{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T01:41:24Z","timestamp":1791942084667,"version":"4.3.4"},"reference-count":54,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T00:00:00Z","timestamp":1776902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Molecules"],"abstract":"<jats:p>Diabetic kidney disease (DKD) affects approximately 40% of patients with diabetes mellitus and remains a leading cause of end-stage renal disease worldwide. Early diagnosis and identification of therapeutic targets are critical for improving patient outcomes, yet reliable biomarkers are lacking. This study integrated transcriptomic data from the Gene Expression Omnibus (GEO) database (GSE96804, GSE30528, and GSE142025) with machine learning algorithms and Mendelian randomization (MR) to identify diagnostic biomarkers for DKD. Differentially expressed genes (DEGs) were identified and intersected with key modules from weighted gene co-expression network analysis (WGCNA). Four machine learning methods\u2014least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and extreme gradient boosting (XGBoost)\u2014were applied for feature selection. Five hub genes (SPP1, CD44, VCAM1, C3, and TIMP1) were identified at the intersection of these approaches. Two-sample MR analysis using eQTL data from the eQTLGen Consortium and kidney function GWAS from the CKDGen Consortium provided evidence supporting potential causal associations between SPP1, C3, and TIMP1 expression and estimated glomerular filtration rate decline. Immune infiltration analysis via CIBERSORT estimated elevated proportions of M1 macrophages and activated CD4+ memory T cells in DKD samples, with all five hub genes showing correlations with macrophage infiltration. A diagnostic model based on these five genes achieved a cross-validated area under the receiver operating characteristic curve (CV-AUC) of 0.938 in the discovery dataset and AUC values of 0.917 and 0.889 in two independent external validation cohorts. Drug\u2013gene interaction analysis identified 10 candidate compounds targeting the hub genes. These findings provide a computational framework for identifying candidate diagnostic biomarkers and generating hypotheses regarding potential therapeutic targets for DKD; however, all results are derived from in silico analyses and require experimental validation\u2014including qPCR, immunohistochemistry, and prospective clinical cohort studies\u2014before clinical applicability can be established.<\/jats:p>","DOI":"10.3390\/molecules31091390","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T10:10:17Z","timestamp":1776939017000},"page":"1390","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Transcriptomic Profiling Combined with Machine Learning and Mendelian Randomization Identifies Diagnostic Biomarkers and Immune Infiltration Patterns in Diabetic Kidney Disease"],"prefix":"10.3390","volume":"31","author":[{"given":"Haiwen","family":"Liu","sequence":"first","affiliation":[{"name":"School of Basic Medical Sciences, Heilongjiang University of Chinese Medicine, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Basic Medical Sciences, Heilongjiang University of Chinese Medicine, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Basic Medical Sciences, Heilongjiang University of Chinese Medicine, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2032","DOI":"10.2215\/CJN.11491116","article-title":"Diabetic kidney disease: Challenges, progress, and possibilities","volume":"12","author":"Alicic","year":"2017","journal-title":"Clin. J. Am. Soc. Nephrol."},{"key":"ref_2","unstructured":"International Diabetes Federation (2021). IDF Diabetes Atlas, International Diabetes Federation. [10th ed.]."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2864","DOI":"10.2337\/dc14-1296","article-title":"Diabetic kidney disease: A report from an ADA consensus conference","volume":"37","author":"Tuttle","year":"2014","journal-title":"Diabetes Care"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"15018","DOI":"10.1038\/nrdp.2015.18","article-title":"Diabetic kidney disease","volume":"1","author":"Thomas","year":"2015","journal-title":"Nat. Rev. Dis. Prim."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1146\/annurev.pathol.4.110807.092150","article-title":"A glimpse of various pathogenetic mechanisms of diabetic nephropathy","volume":"6","author":"Kanwar","year":"2011","journal-title":"Annu. Rev. Pathol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2333","DOI":"10.1172\/JCI72271","article-title":"Molecular mechanisms of diabetic kidney disease","volume":"124","author":"Reidy","year":"2014","journal-title":"J. Clin. Investig."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1038\/s41581-021-00393-8","article-title":"Pathophysiology of diabetic kidney disease: Impact of SGLT2 inhibitors","volume":"17","author":"DeFronzo","year":"2021","journal-title":"Nat. Rev. Nephrol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1038\/nrneph.2011.51","article-title":"Inflammatory molecules and pathways in the pathogenesis of diabetic nephropathy","volume":"7","year":"2011","journal-title":"Nat. Rev. Nephrol."