目的 基于生物信息学构建自噬相关基因（ATGs）在糖尿病足溃疡（DFU）患者肢端微循环损害中的模型，探索相关机制及诊疗靶点。 方法 从GEO数据库获取GSE 134431、GSE 80178和GSE 68183数据集，分别作为训练集与验证集。数据进行预处理、归一化及批次校正后，进行聚类分析、GO和GSVA富集分析、WGCNA模块识别，并利用多种机器学习方法构建预测模型，通过列线图、校准曲线和决策曲线评估性能。 结果 共筛选出20个差异表达基因。聚类分析将患者分为有无微循环损害两组。GO分析显示基因显著富集于自噬相关结构、泛素连接酶结合及细胞应激反应等过程。GSVA提示自噬、线粒体自噬和FoxO通路显著富集。SVM模型性能最优（AUC=1.000），验证集AUC为0.929，关键基因包括BNIP3、HEPHL1、KLK10等。 结论 ATGs通过调控线粒体自噬和FoxO通路参与DFU微循环损害，SVM模型具有良好预测能力，为临床诊断与靶向治疗提供依据。
关键词：
生物信息学; ; ; 自噬相关基因; ; ; 糖尿病足溃疡; ; ; 肢端微循环损害; ; ; 模型构建
Objective To construct a model of autophagy-related genes (ATGs) in the microcirculatory damage of extremities in patients with diabetic foot ulcers (DFU) based on bioinformatics analysis, and explore the related mechanisms and diagnostic/therapeutic targets. Methods Datasets GSE 134431, GSE 80178, and GSE 68183 were obtained from the GEO database, serving as training and validation sets, respectively. After data preprocessing, normalization, and batch correction, cluster analysis, GO and GSVA enrichment analysis, and WGCNA module identification were performed. Multiple machine learning methods were used to build prediction models, and performance was evaluated using nomograms, calibration curves, and decision curve analysis. Results A total of 20 differentially expressed genes were identified. Cluster analysis divided patients into 2 groups based on the presence or absence of microcirculatory damage. GO analysis revealed significant enrichment of genes in autophagosome-related structures, ubiquitin ligase binding, and cellular stress response processes. GSVA indicated significant enrichment in autophagy, mitophagy, and FoxO signaling pathways. The SVM model performed best (AUC=1.000), with an AUC of 0.929 in the validation set. Key genes included BNIP3, HEPHL1, and KLK10. Conclusions ATGs are involved in DFU microcirculatory damage by regulating mitophagy and the FoxO pathway. The SVM model demonstrates strong predictive ability, providing a basis for clinical diagnosis and targeted therapy.
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