生物信息学2025，Vol.23Issue(2)：131-142,12.DOI:10.12113/202401005
基于未折叠蛋白反应基因特征的儿童脓毒症预测模型构建及分子亚型鉴定
Identification of molecular subtypes and construction of a predictive model for pediatric sepsis based on unfolded protein response genes
摘要
Abstract
By analyzing transcriptomic data of pediatric sepsis from the GEO database,the role of unfolded protein response in the pathogenesis of this disease was investigated.First,candidate genes for the diagnostic model were screened by using random forest and support vector machine recursive feature elimination algorithms,including EXOSC4,EIF2AK3,CEBPB,WIPI1,EXOSC6,EXTL2 and SRPRB.A diagnostic model was constructed by multiple logistic regression and validated with three external datasets.Next,the correlation between these genes and immune cell infiltration was analyzed,revealing a strong correlation with neutrophil infiltration.Furthermore,patients with pediatric sepsis were divided into three subtypes by consensus clustering,and their differences in clinical features and expression of inflammatory factors were compared.Finally,core genes for each subtype were selected through weighted gene co-expression network analysis,and significant differences were found among these three subtypes in biological processes such as the immune system,metabolism,and cell death.Drug prediction results showed that patients with different subtypes may have different sensitivities to different types of drugs.In summary,this study provides new ideas for the diagnosis and precision treatment of pediatric sepsis.关键词
儿童脓毒症/未折叠蛋白反应/免疫浸润/诊断模型/机器学习Key words
Pediatric sepsis/Unfolded protein response/Prognostic model/Machine learning分类
生物学引用本文复制引用
刘爽,冯雯,顾雪锋..基于未折叠蛋白反应基因特征的儿童脓毒症预测模型构建及分子亚型鉴定[J].生物信息学,2025,23(2):131-142,12.基金项目
2024年教师专业发展工程项目(No.A3-0200-24-311008). （No.A3-0200-24-311008）
