首页|血流感染诊断生物标志物预测及免疫细胞浸润分析

血流感染诊断生物标志物预测及免疫细胞浸润分析

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目的 通过生物信息学与机器学习识别血流感染(blood stream infections,BSI)患者的相关诊断生物标志物、发病机制及免疫细胞浸润水平,寻找新的药物靶标.方法 从高通量基因表达数据库(gene expression omnibus,GEO)中获取了BSI相关基因表达数据集.使用R语言进行差异表达基因(differentially expressed gene,DEG)筛选,然后进行基因富集分析.使用加权基因共表达网络分析(weighted correlation network analysis,WGCNA)筛选的关键模块基因.通过使用两种机器学习算法来识别中心基因.在外部数据集中使用受试者工作特征曲线(receiver operating characteristic,ROC)和箱线图模型来验证中心基因的诊断效能.通过CIBERSORT反卷积算法分析免疫细胞浸润水平.结果 本研究得到了330个加权基因共表达网络关键模块基因和DEGs的交集基因.基因富集分析结果显示,免疫和炎症相关通路被显著富集.通过机器学习和外部数据库验证共得到8个潜在生物标志物,ROC分析显示,8个潜在生物标志物曲线下面积(area under curve,AUC)均大于0.9.免疫细胞浸润分析表明,所有诊断标生物标志物都可能与免疫细胞有着不同程度的相关性.结论 通过生物信息学和机器学习方法,确定了潜在生物标志物,并构建了BSI诊断模型.本研究可以为BSI患者提供潜在的外周血诊断生物标志物,为BSI发病机制、新型治疗靶点和新药研发提供新的方向.
Prediction of biomarkers for diagnosis of blood stream infections and analysis of immune cell infiltration
Objective Use bioinformatics and machine learning to find diagnostic biomarkers,pathogenesis,and immune cell infiltration levels that are relevant to people with blood stream infections(BSI)and look for new drug targets.Methods A BSI-related gene expression dataset was obtained from the High-Throughput Gene Expression Omnibus(GEO)database.Use the R language to screen differentially expressed genes(DEG),and then perform gene enrichment analysis.Key module genes were screened using weighted correlation network analysis(WGCNA).Identify central genes by using two machine learning algorithms.Use receiver operating characteristic(ROC)curves and box plot models in external datasets to validate the diagnostic efficacy of central genes.Analyze immune cell infiltration levels using the CIBERSORT deconvolution algorithm.Results This study obtained the intersection genes of 330 weighted gene co-expression network key module genes and DEGs.The results of gene enrichment analysis showed that immune and inflammation-related pathways were significantly enriched.A total of 8 potential biomarkers were obtained through machine learning and external database validation.ROC analysis showed that the area under the curve(AUC)of all 8 potential biomarkers was greater than 0.9.Immunocyte infiltration analysis indicates that all diagnostic biomarkers may have varying degrees of correlation with immune cells.Conclusion Through bioinformatics and machine learning methods,potential biomarkers were identified and a blood flow infection diagnosis model was constructed.This study can provide potential peripheral blood diagnostic biomarkers for patients with bloodstream infections and provide new directions for the pathogenesis of bloodstream infections,new treatment targets,and new drug development.

Bloodstream infectionBioinformaticsMachine learningPrediction of biomarkersImmune cell infiltration

李彬、刘睿鹏、吴磊、杜叶、卢文婷、周山清、刘日慧、邓梦雨、梅汝槐

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成都大学药学院,成都 610106

成都中医药大学药学院,成都 611137

广西中医药大学药学院,南宁 530000

四川省绿阳农业开发有限公司,南充 637700

成都大学食品与生物工程学院,成都 610106

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血流感染 生物信息学 机器学习 诊断生物标志物 免疫细胞浸润分析

四川省科技成果转移转化示范项目成都大学2021-2023年研究生人才培养质量和教学改革立项项目四川省大学生创新训练计划项目暨成都大学省级大学生创业实践项目

2023ZHCG0071cdjgy2022004S202411079006S

2024

中国抗生素杂志
中国医药集团总公司四川抗菌素工业研究所,中国医学科学院医药生物技术研究所

中国抗生素杂志

CSTPCD北大核心
影响因子：1.08
ISSN：1001-8689
年,卷(期)：2024.49(8)