Abstract
Background: Emerging evidence underscores the critical involvement of ferroptosis in the pathophysiology of AKI. However, the role of ferroptosis-related genes (FRGs) in AKI remains insufficiently explored. This study sought to identify potential FRGs associated with AKI through bioinformatics approaches and experimental validation. Methods: AKI-related datasets and FRGs were first collected. Differentially expressed FRGs linked to AKI were identified through analytical methods, followed by an examination of their biological functions. Diagnostic biomarkers were then selected using LASSO, RFE, and RF algorithms. Additionally, small pharmacological molecules associated with DE-FRGs were identified to explore the connection between DE-FRGs and AKI. qRT-PCR analysis revealed FAR1 expression in AKI, while Western blotting and IHC confirmed corresponding FAR1 protein changes in kidney tissues. TUNEL staining confirmed cell death in AKI. ROS production and ferroptosis markers were evaluated in FAR1-knockdown and FAR1-overexpressing HK-2 cells. Results: A total of 106 DE-FRGs were identified, with functional enrichment analysis revealing strong associations with the MAPK and mTOR signaling pathways, as well as ferroptosis. Eight diagnostic biomarkers were selected using multiple algorithms, and their predictive accuracy was validated through ROC curve analysis. Furthermore, 13 pharmacological molecules were identified to establish a relationship between DE-FRGs and AKI. AKI renal tissue exhibited elevated cell death and reduced FAR1 expression. In vitro, FAR1 knockdown in HK-2 cells increased ROS and ferroptosis markers, while FAR1 overexpression rescued these phenotypes. Conclusion: This study identified signaling pathways and small molecules associated with DE-FRGs in AKI. FAR1 was also identified as a potential diagnostic biomarker for AKI.
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CITATION STYLE
Duan, H., Yan, J., Fan, X., Du, Y., Zhong, X., Pan, T., & Wang, Y. (2025). FAR1 as a ferroptosis-related biomarker and potential therapeutic target in acute kidney injury: integrated bioinformatics and experimental validation. Renal Failure, 47(1). https://doi.org/10.1080/0886022X.2025.2547260
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