Gene Set Enrichment Analysis

To interpret the alerted biological functions among the progressive stages of COVID-19 in a protein-expression-dependent manner, we performed gene set enrichment analysis (GSEA). Briefly, proteins were ranked according to their differences in expression between two categories of clinical disease status (SSEP vs MS, MS vs HC, or SSEP vs HC) and subsequently used as input data for GSEA. First, the protein list was queried against pre-defined gene sets in the GO database to associate gene expression with biological functions. By sequential analysis of the listed proteins, the running-sum statistic was elevated by the GSEA algorithm in a protein-expression-dependent fashion when encountering a protein belonging to a gene set; otherwise, the preceding statistic was reduced at a constant rate. Enrichment score (ES) was defined as the maximum deviation of the running-sum statistic from zero. The P-value (without multiplicity adjustment) cutoff was set to 0.1 for determining the significantly enriched pathways. GO terms with the top 10 most significant P-values were considered to be the pivotal pathways responsible for the progression of COVID-19, and the proteins that presented in the leading-edge subsets of the pivotal pathways were defined as core proteins.