Identification and validation of ferroptosis-related genes for diabetic nephropathy

Emerging evidence highlights the pivotal role of ferroptosis in the pathophysiology of diabetic nephropathy (DN). This study aimed to identify potential ferroptosis-related genes (FRGs) in DN through bioinformatics and experimental validation.

Datasets for diabetic nephropathy (DN) and ferroptosis-related gene sets were obtained from the Gene Expression Omnibus (GEO) database and the Ferroptosis Database, respectively. Differential expression analysis identified ferroptosis-related genes (DE-FRGs) in DN, and machine learning was applied to screen key genes. The risk model's accuracy was evaluated using receiver operating characteristic (ROC) curve analysis. Potential small chemical compounds associated with DE-FRGs and DN were also explored. Expression of DE-FRGs was measured by Quantitative Reverse Transcription PCR (qRT-PCR) in kidneys of DN mice and by Enzyme-linked immunosorbent assay (ELISA) in serum from DN patients versus non-DN controls.

Analysis identified 125 DE-FRGs enriched in ferroptosis and DN-related pathways. Machine learning pinpointed nine diagnostic biomarkers, which were validated by ROC curves, and 13 potential therapeutic compounds. Among the DE-FRGs, qRT-PCR verified dysregulation of interleukin-33 (IL-33), retinoic acid receptor responder protein 2 (RARRES2), enhancer of zeste homolog 2 (EZH2), gap junction protein alpha 1 (GJA1), and hypoxia-inducible lipid droplet associated (HILPDA) in DN kidneys. Importantly, serum levels of EZH2 and IL-33 were significantly elevated in DN patients, underscoring their critical role in pathogenesis and potential as therapeutic targets.

In conclusion, this study identified IL-33 and EZH2 as key DE-FRGs in DN, offering new insights into the molecular mechanisms underlying the disease.