Primary Tumor Levels of Human Tissue Kallikreins Affect Surgical Success and Survival in Ovarian Cancer Patients
Bibliographic record
Abstract
PURPOSE: Proteolytic factors of the human tissue kallikrein (hK) family and the plasminogen activation system play a key role in tumor progression in various malignancies. We determined antigen levels of urokinase-type plasminogen activator (uPA), its inhibitor PAI-1, and hK5-8, hK10, hK11, and hK13 by ELISA in primary tumor tissue extracts of 142 International Federation of Gynecology and Obstetrics (FIGO) I to IV ovarian cancer patients (median follow-up 41 months). RESULTS: After radical surgery, absence of macroscopically visible residual tumor (RT) was achieved in 72 patients; all patients received postoperative platinum-containing chemotherapy. Significant univariate predictors of poor progression-free survival (PFS) were RT (>0), FIGO stages (III/IV versus I/II/III), ascites volume >500 mL, nodal status, and the difference between PAI-1 and uPA (fractionally ranked). In multivariate analysis, significant independent factors for poor PFS were RT [hazard ratio (HR), 4.53] and low hK11 fractional rank (HR, 0.30). Univariate predictors of poor overall survival were RT, FIGO stages, nodal status, ascites volume, nuclear grade, and low hK10 and hK13. In multivariate analysis, significant independent factors for poor overall survival were RT (HR, 7.49), ascites (HR, 1.97), and low hK10 (HR, 0.196). We constructed a multivariate scoring model estimating RT probability, based on ascites [odds ratio (OR), 13.1], nuclear grade (OR, 2.92), hK6 (OR, 8.54), and hK13 (OR, 0.14), with good in-sample predictive performance (area under receiver operating characteristic, 0.833). CONCLUSIONS: In view of risks and benefits of radical surgery, such a score could support preoperative risk stratification and identify candidates for alternative therapeutic strategies. These results highlight the distinct roles of the hKs for different disease end points in ovarian cancer and their potential to support individualized therapy decisions.
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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".