Identification of ferroptosis-related genes as potential diagnostic biomarkers for diabetic nephropathy based on bioinformatics

Binbin Guo; Minhui Li; Peipei Wu; Yan Chen

doi:10.3389/fmolb.2023.1183530

Identification of ferroptosis-related genes as potential diagnostic biomarkers for diabetic nephropathy based on bioinformatics

Front Mol Biosci. 2023 Aug 1:10:1183530. doi: 10.3389/fmolb.2023.1183530. eCollection 2023.

Authors

Binbin Guo¹, Minhui Li², Peipei Wu¹, Yan Chen¹

Affiliations

¹ International Special Medical Department, Shengli Oilfield Central Hospital, Dongying, Shandong, China.
² Department of Pediatrics Internal Medicine, Dongying Municipal Children's Hospital, Dongying, Shandong, China.

Abstract

Objective: This study investigated to probe ferroptosis-related diagnostic biomarkers and underlying molecular mechanisms in Diabetic nephropathy (DN). Methods: GSE30122 and GSE1009 from GEO database were used as training and verification sets, respectively, to screen differentially expressed ferroptosis-related genes (FRGs). These genes were further analyzed using GO, KEGG, and GSEA methods, and screened with PPI, LASSO, and SVM-RFE to identify ferroptosis-related diagnostic biomarkers for DN. A diagnostic model was established using the Glm function and verified with ROC curve. The relationship between these biomarkers and immune cell was analyzed, and qRT-PCR and Western blot were used to detect the expression of these biomarkers in kidney tissues and identify the effect of TP53 on DN development. Results: Fifty one differentially expressed FRGs were enriched in bioprocesses such as p53 signaling pathway, oxidative stress and chemical stress response, and mTOR signaling pathway. TP53, RB1, NF2, RRM2, PRDX1, and CDC25A were identified as ferroptosis-related diagnostic biomarkers for DN. TP53 showed the most differential expression. ROC analysis showed that AUC values of TP53, RB1, NF2, RRM2, PRDX1, and CDC25A were 0.751, 0.705, 0.725, 0.882, 0.691, and 0.675, respectively. The AUC value of DN diagnosis model was 0.939 in training set and 1.000 in verification set. qRT-PCR results confirmed significant differences in these six biomarkers between DN and normal kidney tissue (p < 0.05), and correlation analysis showed that five biomarkers were significantly correlated with infiltrating immune cells (p < 0.05). Furthermore, western blots showed that TP53 promotes apoptosis through PI3K-AKT signaling in DN. Conclusion: TP53, RB1, NF2, RRM2, PRDX1, and CDC25A have potential as diagnostic biomarkers for DN. The diagnostic model containing the above six biomarkers performs well in the diagnosis of DN. Five of the six biomarkers are strongly associated with several infiltrating immune cells. TP53 may play an essential role in the development of DN.

Keywords: TP53; diabetic nephropathy; diagnostic biomarkers; ferroptosis; ferroptosis-related genes.