Paracrine production of IL-6 promotes a hypercoagulable state in pancreatic cancer.
Bibliographic record
Abstract
Venous thromboembolism is the most common complication and the secondary cause of death in pancreatic cancer. Moreover, the hypercoagulable state induces microcirculation dysfunction, acidosis and hypoxia, and further enhances tumor immune evasion, tumor growth and metastasis. Numerous studies have revealed that patients with malignant tumors have high levels of IL-6, which stimulates hepatocytes to synthesize thrombopoietin, causing an increase in platelets. This study found that the concentration of IL-6 in pancreatic cancer patient sera was higher than that in healthy donors, while pancreatic cancer cells had low expression levels of IL-6, which was different from other types of cancer. This contradictory result prompted us to uncover the underlying mechanism. Our data revealed that pancreatic cancer enhanced IL-6 production in fibroblasts via the Jagged/Notch axis, while IL-6 further elevated Jagged-1/2 expression in a paracrine positive feedback loop in pancreatic cancer. Inhibition experiments and RNAi studies demonstrated that IL-6-induced Jagged-1/2 production in pancreatic cancer depended on STAT3 and that Jagged-1/2 enhanced IL-6 mRNA expression in HSFs through the NF-κB pathway. Finally, the animal study showed that knockdown of Jagged-1/2 or blockade of the Jagged/Notch pathway by Nirogacestat could alleviate pancreatic cancer-induced hypercoagulability. Accordingly, our findings clarified the key role of the Jagged/Notch/IL-6/STAT3 feedback loop in the development of a hypercoagulable state in pancreatic cancer, which also provides new therapeutic strategies for pancreatic cancer patients who suffer from hypercoagulability.
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 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 | 0.000 | 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) | 0.000 | 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".