Six potential biomarkers in septic shock: a deep bioinformatics and prospective observational study

作者：Chang Kong, Yurun Zhu, Xiaofan Xie, Jiayu Wu, Meizi Qian · 发表于：Frontiers in Immunology · 年份：2023 · DOI：10.3389/fimmu.2023.1184700 · 被引用次数：54 · 研究领域：Sepsis Diagnosis and Treatment、Inflammation biomarkers and pathways、Immune Response and Inflammation

Background Septic shock occurs when sepsis is related to severe hypotension and leads to a remarkable high number of deaths. The early diagnosis of septic shock is essential to reduce mortality. High-quality biomarkers can be objectively measured and evaluated as indicators to accurately predict disease diagnosis. However, single-gene prediction efficiency is inadequate; therefore, we identified a risk-score model based on gene signature to elevate predictive efficiency. Methods The gene expression profiles of GSE33118 and GSE26440 were downloaded from the Gene Expression Omnibus (GEO) database. These two datasets were merged, and the differentially expressed genes (DEGs) were identified using the limma package in R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed. Subsequently, Lasso regression and Boruta feature selection algorithm were combined to identify the hub genes of septic shock. GSE9692 was then subjected to weighted gene co-expression network analysis (WGCNA) to identify the septic shock-related gene modules. Subsequently, the genes within such modules that matched with septic shock-related DEGs were identified as the hub genes of septic shock. To further understand the function and signaling pathways of hub genes, we performed gene set variation analysis (GSVA) and then used the CIBERSORT tool to analyze the immune cell infiltration pattern of diseases. The diagnostic value of hub genes in ...