基于非依赖数据采集的呼出气冷凝液蛋白质组加权基因共表达网络分析

Exhaled breath condensate (EBC) is a kind of respiratory lining fluid, which is easy to collect and noninvasive. EBC is considered to be the ideal sample for the study of pulmonary diseases. Proteomics is one of the novel methods to develop disease biomarkers, and the proteomics of EBC is widely studied due to its tremendous biological potential. It can reflect different disease status by analyzing the components of EBC protein, explore potential biomarkers, and improve the diagnostic ability of lung cancer and other diseases. In this study, an EBC proteomics method based on data independent acquisition (DIA) was established to overcome the disadvantage of low protein concentration of EBC, and 2052 proteins were identified. On this basis, the weighted gene co-expression network analysis (WGCNA) was carried out. WGCNA is a novel bioinformatic analysis technology, which allows multiple analysis of different omics information. A total of 61 hub proteins were screened by cluster analysis, and the hub proteins were analyzed by gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG) and protein-protein interactions (PPIs) analysis. The results showed that the hub proteins mainly existed in the nucleus and cytoplasm, and participated in the metabolic pathways related to human diseases, which indicated that the hub proteins could reflect the disease status and hold the potential to be biomarkers. In conclusion, the DIA-based EBC proteomics combined with WGCNA analysis, could effectively explore the potential biological functions of EBC, which could be applied to large-scale clinical research and contribute to the exploration of biomarkers in the future.