Prognostic Value of the Human Kallikrein Gene 15 Expression in Ovarian Cancer
Bibliographic record
Abstract
PURPOSE: KLK15 is a newly cloned human kallikrein gene. Many kallikreins were found to be differentially expressed in ovarian cancer. Like other kallikreins, KLK15 is regulated by steroid hormones in cancer cell lines. KLK15 is upregulated mainly by androgens and to a lesser extent by progestins. The purpose of this study was to examine the prognostic value of KLK15 in ovarian cancer tissues. MATERIALS AND METHODS: We studied KLK15 expression by quantitative reverse transcriptase polymerase chain reaction (RT-PCR) in 168 consecutive patients with epithelial ovarian cancer. Ten patients with benign ovarian tumors were also included in the study. An optimal cutoff point equal to the 50th percentile was defined based on the ability of KLK15 to predict progression-free survival and overall survival of the study population. RESULTS: KLK15 expression levels were significantly higher in cancerous tissues compared with benign tumors. Kaplan-Meier survival curves showed that KLK15 overexpression is a significant predictor of reduced progression-free survival (PFS; P <.001) and overall survival (OS; P <.009). Univariate and multivariate analyses indicate that KLK15 is an independent prognostic factor for PFS and OS. A weak positive correlation was found between KLK15 expression and serum CA-125 levels. CONCLUSION: KLK15 expression, as assessed by quantitative RT-PCR, is an independent marker of unfavorable prognosis 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) | 0.001 | 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".