Colorectal carcinoma cell production of transforming growth factor beta decreases expression of endothelial cell vascular endothelial growth factor receptor 2
Bibliographic record
Abstract
BACKGROUND: Vascular endothelial growth factor (VEGF) signaling is a target for antiangiogenic cancer therapy. The authors have previously observed that up to 40% of vessels in colorectal carcinoma (CRC) tumors are negative for VEGF receptor 2 (VEGFR2) expression. Differential activity of transforming growth factor beta (TGF-β) is a potential contributor to this receptor heterogeneity because TGF-β contributes to both angiogenesis and CRC tumor progression. METHODS: The authors analyzed VEGFR2 expression by Western blotting, and TGF-β expression in endothelial and CRC cell lines, respectively. In addition, they immunostained endothelial cells in CRC xenografts to find an association between VEGFR2 and TGF-β levels or activity. RESULTS: In bovine aortic endothelial cells (BAECs), TGF-β1 significantly repressed VEGFR2 protein in a time-dependent and dose-dependent fashion (P < .05). Serum-free conditioned media from various malignant human CRC cell lines (HCT116, 379.2, Dks8, and DLD1) induced down-regulation of VEGFR2 in BAECs. This effect was proportional to the total levels of TGF-β1 and TGF-β2 and was blocked by SB-431542 and SD-208, TGF-β receptor I inhibitors. Immunofluorescence staining of subcutaneous mouse xenografts of HCT116, 379.2, Dks8, and SW480 cells revealed vessels with an inverse relationship between TGF-β activity and VEGFR2 expression. Oxygen and bone morphogenetic protein 9 levels were shown to modulate TGF-β-induced VEGFR2 down-regulation. CONCLUSIONS: In combination with other factors, TGF-β may contribute to the vascular heterogeneity in human colorectal tumors.
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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".