Background: Emerging evidence reveals the key role of ferroptosis in the pathophysiological process of acute kidney injury (AKI). Our study aimed to investigate the potential ferroptosis-related gene in AKI through bioinformatics and experimental validation.

Methods: The AKI single-cell sequencing dataset was retrieved from the GEO database and ferroptosis-related genes were extracted from the GENECARD website. The potential differentially expressed ferroptosis-related genes of AKI were selected. Functional enrichment analysis was performed. Machine learning algorithms were used to identify key ferroptosis-related genes associated with AKI. A multi-factor Cox regression analysis was used to construct a risk score model. The accuracy of the risk score model was validated using receiver operating characteristic (ROC) curve analysis. We extensively explored the immune landscape of AKI using CIBERSORT tool. Finally, expressions of ferroptosis DEGs were validated in vivo and in vitro by Western blot, ICH and transfection experiments.

Results: Three hub genes (BAP1, MDM4, SLC2A1) were identified and validated by constructing drug regulatory network and subsequent screening using experimentally determined interactions. The risk mode showed the low-risk group had significantly better prognosis compared to high-risk group. The risk score was independently associated with overall survival. The ROC curve analysis showed that the prognosis model had good predictive ability. Additionally, CIBERSORT immune infiltration analysis suggest that the hub gene may influence cell recruitment and infiltration in AKI. Validation experiments revealed that SLC2A1 functions by regulating ferroptosis.

Conclusions: In summary, our study not only identifies SLC2A1 as diagnostic biomarker for AKI, but also sheds light on the role of it in AKI progression, providing novel insights for the clinical diagnosis and treatment of AKI.