Identification of Potential Early Diagnostic Biomarkers of Sepsis

作者：Zhenhua Li, Bin Huang, YI Wen-feng, Fei Wang, Shizhuang Wei, Huaixing Yan, Pan Qin, Donghua Zou, Rongguo Wei, Nian Chen · 发表于：Journal of Inflammation Research · 年份：2021 · DOI：10.2147/jir.s298604 · 被引用次数：52 · 研究领域：Sepsis Diagnosis and Treatment、Neonatal and Maternal Infections、Inflammation biomarkers and pathways

OBJECTIVE: The goal of this article was to identify potential biomarkers for early diagnosis of sepsis in order to improve their survival. METHODS: We analyzed differential gene expression between adult sepsis patients and controls in the GSE54514 dataset. Coexpression analysis was used to cluster coexpression modules, and enrichment analysis was performed on module genes. We also analyzed differential gene expression between neonatal sepsis patients and controls in the GSE25504 dataset, and we identified the subset of differentially expressed genes (DEGs) common to neonates and adults. All samples in the GSE54514 dataset were randomly divided into training and validation sets, and diagnostic signatures were constructed using least absolute shrink and selection operator (LASSO) regression. The key gene signature was screened for diagnostic value based on area under the receiver operating characteristic curve (AUC). STEM software identified dysregulated genes associated with sepsis-associated mortality. The ssGSEA method was used to quantify differences in immune cell infiltration between sepsis and control samples. RESULTS: A total of 6316 DEGs in GSE54514 were obtained spanning 10 modules. Module genes were mainly enriched in immune and metabolic responses. Screening 51 genes from among common genes based on AUC > 0.7 led to a LASSO model for the training set. We obtained a 25-gene signature, which we validated in the validation set and in the GSE25504 dataset. Among the sig...