Identification and Evaluation of Lipocalin-2 in Sepsis-Associated Encephalopathy via Machine Learning Approaches.

IF 4.1 2区 医学 Q2 IMMUNOLOGY Journal of Inflammation Research Pub Date : 2025-03-14 eCollection Date: 2025-01-01 DOI:10.2147/JIR.S504390
Jia Hu, Ziang Chen, Jinyan Wang, Aoxue Xu, Jinkai Sun, Wenyan Xiao, Min Yang
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Abstract

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.

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通过机器学习方法识别和评估脓毒症相关脑病中的脂钙素-2。
目的:脓毒症相关脑病(SAE)是脓毒症患者预后不良的重要因素。识别和筛选SAE的关键基因,以及探索潜在的靶向治疗方法,对于改善败血症的管理和推进精准医学至关重要。患者和方法:采用单细胞RNA测序(scRNA-seq)方法鉴定与脓毒症患者预后不良相关的细胞亚群。接下来,采用分层动态加权基因共表达网络分析(hdWGCNA)来鉴定与特定中性粒细胞亚群相关的基因。富集分析揭示了这些基因的生物学功能。随后,获得神经炎症相关基因以构建神经炎症相关特征。AddModuleScore算法用于计算每个细胞亚群的神经炎症评分,而CellCall算法用于评估中性粒细胞和其他细胞亚群之间的串扰。为了准确识别关键基因,使用了四种二元分类机器学习算法。最后,采用Western blotting和行为学测试来证实lcn2相关的神经炎症在脓毒症小鼠中的作用。结果:本研究利用scRNA-seq揭示了外周中性粒细胞在脓毒症中的关键作用,确定这些中性粒细胞是导致预后不良和与神经炎症相关的因素。在各种机器学习算法的基础上,我们发现Lipocalin-2 (LCN2)可能是参与中性粒细胞诱导的SAE的关键基因。为了证明这些发现,我们进行了体内实验和动物模型。脓毒症小鼠出现LCN2表达增加和认知功能障碍。星形胶质细胞、小胶质细胞标志物及炎症因子TNF-α、IL-6水平显著升高。所有这些现象都被LCN2的下调所逆转。结论:SAE期间外周中性粒细胞LCN2表达上调是引发中枢神经系统炎症的关键步骤。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Inflammation Research
Journal of Inflammation Research Immunology and Microbiology-Immunology
CiteScore
6.10
自引率
2.20%
发文量
658
审稿时长
16 weeks
期刊介绍: An international, peer-reviewed, open access, online journal that welcomes laboratory and clinical findings on the molecular basis, cell biology and pharmacology of inflammation.
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