Nuclear Ep-ICD Expression Is a Predictor of Poor Prognosis in “Low Risk” Prostate Adenocarcinomas
Bibliographic record
Abstract
INTRODUCTION: Molecular markers for predicting prostate cancer (PCa) that would have poor prognosis are urgently needed for a more personalized treatment for patients. Regulated intramembrane proteolysis of Epithelial cell adhesion molecule results in shedding of the extracellular domain (EpEx) and release of its intracellular domain (Ep-ICD) which triggers oncogenic signaling and might correlate to tumor aggressiveness. This study aimed to explore the potential of Ep-ICD and EpEx to identify PCa that have poor prognosis. METHODS: Immunohistochemical analysis of Ep-ICD and EpEx was carried out in normal prostate tissues (n = 100), benign prostate hyperplasia (BPH, n = 83), and prostate cancer (n = 249) using domain specific antibodies. The expression of Ep-ICD and EpEx was correlated with clinico- pathological parameters and disease free survival (DFS). RESULTS: Reduced expression of nuclear Ep-ICD and membrane EpEx was observed in PCa in comparison with BPH and normal prostate tissues (p = 0.006, p < 0.001 respectively). For patients who had PCa with Gleason Score less than 7, preserved nuclear Ep-ICD emerged as the most significant marker in multivariate analysis for prolonged DFS, where these patients did not have recurrence during follow up of up to 12 years (p = 0.001). CONCLUSION: Reduced expression of nuclear Ep-ICD was associated with shorter disease free survival in patients with a Gleason Score less than 7 and may be useful in identifying patients likely to have aggressive tumors with poor prognosis. Furthermore, nuclear Ep-ICD can differentiate between normal and prostate cancer tissues for ambiguous cases.
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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 | 0.000 | 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".