Machine learning revealed novel ferroptosis-related genes and construction ceRNA network in dermal lymphatic endothelial cells of diabetic foot ulcer

Author:

Wang Xingkai1,Zou LinXuan2,Meng Lei3,Song Mingzhi2,Sun Xiaohong2,Jia Zhuqiang4,Zhao Lin5,Han Xin6,Wang Huan2,Wang Peng2,Lu Ming7,Zong JunWei2,Wang Shouyu2

Affiliation:

1. Department of Trauma and Tissue Repair Surgery, Dalian Municipal Central Hospital; Department of Orthopaedic Surgery, The First Affiliated Hospital of Dalian Medical University

2. Department of Orthopaedic Surgery, The First Affiliated Hospital of Dalian Medical University

3. The First Affiliated Hospital of Nanhua Medical University

4. The First Affiliated Hospital of Dalian Medical University; Naqu People's Hospital

5. Department of Quality Management, Dalian Municipal Central Hospital

6. Naqu People's Hospital; Department of Orthopaedic Surgery, The Second Affiliated Hospital of Dalian Medical University

7. Department of Trauma and Tissue Repair Surgery, Dalian Municipal Central Hospital

Abstract

Abstract Background Diabetic foot ulcer (DFU) is a common chronic and serious complication that impairs patients' quality of life. The relationship between ferroptosis and complications of diabetes has attracted much attention in recent years. Here, this study aims to apply the gene expression profile of dermal lymphatic endothelial cells (DLECs) to build a ceRNA network and explore potential ferroptosis-related biomarkers and pathways related to the molecular mechanism of DFU.Methods The GSE38396 dataset from the Gene Expression Omnibus (GEO) collection was utilized to analyze differentially expressed genes (DEGs) in DLECs of DFU. The protein-protein interaction (PPI) network and enrichment analysis of DEGs were carried out. Subsequently, we performed a comprehensive analysis of hub genes identified from the PPI. To yield the key ferroptosis-related genes strongly associated with DLECs of DFU, we integrated multiple datasets and the least absolute shrinkage and selection operator (LASSO) model, which was validated with external datasets (GSE147890) via receiver operating characteristic (ROC) curves. Meanwhile, GSE147890 and GSE29221 were utilized as external datasets to validate the efficiency of key genes as potential biomarkers for DFU.Results The 149 DEGs in DLECs of DFU were obtained using the GSE38396 dataset. Gene Ontology (GO) analysis showed that the collagen-containing extracellular matrix was primarily enriched. The HIF-1 signaling pathway was considered the key pathway by the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. We obtained 12 hub genes from the subnetwork in the PPI network and identified EGFR as a key ferroptosis-related gene by the LASSO model, which had a high AUC value (0.76). In addition, two external datasets validated EGFR with high efficiency (GSE147890: 0.67, GSE29221: 0.72). Ultimately, we constructed a ceRNA network consisting of 5 lncRNAs, 2 miRNAs, and 1 mRNA around EGFR.Conclusions As a key ferroptosis-related gene related to DLECs of DFU, EGFR may be regulated by upstream lncRNA, which in turn affects the activity of the HIF-1 pathway and affects the occurrence and development of DFU. Thus, the results of this study can provide a certain direction and basis for follow-up studies of DFU and provide new insights into the prevention and treatment of DFU.

Publisher

Research Square Platform LLC

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