[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125396-en":3,"doc-seo-125396-105":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":20,"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},125396,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Development and validation of a comprehensive machine learning framework for a diagnostic model of uremia based on genes involved in major depressive disorder","Major depressive disorder (MDD) and uremia are chronic, debilitating conditions with bidirectional clinical impact, yet the molecular links between them remain insufficiently clarified. This study used transcriptome data from GEO to identify shared differentially expressed genes after batch-effect removal, then applied functional enrichment and ssGSEA to characterize immune infiltration patterns. A diagnostic model for uremia was trained and validated with multiple machine-learning algorithm combinations, and potential uremia drugs were explored using Enrichr.","TYPE Original Research PUBLISHED 02 October 2025 DOI 10.3389/fneph.2025.1576349  \nOPEN ACCESS  \nEDITED BY  \nWenlin Yang,  \nUniversity of Florida, United States  \nREVIEWED BY  \nAlessandro Domenico Quercia, Nephrology and Dialysis ASLCN1, Italy Shinsuke Hidese,  \nTeikyo University, Japan  \n*CORRESPONDENCE  \nBing Zheng  \n [ntzb2008@163.com](ntzb2008@163.com)  \nRECEIVED 13 February 2025  \nACCEPTED 18 September 2025  \nPUBLISHED 02 October 2025  \nCITATION  \nJiang K, Zhang C, Shen C, Fang X, Huang Hand Zheng B (2025) Development and validation of a comprehensive machine learning framework for a diagnostic model of uremia based on genes involved in major depressive disorder.  \nFront. Nephrol. 5:1576349 .  \ndoi: 10.3389/fneph.2025.1576349  \nCOPYRIGHT  \n© 2025 Jiang, Zhang, Shen, Fang, Huang and Zheng. This is an open-access article distributed under the terms of the Creative 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.  \nDevelopment and validation of a comprehensive machine learning framework for a diagnostic model of uremia based on genes involved in major depressive disorder  \nKaiyao Jiang1,2,3, Chi Zhang 4, Cheng Shen 1, Xingxing Fang 5, Huaxing Huang 5 and Bing Zheng 1*  \n1 Department of Urology, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China,  \n2Jiangsu Nantong Urological Clinical Medical Center, Nantong, Jiangsu, China, 3 Department of Emergency Medicine, the Afffliated Suqian Hospital of Xuzhou Medical University, Suqian, China, 4 Department of Nephrology, Sir Run Run Hospital, Nanjing Medical University, Nanjing, China,  \n5 Department of Nephrology, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China  \nBackground: Major depressive disorder (MDD) and uremia are two chronic wasting diseases that have interactive effects and significantly aggravate patients ’ distress. However, the molecular basis linking these diseases remains poorly investigated.  \nMethods: Various machine learning algorithms were used to analyze transcriptome data from the Gene Expression Omnibus (GEO) datasets, including those from MDD and uremia patients, to develop and validate our model. After removing batch effects, differentially expressed genes (DEGs) were identified between each disease group and the control group. Functional enrichment analysis was then performed at the intersection of DEGs from the two diseases. In addition, single-sample gene set enrichment analysis (ssGSEA) quantitative immune infiltration analysis was conducted. The optimal diagnostic model of uremia was constructed by analyzing and verifying the training set with multiple combinations of 12 machine learning algorithms. Finally, potential drugs for uremia were identified using the “Enrichr” platform.  \nResults: According to enrichment analysis, a total of seven key genes closely related to MDD and uremia, mainly involved in the immune process, were identified. Immune infiltration analysis showed that MDD and uremia had different profiles of immune cell infiltration compared to healthy controls. Powerful diagnostic markers of seven genes (IL7R, CD3D, RETN, RAB13, TNNT1, HP, and S100A12) were constructed from these genes, and all showed better performance than published uremia diagnostic models. In addition,  \nFrontiers in Nephrology 01 [frontiersin.org](frontiersin.org)  \ndecitabine and nine other agents were found to be potential agents for the treatment of uremia.  \nConclusion: Our study combined bioinformatics techniques and machine learning methods to develop a diagnostic model for uremia, focusing on common genes between MDD and uremia.  \nKEYWORDS  \nuremia, major depressive disorder, machine learning, diagnostic models, bioinf","cbCaiaJUD4XtLK7x","https://ap.wps.com/l/cbCaiaJUD4XtLK7x","pdf",10100715,1,15,"English","en",105,"# Introduction\n# Methods\n## Data processing and gene identification\n## Functional enrichment and immune infiltration\n## Model construction and validation\n## Drug prediction\n# Results\n## Key shared genes\n## Immune infiltration differences\n## Diagnostic markers performance\n## Potential therapeutic agents\n# Conclusion","[{\"question\":\"What connection between MDD and uremia does the study investigate?\",\"answer\":\"It investigates the molecular basis linking major depressive disorder and uremia by focusing on genes common to both conditions and their related biological processes.\"},{\"question\":\"How was the uremia diagnostic model developed and validated?\",\"answer\":\"The study processed GEO transcriptome data, removed batch effects, identified differentially expressed genes, intersected DEG sets across diseases, and used multiple combinations of 12 machine-learning algorithms on a training set and verification steps.\"},{\"question\":\"Which diagnostic markers were identified for uremia and how did they perform?\",\"answer\":\"Seven gene markers (IL7R, CD3D, RETN, RAB13, TNNT1, HP, and S100A12) were constructed and showed better diagnostic performance than previously published uremia diagnostic models.\"}]","Development and validation of a comprehensive machine learning framework for a diagnostic model of uremia based on genes involved in major depressive disorder | 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