Over-expression of extracellular matrix metalloproteinase inducer in prostate cancer is associated with high risk of prostate-specific antigen relapse after radical prostatectomy
Bibliographic record
Abstract
PURPOSE: The prognostic efficiency of clinical grading and staging in patients with confined or moderately differentiated prostate cancer (PCa) has been markedly improved, which underscores the importance of new prognostic markers. Extracellular matrix metalloproteinase inducer (EMMPRIN) has been demonstrated to be involved in cancerangiogenesis, metastasis and invasion. EMMPRIN expression was evaluated by measuring mRNA and protein levels in a large cohort of patients with PCa following prostatectomy and the findings were compared with clinico-pathological parameters, including prostate-specific antigen (PSA) relapse time. METHODS: EMMPRIN mRNA levels in 20 pairs of normal and cancerous prostate tissues were determined by quantitative real-time PCR. Protein expression in paraffin-embedded specimens of prostates gathered from 300 patients with PCa was detected by immunohistochemistry using a monoclonal antibody against EMMPRIN. The associations of EMMPRIN protein expression with the clinico-pathological parameters and PSA relapse-free time after radical prostatectomy were subsequently assessed. RESULTS: Both EMMPRIN mRNA and protein levels were higher in PCa tissue, compared with adjacent normal tissue. In addition, the positive expression rates of EMMPRIN in PCa tissues were significantly associated with preoperative PSA levels (p=0.008), AJCC stage (p=0.006) and Gleason Score (p < 0.001), Risk classification (p < 0.001), lymph node status post-surgery (p < 0.001) and surgical margin status (p < 0.001) were also determined. Multivariate analysis, using the Cox proportional hazards model, revealed that positive EMMPRIN expression was an independent prognostic factor for an increased risk of PSA relapse. CONCLUSION: Over-expression of EMMPRIN correlated with the aggressiveness of PCa, and the PSA relapse-free time, and may be a novel and useful biomarker for follow-up and treatment decisions for 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 | 0.000 | 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".