Department of Dermatology, Tianjin Beichen Hospital, Tianjin, 300400, China.
Zhu Xianyi Memorial Hospital of Tianjin Medical University, Tianjin, 300134, China.
Hum Genomics. 2024 Jun 11;18(1):62. doi: 10.1186/s40246-024-00629-1.
Diabetic foot ulcers (DFU) is the most serious complication of diabetes mellitus, which has become a global health problem due to its high morbidity and disability rates and the poor efficacy of conventional treatments. Thus, it is urgent to identify novel molecular targets to improve the prognosis and reduce disability rate in DFU patients.
In the present study, bulk RNA-seq and scRNA-seq associated with DFU were downloaded from the GEO database. We identified 1393 DFU-related DEGs by differential analysis and WGCNA analysis together, and GO/KEGG analysis showed that these genes were associated with lysosomal and immune/inflammatory responses. Immediately thereafter, we identified CLU, RABGEF1 and ENPEP as DLGs for DFU using three machine learning algorithms (Randomforest, SVM-RFE and LASSO) and validated their diagnostic performance in a validation cohort independent of this study. Subsequently, we constructed a novel artificial neural network model for molecular diagnosis of DFU based on DLGs, and the diagnostic performance in the training and validation cohorts was sound. In single-cell sequencing, the heterogeneous expression of DLGs also provided favorable evidence for them to be potential diagnostic targets. In addition, the results of immune infiltration analysis showed that the abundance of mainstream immune cells, including B/T cells, was down-regulated in DFUs and significantly correlated with the expression of DLGs. Finally, we found latamoxef, parthenolide, meclofenoxate, and lomustine to be promising anti-DFU drugs by targeting DLGs.
CLU, RABGEF1 and ENPEP can be used as novel lysosomal molecular signatures of DFU, and by targeting them, latamoxef, parthenolide, meclofenoxate and lomustine were identified as promising anti-DFU drugs. The present study provides new perspectives for the diagnosis and treatment of DFU and for improving the prognosis of DFU patients.
糖尿病足溃疡（DFU）是糖尿病最严重的并发症，由于其高发病率和致残率以及传统治疗方法效果不佳，已成为全球性的健康问题。因此，迫切需要确定新的分子靶点，以改善 DFU 患者的预后并降低残疾率。
本研究从 GEO 数据库中下载了与 DFU 相关的 bulk RNA-seq 和 scRNA-seq。通过差异分析和 WGCNA 分析共鉴定出 1393 个 DFU 相关的 DEGs，GO/KEGG 分析表明这些基因与溶酶体和免疫/炎症反应有关。随后，我们使用三种机器学习算法（Randomforest、SVM-RFE 和 LASSO），并在独立于本研究的验证队列中验证了它们的诊断性能，鉴定出 CLU、RABGEF1 和 ENPEP 作为 DFU 的 DLGs。随后，我们基于 DLGs 构建了一种新的用于 DFU 分子诊断的人工神经网络模型，在训练和验证队列中的诊断性能良好。单细胞测序中，DLGs 的异质性表达也为它们成为潜在的诊断靶点提供了有利证据。此外，免疫浸润分析结果表明，包括 B/T 细胞在内的主流免疫细胞的丰度在 DFU 中下调，并与 DLGs 的表达显著相关。最后，我们发现拉他莫头孢、紫衫醇、美克洛酚酯和洛莫司汀通过靶向 DLGs 成为有前途的抗 DFU 药物。
CLU、RABGEF1 和 ENPEP 可作为 DFU 的新型溶酶体分子标志物，通过靶向它们，拉他莫头孢、紫衫醇、美克洛酚酯和洛莫司汀被鉴定为有前途的抗 DFU 药物。本研究为 DFU 的诊断和治疗以及改善 DFU 患者的预后提供了新的视角。
