Sun Keyu, Wu Fupeng, Zheng Jiayi, Wang Han, Li Haidong, Xie Zichen
Emergency Department, Minhang Hospital, Fudan University, Shanghai 201100, China.
Research and Translational Laboratory of Acute Injury and Secondary Infection, Minhang Hospital, Fudan University, Shanghai 201199, China.
Int J Biol Macromol. 2024 Dec;282(Pt 3):136961. doi: 10.1016/j.ijbiomac.2024.136961. Epub 2024 Oct 31.
In this study, we aimed to identify an essential blood molecular signature for chacterizing the progression of sepsis-induced acute lung injury using integrated bioinformatic and machine learning analysis. The results showed that a total of 88 functionally related ALI-associated hub genes in sepsis were identified by MCODE analysis and they were enriched in infection and inflammtory responses, lung and cardiovascular disease pathways. These hub genes stratified ALI-sepsis and sepsis and further stratified two subtypes of sepsis-ALI with differential ALI scores, hub gene expression patterns, and levels of immune cells. A seven-gene signature including TNFRSF1A, NFKB1, FCGR2A, NFE2L2, ICAM1 and SOCS3 and PDCD1 was derived from the hub genes. These genes were significantly implicated in immune and metabolism pathways. They were expressed in six circulatory immune cells based on analysis of a single cell RNA sequencing dataset. Furthermore, the seven-gene signature was corrobarated using by integrating 12 machine learning algorithms. A premium three-gene signature NFE2L2, FCGR2A and PDCD1 for differentiating ALI-sepsis from sepsis were also derived from the seven-gene signature based on analysis of the seven core hub genes by the machine learning algorithms. Furthermore, the expressions of hub genes were verified in sepsis mice models. Therefore, our study provided an avenue to develop a molecular tool for identify and characterize progression of acute lung injury associated with sepsis.
在本研究中，我们旨在通过整合生物信息学和机器学习分析，确定一个用于表征脓毒症诱导的急性肺损伤进展的关键血液分子特征。结果表明，通过MCODE分析共鉴定出88个脓毒症中与急性肺损伤功能相关的枢纽基因，它们富集于感染和炎症反应、肺部和心血管疾病通路。这些枢纽基因对急性肺损伤-脓毒症和脓毒症进行了分层，并进一步将脓毒症-急性肺损伤的两个亚型进行分层，其急性肺损伤评分、枢纽基因表达模式和免疫细胞水平存在差异。从枢纽基因中得出了一个包括TNFRSF1A、NFKB1、FCGR2A、NFE2L2、ICAM1、SOCS3和PDCD1的七基因特征。这些基因在免疫和代谢途径中具有显著意义。基于单细胞RNA测序数据集的分析，它们在六种循环免疫细胞中表达。此外，通过整合12种机器学习算法对七基因特征进行了验证。基于机器学习算法对七个核心枢纽基因的分析，还从七基因特征中得出了一个用于区分急性肺损伤-脓毒症和脓毒症的优质三基因特征NFE2L2、FCGR2A和PDCD1。此外，在脓毒症小鼠模型中验证了枢纽基因的表达。因此，我们的研究为开发一种用于识别和表征与脓毒症相关的急性肺损伤进展的分子工具提供了一条途径。
