Identification of Renal Ischemia-Reperfusion Injury Subtypes and Predictive Model for Graft Loss after Kidney Transplantation Based on Programmed Cell Death-Related Genes - Jing Ji; Yuan Ma; Xintong Liu; Qingqing Zhou; Xizi Zheng; Ying Chen; Zehua Li; Li Yang (Karger Publishers, 2024)
Introduction: Ischemia-reperfusion injury (IRI) is detrimental to kidney transplants and may contribute to poor long-term outcomes of transplantation. Programmed cell death (PCD), a regulated cell death form triggered by IRI, is often indicative of an unfavorable prognosis following transplantation. However, given the intricate pathophysiology of IRI and the considerable variability in clinical conditions during kidney transplantation, the specific patterns of cell death within renal tissues remain ambiguous. Consequently, accurately predicting the outcomes for transplanted kidneys continues to be a formidable challenge. Methods: Eight Gene Expression Omnibus datasets of biopsied transplanted kidney samples post-IRI and 1,548 PCD-related genes derived from 18 PCD patterns were collected in our study. Consensus clustering was performed to identify distinct IRI subtypes based on PCD features (IRI PCD subtypes). Differential enrichment analysis of cell death, metabolic signatures, and immune infiltration across these subtypes was evaluated. Three machine learning algorithms were used to identify PCD patterns related to prognosis. Genes associated with graft loss were screened for each PCD type. A predictive model for graft loss was constructed using 101 combinations of 10 machine learning algorithms. Results: Four IRI subtypes were identified: PCD-A, PCD-B, PCD-C, and PCD-D. PCD-A, characterized by high enrichment of multiple cell death patterns, significant metabolic paralysis, and immune infiltration, showed the poorest prognosis among the four subtypes. While PCD-D involved the least kind of cell death patterns with the features of extensive activation of metabolic pathways and the lowest immune infiltration, correlating with the best prognosis in the four subtypes. Using various machine learning algorithms, 10 cell death patterns and 42 PCD-related genes were identified as positively correlated with graft loss. The predictive model demonstrated high sensitivity and specificity, with area under the curve values for 0.5-, 1-, 2-, 3-, and 4-year graft survival at 0.888, 0.91, 0.926, 0.923, and 0.923, respectively. Conclusion: Our study explored the comprehensive features of PCD patterns in transplanted kidney samples post-IRI. The prediction model shows great promise in forecasting graft loss and could aid in risk stratification in patients following kidney transplantation.