Tissue factor positive microparticles as a biomarker for increased risk of breast cancer-associated thrombosis: a mini review
Bibliographic record
Abstract
PURPOSE OF REVIEW: Cancer-associated thrombosis (CAT), such as venous thromboembolism (VTE), is a frequent complication in cancer patients, resulting in poor prognosis. Breast cancer is not highly thrombogenic but is highly prevalent, resulting in increased VTE cases. Many cancers express tissue factor (TF), a glycoprotein that triggers coagulation. The cancer cells were shown to express and release substantial amounts of TF-positive microparticles (MPTF), associated with a prothrombotic state. This narrative review evaluated the current use of the procoagulant MPTF as a biomarker for thrombosis risk in breast cancer. RECENT FINDINGS: Tumors of epithelial origin with elevated TF expression have been associated with increased VTE incidence. Thus, studies have affirmed the use of MPTF biomarkers for VTE risk in many cancers. Patients with metastatic breast cancer and CAT were found to exhibit elevated procoagulant microparticles in vitro, due to TF expression. The silencing of TF was associated with decreased microparticle release in breast carcinoma cell lines, associated with decreased coagulation. SUMMARY: CAT is a multifactorial condition, with several various underlying diseases. It is proposed that MPTF may be an effective biomarker for thrombosis risk in breast cancer patients but requires a more systemic evaluation utilizing standardized quantification methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".