{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T01:41:26Z","timestamp":1791942086530,"version":"4.3.4"},"reference-count":53,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T00:00:00Z","timestamp":1737590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Diabetic nephropathy (DN) is a complication of systemic microvascular disease in diabetes mellitus. Abnormal glycolysis has emerged as a potential factor for chronic renal dysfunction in DN. The current lack of reliable predictive biomarkers hinders early diagnosis and personalized therapy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      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\n                      <jats:italic>in vitro<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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.\n                      <jats:italic>In vitro<\/jats:italic>\n                      , ELF3, LCN2 and CD163 were induced in high glucose-induced HK-2 cell lines.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>An integrative machine learning frame established a novel diagnostic signature using glycolysis-related genes. This study provides a new direction for the early diagnosis and treatment of DN.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2024.1427626","type":"journal-article","created":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T01:56:50Z","timestamp":1737597410000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence"],"prefix":"10.3389","volume":"15","author":[{"given":"Xiaoyin","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Buyu","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingyu","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxuan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianqian","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahui","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yichen","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yumei","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Songbo","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,1,23]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phrs.2019.01.043","article-title":"Diabetic nephropathy: the regulatory interplay between epigenetics and micrornas","volume":"141","author":"Sankrityayan","year":"2019","journal-title":"Pharmacol Res"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdiacomp.2024.108740","article-title":"Risk factors for chronic kidney disease in middle eastern patients with type 2 diabetes mellitus: A cross-sectional study using the kdigo classification","volume":"38","author":"Farah","year":"2024","journal-title":"J Diabetes Complications"},{"key":"B3","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.semcancer.2024.03.001","article-title":"Glut and hk: two primary and essential key players in tumor glycolysis","volume":"100","author":"Yadav","year":"2024","journal-title":"Semin Cancer Biol"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2022.970601","article-title":"Metabolic reprogramming: A novel therapeutic target in diabetic kidney disease","volume":"13","author":"Wang","year":"2022","journal-title":"Front Pharmacol"},{"key":"B5","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1038\/s41419-022-04803-y","article-title":"Igfbp5 promotes diabetic kidney disease progression by enhancing pfkfb3-mediated endothelial glycolysis","volume":"13","author":"Song","year":"2022","journal-title":"Cell Death Dis"},{"key":"B6","doi-asserted-by":"publisher","first-page":"455","DOI":"10.3390\/antiox13040455","article-title":"Oxidative stress: A culprit in the progression of diabetic kidney disease","volume":"13","author":"Wang","year":"2024","journal-title":"Antioxidants (Basel)"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbrc.2016.12.060","article-title":"Ship, a novel factor to ameliorate extracellular matrix accumulation via suppressing pi3k\/akt\/ctgf signaling in diabetic kidney disease","volume":"482","author":"Li","year":"2017","journal-title":"Biochem Biophys Res Commun"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1101\/gr.155697.113","article-title":"Defining cell-type specificity at the transcriptional level in human disease","volume":"23","author":"Ju","year":"2013","journal-title":"Genome Res"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1136\/annrheumdis-2017-212935","article-title":"Metabolic pathways and immunometabolism in rare kidney diseases","volume":"77","author":"Grayson","year":"2018","journal-title":"Ann Rheum Dis"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.2337\/db17-0755","article-title":"Dissection of glomerular transcriptional profile in patients with diabetic nephropathy: srgap2a protects podocyte structure and function","volume":"67","author":"Pan","year":"2018","journal-title":"Diabetes"},{"key":"B11","doi-asserted-by":"publisher","first-page":"S15","DOI":"10.2337\/dc21-S002","article-title":"2. 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