Identification of Subtypes and a Delayed Graft Function Predictive Signature Based on Ferroptosis in Renal Ischemia-Reperfusion Injury - Wei, Xiangling; Deng, Weiming; Dong, Zhanwen; Xie, Zhenwei; Zhang, Jinhua; Wang, Ruojiao; Zhang, Rui; Na, Ning; Zhou, Yu (Frontiers in Cell and Developmental Biology, 2022)
Renal ischemia reperfusion injury (IRI) is an inevitable process in kidney transplantation, leading to acute kidney injury, delayed graft function (DGF), and even graft loss. Ferroptosis is an iron-dependent regulated cell death in various disease including IRI. We aimed to identify subtypes of renal IRI and construct a robust DGF predictive signature based on ferroptosis-related genes (FRG). Consensus clustering analysis was applied to identified ferroptosis-associated subtypes of 203 renal IRI samples in GSE43974 dataset. The FRG-associated DGF predictive signature was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) and its robustness was further verified in validation set GSE37838. The present study revealed two ferroptosis-related clusters (pBECN1 and pNF2 clusters) in renal IRI samples based on distinct expression patterns of BECN1 gene cluster and NF2 gene cluster. Cluster pBECN1 was metabolic active and closely correlated with less DGF while pNF2 was regarded as the metabolic exhausted subtype with higher incidence of DGF. Additionally, a six-gene (ATF3, SLC2A3, CXCL2, DDIT3, ZFP36) ferroptosis-associated signature was constructed to predict occurrence of DGF in renal IRI patients and exhibited robust efficacy in both the training and validation set. High-risk patients tended to have more infiltration of dendritic cells, macrophages, and T cells, and significantly enriched in chemokine-related pathway, WNT/β-catenin signaling pathway, and allograft rejection. Patients with low risks of DGF were associated with ferroptosis-related pathways such as glutathione and fatty acid metabolism pathways. In conclusion, the patient stratification with distinct metabolic activities based on ferroptosis may help distinguish patients who may response to metabolic therapeutics. Moreover, the DGF predictive signature based on FRGs may guide advanced strategies toward prevention of DGF in early stage.