Single-cell RNA sequencing and transcriptomic analysis reveal key genes and regulatory mechanisms in sepsis

The pathogenesis of sepsis, with a high mortality rate and often poor prognosis, has not been fully elucidated. Therefore, an in-depth study on the pathogenesis of sepsis at the molecular level is essential to identify key sepsis-related genes. The aim of this study was to explore the key genes and potential molecular mechanisms of sepsis using a bioinformatics approach. In addition, key genes with miRNA network correlation analysis and immune infiltration correlation analysis were investigated. The scRNA dataset (GSE167363) and RNA-seq dataset (GSE65682, GSE134347) from GEO database were used for screening out differentially expressed genes using single-cell sequencing and transcriptome sequencing. The analysis of immune infiltration was evaluated by the CIBERSORT method. Key genes and possible mechanisms were identified by WGCNA analysis, GSVA analysis, GSEA enrichment analysis and regulatory network analysis, and miRNA networks associated with key genes were constructed. Nine key genes associated with the development of sepsis, namely IL7R, CD3D, IL32, GPR183, HLA-DPB1, CD81, PEBP1, NCL, and ETS1 were screened, and the specific signaling mechanisms associated with the key genes causing sepsis were predicted. Immune profiling showed immune heterogeneity between control and sepsis samples. A regulatory network of 82 miRNAs, 266 pairs of mRNA-miRNA relationship pairs was also constructed. These nine key genes have the potential to become biomarkers for the diagnosis of sepsis and provide new targets and research directions for the treatment of sepsis.

脓毒症（sepsis）具有高死亡率且预后通常不佳，其发病机制尚未完全阐明。因此，深入开展脓毒症的分子水平发病机制研究，对筛选脓毒症相关关键基因至关重要。本研究旨在通过生物信息学方法，探究脓毒症的关键基因及潜在分子机制。此外，本研究还针对关键基因开展了miRNA网络关联分析与免疫浸润关联分析。本研究从基因表达综合数据库（GEO, Gene Expression Omnibus）获取单细胞RNA测序数据集（GSE167363）与RNA测序数据集（GSE65682、GSE134347），通过单细胞测序与转录组测序筛选差异表达基因。采用CIBERSORT算法评估免疫浸润情况，通过加权基因共表达网络分析（WGCNA, Weighted Gene Co-expression Network Analysis）、基因集变异分析（GSVA, Gene Set Variation Analysis）、基因集富集分析（GSEA, Gene Set Enrichment Analysis）及调控网络分析，鉴定关键基因及其潜在作用机制，并构建了关键基因相关的miRNA调控网络。本研究最终筛选出9个与脓毒症发生发展相关的关键基因，分别为IL7R、CD3D、IL32、GPR183、HLA-DPB1、CD81、PEBP1、NCL及ETS1，并预测了与这些关键基因相关的脓毒症潜在信号通路机制。免疫谱分析结果显示，对照组与脓毒症样本间存在免疫异质性。本研究还构建了包含82个miRNA、266对mRNA-miRNA调控关系对的调控网络。上述9个关键基因有望成为脓毒症诊断的生物标志物，可为脓毒症的治疗提供全新靶点与研究方向。