ORIGINAL RESEARCH article
Front. Nephrol.
Sec. Critical Care Nephrology
Volume 5 - 2025 | doi: 10.3389/fneph.2025.1576349
This article is part of the Research TopicRising Stars in Nephrology 2024: Illuminating the Future of Kidney HealthView all 4 articles
Development and validation of a comprehensive machine learning framework for a diagnostic model of Uremia based on genes involved in major depressive disorder
Provisionally accepted
- 1Department of Urology, Second Affiliated Hospital of Nantong University, Nantong, China
- 2Sir Run Run Hospital, Nanjing Medical University, Nanjing, Liaoning Province, China
- 3Department of Nephrology, Second Affiliated Hospital of Nantong University, Jiangsu, China
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Background：Major depressive disorder (MDD) and Uremia are two chronic wasting diseases which have interactive effects and significantly aggravate patients' distress. But the molecular basis linking these diseases remains poorly investigated. Methods: Various machine learning algorithms analyze transcriptome data from GEO datasets, including those from MDD and Uremic patients, to develop and validate our model. After removing batch effects, differentially expressed genes (DEGs) were identified between each disease group and the control group. Functional enrichment analysis was then performed on the intersection of DEGs from the two diseases. In addition, ssGSEA quantitative immune infiltration analysis. The optimal diagnostic model of Uremia was constructed by analyzing and verifying the training set with multiple combinations of 12 machine learning algorithms. Finally, potential drugs for Uremia were identified using the "Enrichr" platform. Results: According to enrichment analysis, a total of 7 key genes closely related to MDD and Uremia, mainly involved in the immune process, were identified. Immuno-infiltration analysis showed that MDD and Uremia had different profiles of immune cell infiltration compared to healthy controls. A powerful diagnostic marker of seven genes (IL7R, CD3D, RETN, RAB13, TNNT1, HP, S100A12) was constructed from these genes, and all showed better performance to published Uremia diagnostic models. In addition, Decitabine and nine other agents were found to be potential agents for the treatment of Uremia. Conclusion: Our study combined bioinformatics techniques and machine learning methods to develop a diagnostic model for Uremia, focusing on common genes between MDD and Uremia.
Keywords: Uremia, Major Depressive Disorder, machine learning, Diagnostic models, bioinformatics
Received: 13 Feb 2025; Accepted: 18 Sep 2025.
Copyright: © 2025 Jiang, Zhang, Cheng, Fang, Huang and Zheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor 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.
* Correspondence: Bing Zheng, ntzb2008@163.com
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