中国中医基础医学杂志2026，Vol.32Issue(3)：510-517,8.
整合生物信息学与机器学习的冠心病早期诊断生物标志物筛选及中药治疗预测
Screening of Biomarkers for Early Diagnosis of Coronary Artery Disease and Prediction of Chinese Materia Medica Treatment by Integrating Bioinformatics and Machine Learning
摘要
Abstract
Objective To integrate bioinformatics and machine learning methods to screen biomarkers for early diagnosis of coronary artery disease(CAD)and predict effective traditional Chinese medicine(TCM),providing a basis and methodological reference for the application of TCM in the early prevention and treatment of CAD.Methods This study obtained peripheral blood miRNA expression data of CAD patients from the GEO database and used R software to screen differentially expressed genes(DEGs).Weighted gene co-expression network analysis(WGCNA)was employed to identify co-expressed gene modules with high biological significance.Enrichment analysis was performed on the overlapping genes between DEGs and the modules screened by WGCNA.The LASSO algorithm was used to predict characteristic genes of CAD,and hub genes were screened based on the STRING database and protein-protein interaction(PPI)network.The overlapping genes were considered potential biomarkers.Subsequently,the differential expression of key genes was validated in different datasets,and their diagnostic value was evaluated using receiver operating characteristic(ROC)curves.Gene set enrichment analysis(GSEA)was conducted to identify pathways significantly enriched for key genes in CAD.Finally,based on the key genes,targeted TCM for CAD treatment was predicted using the Coremine Medical platform,and their efficacy,properties,and meridian tropism were analyzed in combination with TCM theory.Results A total of 391 DEGs were identified in this study,and 156 genes were screened through WGCNA.The intersection yielded 48 genes closely associated with CAD.Enrichment analysis indicated that these genes were significantly enriched in pathways such as the peroxisome proliferator-activated receptor(PPAR)signaling pathway and osteoclast differentiation.Through algorithms such as LASSO regression,aquaporin 9(AQP9)and FK-506 binding protein 5(FKBP5)were screened as potential biomarkers for CAD,and ROC curve analysis confirmed their high diagnostic efficacy(AUC>0.78).GSEA further revealed that AQP9 and FKBP5 were significantly enriched in biological processes such as immune regulation,metabolic regulation,and cellular signal transduction in CAD.Finally,15 types of Chinese materia medica,including Chishao(Paeoniae Radix Rubra),Danshen(Salviae Miltiorrhizae Radix et Rhizoma),Zhishi(Aurantii Fructus Immaturus),and Baizhu(Atractylodis Macrocephalae Rhizoma),were predicted for the treatment of CAD.Conclusion This study utilized bioinformatics and machine learning techniques to screen biomarkers for early diagnosis of CAD and targeted Chinese materia medica,providing a reference for the early prevention and treatment of CAD with TCM.关键词
冠心病/早期诊断/生物标志物/机器学习/生物信息学/中药预测Key words
Coronary artery disease/Early diagnosis/Biomarkers/Machine learning/Bioinformatics/Chinese materia medica prediction分类
医药卫生引用本文复制引用
于佳田,周聪慧,佟旭..整合生物信息学与机器学习的冠心病早期诊断生物标志物筛选及中药治疗预测[J].中国中医基础医学杂志,2026,32(3):510-517,8.基金项目
国家自然科学基金项目(82305439) （82305439）
中国中医科学院基本科研业务费优秀青年科技人才培养专项(ZZ16-YQ-053) （ZZ16-YQ-053）
中央级公益性科研院所基本科研业务费专项(YZX-202422) （YZX-202422）
