Expression and Functional Characterization of the Cancer-related Serine Protease, Human Tissue Kallikrein 14
Bibliographic record
Abstract
Human tissue kallikrein 14 (KLK14) is a novel extracellular serine protease. Clinical data link KLK14 expression to several diseases, primarily cancer; however, little is known of its (patho)-physiological role. To functionally characterize KLK14, we expressed and purified recombinant KLK14 in mature and proenzyme forms and determined its expression pattern, specificity, regulation, and in vitro substrates. By using our novel immunoassay, the normal and/or diseased skin, breast, prostate, and ovary contained the highest concentration of KLK14. Serum KLK14 levels were significantly elevated in prostate cancer patients compared with healthy males. KLK14 displayed trypsin-like specificity with high selectivity for P1-Arg over Lys. KLK14 activity could be regulated as follows: 1) by autolytic cleavage leading to enzymatic inactivation; 2) by the inhibitory serpins alpha1-antitrypsin, alpha2-antiplasmin, antithrombin III, and alpha1-antichymotrypsin with second order rate constants (k(+2)/Ki) of 49.8, 23.8, 1.48, and 0.224 microM(-1) min(-1), respectively, as well as plasminogen activator inhibitor-1; and 3) by citrate and zinc ions, which exerted stimulatory and inhibitory effects on KLK14 activity, respectively. We also expanded the in vitro target repertoire of KLK14 to include collagens I-IV, fibronectin, laminin, kininogen, fibrinogen, plasminogen, vitronectin, and insulin-like growth factor-binding proteins 2 and 3. Our results indicate that KLK14 may be implicated in several facets of tumor progression, including growth, invasion, and angiogenesis, as well as in arthritic disease via deterioration of cartilage. These findings may have clinical implications for the management of cancer and other disorders in which KLK14 activity is elevated.
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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) | 0.000 | 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 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".