Neutrophil Extracellular Trap–Associated CEACAM1 as a Putative Therapeutic Target to Prevent Metastatic Progression of Colon Carcinoma
Bibliographic record
Abstract
Neutrophils promote tumor growth and metastasis at multiple stages of cancer progression. One mechanism through which this occurs is via release of neutrophil extracellular traps (NETs). We have previously shown that NETs trap tumor cells in both the liver and the lung, increasing their adhesion and metastasis following postoperative complications. Multiple studies have since shown that NETs play a role in tumor progression and metastasis. NETs are composed of nuclear DNA-derived web-like structures decorated with neutrophil-derived proteins. However, it is unknown which, if any, of these NET-affiliated proteins is responsible for inducing the metastatic phenotype. In this study, we identify the NET-associated carcinoembryonic Ag cell adhesion molecule 1 (CEACAM1) as an essential element for this interaction. Indeed, blocking CEACAM1 on NETs, or knocking it out in a murine model, leads to a significant decrease in colon carcinoma cell adhesion, migration and metastasis. Thus, this work identifies NET-associated CEACAM1 as a putative therapeutic target to prevent the metastatic progression of colon carcinoma.
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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".