The secretory proprotein convertases furin, PC5, and PC7 activate VEGF-C to induce tumorigenesis
Bibliographic record
Abstract
The secretory factor VEGF-C has been directly implicated in various physiological processes during embryogenesis and human cancers. However, the importance of the conversion of its precursor proVEGF-C to mature VEGF-C in tumorigenesis, and vessel formation and the identity of the protease(s) that regulate these processes is/are not known. The intracellular processing of proVEGF-C that occurs within the dibasic motif HSIIRR(227)SL suggests the involvement of the proprotein convertases (PCs) in this process. In addition, furin and VEGF-C were found to be coordinately expressed in adult mouse tissues. Cotransfection of the furin-deficient colon carcinoma cell line LoVo with proVEGF-C and different PC members revealed that furin, PC5, and PC7 are candidate VEGF-C convertases. This finding is consistent with the in vitro digestions of an internally quenched synthetic fluorogenic peptide mimicking the cleavage site of proVEGF-C ((220)Q-VHSIIRR downward arrow SLP(230)). The processing of proVEGF-C is blocked by the inhibitory prosegments of furin, PC5, and PACE4, as well as by furin-motif variants of alpha2-macroglobulin and alpha1-antitrypsin. Subcutaneous injection of CHO cells stably expressing VEGF-C into nude mice enhanced angiogenesis and lymphangiogenesis, but not tumor growth. In contrast, expression of proVEGF-C obtained following mutation of the cleavage site (HSIIRR(227)SL to HSIISS(227)SL) inhibits angiogenesis and lymphangiogenesis as well as tumor growth. Our findings demonstrate the processing of proVEGF-C by PCs and highlight the potential use of PC inhibitors as agents for inhibiting malignancies induced by VEGF-C.
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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".