Aim Introducing possible diagnostic and therapeutic biomarker candidates via the identification of chief dysregulated proteins in COVID-19 patients is the aim of this study. Background Molecular studies, especially proteomics, can be considered as suitable approaches for discovering the hidden aspect of the disease. Methods Differentially expressed proteins (DEPs) of three patients with demonstrated severe condition (S-COVID-19) were compared to healthy cases by a proteomics study. Cytoscape software and STRING database were used to construct the protein-protein interaction (PPI) network. The central DEPs were identified through topological analysis of the network. ClueGO+CluePedia were applied to find the biological processes related to the central nodes. MCODE molecular complex detection (MCODE) was used to discover protein complexes. Results A total of 242 DEPs from among 256 query ones were included in the network. Centrality analysis of the network assigned 16 hub-bottlenecks, nine of which were presented in the highest-scored protein complex. Ten protein complexes were determined. APOA1 was identified as the protein complex seed, and APP, EGF, and C3 were the top hub-bottlenecks of the network. The results specify that up-regulation of C3 and down-regulation of APOA1 in urine play a role in the stiffness in respiration and, accordingly, the severity of COVID-19. Moreover, dysregulation of APP and APOA1 could both contribute to the possible adverse effects of COVID-19 on the nervous system. Conclusion The introduced central proteins of the S-COVID-19 interaction network, particularly APOA1, can be considered as diagnostic and therapeutic targets related to the coronavirus disease after being approved with complementary studies.
Introducing APOA1 as a key protein in COVID-19 infection: a bioinformatics approach.