TP53INP1 overexpression in prostate cancer correlates with poor prognostic factors and is predictive of biological cancer relapse
Bibliographic record
Abstract
BACKGROUND: Tumor protein 53-induced nuclear protein 1 (TP53INP1) is a proapoptotic protein involved in cell stress response. Whereas there is an overexpression of TP53INP1 in numerous tissues submitted to stress agents, TP53INP1 is down-expressed in stomach, pancreatic, and inflammation-mediated colic carcinomas. In medullary thyroid carcinomas, TP53INP1 overexpression correlates with poor prognosis. TP53INP1 expression has never been reported in Prostate Cancer (PC). Our aim was to investigate variations of TP53INP1 expression and their correlation to clinicopathological parameters in PC. METHODS: Quantitative measurements of immunohistochemical expression of TP53INP1 using high-throughput densitometry, assessed on digitized microscopic tissue micro-array images, were correlated with clinicopathological parameters in 91 human PC. Treatment of LNCaP tumor cells in vitro with cytokines and with TP53INP1 antisense oligonucleotide (ASO) was also analyzed. RESULTS: In normal prostate tissues, TP53INP1 is only expressed in prostate basal cells. There is a de novo TP53INP1 expression in prostate luminal cells in inflammatory prostate tissues, high grade PIN lesions and in PC. Stimulation of LNCaP cells with inflammatory cytokines enhances the level of TP53INP1 mRNA. In PC, TP53INP1 overexpression correlates with high Gleason grade, unfavorable D'Amico score and lymph node invasion, and is an independent factor of biological cancer relapse. Moreover, treatment of LNCaP cells with a TP53INP1 ASO down-regulates TP53INP1 protein level, inhibits proliferation, and induces apoptosis. CONCLUSION: TP53INP1 overexpression in PC seems to be a worse prognostic factor, particularly predictive of biological cancer relapse. Results in vitro suggest that TP53INP1 could be considered as a relevant target for potential specific therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".