Tumour Extracellular Vesicles Induce Neutrophil Extracellular Traps To Promote Lymph Node Metastasis
Bibliographic record
Abstract
Abstract Lymph nodes (LNs) are frequently the first sites of metastasis. Currently, the only prognostic LN assessment is determining metastasis status. However, there is evidence suggesting that LN metastasis is facilitated by a pre-metastatic niche induced by tumour derived extracellular vehicles (EVs). Therefore, it is important to detect and modify the LN environmental changes. We have previously reported that neutrophil extracellular traps (NETs) can sequester and promote distant metastasis. Here, we first confirmed that LN NETs are associated with reduced patient survival. Next, we demonstrated that NETs deposition precedes LN metastasis and NETs inhibition abolishes LN metastases in animal mode. Furthermore, we discovered that EVs are essential to the formation of LN NETs. Lymphatic endothelial cells secrete CXCL8/2 in response to EVs inducing NETs formation and the promotion of LN metastasis. Our findings are the first to reveal the role of EV induced NETs in LN metastasis and provide potential immunotherapeutic vulnerabilities. Graphic Abstract Illustrative demonstration of the LNs premetastatic niche formation induced by EVs and NETs. Primary tumour constantly secretes EVs, which were actively uptaken by LECs. LECs subsequently secretes CXCL8 or CXCL2 upon EV reception. CXCL8 and CXCL2 are both neutrophil chemoattractants and potent NETs inducers. The following neutrophil recruitment and NETs formation lead to increased LN metastasis burden.
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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) | 0.009 | 0.002 |
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".