Aberrant expression of kallikrein‐related peptidase 7 is correlated with human melanoma aggressiveness by stimulating cell migration and invasion
Bibliographic record
Abstract
Members of the tissue kallikrein-related peptidase (KLK) family not only regulate several important physiological functions, but aberrant expression has also been associated with various malignancies. Clinically, KLKs have been suggested as promising biomarkers for diagnosis and prognosis in many types of cancer. As of yet, expression of KLKs and their role in skin cancers are, however, poorly addressed. Malignant melanoma is an aggressive disease associated with poor prognosis. Hence, diagnostic biomarkers to monitor melanoma progression are needed. Herein, we demonstrate that although mRNA of several KLKs are aberrantly expressed in melanoma cell lines, only the KLK7 protein is highly secreted in vitro. In line with these findings, ectopic expression of KLK7 in human melanomas and its absence in benign nevi were demonstrated by immunohistochemistry in vivo. Interestingly, overexpression of KLK7 induced a significant reduction in melanoma cell proliferation and colony formation. Moreover, KLK7 overexpression triggered an increase in cell motility and invasion associated with decreased expression of E-cadherin and an upregulation of MCAM/CD146. Our results demonstrate, for the first time, that aberrant KLK7 expression leads to a switch from proliferative to invasive phenotype, suggesting a potential role of KLK7 in melanoma progression. Thus, we hypothesize that KLK7 may represent a potential biomarker for melanoma progression.
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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".