Prognostication of prostate cancer based on NUCB2 protein assessment: NUCB2 in prostate cancer
Bibliographic record
Abstract
BACKGROUND: Nucleobindin 2 (NUCB2) protein, a novel oncoprotein, is overexpressed in breast cancer. To date, there have been no published data regarding the role of NUCB2 protein expression in prostate cancer (PCa). Therefore, this study was performed to investigate the correlations between NUCB2 protein expression and prognosis in patients with PCa. METHODS: Through immunohistochemistry, NUCB2 protein expression was evaluated in 60 benign prostatic hyperplasia (BPH) specimens and 180 PCa specimens. The correlation of NUCB2 protein expression with clinicopathological parameters was assessed using χ2 analysis. Kaplan-Meier analysis and Cox proportional hazards regression models were used to investigate the correlation between NUCB2 protein expression and prognosis of PCa patients. RESULTS: The immunohistochemistry results showed that the expression level of NUCB2 in PCa cases was significantly higher than that in BPH tissues (P < 0.001). Moreover, statistical analysis also showed that high NUCB2 protein expression was positively related to seminal vesicle invasion, lymph node metastasis, angiolymphatic invasion, higher Gleason score, biochemical recurrence (BCR), and higher preoperative prostate-specific antigen (PSA). Furthermore, it was also shown that patients with high NUCB2 protein expression had significantly poorer overall survival and BCR- free survival compared with patients with low expression of NUCB2 protein. Multivariate Cox regression analysis revealed that high NUCB2 protein expression level was an independent prognostic factor for overall survival and BCR-free survival of patients with PCa. CONCLUSIONS: NUCB2 protein expression showed a strong association with the potencies of BCR and progression of PCa, and that may be applied as a novel biomarker for the prediction of BCR, and helpful for improving the diagnosis, prognosis and treatment of PCa.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".