Mammalian Neuraminidase-1 is a Critical Target of Thromboxane Induced T-Cell Immunosuppression
Bibliographic record
Abstract
Abstract Aspirin or celecoxib in combination with chemotherapy significantly reduced the risk for disease recurrence and improved survival after colorectal cancer surgery in the approximately 20% of patients harbouring activating mutations in the PIK3CA gene 1-3 . The biological mechanisms behind the anti-cancer effects of these medications are an area of intense research interest. Recently, Yang et al. 4 found that platelet-derived thromboxane A 2 (TXA 2 ) inhibits T-cell activity through the expression of the guanine exchange factor ARHGEF1, and that aspirin was able to inhibit ARHGEF1 expression and preserve T-cell functionality and cancer immunity by blocking TXA 2 production 4 . TXA 2 production is inhibited by the effect of aspirin on cyclooxygenase-1 (COX-1). Celecoxib, a selective COX-2 inhibitor, does not inhibit platelet TXA 2 production but has shown almost identical efficacy to aspirin in controlled clinical trials 3 . Here, we show that the enzyme mammalian neuraminidase 1 (NEU1) is involved in the activation and downstream signaling of the TXA 2 receptor (TP) on T cells in response to TXA 2 . Both aspirin and celecoxib significantly inhibited TXA 2 -induced NEU1 activity and reduced ARHGEF1 expression. This regulation of the TP receptor by NEU1 may explain why aspirin and celecoxib demonstrated comparable clinical efficacy despite only aspirin being able to inhibit TXA 2 synthesis. Targeting NEU1 may provide a novel therapeutic strategy against cancer cell metastasis by preserving a functional T-cell response against cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".