Identification of key genes related to immune cells in patients with gram-negative sepsis based on weighted gene co-expression network analysis.

Background: Gram-negative sepsis is closely related to the immune response, involving collaborative efforts of different immune cells. However, the mechanisms underlying immune cell regulation in gram-negative sepsis remain unclear. Therefore, this study investigated the potential regulatory mechanisms and identified the key genes related to immune cells in gram-negative sepsis. Methods: The RNA-sequencing data for gram-negative sepsis samples and normal samples were collected from the Gene Expression Omnibus (GEO) dataset GSE9960. CIBERSORT was performed to analyze the proportion of 22 types of immune cells in gram-negative sepsis and normal samples. Weighted gene co-expression network analysis (WGCNA) was used to determine the networks that are associated with the differentially distributed immune cells in the two groups. Differentially expressed genes were identified using the limma package. The least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms were applied to ascertain hub gene signatures. The gene interaction network of hub gene signatures was determined by ingenuity pathway analysis. Furthermore, the expression levels of the key genes were verified using quantitative real-time polymerase chain reaction (qRT-PCR). Results: CIBERSORT analysis showed that the proportions of plasma cells, resting CD4 Conclusions: This investigation provides novel insights into the molecular mechanisms of immune cells involved in the pathogenesis of gram-negative sepsis. NLRC4 and RGL4 were identified as key gene signatures related to immune cells and may act as potential diagnostic biomarkers for gram-negative sepsis.