IL-37/IL-1R8 axis: a novel major mechanism of control at the interface between tumor and immune cells
Bibliographic record
Abstract
Interleukin (IL)-37 is one of the "youngest" IL-1 family members and one of the few molecules exerting anti-inflammatory activity. Upon inflammasome activation, the cytokine precursor is converted into its mature form, which acts intracellularly as a nuclear transcription factor, impairing the production of pro-inflammatory cytokines, and extracellularly by forming the IL-37/IL-18Rα/IL-1R8 complex, favoring IL-1R8 inhibitory signaling with immunosuppressive function. IL-1R8, which is mostly expressed in a number of cell types, negatively regulates both IL-1R/TLR signaling, blocking the NF-kB/JNK pathway and the production of pro-inflammatory cytokines. Owing to its ability to inhibit both innate and adaptive immunity, IL-37 has been reported to control inflammation in many chronic disorders, including cancer. IL-37 impairs the proliferation and migration of tumor cells, mediates anti-angiogenetic mechanisms, and favors immunoregulation in the tumor microenvironment (TME). This review aims to provide a current overview of IL-37 genetic and biological features and of its active interaction with IL-1R8, inducing anti-inflammatory effects on the immune system and affecting cancer cell dynamics in the TME. Moreover, it analyzes the rare pro-tumoral effects of IL-37 in some tumors and discusses their possible mechanisms. It concludes that, due to its strong anti-inflammatory property, IL-37 can be considered a potential regulator in the pathogenesis of a variety of cancers, slowing tumor progression through multiple pathways and providing valuable information for tumor immune target therapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".