Integration of mitochondrial gene expression and immune landscape in acute kidney injury prediction.

Background: Acute kidney injury (AKI) is a life-threatening condition with limited early biomarkers. Mitochondrial dysfunction is central to AKI pathophysiology, yet its potential for predicting AKI remains underexplored.

Methods: Gene expression data from three publicly available AKI datasets (GSE30718, GSE61739, and GSE139061) were analyzed to identify differentially expressed genes (DEGs). A set of 11 mitochondrial-related genes was selected and used to construct a mitochondrial risk score (MRS) model via Lasso and elastic net regression. The model was validated across multiple datasets. Immune infiltration was assessed using the xCell algorithm to explore the relationship between MRS and immune cell dynamics in AKI. Stable HK-2 cells were constructed of XRCC3 knockdown and overexpression to investigate the effects of XRCC3 on cell activities. Additionally, the impact of XRCC3 on mitochondrial structure and function was examined in vivo and in vitro.

Results: Eleven mitochondrial-related genes were consistently dysregulated across all datasets. PCA demonstrated a clear separation between AKI and normal samples. Functional enrichment analysis revealed that upregulated genes were linked to extracellular matrix remodeling and stress responses, while downregulated genes were associated with mitochondrial dysfunction. The MRS model showed strong predictive performance. We found that XRCC3 significantly promoted the activities of HK-2 cells and improved the integrity of mitochondrial structure and function in vivo and in vitro.

Conclusion: The mitochondrial gene-based MRS model is a robust tool for predicting AKI. Our findings underscore the critical role of mitochondrial dysfunction and immune modulation in AKI, offering potential avenues for targeted therapeutic strategies.