[Application value of plasma proteomics in the diagnosis and pathogenesis of high altitude polycythemia]

Objective: To explore the application value of plasma proteomics in the diagnosis and pathogenesis of high altitude polycythemia (HAPC). Methods: Ten patients with HAPC in Qinghai Provincial People's Hospital from January 2020 to January 2021 were selected as the experimental group, including 4 males and 6 females, aged (46±4) years. Ten healthy controls at the same altitude in the same period were selected as the control group, including 5 males and 5 females, aged (44±4) years. The differential proteins were identified and quantified by high performance liquid chromatography-mass spectrometry (HPLC-MS), and the gene ontology (GO) functional enrichment analysis, the Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis and interaction network analysis were conducted for the selected differential proteins. Results: A total of 117 differential proteins with quantitative values were screened from the experimental group and the control group, 45 significantly up-regulated proteins with quantitative values only in the experimental group, 40 significantly down-regulated proteins with quantitative values only in the control group, and 32 differentially expressed proteins with quantitative values in both experimental and control groups were detected. Compared with the control group, 11 of the 32 differentially expressed proteins in the experimental group were down-regulated and 21 were up-regulated. The results of GO functional enrichment analysis showed that the biological processes involved by differential proteins mainly included immune response, complement activation, activated protein cascade and coagulation system. The results of KEGG function enrichment analysis showed that the main biochemical metabolic pathways and signal transduction pathways involved by differential proteins were axon guidance, lysosomes, cell adhesion molecules, lipid and atherosclerosis, hematopoietic cell lineage and cholesterol metabolism. The abundant domains are mainly in immunoglobulin-like domain, EGF-like domain, fibronectin type Ⅲ superfamily, serine proteases, Sushi/SCR/CCP superfamily. The results of differential protein interaction analysis showed that the interaction score was>700, and the top 10 differential proteins with the largest number of nodes were MPO, RPS27A, ARG1, GM2A, TIMP1, CRP, FABP5, HBB, S100A7 and RHOA, respectively. Conclusion: Plasma proteomics analysis technique is helpful to identify the related protein markers in the development of HAPC, and provide reference for the diagnosis and pathogenesis of HAPC.