Presepsin Predicts Severity and Secondary Bacterial Infection in COVID-19 by Bioinformatics Analysis

Abstract

Introduction. Novel coronavirus pneumonia (COVID-19) is an acute respiratory disease caused by the novel coronavirus SARS-CoV-2. Severe and critical illness, especially secondary bacterial infection (SBI) cases, accounts for the vast majority of COVID-19-related deaths. However, the relevant biological indicators of COVID-19 and SBI are still unclear, which significantly limits the timely diagnosis and treatment. Methods. The differentially expressed genes (DEGs) between severe COVID-19 patients with SBI and without SBI were screened through the analysis of GSE168017 and GSE168018 datasets. By performing Gene Ontology (GO) enrichment analysis for significant DEGs, significant biological processes, cellular components, and molecular functions were selected. To understand the high-level functions and utilities of the biological system, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed. By analyzing protein-protein interaction (PPI) and key subnetworks, the core DEGs were found. Results. 85 DEGs were upregulated, and 436 DEGs were downregulated. The CD14 expression was significantly increased in the SBI group of severe COVID-19 patients ( $P<0.01$ ). The area under the curve (AUC) of CD14 in the SBI group in severe COVID-19 patients was 0.9429. The presepsin expression was significantly higher in moderate to severe COVID-19 patients ( $P<0.05$ ). Presepsin has a diagnostic value for moderate to severe COVID-19 with the AUC of 0.9732. The presepsin expression of COVID-19 patients in the nonsurvivors was significantly higher than that in the survivors ( $P<0.05$ ). Conclusion. Presepsin predicts severity and SBI in COVID-19 and may be associated with prognosis in COVID-19.