GOLPH3 and YB-1 Are Novel Markers Correlating With Poor Prognosis in Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: Prostate cancer is a common and aggressive cancer among men. Despite advances in the treatment, the mechanisms involved in progression are still unclear. New prognostic markers should be explored for better design of patient-specific therapeutic regimens. METHODS: This study was performed on 120 patients stratified as 76 with prostatic carcinoma, 12 with low-grade prostate intraepithelial lesion, 12 with high-grade prostate intraepithelial lesion and 20 with benign prostate hyperplasia. Immunohistochemical study was done for Golgi phosphoprotein 3 (GOLPH3) and Y-box binding protein-1 (YB-1) analysis. Correlation with clinicopathological data and overall survival was analyzed. RESULTS: Both GOLPH3 and YB-1 showed increased expression from benign to malignant tumors. In prostatic carcinoma, cytoplasmic GOLPH3 was associated with Gleason score, stage and androgen receptor (P = 0.034, P < 0.001, and P = 0.008 respectively). Nuclear YB-1 expression was associated with Gleason score and androgen receptor (P = 0.018 and P = 0.024 respectively). Cytoplasmic YB-1 expression was associated with Gleason score, stage and androgen receptor (P = 0.008, P = 0.027, and P < 0.001 respectively). High Gleason score (P = 0.004), high stage (P < 0.001) and androgen receptor (P = 0.006) were the only detected adverse prognostic clinicopathological factors. Moderate/intense GOLPH3 and high nuclear and cytoplasmic YB-1 expression were correlated with shorter overall survival (P < 0.001, P = 0.020, and P < 0.001 respectively). In the multivariate analysis, moderate/intense GOLPH3 expression was the only predictor of overall survival (P = 0.025). CONCLUSIONS: High GOLPH3 and nuclear/cytoplasmic YB-1 expression correlated with poor prognosis in prostate cancer. Both markers can be promising targets for new treatment strategies.
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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 | 0.001 |
| 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".