Function and clinical relevance of kallikrein-related peptidases and other serine proteases in gynecological cancers
Bibliographic record
Abstract
Gynecological cancers, including malignant tumors of the ovaries, the endometrium and the cervix, account for approximately 10% of tumor-associated deaths in women of the Western world. For screening, diagnosis, prognosis, and therapy response prediction, the group of enzymes known as serine (Ser-)proteases show great promise as biomarkers. In the present review, following a summary of the clinical facts regarding malignant tumors of the ovaries, the endometrium and the cervix, and characterization of the most important Ser-proteases, we thoroughly review the current state of knowledge relating to the use of proteases as biomarkers of the most frequent gynecological cancers. Within the Ser-protease group, the kallikrein-related peptidase (KLK) family, which encompasses a subgroup of 15 members, holds particular promise, with some acting via a tumor-promoting mechanism and others behaving as protective factors. Further, the urokinase-type plasminogen activator (uPA) and its inhibitor PAI-1 (plasminogen activator inhibitor-1) seem to play an unfavorable role in gynecological tumors, while down-regulation of high-temperature requirement proteins A 1, 2 and 3 (HtrA1,2,3) is associated with malignant disease and cancer progression. Expression/activity levels of other Ser-proteases, including the type II transmembrane Ser-proteases (TTSPs) matriptase, hepsin (TMPRSS1), and the hepsin-related protease (TMPRSS3), as well as the glycosyl-phosphatidylinositol (GPI)-anchored Ser-proteases prostasin and testisin, may be of clinical relevance in gynecological cancers. In conclusion, proteases are a rich source of biomarkers of gynecological cancer, though the enzymes' exact roles and functions merit further investigation.
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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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".