},{"key":"ref_9","first-page":"987","article-title":"What is the mechanism of microalbuminuria in diabetes: A role for the glomerular endothelium?","volume":"47","author":"Satchell","year":"2004","journal-title":"Diabetologia"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.2337\/diabetes.51.4.1157","article-title":"Increased urinary albumin excretion, endothelial dysfunction, and chronic low-grade inflammation in type 2 diabetes","volume":"51","author":"Stehouwer","year":"2002","journal-title":"Diabetes"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"(2022). Kidney Disease: Improving Global Outcomes (KDIGO) Diabetes Work Group. KDIGO 2022 clinical practice guideline for diabetes management in chronic kidney disease. Kidney Int., 102, S1\u2013S127.","DOI":"10.1016\/j.kint.2022.06.008"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3075","DOI":"10.2337\/dci22-0027","article-title":"Diabetes management in chronic kidney disease: A consensus report by the American Diabetes Association (ADA) and Kidney Disease: Improving Global Outcomes (KDIGO)","volume":"45","author":"Khunti","year":"2022","journal-title":"Diabetes Care"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1038\/ki.2009.399","article-title":"In patients with type 1 diabetes and new-onset microalbuminuria the development of advanced chronic kidney disease may not require progression to proteinuria","volume":"77","author":"Perkins","year":"2010","journal-title":"Kidney Int."},{"key":"ref_14","first-page":"1041","article-title":"Diabetic kidney disease: New clinical and therapeutic issues","volume":"30","author":"Pugliese","year":"2020","journal-title":"Nutr. Metab. Cardiovasc. Dis."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1681\/ASN.2010010010","article-title":"Pathologic classification of diabetic nephropathy","volume":"21","author":"Tervaert","year":"2010","journal-title":"J. Am. Soc. Nephrol."},{"key":"ref_16","first-page":"941","article-title":"Proteomic biomarkers in kidney disease: Issues in development and implementation","volume":"6","author":"Mischak","year":"2010","journal-title":"Nat. Rev. Nephrol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"The EMPA-KIDNEY Collaborative Group (2023). Empagliflozin in patients with chronic kidney disease. N. Engl. J. Med., 388, 117\u2013127.","DOI":"10.1056\/NEJMoa2204233"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1056\/NEJMoa2403347","article-title":"Effects of semaglutide on chronic kidney disease in patients with type 2 diabetes","volume":"391","author":"Perkovic","year":"2024","journal-title":"N. Engl. J. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2354","DOI":"10.2337\/db10-1181","article-title":"Transcriptome analysis of human diabetic kidney disease","volume":"60","author":"Woroniecka","year":"2011","journal-title":"Diabetes"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"717","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":"ref_21","doi-asserted-by":"crossref","unstructured":"Rajula, H.S.R., Verlato, G., Manchia, M., Antonucci, N., and Fanos, V. (2020). Comparison of conventional statistical methods with machine learning in medicine: Diagnosis, drug development, and treatment. Medicina, 56.","DOI":"10.3390\/medicina56090455"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1038\/s41586-023-05769-3","article-title":"An atlas of healthy and injured cell states and niches in the human kidney","volume":"619","author":"Lake","year":"2023","journal-title":"Nature"},{"key":"ref_23","first-page":"1","article-title":"\u2018Mendelian randomization\u2019: Can genetic epidemiology contribute to understanding environmental determinants of disease?","volume":"32","author":"Ebrahim","year":"2003","journal-title":"Int. J. Epidemiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3253","DOI":"10.1681\/ASN.2016010098","article-title":"Mendelian randomization as an approach to assess causality using observational data","volume":"27","author":"Sekula","year":"2016","journal-title":"J. Am. Soc. Nephrol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1093\/ije\/dyv080","article-title":"Mendelian randomization with invalid instruments: Effect estimation and bias detection through Egger regression","volume":"44","author":"Bowden","year":"2015","journal-title":"Int. J. Epidemiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1038\/s41588-019-0407-x","article-title":"A catalog of genetic loci associated with kidney function from analyses of a million individuals","volume":"51","author":"Wuttke","year":"2019","journal-title":"Nat. Genet."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2183","DOI":"10.1042\/CS20160636","article-title":"Diabetic nephropathy\u2014Is this an immune disorder?","volume":"131","author":"Tesch","year":"2017","journal-title":"Clin. Sci."},{"key":"ref_28","first-page":"206","article-title":"Innate immunity in diabetic kidney disease","volume":"17","author":"Tang","year":"2021","journal-title":"Nat. Rev. Nephrol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1038\/ni.1923","article-title":"Complement: A key system for immune surveillance and homeostasis","volume":"11","author":"Ricklin","year":"2010","journal-title":"Nat. Immunol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1038\/nmeth.3337","article-title":"Robust enumeration of cell subsets from tissue expression profiles","volume":"12","author":"Newman","year":"2015","journal-title":"Nat. Methods"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xu, M., Zhou, H., Liu, L., Liu, X., Wang, S., Pan, Y., and Hu, P. (2023). Identification and validation of immune and oxidative stress-related diagnostic markers for diabetic nephropathy by WGCNA and machine learning. Front. Immunol., 14.","DOI":"10.3389\/fimmu.2023.1084531"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Guo, C., Wang, W., Dong, Y., and Han, Y. (2024). Identification of key immune-related genes and potential therapeutic drugs in diabetic nephropathy based on machine learning algorithms. BMC Med. Genom., 17.","DOI":"10.1186\/s12920-024-01995-4"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1046\/j.1523-1755.2001.00032.x","article-title":"Expression, roles, receptors, and regulation of osteopontin in the kidney","volume":"60","author":"Xie","year":"2001","journal-title":"Kidney Int."