Secretion of Vascular Endothelial Growth Factor by Primary Human Fibroblasts at Senescence
Bibliographic record
Abstract
Cellular senescence prevents the proliferation of cells at risk for neoplastic transformation. Nonetheless, the senescence response is thought to be antagonistically pleiotropic and thus contribute to aging phenotypes, including, ironically, late life cancers. The cancer-promoting activity of senescent cells is likely due to secreted molecules, the identity of which remains largely unknown. Here, we have shown that senescent fibroblasts, much more than presenescent fibroblasts, stimulate tumor vascularization in mice. Weakly malignant epithelial cells co-injected with senescent fibroblasts had larger and greater numbers of blood vessels compared with controls. Accordingly, increased vascular endothelial growth factor (VEGF) expression was a frequent characteristic of senescent human and mouse fibroblasts in culture. Importantly, conditioned medium from senescent fibroblasts, more than medium from presenescent cells, stimulates cultured human umbilical vein endothelial cells to invade a basement membrane, a hallmark of angiogenesis. Increased VEGF expression was specific to the senescent phenotype and increased whether senescence was induced by replicative exhaustion, overexpression of p16(Ink4a), or overexpression of oncogenic RAS. The senescence-dependent increase in VEGF production was accompanied by very little increase in hypoxic-inducible (transcription) factor 1 alpha protein levels, and hypoxia further induced VEGF in senescent cells. This result suggests the rise in VEGF expression at senescence is not a hypoxic response. Our findings may in part explain why senescent cells stimulate tumorigenesis in vivo and support the idea that senescent cells may facilitate age-associated cancer development by secreting factors that promote malignant progression.
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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 | 0.001 |
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".