PURPOSE: Sepsis-associated encephalopathy (SAE) critically contributes to poor prognosis in septic patients. Identifying and screening key genes responsible for SAE, as well as exploring potential targeted therapies, are vital for improving the management of sepsis and advancing precision medicine. PATIENTS AND METHODS: Single-cell RNA sequencing (scRNA-seq) was administrated to identify cell subpopulations related to poor prognosis in septic patients. Next, hierarchical dynamic weighted gene co-expression network analysis (hdWGCNA) was employed to identify genes associated with specific neutrophil subpopulations. Enrichment analysis revealed the biological functions of these genes. Subsequently, neuroinflammation-related genes were obtained to construct a neuroinflammation-related signature. The AddModuleScore algorithm was used to calculate neuroinflammation scores for each cell subpopulation, whereas the CellCall algorithm was used to assess the crosstalk between neutrophils and other cell subpopulations. To identify key genes accurately, four binary classification machine learning algorithms were utilized. Finally, Western blotting and behavioral tests were used to confirm the role of LCN2-related neuroinflammation in septic mice. RESULTS: This study utilized scRNA-seq to reveal the critical role of peripheral neutrophils during sepsis, identifying these neutrophils as contributors to poor prognosis and associated with neuroinflammation. On the basis of various machine learning algorithms, we discovered that Lipocalin-2 (LCN2) may be the key gene involved in neutrophil-induced SAE. To prove these findings, we conducted in vivo experiments and an animal model. Increased LCN2 expression and cognitive dysfunction occurred in septic mice. Additionally, the levels of markers of astrocytes and microglia and inflammatory factors such as TNF-α and IL-6 were significantly increased. All these phenomena were reversed by the downregulation of LCN2. CONCLUSION: The upregulation of LCN2 expression on peripheral neutrophils is a critical step that triggers neuroinflammation in the central nervous system during SAE.
Identification and Evaluation of Lipocalin-2 in Sepsis-Associated Encephalopathy via Machine Learning Approaches.
利用机器学习方法识别和评估脓毒症相关性脑病中的脂质运载蛋白-2
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作者:Hu Jia, Chen Ziang, Wang Jinyan, Xu Aoxue, Sun Jinkai, Xiao Wenyan, Yang Min
| 期刊: | Journal of Inflammation Research | 影响因子: | 4.100 |
| 时间: | 2025 | 起止号: | 2025 Mar 14; 18:3843-3858 |
| doi: | 10.2147/JIR.S504390 | 研究方向: | 免疫/内分泌 |
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