Cancer and platelet crosstalk: opportunities and challenges for aspirin and other antiplatelet agents
Bibliographic record
Abstract
Platelets have long been recognized as key players in hemostasis and thrombosis; however, growing evidence suggests that they are also significantly involved in cancer, the second leading cause of mortality worldwide. Preclinical and clinical studies showed that tumorigenesis and metastasis can be promoted by platelets through a wide variety of crosstalk between platelets and cancer cells. For example, cancer changes platelet behavior by directly inducing tumor-platelet aggregates, triggering platelet granule and extracellular vesicle release, altering platelet phenotype and platelet RNA profiles, and enhancing thrombopoiesis. Reciprocally, platelets reinforce tumor growth with proliferation signals, antiapoptotic effect, and angiogenic factors. Platelets also activate tumor invasion and sustain metastasis via inducing an invasive epithelial-mesenchymal transition phenotype of tumor cells, promoting tumor survival in circulation, tumor arrest at the endothelium, and extravasation. Furthermore, platelets assist tumors in evading immune destruction. Hence, cancer cells and platelets maintain a complex, bidirectional communication. Recently, aspirin (acetylsalicylic acid) has been recognized as a promising cancer-preventive agent. It is recommended at daily low dose by the US Preventive Services Task Force for primary prevention of colorectal cancer. The exact mechanisms of action of aspirin in chemoprevention are not very clear, but evidence has emerged that suggests a platelet-mediated effect. In this article, we will introduce how cancer changes platelets to be more cancer-friendly and highlight advances in the modes of action for aspirin in cancer prevention. We also discuss the opportunities, challenges, and opposing viewpoints on applying aspirin and other antiplatelet agents for cancer prevention and treatment.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".