{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T17:06:55Z","timestamp":1786122415719,"version":"build-2736575974"},"reference-count":58,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T00:00:00Z","timestamp":1786060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>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\u2013based integration of exposome and multi-omics data provide new opportunities for dissecting this complex comorbidity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study established a sequential, cross-scale framework integrating global epidemiological analysis of GBD data (1990\u20132021), 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\u2013epithelial Transwell co-culture.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>These findings provide a cross-scale perspective linking environmental exposures, macrophage-centered immune alterations, and COPD\u2013LC 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1875146","type":"journal-article","created":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T09:47:40Z","timestamp":1786096060000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["SHAP-interpretable machine learning integrating exposures and multi-omics reveals immune alterations and biomarkers in COPD\u2013lung cancer comorbidity"],"prefix":"10.3389","volume":"17","author":[{"given":"Yiran","family":"Fei","sequence":"first","affiliation":[{"name":"The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine)","place":["Hangzhou, Zhejiang, 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