Gao Hengkun, Chen Sibing, Li Jiannan, Liu Yanxi
Department of Wound Repair, Plastic and Reconstructive Surgery, China-Japan Union Hospital of Jilin University, No.126 Xiantai Street, Changchun 130033, Jilin, China.
J Diabetes Complications. 2025 Aug;39(8):108957. doi: 10.1016/j.jdiacomp.2025.108957. Epub 2025 Jan 29.
Diabetic foot ulcer (DFU) constitutes a major complication in diabetes management. This study aimed to develop and validate an immune-related diagnostic model for DFU by identifying key genes and analyzing their functional enrichment.
We utilized the datasets GSE199939, GSE134431, and GSE80178 from the Gene Expression Omnibus (GEO) database. Weighted Gene Co-Expression Network Analysis (WGCNA) was employed to identify gene modules associated with DFU. Differentially expressed genes (DEGs) were pinpointed using the "limma" package, and functional enrichment was executed using "clusterProfiler". A risk score for diagnosing DFU was developed using the Least Absolute Shrinkage and Selection Operator (LASSO) model. The CIBERSORT algorithm was utilized to assess immune cell infiltration. The diagnostic effectiveness of the risk score was gauged through the receiver operating characteristic (ROC) curve, and drug target prediction was performed using the DGIdb database.
WGCNA identified a DFU-related gene module containing 2184 genes. Functional enrichment analysis revealed important pathways, including proteasome and cell cycle. Nine DEGs were recognized as immune-related candidates for DFU, predominantly involved in signaling cascades like cytokine-cytokine receptor interaction. The LASSO model selected four key genes (APOD, ULBP2, TGFBR3, TNFRSF12A) to construct a risk score, which showed high diagnostic accuracy in datasets GSE134431, GSE199939 and GSE80178 (AUC = 0.990, 1.000, and 0.926, respectively). Pronounced disparities in infiltrating immune cells were observed among DFU patient groups with disparate risk factors. Drug prediction analyses identified potential therapeutic targets for the key genes.
This study developed a powerful immune-related diagnostic model for DFU, highlighting the key genes and pathways involved in its pathogenesis. The risk score provides a valuable tool for DFU diagnosis, while the identified drug targets provide avenues for potential therapeutic intervention.
糖尿病足溃疡（DFU）是糖尿病管理中的主要并发症。本研究旨在通过识别关键基因并分析其功能富集情况，开发并验证一种用于DFU的免疫相关诊断模型。
我们使用了来自基因表达综合数据库（GEO）的数据集GSE199939、GSE134431和GSE80178。采用加权基因共表达网络分析（WGCNA）来识别与DFU相关的基因模块。使用“limma”软件包确定差异表达基因（DEG），并使用“clusterProfiler”进行功能富集分析。使用最小绝对收缩和选择算子（LASSO）模型建立DFU诊断风险评分。利用CIBERSORT算法评估免疫细胞浸润情况。通过受试者工作特征（ROC）曲线评估风险评分的诊断效能，并使用DGIdb数据库进行药物靶点预测。
WGCNA识别出一个包含2184个基因的DFU相关基因模块。功能富集分析揭示了包括蛋白酶体和细胞周期等重要通路。九个DEG被确定为DFU的免疫相关候选基因，主要参与细胞因子-细胞因子受体相互作用等信号级联反应。LASSO模型选择了四个关键基因（载脂蛋白D（APOD）、UL16结合蛋白2（ULBP2）、转化生长因子β受体3（TGFBR3）、肿瘤坏死因子受体超家族成员12A（TNFRSF12A））构建风险评分，该评分在数据集GSE134431、GSE199939和GSE80178中显示出较高的诊断准确性（AUC分别为0.990、1.000和0.926）。在具有不同风险因素的DFU患者组中观察到免疫细胞浸润存在明显差异。药物预测分析确定了关键基因的潜在治疗靶点。
本研究开发了一种强大的DFU免疫相关诊断模型，突出了其发病机制中涉及的关键基因和通路。风险评分为DFU诊断提供了有价值的工具，而确定的药物靶点为潜在的治疗干预提供了途径。
