{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T16:35:50Z","timestamp":1792427750502,"version":"4.5.0"},"reference-count":24,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T00:00:00Z","timestamp":1755216000000},"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. Cell Dev. Biol."],"abstract":"\n Background<\/jats:title>\n Early diagnosis and intervention for chronic kidney disease (CKD) can significantly improve patient\u2019s quality of life and prognosis. Besides routine laboratory indicators and medical history, risk prediction models can predict CKD outcome. However, there is currently a lack of CKD prognostic prediction models based on transcriptomics and machine learning.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Utilizing weighted correlation network analysis (WGCNA) and random forest algorithms in GSE137570, three core gene sets of different sizes were constructed, which were externally validated in GSE66494 and GSE180394, and evaluated for their predictive performance in GSE45980 by receiver operating characteristic (ROC) curves. Predictive models were built using Cox regression, LASSO regression, and logistic regression in GSE60861. And the reliability of human CKD transcriptomic analysis and the feasibility of functional studies were validated in a mouse UUO model.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Combining WGCNA and differential gene analysis, 9 genes positively associated with CKD occurrence and development and 20 genes negatively associated with that were identified. By random forest algorithm, three gene sets were constructed: minimal gene set (\n CCL2<\/jats:italic>\n ,\n SUCLG1<\/jats:italic>\n ,\n ACADM<\/jats:italic>\n ), medium gene set (\n CCL2<\/jats:italic>\n ,\n GGT6<\/jats:italic>\n ,\n PCK2<\/jats:italic>\n ,\n SFXN2<\/jats:italic>\n ,\n SLC34A3<\/jats:italic>\n ,\n ALPL<\/jats:italic>\n ,\n GLTPD2<\/jats:italic>\n ,\n ACADM<\/jats:italic>\n ,\n SUCLG1<\/jats:italic>\n ), and maximal gene set (\n CCL2<\/jats:italic>\n ,\n MMP7<\/jats:italic>\n ,\n GGT6<\/jats:italic>\n ,\n PCK2<\/jats:italic>\n ,\n SFXN2<\/jats:italic>\n ,\n SLC34A3<\/jats:italic>\n ,\n ALPL<\/jats:italic>\n ,\n GLTPD2<\/jats:italic>\n ,\n ACADM<\/jats:italic>\n ,\n SUCLG1<\/jats:italic>\n ). In external validation, the maximal plage score had best classification performance for CKD (AUC:0.767) in GSE66494 and in GSE180394 (AUC:0.760), the medium plage score achieved a predictive performance for CKD progression (AUC = 0.758) in GSE45980. In the multivariate model, Cox regression analysis constructed a risk model with only minimal z-score, further LASSO regression analysis included gender and minimal z-score, but logistic regression multivariate analysis failed to be constructed with any score. A high degree of similarity between mouse CKD and human CKD in KEGG enrichment was observed in the mouse unilateral ureteral obstruction model, and the core genes related to the occurrence and progression of human CKD remained diagnostically valuable in mice.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n This study provides a transcriptomics-based risk prediction model for the occurrence and development of CKD based on machine learning, offering potential target genes for the further experimental research of CKD.<\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fcell.2025.1627355","type":"journal-article","created":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T13:01:51Z","timestamp":1755262911000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Identification of progression-related genes and construction of prognostic model for chronic kidney disease by machine learning"],"prefix":"10.3389","volume":"13","author":[{"given":"Bingkun","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hu","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shijie","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,8,15]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"12649","DOI":"10.3390\/ijerph182312649","article-title":"Validation of a novel predictive algorithm for kidney failure in patients suffering from chronic kidney disease: the prognostic reasoning system for chronic kidney disease (PROGRES-CKD)","volume":"18","author":"Bellocchio","year":"2021","journal-title":"International J. 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