Angiogenic factors FGF2 and PDGF-BB synergistically promote murine tumor neovascularization and metastasis
Bibliographic record
Abstract
Tumors produce multiple growth factors, but little is known about the interplay between various angiogenic factors in promoting tumor angiogenesis, growth, and metastasis. Here we show that 2 angiogenic factors frequently upregulated in tumors, PDGF-BB and FGF2, synergistically promote tumor angiogenesis and pulmonary metastasis. Simultaneous overexpression of PDGF-BB and FGF2 in murine fibrosarcomas led to the formation of high-density primitive vascular plexuses, which were poorly coated with pericytes and VSMCs. Surprisingly, overexpression of PDGF-BB alone in tumor cells resulted in dissociation of VSMCs from tumor vessels and decreased recruitment of pericytes. In the absence of FGF2, capillary ECs lacked response to PDGF-BB. However, FGF2 triggers PDGFR-alpha and -beta expression at the transcriptional level in ECs, which acquire hyperresponsiveness to PDGF-BB. Similarly, PDGF-BB-treated VSMCs become responsive to FGF2 stimulation via upregulation of FGF receptor 1 (FGFR1) promoter activity. These findings demonstrate that PDGF-BB and FGF2 reciprocally increase their EC and mural cell responses, leading to disorganized neovascularization and metastasis. Our data suggest that intervention of this non-VEGF reciprocal interaction loop for the tumor vasculature could be an important therapeutic target for the treatment of cancer and metastasis.
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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".