Impact of acetylsalicylic acid on tumor angiogenesis and lymphangiogenesis through inhibition of VEGF signaling in a murine sarcoma model
Bibliographic record
Abstract
Aspirin is a salicylate drug that is widely used, and recently it has been shown to influence the development of various types of cancers. Our previous study revealed that aspirin had an inhibitory effect on the growth of S180 sarcoma and 3AO human ovarian cancer cells. The present study utilized a murine S180 sarcoma model to investigate the molecular mechanisms involved in aspirin-induced tumor growth inhibition. Tumor-bearing mice were randomly divided into five groups with 10 mice in each group: i) control; ii) 5-fluorouracil (5-FU); iii) high-dose aspirin (250 mg/kg); iv) low-dose aspirin (50 mg/kg); and v) combination of 5-FU and aspirin (50 mg/kg). The effect of aspirin on tumor growth was observed by measuring tumor volume and evaluating the antitumor effect. Tumor histology and immunohistochemistry were performed to detect the microvessel density (MVD), lymphatic vessel density (LVD), and the expression levels of vascular endothelial growth factor A (VEGF-A) and VEGF-C. The expression of VEGF-A and VEGF-C was also confirmed and quantified by western blotting. We discovered significant growth delay in murine S180 sarcoma as a result of aspirin treatment. The inhibition rate of tumor growth induced by high-dose and low-dose aspirin was 33.5 and 22.2%, respectively (P<0.05). The expression of VEGF-A and VEGF-C in tumor tissues inhibited by aspirin was demonstrated by immunohistochemistry, and the MVD was decreased in a dose-dependent manner (p<0.05). Reduced LVD was particularly apparent in the high-dose aspirin group (p<0.05). Western blot data showed that the expression of both VEGF-A and VEGF-C was reduced after treatment with aspirin. In conclusion, the impact of aspirin-induced tumor growth delay of murine S180 sarcoma may correlate with the inhibition of angiogenesis and lymphangiogenesis by reducing VEGF-A and VEGF-C expression in tumor tissues.
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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 | 0.001 |
| 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".