Steroid Hormone Regulation and Prognostic Value of the Human Kallikrein Gene 14 in Ovarian Cancer
Bibliographic record
Abstract
To study KLK14 gene expression in endocrine-related cancers, we studied its hormonal regulation in breast and ovarian cancer cell lines. Our kinetic and blocking experiments suggest that this up-regulation is mediated through the androgen receptor. We then studied the expression of KLK14 by quantitative reverse transcriptase-polymerase chain reaction in 155 consecutive ovarian tumors and correlated these findings with clinicopathologic parameters, response to chemotherapy, and survival. A stepwise reduction was observed in the levels of KLK14 messenger RNA in normal, benign, and cancerous tissues (P < .001). Expression levels were significantly higher in patients with early stage disease and optimal debulking and in patients who responded to chemotherapy. Kaplan-Meier survival curves demonstrated longer progression-free and overall survival in patients with KLK14-positive tumors than in patients with KLK14-negative tumors (P < .001). When all other prognostic variables were controlled in the multivariate analysis, KLK14 retained its prognostic significance (progression-free and overall survival, respectively, hazard ratios, 0.43 and 0.53; P = .027 and .014). A weak negative correlation was found between KLK14 expression and serum CA-125. KLK14 is a new, independent, and favorable 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 | 0.000 | 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) | 0.000 | 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".