Hu Yungang, Wang Yiwen, Zhi Lin, Yu Lu, Hu Xiaohua, Shen Yuming, Du Weili
Department of Burns and Plastic Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing 100035, China; Department of Plastic Surgery, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China.
Department of Burns and Plastic Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing 100035, China.
Int J Biol Macromol. 2024 Dec;283(Pt 2):137789. doi: 10.1016/j.ijbiomac.2024.137789. Epub 2024 Nov 17.
Diabetic foot ulcers (DFU) is a complication associated with diabetes characterised by high morbidity, disability, and mortality, involving chronic inflammation and infiltration of multiple immune cells. We aimed to identify the critical genes in nonhealing DFU using single-cell RNA sequencing, transcriptomic analysis and machine learning. The GSE165816, GSE134431, and GSE143735 datasets were downloaded from the GEO database. We processed and screened the datasets, and identified the cell subsets. Each cell subtype was annotated, and the predominant cell types contributing to the disease were analysed. Key genes were identified using the LASSO regression algorithm, followed by verification of model accuracy and stability. We investigated the molecular mechanisms and changes in signalling pathways associated with this disease using immunoinfiltration analysis, GSEA, and GSVA. Through scRNA-seq analysis, we identified 12 distinct cell clusters and determined that the basalKera cell type was important in disease development. A high accuracy and stability prediction model was constructed incorporating five key genes (TXN, PHLDA2, RPLP1, MT1G, and SDC4). Among these five genes, SDC4 has the strongest correlation and plays an important role in the development of DFU. Our study identified SDC4 significantly associated with nonhealing DFU development, potentially serving as new prevention and treatment strategies for DFU.
糖尿病足溃疡（DFU）是一种与糖尿病相关的并发症，其特征为高发病率、致残率和死亡率，涉及慢性炎症以及多种免疫细胞的浸润。我们旨在通过单细胞RNA测序、转录组分析和机器学习来鉴定难愈合性DFU中的关键基因。从GEO数据库下载了GSE165816、GSE134431和GSE143735数据集。我们对这些数据集进行了处理和筛选，并鉴定了细胞亚群。对每个细胞亚型进行了注释，并分析了导致该疾病的主要细胞类型。使用LASSO回归算法鉴定关键基因，随后验证模型的准确性和稳定性。我们通过免疫浸润分析、基因集富集分析（GSEA）和基因集变异分析（GSVA）研究了与该疾病相关的分子机制和信号通路变化。通过单细胞RNA测序分析，我们鉴定出12个不同的细胞簇，并确定基底角质形成细胞类型在疾病发展中很重要。构建了一个包含五个关键基因（TXN、PHLDA2、RPLP1、MT1G和SDC4）的高精度和稳定性预测模型。在这五个基因中，SDC4的相关性最强，在DFU的发展中起重要作用。我们的研究确定SDC4与难愈合性DFU的发展显著相关，可能成为DFU新的预防和治疗策略。
