Key platelet genes play important roles in predicting the prognosis of sepsis

作者：Leiting Shen, Chang Tao, Kun Zhu, Linghao Cai, Sisi Yang, Jingyi Jin, Yichao Ren, Yi Xiao, Yuebai Zhang, Dengming Lai, Jinfa Tou · 发表于：Scientific Reports · 年份：2024 · DOI：10.1038/s41598-024-74052-w · 被引用次数：7 · 研究领域：Adipokines, Inflammation, and Metabolic Diseases、Inflammatory Biomarkers in Disease Prognosis、Neutrophil, Myeloperoxidase and Oxidative Mechanisms

Sepsis is a life-threatening organ malfunction induced by an imbalanced immunological reaction to infection in the host. Many studies have utilized traditional RNA sequencing (RNA-seq) data to identify important biological targets to predict sepsis prognosis. However, alterations in core cells and functional status cannot be effectively detected in sepsis patients. The goal of this study was to identify key cells through single-cell RNA-seq (scRNA-seq), and combine bulk RNA-seq data and multiple algorithm analysis to construct a stable prognostic model for sepsis. The scRNA-seq and bulk RNA-seq data from sepsis patients were collected from the Gene Expression Omnibus (GEO) database. The R package "Seurat" was used to process the scRNA-seq data. Cell communication was investigated using the R package "CellChat". The pseudo-time of the cells was calculated using the R package "monocle". The R package "limma" was used to identify differentially expressed genes (DEGs) between the sepsis group and the control group. Weighted gene correlation network analysis (WGCNA) was used to identify critical modules. Eight kinds of machine learning and 90 algorithm combinations were used to construct the prognostic model for sepsis. Quantitative real-time PCR (qRT‒PCR) was performed to determine the expression of key genes in the cecal ligation and puncture (CLP)-induced sepsis mouse model. The immunological status and related properties of DEGs were then investigated in the high- and low-risk...