Delphinidin, a dietary anthocyanidin, inhibits platelet-derived growth factor ligand/receptor (PDGF/PDGFR) signaling
Bibliographic record
Abstract
Most cancers are dependent on the growth of tumor blood vessels and inhibition of tumor angiogenesis may thus provide an efficient strategy to retard or block tumor growth. Recently, tumor vascular targeting has expanded to include not only endothelial cells (ECs) but also smooth muscle cells (SMCs), which contribute to a mature and functional vasculature. We have reported previously that delphinidin, a major biologically active constituent of berries, inhibits the vascular endothelial growth factor-induced phosphorylation of vascular endothelial growth factor receptor-2 and blocks angiogenesis in vitro and in vivo. In the present study, we show that delphinidin also inhibits activation of the platelet-derived growth factor (PDGF)-BB receptor-beta [platelet-derived growth factor receptor-beta (PDGFR-beta)] in SMC and that this inhibition may contribute to its antitumor effect. The inhibitory effect of delphinidin on PDGFR-beta was very rapid and led to the inhibition of PDGF-BB-induced activation of extracellular signal-regulated kinase (ERK)-1/2 signaling and of the chemotactic motility of SMC, as well as the differentiation and stabilization of EC and SMC into capillary-like tubular structures in a three-dimensional coculture system. Using an anthocyan-rich extract of berries, we show that berry extracts were able to suppress the synergistic induction of vessel formation by basic fibroblast growth factor-2 and PDGF-BB in the mouse Matrigel plug assay. Oral administration of the berry extract also significantly retarded tumor growth in a lung carcinoma xenograft model. Taken together, these results provide new insight into the molecular mechanisms underlying the antiangiogenic activity of delphinidin that will be helpful for the development of dietary-based chemopreventive strategies.
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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".