KLK15 is a prognostic marker for progression‐free survival in patients with radical prostatectomy
Bibliographic record
Abstract
In search of biomarkers for prostate cancer, we evaluated the expression of the human kallikrein-related peptidase KLK15 in samples of prostatic adenocarcinomas from radical prostatectomies. Twenty-five pairs of cancerous and adjacent normal prostatic tissue were selected by laser capture microdissection. The tissue was used for quantification of KLK15 mRNA by reverse-transcriptase polymerase chain reaction. Immunohistochemical expression of the KLK15 protein in 193 samples of prostatic adenocarcinoma was analysed in relation to clinicopathological parameters of the patients and disease progression. Expression of KLK15 correlated with the pathological tumour stage and Gleason score of the cases, both at mRNA and at protein level. While mRNA expression in the tumour was elevated, the protein level of KLK15 was reduced compared with adjacent normal tissue and to prostatic intraepithelial neoplasia. Univariate Kaplan-Meier analysis showed a significant association of dichotomised KLK15 levels with disease progression defined by prostate-specific antigen relapse (p = 0.001). Multivariate analysis according to the Cox proportional hazards regression model identified dichotomised KLK15 expression, corrected for the patient parameters age, preoperative prostate-specific antigen level, pathological tumour stage, Gleason score and surgical margin status, as an independent prognostic factor for poor outcome (inclusion model, hazard ratio 1.802, 95% confidence interval 1.037-3.132, p = 0.037). We suggest KLK15 as a new independent tumour marker for patients at risk for disease progression after radical prostatectomy.
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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".