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- Title
Weighted Gene Co-Expression Network Analysis on Proteomics of Exhaled Breath Condensate Based on Data-Independent Acquisition.
- Authors
MA Lin; SUN Dongxiao; ZHEN Huajun; XIU Guangli
- Abstract
Exhaled breath condensate (EBC) is a kind of respiratory lining fluid, which is easy to collect and non- invasive. 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 2 052 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.
- Publication
Journal of East China University of Science & Technology, 2022, Vol 48, Issue 5, p649
- ISSN
1006-3080
- Publication type
Academic Journal
- DOI
10.14135/j.cnki.1006-3080.20210824001