via stabilization of BMI1
Bibliographic record
Abstract
BACKGROUND: Cancer stem-like traits contribute to prostate cancer (PCa) progression and metastasis. Deciphering the novel molecular mechanisms underlying stem-like traits may provide important insight for developing novel therapeutics. METHODS: Immunohistochemistry and immunofluorescence assays in prostatic tissues; gain- and loss-of-function analyses using ectopic overexpression and shRNAs in PCa cell lines; measurements of tumorigenic and stemness properties, and transcription in vitro and in vivo; transcriptional analysis in public databases. RESULTS: We identified that overexpression of BTF3 in PCa tissues and BTF3 expression highly correlates to stem-like traits. Cancer stem-like characteristics in PCa including self-renewal and metastatic potential were impaired by BTF3 loss and promoted by BTF3 overexpression. Mechanistically, BTF3 could stabilize BMI1, which is a crucial regulator of prostate stem cell self-renewal. More importantly, our data revealed that BTF3 is highly predictive of poor prognosis and may help in risk stratification of PCa patients. CONCLUSIONS: BTF3 promotes PCa progression though modeling stem-like traits in PCa. BTF3 represents a stratification marker in PCa progression and outcomes.
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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) | 0.002 | 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".