PANoptosis-Based Diagnostic Model Using Single-Cell and Transcriptome RNA Sequencing to Predict Rejection in Kidney Transplant Patients.

OBJECTIVES: Kidney transplantation is the most effective treatment for end-stage renal failure, but transplant rejection remains a major challenge. The role of PANoptosis in rejection is not fully understood. MATERIALS AND METHODS: We performed single-cell analysis of PANoptosis-related differentially expressed genes in kidney transplant rejection using data from the GEO database. We identified 7 core PANoptosis genes associated with rejection from 2 machine learning algorithms. We constructed a clinical predictive model, which we evaluated for efficacy and calibration. We predicted potential therapeutic drugs by using the DSigDB database. RESULTS: Compared with nonrejection samples, rejection samples showed increased proportions of endothelial cells and macrophages and decreased proximal tubular cells and fibroblasts. Among 134 PANoptosis-related differentially expressed genes, 7 core genes were significantly positively correlated. The predictive model based on these genes demonstrated good accuracy and calibration. Drug prediction identified tosyllysyl chloromethane targeting NFKBIA as a promising candidate for treatment of rejection. CONCLUSIONS: Our findings provide a proof-of-concept diagnostic model that required clinical validation of 7 core PANoptosis-related genes in kidney transplant rejection through single-cell and machine learning analyses. Tosyllysyl chloromethane targeting NFKBIA emerged as a potential therapeutic agent, offering new insights into personalized diagnosis and treatment strategies for renal transplant rejection.