医学信息2026，Vol.39Issue(9)：1-13,31,14.DOI:10.3969/j.issn.1006-1959.2026.09.001
基于多队列机器学习构建阿尔茨海默病血-脑共性分子标志物诊断模型并联合miRNA-107预测高信度靶基因
Construction of a Diagnosis Model for Alzheimer Disease Based on Multi-queue Machine Learning for Blood-brain Common Molecular Markers and Prediction of High-confidence Target Genes in Combination with miRNA-107
摘要
Abstract
Objective To identify blood-brain common molecular markers for Alzheimer disease(AD)and construct a novel diagnostic model,while exploring the potential regulatory mechanism with upstream miRNAs.Methods Sixteen public datasets from the GEO platform were integrated(15 transcriptome datasets:4 whole blood and 11 brain tissue;1 serum miRNA dataset:GSE120584).Batch correction was performed using SVA/ComBat,and differential expression analysis was conducted using limma in whole blood and brain tissue separately(|log2FC|>0.2,FDR<0.05),with the intersection defining candidate common molecular markers.GSE63060+GSE63061 were used as the training set,and 113 machine learning methods/combinations were systematically evaluated to construct diagnostic models,which were externally validated in the remaining 15 datasets.ClusterProfiler was used for GO and KEGG enrichment analysis,MANIA and Cytoscape for PPI network construction,CIBERSORT for immune infiltration assessment,while miRDB and miRTarBase and TargetScan for joint prediction of high-confidence target genes of differentially expressed miRNAs.Results After the batch effect was corrected by the ComBat function,the expression matrix was subjected to principal component analysis,and the samples showed a more consistent distribution in the PCA space.A total of 106 DEGs(20 up-regulated and 86 down-regulated)were identified in whole blood data,and 2006 DEGs(934 up-regulated and 1072 down-regulated)were identified in brain tissue data(threshold:|log2FC|>0.2,FDR<0.05).There were 11 genes in the intersection of the two,and 8 genes in the same direction of difference.Among them,VCAN and FOS were up-regulated,and NDUFA4,COX6C,HINT1,ACTR6,LSM3 and ZC3H15 were down-regulated,which were defined as candidate blood-brain common molecular markers.GO enrichment analysis showed that respiratory electron transport chain,electron transport coupled with mitochondrial ATP synthesis,and electron transport of cytochrome c to oxygen(complex Ⅳ)were significantly enriched(FDR<0.05).KEGG analysis showed that oxidative phosphorylation,heat production and reactive oxygen species-related pathways were also significant(FDR<0.05);in addition,neurodegenerative diseases such as AD,Parkinson's disease,Huntington's disease,amyotrophic lateral sclerosis and prion disease were clustered;at the same time,nonalcoholic fatty liver disease and diabetic cardiomyopathy were also enriched.The random forest model constructed with these 8 genes performed best:AUC=0.999(95％CI:0.998-1.000)in the training set,and AUC was 0.932-1.000 in the external 15 cohorts.The PPI network showed NDUFA4 and COX6C as core nodes.CIBERSORT revealed peripheral immune profile remodeling in AD(e.g.,increased neutrophils,M0 macrophages;decreased naive B cells,activated NK cells).The intersection of miRNA predictions indicated that miR-107's high-confidence target genes included VCAN,suggesting a potential key axis of"miR-107-VCAN-AD".Conclusion This study,within a multi-cohort and cross-tissue integration framework,identify 8 genes as blood-brain common molecular markers and establish a stable diagnostic model in multiple external cohorts.Mechanistic evidence consistently point to mitochondrial OXPHOS/oxidative stress and ECM/immune pathways,with the miR-107/VCAN axis as a potential upstream regulatory target.This study provides generalizable clues and tools for non-invasive molecular diagnosis and mechanism research of AD.关键词
阿尔茨海默病/血-脑共性标志物/氧化磷酸化/随机森林/免疫浸润/miR-107/VCANKey words
Alzheimer disease/Blood-brain common markers/Oxidative phosphorylation/Random forest/Immune infiltration/miR-107/VCAN分类
医药卫生引用本文复制引用
刘淼,陈宇昱,王涛,杨可欣,陶佳晅,肖荣..基于多队列机器学习构建阿尔茨海默病血-脑共性分子标志物诊断模型并联合miRNA-107预测高信度靶基因[J].医学信息,2026,39(9):1-13,31,14.基金项目
国家自然科学基金面上项目(编号:82173501) （编号:82173501）
