The serum concentration of human kallikrein 10 represents a novel biomarker for ovarian cancer diagnosis and prognosis.
Bibliographic record
Abstract
Human kallikrein 10 (hK10) is a secreted serine protease that is highly expressed in ovarian tissue. We hypothesized that hK10 might represent a novel serological marker for ovarian cancer. We quantified by immunoassay, hK10 in sera from 97 normal women (controls), 141 patients with benign gynecologic diseases, and 146 patients with ovarian cancer. We then examined the diagnostic and prognostic value of this measurement in ovarian cancer. We found that normal serum hK10 ranged from 50 to 1040 ng/liter (mean = 439 ng/liter). hK10 concentration is significantly elevated in serum of presurgical ovarian cancer patients (range: 106-11,746 ng/liter; mean = 1067 ng/liter) but not in serum of patients with benign gynecologic diseases (range: 120-1200 ng/liter; mean = 447 ng/liter). When a cutoff of 700 ng/liter was selected (diagnostic specificity = 90%), the diagnostic sensitivity for ovarian cancer is 54%. About 35% of CA125-negative ovarian cancer patients (CA125 < 23 kU/liter) were hK10 positive at 90% specificity. In patients with stage I/II ovarian cancer, use of these two markers in combination results in a 21% increase in sensitivity, at 90% specificity, compared with CA125 alone. High serum hK10 was strongly associated with serous epithelial type, late-stage, advanced grade, large residual tumor (>1 cm), suboptimal debulking, and no response to chemotherapy (all Ps < 0.001). In univariate Cox survival analysis, high serum hK10 is associated with increased risk for relapse and death (hazard ratio = 2.59 and 3.15, respectively, P </= 0.003). This prognostic value remains significant for overall survival in the multivariate analysis. Kaplan-Meier survival curves demonstrated similar findings. Serum hK10 represents a novel biomarker for ovarian cancer. We conclude that preoperative serum hK10 concentration is a strong and independent unfavorable prognostic marker for ovarian 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 | 0.000 |
| 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".