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A computational pipeline distinguishes kidney-originating urinary sediment cells, identifies injury-associated gene signatures, and refines them into a validated diagnostic model. 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Zheng Y, Jiang X, Yang Z, Liu X (2026) A non-invasive urinary diagnostic signature for diabetic kidney disease revealed by machine learning and single-cell analysis. PLoS One 21(1): e0340096. [https://](https://)[ ](https://)[doi.org/10.1371/journal.pone.0340096](doi.org/10.1371/journal.pone.0340096)  \nEditor: Yusuf Oloruntoyin Ayipo, Kwara State University, NIGERIA  \nReceived: October 20, 2025  \nAccepted: December 15, 2025  \nPublished: January 2, 2026  \nCopyright: © 2026 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData availability statement: All relevant data are within the manuscript and its Supporting  Information files.  \nFunding: The author(s) received no specific funding for this work.  \nRESEARCH ARTICLE  \nA non-invasive urinary diagnostic signature for diabetic kidney disease revealed by machine learning and single-cell analysis  \nYonggang Chen1☯, Jintai Luo2,3☯, Yingying Zheng4☯, Xiaomei Jiang5, Zixiang Yang1, Xiaobing Liu1*  \n1 Department of Urology, Loudi Central Hospital, Loudi, Hunan Province, China, 2 Department of Urology and Andrology, Minimally Invasive Surgery Center, Guangdong Provincial Key Laboratory of Urology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China,  \n3 Loudi Hospital, First Affiliated Hospital of Guangzhou Medical University, Loudi, Hunan Province, China, 4 Fangchenggang Hospital of Traditional Chinese Medicine, Guangxi University of Chinese Medicine, Fangchenggang, Guangxi, China, 5 Department of Cardiovascular Medicine, Loudi Central Hospital, Loudi, Hunan Province, China  \n☯ These authors contributed equally to the work.  \n* [ldlxb216399@163.com](ldlxb216399@163.com)  \nAbstract  \nBackground  \nDiabetic kidney disease (DKD) poses a significant health burden with inadequate diagnostic sensitivity. This study develops non-invasive biomarkers by integrating urinary and renal single-cell sequencing with machine learning.  \nMethods  \nThis study analyzed DKD single-cell and bulk transcriptomic data from public repositories. We established a computational pipeline to distinguish kidney-originating cells in urinary sediments, enabling the identification of injury-associated gene signatures. These signatures were refined using machine learning to develop a diagnostic model, which was validated in independent cohorts. The biomarkers were further verified in DKD renal tissues at single-cell resolution and across multiple nephropathies. Functional and spatial analyses confirmed biological relevance using transcriptomic and histological validation.  \nResults  \nSingle-cell analysis of 2,089 urine-derived cells identified eight renal cell types, including injured proximal tubule cells (Inj-PTC) showing upregulated injury markers (HAVCR1, VCAM1) and enriched apoptotic/TGF-β pathways. A machine learningselected biomarker panel (PDK4, RHCG, FBP1) demonstrated strong diagnostic value (area under the curve, AUC >0.9), with consistent downregulation across  \nPLOS One | [https://doi.org/10.1371/journal.pone.0340096](https://doi.org/10.1371/journal.pone.0340096) January 2, 2026 1 / 19  \nCompeting interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  \nAbbreviations: DKD: diabetic kidney disease; CKD: chronic kidney disease; ESRD: end-stage renal disease; eGFR: estimated glomerular filtration rate; GSVA: gene set variation analysis; GEO: Gene Expression Omnibus; KEGG: Kyoto  \nEncyclopedia of Genes and Genomes; LASSO: least absolute shrinkage and selection operator; DEGs: Differentially expressed genes; FDR:  \nfalse discovery rate; ROC: receiver operating characteristic; LR: logistic regression; XGBoost:  \neXtre","cbCaijkoVwXhkpA0","https://ap.wps.com/l/cbCaijkoVwXhkpA0","pdf",2260240,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusions\n# Introduction","[{\"question\":\"What diagnostic challenge in diabetic kidney disease does this study address?\",\"answer\":\"It addresses inadequate diagnostic sensitivity of current clinical markers for DKD by developing non-invasive biomarker signatures with machine learning.\"},{\"question\":\"How were urinary-derived cells and injury gene signatures identified?\",\"answer\":\"A computational pipeline distinguished kidney-originating cells in urinary sediments, enabling identification of injury-associated gene signatures from single-cell and bulk transcriptomic data.\"},{\"question\":\"Which biomarker panel showed diagnostic value in the results?\",\"answer\":\"A three-gene panel (PDK4, RHCG, FBP1) showed strong diagnostic performance with AUC values above 0.9, alongside injury-related pathway enrichment.\"}]","A non-invasive urinary diagnostic signature for diabetic kidney disease revealed by machine learning and single-cell analysis | PDF",1790617111,48]