The role of human tissue kallikreins 7 and 8 in intracranial malignancies
Bibliographic record
Abstract
Recent evidence suggests that many tissue kallikreins are implicated in carcinogenesis. Kallikrein 8 (KLK8) plays a role in the physiology of the central nervous system. Kallikrein 7 (KLK7) takes part in skin desquamation. Both show altered expression in ovarian and breast cancer. In this study, we examined the level of mRNA expression of the KLK7 and KLK8 genes in 73 intracranial tumors using qualitative RT-PCR. The results were correlated with clinical and histomorphological variables and patient outcome. The expression of both genes was also examined in the brain cancer cell lines U-251 MG, D54 and SH-SY5Y and the invasive capacity of glioblastoma cells U-251 MG overexpressing hK7 or hK8 was also investigated in an in vitro Matrigel assay. Follow-up analysis revealed that expression of KLK7 mRNA was associated with shorter overall survival (OS) compared to patients with no KLK7 expression, as determined by Cox proportional hazard regression analysis. Overexpression of hK7 protein by cultivated brain tumor cells significantly enhanced the invasive potential in the Matrigel invasion assay, in contrast to cells overexpressing hK8 protein. Our data suggest that hK7 protein overexpression is associated with a more aggressive phenotype in brain cancer cells.
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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 | 0.000 |
| 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 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".