IL-18 and Lower Risk for Lung Cancer: Triangulated Evidence from Germline Predictions, Pre-Diagnostic Measurements, and Tumor Expression
Bibliographic record
Abstract
Abstract Lung cancer remains the most common cause of cancer death globally. Dysregulation of immune response and inflammatory signaling is known to play an important role in lung tumorigenesis, but the causal drivers of this process have yet to be elucidated. To identify circulating inflammatory and immune-related proteins that influence risk for lung cancer we related genetically predicted plasma levels for 85 inflammation and immune proteins with susceptibility to lung cancer. Mendelian randomization (MR) analyses in 29,266 cases and 56,450 controls identified a candidate causal marker, IL-18, which conferred lower risk of lung cancer (OR per standard deviation increase: 0.85 [95% CI: 0.79-0.92]), in particular for adenocarcinoma (OR: 0.80 [95% CI: 0.72-0.89]). We subsequently validated this association using polygenic IL-18 predictions in the UK Biobank (HR highest vs. lowest quartile: 0.83 [95% CI: 0.72-0.95]) and using pre-diagnostic blood concentrations of IL-18 in 732 cases and 732 controls after controlling for the inhibitory role of IL-18BP (OR highest vs. lowest quartile: 0. 63 [95% CI: 0. 41-0.91]). Genetic colocalization suggested that IL-18 may act on lung cancer risk locally via lung tissue expression, and joint MR and tumor microenvironment analyses highlight CD8 T cells and NK cells as potential mediators. In addition to risk, IL-18 expression in adenocarcinoma tumor tissue was found to be associated with all-cause mortality in 480 TCGA samples after controlling for IL-18BP (HR per SD: 0.87 [95% CI: 0.78, 0.98]), which is in line with recent studies showing anti-tumor effects of IL-18. Our comprehensive genomic triangulation study thus highlights the potential for IL-18 as an aetiological biomarker and targetable for immune-oncology therapies.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".