MATRIX METALLOPROTEINASE 26 (MMP-26) OVEREXPRESSION IN PROSTATIC ADENOCARCINOMA
Bibliographic record
Abstract
ABSTRACT Introduction Matrix metalloproteinases (MMP) have been identified as biomarkers for several diseases, including cancer. MMP-26 is constitutively expressed in some cancer cells of epithelial origin. Despite this, there is a lack of studies regarding the expression of MMP-26 on prostatic carcinoma. Aim Here, we investigate the expression of the MMP-26 peptide in benign and malign prostatic tissues. Patients and Methods For this, 150 specimens, including atrophy (N = 25), prostatic intraepithelial neoplasia (PIN) (N = 25), benign prostatic hyperplasia (BPH) (N = 50), and prostatic adenocarcinoma (PA) (N = 50), were immunohistochemically (IHC) examined for the expression of MMP-26. Results MMP-26 expression was positive in 70 (46.7%) out of the 150 samples, being more prevalent in the PA group (46/50 cases,92%), followed by PIN (22/25 cases, 88%). The BPH group showed only 2/50 (4%) positive cases, and the atrophy group showed no reactivity. ROC curve analysis showed that MMP-26 immunoexpression had a higher area under the curve between PA vs atrophy+PIN+BPH (AUC=0.94; 95% CI 0.9-0.98), PA+PIN vs atrophy+BPH (AUC=0.97; 95% CI 0.94-0.99) and PA vs atrophy+BPH (AUC=0.97; 95% CI 0.95-1.00) groups. In addition, the expression and intensity of the MMP-26 reaction showed a significant association with total PSA values ( P =0.001). Conclusions Our results showed that MMP-26 immunoexpression was useful to differentiate a group of benign and malignant samples in prostate tumors. This characteristic could assist in the predictive assessment and, consequently, in the development of new strategies for the diagnosis, prognosis, and treatment of prostate cancer.
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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".