[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128051-en":3,"doc-seo-128051-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128051,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy - a study based on integrative machine learning and single-cell sequence","Diabetic nephropathy (DN) is a diabetes complication linked to chronic renal dysfunction, and abnormal glycolysis is emerging as a potential contributor. Reliable predictive biomarkers remain limited, restricting early diagnosis and personalized treatment. Using transcriptomic data from GEO, the study identifies glycolysis-related genes, builds an integrative machine-learning diagnostic signature (GScore), and assesses performance with decision and calibration curves. Single-cell RNA sequencing explores cellular subtypes and microenvironment signals, while cMAP and in vitro experiments support target agent discovery and signature validation.","TYPE Original Research PUBLISHED 23 January 2025  \nDOI 10.3389/fimmu.2024.1427626  \nOPEN ACCESS  \nEDITED BY  \nChaofeng Han,  \nSecond Military Medical University, China  \nREVIEWED BY  \nBjörn Koos,  \nUniversity Hospital Bochum GmbH, Germany Ruifeng Ding,  \nChangzheng Hospital, China  \n*CORRESPONDENCE  \nSongbo Fu  \n [fusb@lzu.edu.cn](fusb@lzu.edu.cn)  \nRECEIVED 04 May 2024  \nACCEPTED 12 November 2024  \nPUBLISHED 23 January 2025  \nCITATION  \nWu X, Guo B, Chang X, Yang Y, Liu Q, Liu J, Yang Y, Zhang K, Ma Y and Fu S (2025) Identification and validation of glycolysisrelated diagnostic signatures  \nin diabetic nephropathy: a study based on integrative machine learning and single-cell sequence.  \nFront. Immunol. 15:1427626 .  \ndoi: 10.3389/fimmu.2024.1427626  \nCOPYRIGHT  \n© 2025 Wu, Guo, Chang, Yang, Liu, Liu, Yang, Zhang, Ma and Fu. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) .  \nThe 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.  \nIdentification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence  \nXiaoyin Wu 1,2, Buyu Guo 2,3, Xingyu Chang 4,5, Yuxuan Yang 2,3, Qianqian Liu 2,3, Jiahui Liu 2,3, Yichen Yang 2,3, Kang Zhang 6, Yumei Ma 7 and Songbo Fu 1,3,8*  \n1School of Basic Medical Sciences, Lanzhou University, Lanzhou, China, 2The First Clinical Medical College, Lanzhou University, Lanzhou, China, 3 Department of Endocrinology, First Hospital of Lanzhou University, Lanzhou, China, 4Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China, 5Shanghai Key Laboratory Female Reproductive Endocrine-Related Diseases, Shanghai, China, 6Xifeng District People ’s Hospital, Qingyang, China, 7Qilihe District People ’s Hospital, Lanzhou, China, 8Gansu Provincial Endocrine Disease Clinical Medicine Research Center,  \nLanzhou, China  \nBackground: Diabetic nephropathy (DN) is a complication of systemic microvascular disease in diabetes mellitus. Abnormal glycolysis has emerged asa potential factor for chronic renal dysfunction in DN. The current lack of reliable predictive biomarkers hinders early diagnosis and personalized therapy.  \nMethods: Transcriptomic profiles of DN samples and controls were extracted from GEO databases. Differentially expressed genes (DEGs) and their functional enrichments were identified. Glycolysis-related genes (GRGs) were selected by combining DEGs, weighted gene co-expression network, and glycolysis candidate genes. We established a diagnostic signature termed GScore via integrative machine learning framework. The diagnostic efficacy was evaluated by decision curve and calibration curve. Single-cell RNA sequence data was used to identify cell subtypes and interactive signals. The cMAP database was used to find potential therapeutic agents targeting GScore for DN. The expression levels of diagnostic signatures were verified in vitro.  \nResults: Through the 108 combinations of machine learning algorithms, we selected 12 diagnostic signatures, including CD163, CYBB, ELF3, FCN1, PROM1, GPR65, LCN2, LTF, S100A4, SOX4, TGFB1 and TNFAIP8 . Based on them, an integrative model named GScore was established for predicting DN onset and stratifying clinical risk. We observed distinct biological characteristics and immunological microenvironment states between the high-risk and low-risk groups. GScore was significantly associated with neutrophils and non-classical monocytes. Potential agents including esmolol, estradiol, ganciclovir, and felbamate, targeting the 12 diagnostic signatures were identified. In vitro, ELF3, LCN2 and CD163 were induced in high glucose-induced HK-2 c","cbCaiiCfqm0amLws","https://ap.wps.com/l/cbCaiiCfqm0amLws","pdf",26262332,2,1,18,"English","en",105,"# Background\n## Diabetic nephropathy and glycolysis\n# Methods\n## Data sources and differential expression\n## Feature selection and model building (GScore)\n## Diagnostic evaluation\n## Single-cell analysis and therapeutic targeting\n## In vitro validation\n# Results\n## Selected diagnostic signatures and risk stratification\n## Immune microenvironment associations\n## Candidate therapeutic agents and cell validation\n# Conclusion","[{\"question\":\"What clinical problem does the study address in diabetic nephropathy?\",\"answer\":\"The study targets the lack of reliable predictive biomarkers that limits early diagnosis and personalized therapy for diabetic nephropathy progression.\"},{\"question\":\"How is the diagnostic signature GScore constructed?\",\"answer\":\"GScore is built by selecting glycolysis-related genes using differential expression analysis, weighted gene co-expression network methods, and glycolysis candidate genes, then training an integrative machine-learning model.\"},{\"question\":\"What evidence supports the biological relevance of the signatures?\",\"answer\":\"Single-cell RNA sequencing distinguishes cell subtypes and microenvironment states between high- and low-risk groups, and associations are reported with neutrophils and non-classical monocytes. In vitro validation further shows induction of selected genes under high-glucose conditions.\"}]","Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy - a study based on integrative machine learning and single-cell sequence | PDF",1785944481,45,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"identification-and-validation-of-glycolysis-related-diagnostic-signatures-in-diabetic-nephropathy-a-study-based-on-integrative-machine-learning-and-single-cell-sequence","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/identification-and-validation-of-glycolysis-related-diagnostic-signatures-in-diabetic-nephropathy-a-study-based-on-integrative-machine-learning-and-single-cell-sequence/128051/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What clinical problem does the study address in diabetic nephropathy?","Question",{"text":76,"@type":77},"The study targets the lack of reliable predictive biomarkers that limits early diagnosis and personalized therapy for diabetic nephropathy progression.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the diagnostic signature GScore constructed?",{"text":81,"@type":77},"GScore is built by selecting glycolysis-related genes using differential expression analysis, weighted gene co-expression network methods, and glycolysis candidate genes, then training an integrative machine-learning model.",{"name":83,"@type":74,"acceptedAnswer":84},"What evidence supports the biological relevance of the signatures?",{"text":85,"@type":77},"Single-cell RNA sequencing distinguishes cell subtypes and microenvironment states between high- and low-risk groups, and associations are reported with neutrophils and non-classical monocytes. 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