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筛选动脉粥样硬化（atherosclerosis， AS）内质网应激（endoplasmic reticulum stress， ERS）相关诊断标志物，分析其免疫微环境特征并识别分子亚型，为AS精准诊疗提供一定参考依据。利用基因表达综合数据库（gene expression omnibus， GEO）中获取AS相关数据集，筛选出差异表达的内质网应激相关基因（differentially expressed endoplasmic reticulum stress-related genes，DE-ERRGs）；运用基因本体（gene ontology， GO）、京都基因与基因组百科全书（kyoto encyclopedia of genes and genomes， KEGG）、基因集富集分析（gene set enrichment analysis， GSEA）、基因集变异分析（gene set variation analysis， GSVA）及加权基因共表达网络分析（weighted gene co-expression network analysis， WGCNA）开展功能富集与关键基因模块的挖掘；将最小绝对收缩与选择算子（least absolute shrinkage and selection operator， LASSO），支持向量机（support vector machine， SVM），随机森林（random forest， RF）三种机器学习算法联合起来筛选诊断标志物并构建模型，分析免疫细胞浸润特征，依据内质网相关基因（endoplasmic reticulum stress-related genes ， ERRGs）的表达谱对分子亚型进行聚类。通过三种机器学习，筛选出TRPM2、NLRP3为AS内质网应激核心标志物，二者为独立危险因素，双基因诊断模型受试者工作特征（receiver operating characteristic， ROC）的曲线下面积（area under the curve， AUC）为0.879；AS组效应记忆CD8?T细胞、髓源性抑制细胞及调节性T细胞（regulatory T cells， Tregs) 浸润异常，Ⅰ型干扰素反应、CC类趋化因子受体等免疫通路差异表达，鉴定出2种免疫浸润特征迥异的ERS分子亚型。本研究系统阐释了AS中ERS相关的分子特征与免疫微环境异质性，为解析AS发病的分子机制、挖掘新型诊疗靶点及优化个体化干预策略提供科学依据与新思路。
Abstract：This study aimed to screen for diagnostic biomarkers related to endoplasmic reticulum stress (ERS) in atherosclerosis (AS)， characterize the immune microenvironment， and identify molecular subtypes， thereby providing a reference for the precise diagnosis and treatment of AS. AS-related datasets were obtained from the gene expression omnibus (GEO) database， and differentially expressed endoplasmic reticulum stress-related genes (DE-ERRGs) were identified. Functional enrichment analyses and the identification of key gene modules were performed using gene ontology (GO)， the kyoto encyclopedia of genes and genomes (KEGG)， gene set enrichment analysis (GSEA)， gene set variation analysis (GSVA)， and weighted gene co-expression network analysis (WGCNA). Three machine learning algorithms—least absolute shrinkage and selection operator (LASSO)， support vector machine (SVM)， and random forest (RF)—were integrated to screen for diagnostic biomarkers， construct a diagnostic model， and analyze immune cell infiltration characteristics. Molecular subtypes were clustered based on the expression profiles of endoplasmic reticulum stress-related genes (ERRGs). Using three machine learning methods， TRPM2 and NLRP3 were identified as core markers of endoplasmic reticulum stress in AS. Both factors were identified as independent risk factors. Receiver operating characteristic (ROC) curve analysis revealed that the two-gene diagnostic model had an area under the curve (AUC) of 0.879. The AS group exhibited abnormal infiltration of effector memory CD8? T cells， myeloid-derived suppressor cells ， and regulatory T cells (Tregs)， as well as differential expression of immune pathways such as the type I interferon response and CC-type chemokine receptors. This led to the identification of two ERS molecular subtypes with markedly distinct immune infiltration profiles. This study systematically elucidates the molecular characteristics of endoplasmic reticulum stress in AS and the heterogeneity of the immune microenvironment， providing a scientific basis and a new research approach for elucidating the molecular mechanisms underlying AS pathogenesis， identifying novel therapeutic targets， and optimizing personalized intervention strategies.
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基本信息:
中图分类号:R543.5;TP181
引用信息:
[1]王鑫,要文,姜雪娇,等.基于机器学习的动脉粥样硬化内质网应激相关基因筛选、亚型识别及诊断[J].基因组学与应用生物学().
基金信息:
内蒙古自然科学基金项目（2027QN08048）资助
2026-07-28
2026-07-28
2026-07-28