Biomarkers and Potential Therapeutic Agents for Chronic Kidney Disease in the Context of Aging: An Integrated Bioinformatics and Machine Learning Study.

Aging is a key factor accelerating the progression of chronic kidney disease (CKD). This study aims to identify core therapeutic targets for aging-related CKD and predict potential therapeutic agents. CKD expression profile was constructed by combining the GSE37171 and GSE62792 datasets. Differentially expressed genes (DEGs) were identified through differential expression analysis, followed by the intersection of these DEGs with key module genes from weighted gene co-expression network analysis and aging-related genes to pinpoint aging-related DEGs (ARDEGs). Machine learning algorithms were employed to identify hub genes, while ROC curves and nomogram models were used to evaluate the diagnostic performance of these genes. Gene set enrichment analysis (GSEA) was conducted to explore the molecular mechanisms underlying the hub genes. Finally, the DGIdb database was utilized to predict potential agents. Three hub genes-AGER, PDGFRB, and SOD2-were selected through machine learning. GSEA revealed its involvement in the CKD process. ROC analysis demonstrated the potential diagnostic value, while the nomogram model exhibited strong predictive performance. Seven potential therapeutic agents were predicted through drug-target interaction analysis. AGER, PDGFRB, and SOD2 were identified as potential key targets linking aging to CKD, specifically through their roles in CKD-related fibrosis and oxidative stress. The predicted drugs targeting these mechanisms show promise for therapeutic intervention. However, experimental validation is required to confirm the therapeutic potential of these findings. This study identifies key targets for CKD in the context of aging and predicts agents with potential therapeutic value, providing insights for treatment development.