EGCG Suppresses Melanoma Tumor Angiogenesis and Growth without Affecting Angiogenesis and VEGF Expression in the Heart and Skeletal Muscles in Mice
Bibliographic record
Abstract
Melanoma is a highly malignant cancer with a potent capacity to metastasize distantly and has a higher mortality. There is no effective therapy for high risk melanoma patients to prevent relapse or distant metastasis. Therefore effective chemoprevention strategies are needed. The present study mainly evaluates the effects of EGCG on melanoma angiogenesis, growth, and capillary density (CD) in the heart and skeletal muscles of mice. 5 x 10^5 B16F10 cells were inoculated into the right proximal dorsal of the back in the eight week old male mice (n=12). Then, 6 mice received EGCG at 50-100 mg/kg/d in drinking water for 4 weeks and 6 control mice received drinking water only. Tumor size was monitored using dial calipers. At the end of the experiment, blood samples, tumors, hearts, and limb muscles were collected and measured for VEGF expression using ELISA and capillary density (CD) using CD31 immunohistochemistry. Compared to the control, EGCG treatment significantly reduced tumor weight (2.9±0.5 vs. 5.9±1.1 g; P<0.01; n=6), melanoma CD (117±9 vs. 167±23; P<0.01), and melanoma VEGF expression (32±1.5 vs. 42±2 pg/mg; P < 0.01), respectively. Also EGCG had no effects on body weight, heart weight, angiogenesis or VEGF expression in the heart and skeletal muscle of mice. EGCG (20-50 µg/ml) significantly inhibited the proliferation, migration, VEGF expression, and the activation of HIF-1α and NFαB in cultured B16F10 cells, respectively. These findings support the hypothesis that EGCG, a major green tea polyphenol, directly targets tumor cells and tumor vasculature, thereby inhibiting tumor growth, proliferation, migration, and angiogenesis of melanoma, and that the down-regulation of VEGF expression by EGCG is associated with the inhibition of HIF-1α and NFkB activation. EGCG has great potential as a chemopreventive agent because it has no effect on angiogenesis in normal tissue and has low toxicity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".