Purpose: As a common respiratory disease, chronic obstructive pulmonary disease (COPD) has a high morbidity and mortality. Current clinical therapies are not ideal and do not improve lung function or long-term life quality. It is very important to find new potential pathogenic mechanisms, biomarkers, and targets with therapeutic value in COPD.
Methods: Serum samples collected from acute exacerbation and stable COPD and healthy participants were analyzed using label-free liquid chromatography tandem mass spectrometry to identify the differentially expressed proteins (DEPs) between two groups. Bioinformatics analyses were performed to determine the biological processes associated with those DEPs. Key proteins were validated by enzyme linked immunosorbent assay (ELISA).
Results: In total, 661 proteins were detected in serum from patients with COPD and healthy participants. Compared with healthy participants, patients with acute exacerbation of COPD had 45 DEPs, 13 were upregulated and 32 were downregulated; and patients with stable COPD had 79 DEPs, 18 were upregulated and 61 were downregulated. Gene Ontology functional annotation results indicated that the DEPs identified in patients with COPD were associated with the terms cellular anatomical entity, binding, and cellular process. Kyoto Encyclopedia of Genes and Genomes functional annotation analysis and the Clusters of Orthologous Genes database analysis indicated that the functions of these DEPs were primarily in signal transduction mechanisms and amino acid transport and metabolism. The ELISA results for three key proteins of IGFBP2, LRG1 and TAGLN were consistent with the LC-MS/MS results and the area under the receiver operating characteristic of the combined index was 0.893 (95% CI: 0.813, 0.974).
Conclusion: Our findings suggested pathogenic mechanisms underlying COPD stages and indicated three key proteins that may warrant further study as potential biomarkers for early diagnosis or prognosis of COPD or as therapeutic targets.
Keywords: chronic obstructive pulmonary disease, respiration, proteomics, differentially expressed proteins, bioinformatics, LC-MC/MS