NK4, an antagonist of hepatocyte growth factor (HGF), inhibits growth of multiple myeloma cells: molecular targeting of angiogenic growth factor
Bibliographic record
Abstract
Hepatocyte growth factor (HGF) promotes cell growth and motility and also increases neovascularization. Multiple myeloma (MM) cells produce HGF, and the plasma concentration of HGF is significantly elevated in patients with clinically active MM, suggesting that HGF might play a role in the pathogenesis of MM. NK4, an antagonist of HGF, is structurally homologous to angiostatin, and our previous report showed that NK4 inhibited the proliferation of vascular endothelial cells induced by HGF stimulation. The purposes of this study were to elucidate the contribution of HGF to the growth of MM cells as well as to investigate the possibility of the therapeutic use of NK4. In vitro study showed that NK4 protein stabilized the growth of MM cell lines and regulated the activation of c-MET, ERK1/2, STAT3, and AKT-1. Recombinant adenovirus containing NK4 cDNA (AdCMV.NK4) was injected intramuscularly into Icr/scid mice bearing tumors derived from HGF-producing MM cells. AdCMV.NK4 significantly inhibited the growth of these tumors in vivo. Histologic examination revealed that AdCMV.NK4 induced apoptosis of MM cells, accompanied by a reduction in neovascularization in the tumors. Thus, NK4 inhibited the growth of MM cells via antiangiogenic as well as direct antitumor mechanisms. The molecular targeting of HGF by NK4 could be applied as a novel therapeutic approach to MM.
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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".