SDC4 protein action and related key genes in nonhealing diabetic foot ulcers based on bioinformatics analysis and machine learning
Yungang Hu, Yiwen Wang, Lin Zhi, Yu Lu, Xiaohua Hu, Yuming Shen, Weili Du
Abstract
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.