Identification of potential necroinflammation-associated necroptosis-related biomarkers for delayed graft function and renal allograft failure: a machine learning-based exploration in the framework of predictive, preventive, and personalized medicine

Abstract

Delayed graft function (DGF) is one of the key post-operative challenges for a subset of kidney transplantation (KTx) patients. Graft survival is significantly lower in recipients who have experienced DGF than in those who have not. Assessing the risk of chronic graft injury, predicting graft rejection, providing personalized treatment, and improving graft survival are major strategies for predictive, preventive, and personalized medicine (PPPM/3PM) to promote the development of transplant medicine. However, since PPPM aims to accurately identify disease by integrating multiple omics, current methods to predict DGF and graft survival can still be improved. Renal ischemia/reperfusion injury (IRI) is a pathological process experienced by all KTx recipients that can result in varying occurrences of DGF, chronic rejection, and allograft failure depending on its severity. During this process, a necroinflammation-mediated necroptosis-dependent secondary wave of cell death significantly contributes to post-IRI tubular cell loss. In this article, we obtained the expression matrices and corresponding clinical data from the GEO database. Subsequently, nine differentially expressed necroinflammation-associated necroptosis-related genes (NiNRGs) were identified by correlation and differential expression analysis. The subtyping of post-KTx IRI samples relied on consensus clustering; the grouping of prognostic risks and the construction of predictive models for DGF (the area under the receiver operating characteristic curve (AUC) of the internal validation set and the external validation set were 0.730 and 0.773, respectively) and expected graft survival after a biopsy (the internal validation set’s 1-year AUC: 0.770; 2-year AUC: 0.702; and 3-year AUC: 0.735) were based on the least absolute shrinkage and selection operator regression algorithms. The results of the immune infiltration analysis showed a higher infiltration abundance of myeloid immune cells, especially neutrophils, macrophages, and dendritic cells, in the cluster A subtype and prognostic high-risk groups. Therefore, in the framework of PPPM, this work provides a comprehensive exploration of the early expression landscape, related pathways, immune features, and prognostic impact of NiNRGs in post-KTx patients and assesses their capabilities as. predictors of post-KTx DGF and graft loss, targets of the vicious loop between regulated tubular cell necrosis and necroinflammation for targeted secondary and tertiary prevention, and references for personalized immunotherapy. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-023-00320-w.

Identification of RNA-binding protein genes associated with renal rejection and graft survival

“…ROC curve analysis showed that our model was significant and sensitive, and was valuable in guiding the treatment and prognosis of renal transplant recipients. Among them, the AUC of risk score for 3 years of survival in the validation set is 0.852, which is better than NiNRG predictive model (AUC 0.735), which is a long-term graft survival prediction model constructed on five necroinflammation-associated necroptosis-related genes (NiNRG) [ 38 ]. In addition, our model also had better indications in long-term survival prediction at 10 years and 20 years.…”

Comprehensive analysis of necroptosis-related genes in renal ischemia-reperfusion injury

“…Given the development of massive sequencing and genetic diagnosis techniques, it is essential to investigate the role of necroptosis in gene-based diagnostic and therapeutic strategies for DGF. It has been reported that necroptosis-related genes (NRGs) are upregulated early in the renal allograft and serve as risk factors for subsequent DGF ( 14 ). However, the association between NRGs and specific cell types or their role in the induction of specific immune responses during the process of renal IRI remains ambiguous.…”