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结合铁死亡建立遗传算法优化的反向传播神经网络脓毒症预后模型OA北大核心CSTPCD

Combined machine learning with ferroptosis to establish the prognosis model for sepsis based on backward propagation neural network optimized using genetic algorithm

中文摘要英文摘要

目的 将铁死亡与机器学习相结合,建立一种基于遗传算法的反向传播网络(GA-BPNN)模型,以预测脓毒症患者的28d存活情况.方法 通过高通量基因表达数据库(GEO)下载脓毒症相关数据.从分子签名数据库(MSigDB)下载与铁死亡相关的基因65个.使用基因集富集分析(GSEA)、基因集变异分析(GSVA)、加权基因共表达网络分析(WGCNA)和蛋白质-蛋白质相互作用(PPI)网络分析筛选脓毒症患者的铁死亡相关基因.同时利用短时间序列挖掘分析(STEM)筛选与脓毒症发展进程相关的基因,并与脓毒症患者铁死亡相关基因取交集,获得脓毒症预后的关键基因.在传统的反向传播网络(BPNN)的基础上,采用遗传算法(GA)优化权值和阈值,建立脓毒症预后预测GA-BPNN模型.结果 脓毒症存活组和未存活组之间铁死亡相关基因集活性差异显著.共筛选出97个脓毒症铁死亡相关基因.同时确定了191个在脓毒症发展进程中显著上调或下调的基因.取交集基因建立脓毒症四基因预测预后GA-BPNN模型.在训练集中经过迭代后训练的均方误差在0.05以下,曲线下面积(AUC)为0.98.为验证GA-BPNN模型的泛化能力和分类效果,在外部数据验证集中,将GA-BPNN模型与支持向量机(SVM)、BPNN和随机森林(RF)进行比较.结果 表明,在验证集中GA-BPNN模型的AUC值均为0.92,高于SVM、RF和BPNN.结论 基于GA-BPNN提出了四基因模型预测脓毒症患者的预后效果是可靠且稳定的,为脓毒症中的铁死亡提供新的见解.

Objective Combining ferroptosis with machine learning,a backward propagation network model based on a genetic algo-rithm(GA-BPNN)was established to predict the 28-day survival of sepsis patients.Methods Data related to sepsis were downloaded from gene expression omnibus(GEO).A total of 65 genes associated with ferroptosis were downloaded from molecular signatures database(MSigDB).The gene set enrichment analysis(GSEA),gene set variation analysis(GSVA),weighted gene co-expression network analysis(WGCNA),and protein-protein interaction(PPI)network analysis were then used to screen the genes associated with ferroptosis in sepsis patients.The short time series mining analysis(STEM)was used to screen the genes related to the development of sepsis.The intersection of genes related to ferroptosis in sepsis patients was made to obtain the key genes for the prognosis of sepsis.Based on the traditional back propagation network(BPNN),a genetic algorithm(GA)was used to optimize the weights and thresholds to establish a prognostic model of sepsis,namely GA-BPNN.Results GSVA showed a significant difference in the activity of the ferroptosis-related gene set between the sepsis survival group and nonsurviving group.Ninety-seven genes related to ferroptosis in sepsis were screened.A total of 191 genes that were significantly up-regulated or down-regulated in the development of sepsis were identified.A GA-BPNN model for predicting the prog-nosis of four genes in sepsis was established by the intersection of genes related to ferroptosis and differentially expressed genes in sepsis.The mean square error of the training after the iteration in the training set is below 0.05,and the AUC value is 0.98.In order to verify the classification effect of the GA-BPNN model,the GA-BPNN model was compared with the support vector machine(SVM),backpropaga-tion network(BPNN),and random forest(RF)in the verification set.The results show that the AUC value of the GA-BPNN model in the validation set were all 0.92,higher than that of the SVM,RF,and BPNN.Conclusion The four-gene model based on the GA-BPNN is reliable and stable in predicting the prognosis of sepsis patients,which provides a new insight into ferroptosis in sepsis.

曾媛媛;常莉;池晴佳;封顺;田菲菲

西南交通大学生命科学与工程学院生物工程系,成都 610036武汉理工大学理学院工程结构与力学系,武汉 430070

生物工程

脓毒症铁死亡生物标志物预后模型

sepsisferroptosisbiomarkerprognostic model

《中国医科大学学报》 2024 (004)

295-301 / 7

国家自然科学基金(22174117)

10.12007/j.issn.0258-4646.2024.04.002

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