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Presepsin Predicts Severity and Secondary Bacterial Infection in COVID-19 by Bioinformatics Analysis
Yufei Chang1, Linan Liu1, Hui Wang1
1Department of Emergency, Beijing Ditan Hospital Capital Medical University, Beijing, China.
Computational and Mathematical Methods in Medicine
|September 16, 2022
Summary
Presepsin is a promising biomarker for predicting COVID-19 severity and secondary bacterial infections (SBI). Elevated presepsin levels correlate with worse outcomes and mortality in patients with novel coronavirus pneumonia.
Area of Science:
- Biochemistry
- Genomics
- Infectious Diseases
Background:
- Novel coronavirus pneumonia (COVID-19) is an acute respiratory illness caused by SARS-CoV-2.
- Secondary bacterial infections (SBI) significantly increase mortality in severe COVID-19 cases.
- Clear biological indicators for COVID-19 and SBI are needed for timely diagnosis and treatment.
Purpose of the Study:
- Identify differentially expressed genes (DEGs) between severe COVID-19 patients with and without SBI.
- Investigate potential biomarkers for predicting COVID-19 severity and SBI.
- Analyze the diagnostic and prognostic value of identified biomarkers.
Main Methods:
- Analyzed gene expression datasets (GSE168017, GSE168018) to identify DEGs.
- Performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses.
- Utilized protein-protein interaction (PPI) analysis to identify core DEGs.
Main Results:
- Identified 85 upregulated and 436 downregulated DEGs.
- CD14 expression was significantly increased in severe COVID-19 patients with SBI (AUC=0.9429).
- Presepsin expression was higher in moderate to severe COVID-19 patients (AUC=0.9732) and non-survivors.
Conclusions:
- Presepsin shows significant diagnostic value for moderate to severe COVID-19.
- Presepsin can predict COVID-19 severity and the presence of SBI.
- Presepsin levels may be associated with prognosis in COVID-19 patients.
