Prognostic significance of human tissue kallikrein-related peptidases 6 and 10 in gastric cancer
Bibliographic record
Abstract
The prognosis of patients following surgery for gastric cancer is often poor and is estimated using traditional clinicopathological parameters, which can be inaccurate predictors of future survival. Kallikreins are a group of serine proteases, which are differentially expressed in many human tumors and are being investigated as potential cancer biomarkers. This study assessed the prognostic utility of human tissue kallikrein-like peptidases 6 and 10 (KLK6 and KLK10) and correlated their expression with histopathological and clinical parameters in gastric cancer. We constructed a gastric tumor tissue microarray from 113 gastrectomy specimens and quantified KLK6 and KLK10 expression using immunohistochemistry. To overcome the problem of inter-observer variability and subjectivity in immunohistochemistry interpretation, a whole-slide scanned image of the tissue microarray was analyzed using an automated algorithm to quantify staining intensity. KLK6 expression was positively correlated with nodal involvement (p=0.002) and was predictive of advanced-stage disease (p<0.05). Kaplan-Meier survival curves revealed that tumors expressing high levels of KLK6 were significantly associated with significantly lower overall survival (p=0.04). KLK10 overexpression was also a predictor of advanced-stage disease (p<0.01), but was not significantly correlated with lymph node involvement or survival period. Our results show the potential ability of KLK6 as a prognostic marker for gastric cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".