Identification and validation of SPP1 as a potential biomarker for COPD through comprehensive bioinformatics analysis

Abstract

Background: Chronic Obstructive Pulmonary Disease (COPD) is a challenging respiratory condition characterized by persistent airflow limitation and progressive lung function decline. The identification of robust biomarkers is crucial for early diagnosis, monitoring disease progression, and guiding therapeutic strategies.

Methods: In this study, we employed a comprehensive bioinformatics approach utilizing multiple Gene Expression Omnibus (GEO) datasets to identify potential COPD biomarkers. Differentially expressed genes (DEGs) were identified from GSE38974 and GSE76925, and Weighted Gene Co-expression Network Analysis (WGCNA) on GSE76925 revealed significant gene modules associated with COPD traits.

Results: Integrative analysis highlighted five candidate genes, with Secreted Phosphoprotein 1 (SPP1) emerging as a promising biomarker. SPP1 exhibited consistent negative correlations with lung function parameters in human datasets (GSE103174) and significant upregulation in COPD-relevant animal models (GSE36174 and GSE52509). Moreover, SPP1 levels were elevated across various respiratory samples, including alveolar epithelium, alveolar macrophages, sputum, and lung tissue, from COPD patients.

Conclusion: These findings highlight the potential of SPP1 as a diagnostic and prognostic biomarker for COPD, emphasizing the need for further investigation into its role in COPD pathogenesis and its effectiveness in clinical applications.

Keywords: Differentially expressed genes (DEGs); Gene expression omnibus (GEO) datasets; Lung function decline; Weighted gene Co-Expression network analysis (WGCNA).