Function and clinical relevance of kallikrein-related peptidases and other serine proteases in gynecological cancers
Bibliographic record
Abstract
Julia Dorna, Nathalie Beaufortb, Manfred Schmitta, Eleftherios P. Diamandisc, Peter Goettigd & Viktor Magdolen*aa Klinische Forschergruppe der Frauenklinik der Technischen Universität München, Klinikum rechts der Isar MunichGermanyb Institute for Stroke and Dementia Research (ISD), Klinikum der Universität München MunichGermanyc Department of Pathology and Laboratory Medicine, Mount Sinai Hospital TorontoCanadad Structural Biology Group, University of Salzburg SalzburgAustriaReferee: Dr. Judith Clements, Scientific Director, Australian Prostate Cancer Research Centre-Queensland; Professor and Program Leader, Cancer Program, The Institute of Health and Biomedical Innovation, Queensland University of Technology; Adjunct Professor, School of Medicine, University of Queensland, Australia.*Both authors contributed equally to this work. Prof. Dr Viktor Magdolen, Klinische Forschergruppe, Frauenklinik der Technischen Universität München, Klinikum rechts der Isar, Ismaninger Str. 22, D-81675 MünchenGermany+49 89 4140 2493+49 89 4140 7410viktor.magdolen@lrz.tum.deAbstractGynecological 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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".