[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124710-en":3,"doc-seo-124710-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124710,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Construction of Predictive Model of Interstitial Fibrosis and Tubular Atrophy After Kidney Transplantation with Machine Learning Algorithms","Interstitial fibrosis and tubular atrophy (IFTA) is a key histopathological manifestation of chronic kidney disease and a major driver of long-term graft loss after kidney transplantation. This study leverages necroptosis-related genes and 13 machine learning algorithms to build diagnostic models for IFTA. GEO cohorts were processed to identify necroptosis-related differentially expressed genes, model performance was screened by AUC, and patient groups were characterized using clustering, survival analysis, enrichment, CIBERSOFT, and ssGSEA.","TYPE Original Research PUBLISHED 01 November 2023 DOI 10.3389/fgene.2023.1276963  \nOPEN ACCESS  \nEDITED BY  \nNan Jiang,  \nPeking University Hospital of Stomatology, China  \nREVIEWED BY  \nXingyi Shi,  \nNovartis Institutes for BioMedical Research, United States Tao Sun,  \nRenmin University of China, China Dulat Bekbolsynov,  \nUniversity of Toledo, United States  \n*CORRESPONDENCE  \nRuoyun Tan,  \n [tanruoyun112@vip.sina.com](tanruoyun112@vip.sina.com)[ ](tanruoyun112@vip.sina.com)Min Gu,  \n [njmuwzj1990@hotmail.com](njmuwzj1990@hotmail.com)[ ](njmuwzj1990@hotmail.com)Xiaobing Ju,  \n [doctorjxb73@njmu.edu.cn](doctorjxb73@njmu.edu.cn)  \n†These authors have contributed equally to this work  \nRECEIVED 13 August 2023  \nACCEPTED 11 October 2023  \nPUBLISHED 01 November 2023  \nCITATION  \nYin Y, Chen C, Zhang D, Han Q, Wang Z, Huang Z, Chen H, Sun L, Fei S, Tao J, Han Z, Tan R, Gu M and Ju X (2023), Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms.  \nFront. Genet. 14:1276963 .  \ndoi: 10.3389/fgene.2023.1276963  \nCOPYRIGHT  \n© 2023 Yin, Chen, Zhang, Han, Wang, Huang, Chen, Sun, Fei, Tao, Han, Tan, Gu and Ju. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nConstruction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms  \nYu Yin 1†, Congcong Chen 1†, Dong Zhang 1†, Qianguang Han 1, Zijie Wang 1, Zhengkai Huang 1, Hao Chen 1, Li Sun 1, Shuang Fei 1, Jun Tao 1, Zhijian Han 1, Ruoyun Tan 1*, Min Gu 1,2* and Xiaobing Ju 1*  \n1Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China, 2Department of Urology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China  \nBackground: 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.  \nMethods: 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 .  \nResults: A total of 55 necroptosis-related DEGs were identified by taking the intersection of the DEGs and necroptosis-related gene sets. Stepglm[both]+RF is the optimal model with an average AUC of 0 .822. A total of four molecular subgroups of renal transplantation patients were obtained by clustering, and significant upregulation of fibrosis-related","cbCaibg2rANzSiI9","https://ap.wps.com/l/cbCaibg2rANzSiI9","pdf",3733122,1,10,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusion","[{\"question\":\"What clinical problem does the study address?\",\"answer\":\"The study focuses on interstitial fibrosis and tubular atrophy (IFTA), which predicts chronic deterioration and long-term loss of function in transplanted kidneys.\"},{\"question\":\"How were the predictive models constructed?\",\"answer\":\"Necroptosis-related genes were used, and 13 machine learning algorithms were applied to build 114 IFTA diagnostic models, with optimal models selected by AUC values.\"},{\"question\":\"How were patient subgroups and prognosis evaluated?\",\"answer\":\"Post-transplantation patients were clustered into molecular subgroups, then analyzed with PCA, Kaplan–Meier survival analysis, functional enrichment, CIBERSOFT, and single-sample gene set enrichment analysis (ssGSEA).\"}]","Construction of Predictive Model of Interstitial Fibrosis and Tubular Atrophy After Kidney Transplantation with Machine Learning Algorithms | 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