Identification of serum prognostic biomarkers of severe COVID-19 using a quantitative proteomic approach

Abstract The COVID-19 pandemic is an unprecedented threat to humanity that has provoked global health concerns. Since the etiopathogenesis of this illness is not fully characterized, the prognostic factors enabling treatment decisions have not been well documented. Accurately predicting the progression of the disease would aid in appropriate patient categorization and thus help determine the best treatment option. Here, we have introduced a proteomic approach utilizing data-independent acquisition mass spectrometry (DIA-MS) to identify the serum proteins that are closely associated with COVID-19 prognosis. Twenty-seven proteins were differentially expressed between severely ill COVID-19 patients with an adverse or favorable prognosis. Ingenuity Pathway Analysis revealed that 15 of the 27 proteins might be regulated by cytokine signaling relevant to interleukin (IL)-1β, IL-6, and tumor necrosis factor (TNF), and their differential expression was implicated in the systemic inflammatory response and in cardiovascular disorders. We further evaluated practical predictors of the clinical prognosis of severe COVID-19 patients. Subsequent ELISA assays revealed that CHI3L1 and IGFALS may serve as highly sensitive prognostic markers. Our findings can help formulate a diagnostic approach for accurately identifying COVID-19 patients with severe disease and for providing appropriate treatment based on their predicted prognosis.