Acetylsalicylic Acid Exerts Potent Antitumor and Antiangiogenic Effects in Cutaneous and Uveal Melanoma Cell Lines
Bibliographic record
Abstract
INTRODUCTION: Acetylsalicylic acid (ASA) has been investigated for a potential anticancer role in several cancers, such as colorectal, ovarian, and endometrial cancer. Moreover, ASA has been shown to abrogate various processes that contribute to tumor growth and progression. OBJECTIVE: The aim of this study was to evaluate the effects of ASA on cutaneous melanoma (CM) and uveal melanoma (UM). METHODS: Human CM and UM cells were treated with 5 mM ASA and assessed for changes in cellular functions. Antiangiogenic effects of ASA were determined using an ELISA-based assay for 10 proangiogenic cytokines, and then validated by Western blot. Finally, proteomic analysis of ASA-treated cells was performed to elucidate the changes that may be responsible for ASA-mediated effects in melanoma cells. RESULTS: Treatment with ASA significantly inhibited the proliferation, invasion, and migration capabilities, and caused a significant decrease in angiogenin and PIGF secretion in both CM and UM. Mass spectrometry revealed 179 protein changes associated with ASA in the CM and UM cell lines. CONCLUSIONS: These results suggest that ASA may be effective as an adjuvant therapy in metastatic CM and UM. Future studies are needed to determine the regulating targets that are responsible for the antitumor effects of ASA.
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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 | 0.000 |
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".