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通过机器学习方法对脓毒症相关性脑病中脂联素-2的鉴定与评估

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

作者信息

Hu Jia, Chen Ziang, Wang Jinyan, Xu Aoxue, Sun Jinkai, Xiao Wenyan, Yang Min

机构信息

Department of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People's Republic of China.

Key Laboratory of Anesthesiology and Perioperative Medicine of Anhui Higher Education Institutes, Anhui Medical University, Hefei, Anhui Province, People's Republic of China.

出版信息

J Inflamm Res. 2025 Mar 14;18:3843-3858. doi: 10.2147/JIR.S504390. eCollection 2025.

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.

摘要

目的

脓毒症相关脑病(SAE)严重影响脓毒症患者的预后。识别和筛选导致SAE的关键基因,并探索潜在的靶向治疗方法,对于改善脓毒症的治疗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)来识别脓毒症患者中与预后不良相关的细胞亚群。接下来,运用分层动态加权基因共表达网络分析(hdWGCNA)来识别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,获取神经炎症相关基因以构建神经炎症相关特征。使用AddModuleScore算法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与其他细胞亚群之间的相互作用。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,通过蛋白质免疫印迹法和行为测试来证实脂联素2(LCN2)相关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期间的关键作用,确定这些中性粒细胞是预后不良的促成因素且与神经炎症相关。基于各种机器学习算法,我们发现脂质运载蛋白2(LCN2)可能是参与中性粒细胞诱导的SAE的关键基因。为了验证这些发现,我们进行了体内实验和动物模型研究。脓毒症小鼠中LCN2表达增加且出现认知功能障碍。此外,星形胶质细胞、小胶质细胞的标志物水平以及肿瘤坏死因子-α和白细胞介素-6等炎症因子显著升高。LCN2的下调逆转了所有这些现象。

结论

外周中性粒细胞上LCN2表达的上调是SAE期间触发中枢神经系统神经炎症的关键步骤。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93f0/11920642/a853352a137c/JIR-18-3843-g0001.jpg

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