|
|
|
| Bioinformatics analysis of ferroptosis-related genes in patients with atherosclerosis in type 2 diabetes mellitus |
| WU You, DENG Xia, JIA Jue, YUAN Guoyue |
| (Department of Endocrinology and Metabolism, the Affiliated Hospital of Jiangsu University; Institute of Endocrine and Metabolic Diseases, Jiangsu University, Zhenjiang Jiangsu 212001, China) |
|
|
|
Abstract Objective： To explore the hub genes and potential mechanism of ferroptosis in the development of atherosclerosis (AS) in patients with type 2 diabetes mellitus (T2DM) based on bioinformatics. Methods： The datasets GSE20966 (T2DM) and GSE43292 (AS) were obtained from the GEO database. Differentially expressed genes (DEGs) were identified using the Limma R package. Heatmaps and volcano plots were drawn, and crossanalysis was performed to obtain DEGs associated with the two diseases. GO and KEGG enrichment analysis was performed to explore the biological functions of DEGs. Ferroptosis-related genes (FRGs) obtained from the FerrDb database were crosslinked, and hub genes were screened using LASSO regression and random forest analysis. ROC curves and validation sets GSE76895 (T2DM) and GSE28829 (AS) were used for verification. Finally, the gene-miRNA network was drawn. Results： A total of 606 DEGs were identified related to the T2DM and the AS datasets. Twenty potential genes were obtained by cross-analyzing with FRGs. Cyclin-depedent kinase inhibitors 1A (CDKN1A), poly ADPribose polymerase 8 (PARP8), phosphatidylethanolamine binding protein 1 (PEBP1), and progesterone receptor membrane component 1 (PGRMC1) were hub genes that affected AS in T2DM patients through ferroptosis. The area under the curve (AUC) of the ROC curves in the datasets GSE20966 and GSE43292 of the four genes was all greater than 0.7, which had diagnostic value. PEBP1 and PGRMC1 were significantly down-regulated in the validation sets GSE76895 and GSE28829. In addition, 13 miRNAs were closely associated with 4 hub genes. Conclusion： CDKN1A, PARP8, PEBP1 and PGRMC1 are involved in AS in T2DM patients through ferroptosis and may become new therapeutic targets.
［Key words］ferroptosis; type 2 diabetes mellitus; atherosclerosis; bioinformatics; machine learning
|
|
Received: 16 April 2024
|
|
|
|
|
|
|
