An In-Silico Study to Identify Relevant Biomarkers in Sepsis Applying Integrated Bulk RNA Sequencing and Single-Cell RNA Sequencing Analyses.

This study aims to discover sepsis-related biomarkers via in-silico analyses. The single-cell sequencing RNA (sc-RNA) data and metabolism-related genes are obtained from public databases and previous studies, respectively. Cell subpopulations are identified and annotated, followed by performing single-sample geneset enrichment analysis (ssGSEA and identification of differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) is applied to classify specific gene modules, and the key module is subjected to immune infiltration analysis. The communication between the subclusters of monocytes is visualized. Five cell subpopulations (subcluster C1-5) containing a relatively higher percentage of monocytes are identified, with subcluster C4 having the lowest enrichment score of metabolism-related genes. Genes with a higher expression in the subclusters are enriched for antigen processing and presentation of exogenous antigen, lymphocyte differentiation, and leukocyte activation. Subcluster C5 affected other subclusters through galectin 9 (LGALS9)-CD45 and LGALS9-CD44, while other subclusters affected subcluster C5 through MIF-(CD74+C-X-C motif chemokine receptor 4 (CXCR4)) and MIF-(CD74+CD44). Six genes (F-Box Protein 4, ; Forkhead Box K1, ; MSH2 with MutS Homolog 2, ; Nop-7-associated 2, ; Transmembrane Protein 128, ; and ) are determined as the hub genes for sepsis. The 6 hub genes are positively correlated with, among others, monocytes and NK cells, but negatively correlated with neutrophils. This study identifies accurate biomarkers for sepsis, contributing to the diagnosis and treatment of the disease.