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SHAP-interpretable machine learning integrating exposures and multi-omics reveals immune alterations and biomarkers
Yiran Fei1, Yinying Chai1, Shiyuan Tong2
1The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang, China.
Introduction:
Chronic obstructive pulmonary disease (COPD) and lung cancer (LC) frequently co-occur and share environmental and biological determinants, yet their cross-scale associations remain incompletely understood. Artificial intelligence and machine learning-based integration of exposome and multi-omics data provide new opportunities for dissecting this complex comorbidity.
Methods:
This study established a sequential, cross-scale framework integrating global epidemiological analysis of GBD data (1990-2021), exposome-wide risk factor assessment using random forest classification with SHAP interpretation, immune microenvironment characterization via CIBERSORT and single-cell RNA sequencing with LLM-assisted annotation, candidate gene prioritization through SMR analysis, LASSO regression, and machine learning classifiers, and experimental validation using RT-qPCR, western blotting, immunohistochemistry, dual immunofluorescence, and macrophage-epithelial Transwell co-culture.
Results:
COPD and LC demonstrated persistent global co-occurrence patterns across 204 countries and territories. SHAP analysis identified smoking, particulate matter pollution, and residential radon as major shared risk factors. Bulk and single-cell transcriptomic analyses revealed consistent immune microenvironment remodeling, with macrophages as the predominant shared immune population. Multi-omics intersection and machine learning prioritization identified TREM1 and ODF2L as shared hub genes, significantly downregulated in COPD and LC tissues. Macrophage-specific silencing of TREM1 or ODF2L attenuated macrophage-mediated promotion of A549 cell migration and proliferation, and dual immunofluorescence confirmed macrophage-associated localization of both proteins.
Discussion:
These findings provide a cross-scale perspective linking environmental exposures, macrophage-centered immune alterations, and COPD-LC comorbidity. TREM1 and ODF2L represent promising macrophage-associated candidate biomarkers and potential therapeutic targets. Integrating interpretable machine learning with exposome and multi-omics data offers a robust framework for biomarker discovery in chronic respiratory disease comorbidity.
