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.

败血症（sepsis）具有高死亡率且预后常不佳，其发病机制尚未完全阐明。因此，从分子层面深入研究败血症的发病机制，对于筛选关键败血症相关基因至关重要。本研究旨在通过生物信息学（bioinformatics）方法，探究败血症的关键基因与潜在分子机制。此外，针对关键基因开展了miRNA网络关联分析及免疫浸润（immune infiltration）关联分析。本研究从GEO数据库获取单细胞RNA测序（scRNA-seq）数据集GSE167363以及RNA测序（RNA-seq）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算法对免疫浸润情况进行评估。通过加权基因共表达网络分析（WGCNA）、基因集变异分析（GSVA）、基因集富集分析（GSEA）及调控网络分析，明确了关键基因及其潜在致病机制，并构建了关键基因相关的miRNA调控网络。最终筛选得到9个与败血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，并预测了这些关键基因介导败血症发生的特异性信号通路机制。免疫特征分析结果显示，对照组与败血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。这9个关键基因有望成为败血症诊断的生物标志物（biomarker），并为败血症的治疗提供全新的靶点与研究方向。