Expression of Kallikrein-Related Peptidase 7 Predicts Poor Prognosis in Patients with Unresectable Pancreatic Ductal Adenocarcinoma
Bibliographic record
Abstract
BACKGROUND: Kallikrein-related peptidase 7 (KLK7) is overexpressed in pancreatic ductal adenocarcinomas (PDAC). The aims of this study were to examine the expression of KLK7 during progression of pancreatic intraepithelial neoplasia (PanIN) to invasive PDAC and to assess its prognostic significance for PDAC. METHODS: Immunohistochemistry was used to assess KLK7 expression using a tissue microarray (TMA) and full sections of pancreatic tissue containing normal tissue, PanIN, and invasive adenocarcinoma, and the association between KLK7 expression and prognosis was examined by a population-based pancreatic cancer TMA. RESULTS: Normal pancreatic epithelium was negative for KLK7 in either TMAs or full sections. Analysis by TMAs showed that 91% of cases showed KLK7 positivity in the adenocarcinoma component, which was significantly higher than PanIN 2/3. In full tissue sections of PDAC, KLK7 expression was detected in less than 1% of cells among PanIN 1A lesions, and increased with grade among PanIN 1B and PanIN2/3 lesions before reaching 69% in the invasive PDAC. In patients with unresected PDAC, KLK7 positivity was significantly associated with shorter overall survival. CONCLUSIONS: Aberrant KLK7 expression starts in intermediate-to-late stages of PanIN progression, and KLK7-positive staining is associated with almost a three-fold increase in mortality rate of patients with unresected PDAC. IMPACT: The association of KLK7 expression and poor outcome of patients with unresectable PDAC suggests that inhibiting either KLK7 expression and/or activity could be a therapeutic strategy. Because the vast majority of patients present with unresectable disease, such an intervention could have a significant impact upon the overall survival of this patient population.
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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".