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s00011-018-1200-5","article-title":"The role of osteopontin in kidney diseases","volume":"68","author":"Kaleta","year":"2019","journal-title":"Inflamm. Res."},{"key":"ref_35","first-page":"3382","article-title":"CD44 deficiency increases tubular damage but reduces renal fibrosis in obstructive nephropathy","volume":"17","author":"Rouschop","year":"2006","journal-title":"J. Am. Soc. Nephrol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"F905","DOI":"10.1152\/ajprenal.00421.2006","article-title":"Role of matrix metalloproteinases in renal pathophysiologies","volume":"292","author":"Catania","year":"2007","journal-title":"Am. J. Physiol. Ren. Physiol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"GTEx Consortium (2020). The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science, 369, 1318\u20131330.","DOI":"10.1126\/science.aaz1776"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1093\/bioinformatics\/bts034","article-title":"The sva package for removing batch effects and other unwanted variation in high-throughput experiments","volume":"28","author":"Leek","year":"2012","journal-title":"Bioinformatics"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"e47","DOI":"10.1093\/nar\/gkv007","article-title":"limma powers differential expression analyses for RNA-sequencing and microarray studies","volume":"43","author":"Ritchie","year":"2015","journal-title":"Nucleic Acids Res."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Langfelder, P., and Horvath, S. (2008). WGCNA: An R package for weighted correlation network analysis. BMC Bioinform., 9.","DOI":"10.1186\/1471-2105-9-559"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1093\/nar\/28.1.27","article-title":"KEGG: Kyoto Encyclopedia of Genes and Genomes","volume":"28","author":"Kanehisa","year":"2000","journal-title":"Nucleic Acids Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1089\/omi.2011.0118","article-title":"clusterProfiler: An R package for comparing biological themes among gene clusters","volume":"16","author":"Yu","year":"2012","journal-title":"OMICS"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v033.i01","article-title":"Regularization paths for generalized linear models via coordinate descent","volume":"33","author":"Friedman","year":"2010","journal-title":"J. Stat. Softw."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene selection for cancer classification using support vector machines","volume":"46","author":"Guyon","year":"2002","journal-title":"Mach. Learn."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"D483","DOI":"10.1093\/nar\/gkab835","article-title":"The STRING database in 2021: Customizable protein\u2013protein association networks made functional","volume":"49","author":"Szklarczyk","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2498","DOI":"10.1101\/gr.1239303","article-title":"Cytoscape: A software environment for integrated models of biomolecular interaction networks","volume":"13","author":"Shannon","year":"2003","journal-title":"Genome Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1038\/s41588-021-00913-z","article-title":"Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression","volume":"53","author":"Claringbould","year":"2021","journal-title":"Nat. Genet."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1038\/s41588-018-0099-7","article-title":"Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases","volume":"50","author":"Verbanck","year":"2018","journal-title":"Nat. Genet."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., and M\u00fcller, M. (2011). pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform., 12.","DOI":"10.1186\/1471-2105-12-77"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"D1144","DOI":"10.1093\/nar\/gkaa1084","article-title":"Integration of the Drug\u2013Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts","volume":"49","author":"Freshour","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1002\/jcc.21334","article-title":"AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading","volume":"31","author":"Trott","year":"2010","journal-title":"J. Comput. Chem."}],"container-title":["Molecules"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1420-3049\/31\/9\/1390\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T08:02:03Z","timestamp":1778140923000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1420-3049\/31\/9\/1390"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,23]]},"references-count":54,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["molecules31091390"],"URL":"https:\/\/doi.org\/10.3390\/molecules31091390","relation":{},"ISSN":["1420-3049"],"issn-type":[{"value":"1420-3049","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,23]]}}}