基于DIA蛋白质组学技术筛选脓毒症脑病发病相关关键蛋白质的研究  

Research on screening key proteins related to the pathogenesis of sepsis associated encephalopathy based on DIA proteomics technology

作　　者：吴淑慧 胡宏杰 陆昱汝 祝伟[1,2] Wu Shuhui;Hu Hongjie;Lu Yuru;Zhu Wei(Department of Emergency Medicine,Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan 430030,Hubei,China;Department of Intensive Care Medicine,Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan 430030,Hubei,China)

机构地区：[1]华中科技大学同济医学院附属同济医院急诊医学科,湖北武汉430030 [2]华中科技大学同济医学院附属同济医院重症医学科,湖北武汉430030

出　　处：《生物医学转化》2024年第4期28-36,共9页Biomedical Transformation

基　　金：湖北省重点研发计划项目(2022BCA038)。

摘　　要：目的 通过数据独立采集(DIA)定量蛋白质组学技术和机器学习方法挖掘脓毒症脑病(SAE)患者血浆关键蛋白质,以指导SAE的发病机制研究并助力SAE早期诊断。方法 通过DIA定量蛋白质组学技术检测脓毒症和SAE患者血浆蛋白质水平,同时,通过生物信息学方法明确两组患者间的差异蛋白质并进行功能分析。进一步通过极限梯度提升法(XGBoost)、最小绝对收缩和选择回归算法(LASSO)机器学习模型等方法在差异蛋白质中筛选关键差异蛋白质构建列线图并评价其诊断效能。结果 通过DIA定量蛋白质组学技术和生物信息学分析在患者血浆中共筛选出22个差异表达蛋白质,其中9个蛋白质表达水平在SAE组中上调,13个蛋白质表达水平在SAE组中下调。差异蛋白质涉及丝裂原活化蛋白激酶(MAPK)信号通路、病灶黏附和磷脂酶D等信号通路,参与细胞黏附、信号转导、高分子修饰等生物学过程,同时,肠道疾病与SAE发生可能存在密切关系。进一步通过XGBoost机器学习模型、LASSO回归机器学习模型和五折交叉验证的方法在22个差异蛋白质中筛选出两个关键蛋白质内质网蛋白29 (ERP29)和白细胞介素增强剂结合因子3 (ILF3)。将两个关键蛋白质构建列线图并评价其诊断效果,其中训练集受试者工作特征曲线(ROC)的曲线下面积(AUC)为0.925 6,测试集AUC为0.875。结论 基于DIA定量蛋白质组学技术和机器学习方法明确的蛋白质ERP29和ILF3是SAE发生的关键蛋白质,是SAE发病机制研究的重要靶点。ERP29和ILF3可作为生物学标志物用于SAE早期诊断。Objective To explore the key proteins in plasma of patients with sepsis associated encephalopathy(SAE) through data-independent acquisition(DIA) quantitative proteomics and machine learning techniques,with the aim of guiding research on SAE pathogenesis and aiding early diagnosis.Methods The protein levels of plasma in patients with sepsis and SAE were measured using DIA quantitative proteomics technology.The differential proteins between the two patient groups were identified and functionally analyzed using bioinformatics methods.Key differential proteins were further screened using machine learning models such as Extreme Gradient Boosting(XGBoost) and Least Absolute Shrinkage and Selection Operator(LASSO)regression to construct a nomogram and evaluate its diagnostic performance.Results A total of 22 differentially expressed proteins were identified in the plasma of patients using DIA quantitative proteomics technology and bioinformatics analysis.Among these,the expression levels of 9 proteins were upregulated and 13 proteins were downregulated in the SAE group.These proteins were involved in signaling pathways such as Mitogen-Activated Protein Kinase(MAPK) signaling,focal adhesion and phospholipase D signaling pathways,and were involved in biological processes such as cell adhesion,signaling,macromolecular modification,etc.Meanwhile,intestinal diseases were found to have a potential association with the occurrence of SAE.Further screening was conducted using XGBoost and LASSO regression machine learning model,along with 5-fold cross validation to identify two key differential proteins,Endoplasmic Reticulum Protein 29(ERP29) and Interleukin Enhancer Binding Factor 3(ILF3),among the 22 differentially expressed proteins.A nomogram was constructed with these two key proteins and the diagnostic efficacy was evaluated,with area under curve(AUC) value 0.925 6 for the receiver operating characteristic curve(ROC) in the training set and AUC value 0.875 in the test set.Conclusion ERP29 and ILF3,identified through DIA qua

关 键 词：脓毒症脑病 蛋白质组学 机器学习 生物学标志物 

分 类 号：R631[医药卫生—外科学]