Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms

作者：Yu Yin, Congcong Chen, Dong Zhang, Qianguang Han, Zijie Wang, Zhengkai Huang, Hao Chen, Li Sun, Fei Shuang, Jun Tao, Zhijian Han, Ruoyun Tan, Min Gu, Xiaobing Ju · 发表于：Frontiers in Genetics · 年份：2023 · DOI：10.3389/fgene.2023.1276963 · 被引用次数：11 · 研究领域：Trace Elements in Health、MicroRNA in disease regulation、Biomarkers in Disease Mechanisms

Background: Interstitial fibrosis and tubular atrophy (IFTA) are the histopathological manifestations of chronic kidney disease (CKD) and one of the causes of long-term renal loss in transplanted kidneys. Necroptosis as a type of programmed death plays an important role in the development of IFTA, and in the late functional decline and even loss of grafts. In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes. Methods: We screened all 162 “kidney transplant”–related cohorts in the GEO database and obtained five data sets (training sets: GSE98320 and GSE76882, validation sets: GSE22459 and GSE53605, and survival set: GSE21374). The training set was constructed after removing batch effects of GSE98320 and GSE76882 by using the SVA package. The differentially expressed gene (DEG) analysis was used to identify necroptosis-related DEGs. A total of 13 machine learning algorithms—LASSO, Ridge, Enet, Stepglm, SVM, glmboost, LDA, plsRglm, random forest, GBM, XGBoost, Naive Bayes, and ANNs—were used to construct 114 IFTA diagnostic models, and the optimal models were screened by the AUC values. Post-transplantation patients were then grouped using consensus clustering, and the different subgroups were further explored using PCA, Kaplan–Meier (KM) survival analysis, functional enrichment analysis, CIBERSOFT, and single-sample Gene Set Enrichment Analysis. Results: A total of 55 necroptosis-related DEGs were identif...