Abstract
Aim: To understand the pathological progress of COVID-19 and to explore the potential biomarkers. Background: The COVID-19 pandemic is ongoing. There is metabolomics research about COVID-19 indicating the rich information of metabolomics is worthy of further data mining. Methods: We applied bioinformatics technology to reanalyze the published metabolomics data of COVID-19. Results: Benzoate, β-alanine and 4-chlorobenzoic acid were first reported to be used as potential biomarkers to distinguish COVID-19 patients from healthy individuals; taurochenodeoxycholic acid 3-sulfate, glucuronate and N,N,N-trimethyl-alanylproline betaine TMAP are the top classifiers in the receiver operating characteristic curve of COVID-severe and COVID-nonsevere patients. Conclusion: These unique metabolites suggest an underlying immunoregulatory treatment strategy for COVID-19.