Insulin‐like growth factor binding proteins (IGFBPs) as potential physiological substrates for human kallikreins hK2 and hK3
Bibliographic record
Abstract
Insulin-like growth factors (IGFs) are important growth regulators of both normal and malignant prostate cells. Their action is regulated by six insulin-like growth factor binding proteins (IGFBPs). The proteolytic cleavage of IGFBPs by various proteases decreases dramatically their affinity for their ligands and therefore enhances the bioavailability of IGFs. To elucidate the putative biological role of prostatic kallikreins hK2 and hK3 (prostate-specific antigen) in tumour progression, we analyzed the degradation of IGFBP-2, -3, -4 and -5 by these two tissue kallikreins. We found that hK3, already characterized as an IGFBP-3 degrading protease, cleaved IGFBP-4 but not IGFBP-2 and -5, whereas hK2 cleaved all of the IGFBPs much more effectively, and at concentrations far lower than those reported for other IGFBP-degrading proteases. The proteolytic patterns after cleavage of IGFBPs by hK2 and hK3 were similar and were not modified in the presence of IGF-I. Heparin, but not other glycosaminoglycans, enhanced dramatically the ability of hK3 but not hK2 to degrade IGFBP-3 and IGFBP-4. More importantly, the IGFBP fragments generated by hK2 and hK3 had no IGF-binding capacity, as assessed by Western ligand blotting. Our results suggest that the prostatic kallikreins hK2 and hK3 may influence specifically the tumoral growth of prostate cells through the degradation of IGFBPs, to increase IGF bioavailability.
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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) | 0.002 | 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".