KLK6 and KLK13 predict tumor recurrence in epithelial ovarian carcinoma
Bibliographic record
Abstract
BACKGROUND: The human kallikrein-related peptidase family consists of 15 genes. Twelve of these genes are overexpressed in ovarian cancer and may represent potential markers for diagnosis, prognosis, and/or response to treatment. The aim of this study was to determine the prognostic significance of kallikrein-related peptidase 6 (KLK6) and kallikrein-related peptidase 13 (KLK13) in epithelial ovarian cancer by quantifying gene expression levels with tumour pathology and patient survival data. METHODS: Total RNA was isolated from 106 patients diagnosed with primary ovarian cancer, as well as 8 normal ovary controls. Samples were analysed by quantitative real-time PCR for KLK6 and KLK13 expression. Correlation between kallikrein gene expression and clinical characteristics was evaluated with the chi(2)-test. Survival analysis was performed using Kaplan-Meier and Cox proportional hazards regression models. RESULTS: Expression levels of both KLK6 and KLK13 mRNA were significantly increased in invasive cancers relative to normal ovaries (P=0.002 and 0.039 respectively). High KLK6 and KLK13 expression was an indicator of poor prognosis, with patients having a shorter recurrence-free survival (P=0.002 and 0.027 respectively). High KLK6 expression was also significantly associated with lower overall survival (P=0.011). When subjected to multivariate analysis, patients with either high KLK6 or KLK13 were 3- and 2.2-fold, respectively, more likely to have a recurrence than patients with low kallikrein expression. CONCLUSION: These data show increased mRNA expression of KLK6 and KLK13 in ovarian cancer compared to normal ovarian tissues. High KLK6 or KLK13 expression in primary ovarian tumours can significantly predict prognosis in terms of recurrence-free survival and overall survival. In all, this study shows KLK6 and KLK13 as potential biomarkers and may be therapeutic targets for treatment of 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 | 0.002 |
| 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".