Crosstalk Between Inflammasome Signalling and Epithelial-Mesenchymal Transition in Cancer and Benign Disease: Mechanistic Insights, Context-Dependence, and Therapeutic Opportunities
Bibliographic record
Abstract
Epithelial-mesenchymal transition (EMT) and inflammasome signalling are intercon-nected processes which underpin tumour progression, metastasis, and therapeutic re-sistance. Inflammasomes such as NLRP3 encourage pro-inflammatory states (IL-1β, IL-18, NF-κB) and the activation of signalling pathways like TGF-β that promote mes-enchymal traits crucial for EMT. EMT transcriptional programmes can then in turn modulate the inflammasome via NF-κB/TGF-β signalling, creating self-perpetuating mechanisms of cellular plasticity and dysregulated therapeutic response. We have re-viewed the mechanistic evidence for EMT-inflammasome crosstalk in cancer and discussed the potential therapeutic implications. The function of the EMT-inflammasome axis is clearly context-dependent, with the cancer type, stage, and the complexity of the tumour microenvironment heavily contributing. The crosstalk between EMT and the inflammasome is an overlooked mechanism of tumour evolution, and targeting inflammasomes like NLRP3, or their downstream signalling pathways, offers a promising therapeutic avenue, with the objective of inhibiting metastasis and overcoming drug resistance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".