Extracellular vesicles from pancreatic cancer and its tumour microenvironment promote increased Schwann cell migration
Bibliographic record
Abstract
BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) exhibits a high frequency of neural invasion (NI). Schwann cells (SCs) have been shown to be reprogrammed to facilitate cancer cell migration and invasion into nerves. Since extracellular vesicles (EVs) affect the tumour microenvironment and promote metastasis, the present study analysed the involvement of EVs from pancreatic cancer cells and their microenvironment in altering SC phenotype as part of the early events in the process of NI. METHODS: EVs were isolated from human/murine PDAC cells, pancreatic stellate cells (PSCs), human tissues and plasma to perform a novel 3D migration assay, qRT-PCR and western blot. Kaplan-Meier and Cox regression analyses were employed to evaluate the clinical potential of plasma EV-derived candidate from 165 PDAC patients. RESULTS: The EVs from PDAC cells, PSCs derived from human tumour tissues, other cell types in the tumour microenvironment from tumour tissues and circulating plasma act as drivers of a pro-migratory phenotype of SCs by inducing dedifferentiation in SCs. Notably, p75NTR expression was upregulated in the plasma-derived EVs from patients with NI (Pn1) relative to those without NI (Pn0). High expression of plasma-derived EV p75NTR correlated with reduced overall survival and was identified as an independent prognostic factor. CONCLUSIONS: These findings suggest that EV-mediated SC migration underlies the interactions contributing to PDAC-associated NI with implications for improved outcome and therapeutic strategy.
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) | 0.001 | 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".