Ji Jing, Ma Yuan, Liu Xintong, Zhou Qingqing, Zheng Xizi, Chen Ying, Li Zehua, Yang Li
Renal Division, Peking University Institute of Nephrology, Key Laboratory of Renal Disease-Ministry of Health of China, Key Laboratory of CKD Prevention and Treatment (Peking University)-Ministry of Education of China, Peking University First Hospital, Beijing, China.
Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, China.
Kidney Dis (Basel). 2024 Sep 5;10(6):450-467. doi: 10.1159/000540158. eCollection 2024 Dec.
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.
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.
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.
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.
缺血再灌注损伤（IRI）对肾移植有害，可能导致移植的长期预后不良。程序性细胞死亡（PCD）是由IRI触发的一种受调控的细胞死亡形式，通常预示着移植后预后不佳。然而，鉴于IRI复杂的病理生理学以及肾移植期间临床情况的显著变异性，肾组织内细胞死亡的具体模式仍不明确。因此，准确预测移植肾的预后仍然是一项艰巨的挑战。
我们的研究收集了8个IRI后活检的移植肾样本的基因表达综合数据集以及源自18种PCD模式的1548个PCD相关基因。基于PCD特征（IRI PCD亚型）进行一致性聚类以识别不同的IRI亚型。评估了这些亚型之间细胞死亡、代谢特征和免疫浸润的差异富集分析。使用三种机器学习算法来识别与预后相关的PCD模式。针对每种PCD类型筛选与移植物丢失相关的基因。使用10种机器学习算法的101种组合构建移植物丢失的预测模型。
确定了四种IRI亚型：PCD-A、PCD-B、PCD-C和PCD-D。PCD-A的特征是多种细胞死亡模式高度富集、显著的代谢麻痹和免疫浸润，在四种亚型中预后最差。而PCD-D涉及的细胞死亡模式最少，具有代谢途径广泛激活和最低免疫浸润的特征，与四种亚型中最佳预后相关。使用各种机器学习算法，确定了10种细胞死亡模式和42个PCD相关基因与移植物丢失呈正相关。预测模型显示出高敏感性和特异性，0.5年、1年、2年、3年和4年移植物存活的曲线下面积值分别为0.888、0.91、0.926、0.923和0.923。
我们的研究探索了IRI后移植肾样本中PCD模式的综合特征。该预测模型在预测移植物丢失方面显示出巨大潜力，并有助于肾移植患者的风险分层。
