Background: PANoptosis contributes to chronic obstructive pulmonary disease (COPD) pathogenesis. We aimed to identify PANoptosis-related gene signatures as candidate biomarkers and evaluate their genetic associations with COPD risk. Methods: Three transcriptomic data sets were integrated to identify differentially expressed PANoptosis-related genes in COPD, followed by enrichment analyses and immune infiltration profiling using CIBERSORT and single-sample gene set enrichment analysis. We selected feature genes using machine learning and evaluated their discriminative performance. We used Mendelian randomisation (MR) and multivariable MR (MVMR) to assess genetic associations with COPD risk, using the forced expiratory volume in one second/forced vital capacity (FEV1/FVC) ratio as a proxy for airflow limitation and adjusting for smoking. We validated candidate genes by quantitative real-time polymerase chain reaction (qRT-PCR) in human lung tissues from COPD patients and controls. Results: T helper cell subsets, with decreased M2 macrophages, monocytes, and neutrophils. PIK3CG and IL1B were positively associated with adaptive immune-related signals, whereas UACA showed predominantly negative immune correlations. PIK3CG showed moderate and variable discriminative performance, with an area under the curve of 0.775 in the training data set and 0.614 and 0.743 in validation data sets. The MR and MVMR analyses suggested that IRF1- and UACA-mediated increases in FEV1/FVC were associated with lower COPD risk independently of smoking. Additionally, qRT-PCR confirmed PIK3CG up-regulation in COPD lung tissues, whereas IRF1 and UACA showed no remarkable expression changes. Conclusions: Our results suggest that PIK3CG is a computationally and experimentally supported biomarker for COPD, while IRF1 and UACA act as genetically implicated regulators of airflow limitation. Together, these findings show the potential importance of PANoptosis-driven immune microenvironmental remodelling as a therapeutic direction warranting further functional validation.
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Chen et al. (2026) studied this question.
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