ORIGINAL RESEARCH article
Front. Immunol.
Sec. Inflammation
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1666195
This article is part of the Research TopicThe Experimental Models as a Tool for Studying Therapeutic Targets in COPD: Lessons LearnedView all 3 articles
Integrating Bioinformatics and Molecular Experiments to Reveal the Critical Role of the Cellular Energy Metabolism-Related Marker PLA2G1B in COPD Epithelial Cells
Provisionally accepted
- Peking University Third Hospital, Haidian, China
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Background Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease characterized by small airway lesions and persistent airflow limitation. Recent studies have highlighted impaired cellular energy metabolism (CEM) in COPD, although the underlying mechanisms remain incompletely understood. Material and methods This research identified cell energy metabolism-related differentially expressed genes (CEM-DEGs) by collecting CEM-associated signatures from multiple public databases and integrating these markers with data from the GEO database. Subsequently, five machine learning algorithms—Boruta, Xgboost, GBM, SVM-RFE, and LASSO—were employed to screen for key variables. Gene Set Enrichment Analysis (GSEA) and immune infiltration analysis were then performed on these key CEM-DEGs. Finally, the results of the bioinformatics analysis were verified by in vitro and in vivo experiments in combination with the single-cell data analysis results. Results Bioinformatic analysis identified six critical markers (CYP1B1, CA3, AHRR, MGAM, PNMT, and PLA2G1B) that regulated CEM in the progression of COPD, from which a prognostic model was constructed using a nomogram with an area under the curve (AUC) of 0.814. Functional enrichment analysis further elucidated the intricate interplay between these CEM regulatory factors and key biological processes, including inflammation, oxidative stress, and epithelial-mesenchymal transition. Beyond that, both in vitro and in vivo experiments, along with single-cell data analysis, have conclusively verified the specific downregulation of PLA2G1B in epithelial cells derived from the COPD group. Notably, the knockdown of PLA2G1B in epithelial cells triggered inflammation, oxidative stress, and apoptosis. Conclusions This study identified six CEM-related biomarkers (CYP1B1, CA3, AHRR, MGAM, PNMT, and PLA2G1B) in COPD and established a corresponding prognostic model. Furthermore, in vitro and in vivo experiments validated the regulatory role of PLA2G1B in epithelial cell inflammation, oxidative stress, and apoptosis, thereby elucidating the mechanism underlying CEM in COPD and potentially uncovering novel therapeutic targets for drug development.
Keywords: chronic obstructive pulmonary disease, Cellular energy metabolism, machine learning, PLA2G1B, Single cell sequencing
Received: 15 Jul 2025; Accepted: 29 Sep 2025.
Copyright: © 2025 Shi, Wang, Rao, Li, Luo, Zhang, Pei, Gai and Sun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yongchang Sun, suny@bjmu.edu.cn
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