Identification of progression-related genes and construction of prognostic model for chronic kidney disease by machine learning.

Background: Early diagnosis and intervention for chronic kidney disease (CKD) can significantly improve patient's 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. Methods: 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. Results: 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 ( Conclusion: 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.