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.