Abstract

Chronic kidney disease (CKD) inevitably progresses to end-stage renal disease if intervention does not occur timely. However, there are limitations in predicting the progression of CKD by solely relying on changes in renal function. A biomarker with high sensitivity and specificity that can predict CKD progression early is required. We used the online Gene Expression Omnibus microarray dataset GSE45980 to identify differentially expressed genes (DEGs) in patients with progressive and stable CKD. We then performed functional enrichment and protein-protein interaction network analysis on DEGs and identified key genes. Finally, the expression patterns of key genes were verified using the GSE60860 dataset, and the receiver operating characteristic curve analysis was performed to clarify their predictive ability of progressive CKD. Ultimately, we verified the expression profiles of these hub genes in an in vitro renal interstitial fibrosis model by real-time PCR and western blot analysis. Differential expression analysis identified 50 upregulated genes and 47 downregulated genes. The results of the functional enrichment analysis revealed that upregulated DEGs were mainly enriched in immune response, inflammatory response, and NF-κB signaling pathways, whereas downregulated DEGs were mainly related to angiogenesis and the extracellular environment. Protein-protein interaction network and key gene analysis identified CCR7 as the most important gene. CCR7 mainly plays a role in immune response, and its only receptors, CCL19 and CCL21, have also been identified as DEGs. The receiver operating characteristic curve analysis of CCR7, CCL19, and CCL21 found that CCR7 and CCL19 present good disease prediction ability. CCR7 may be a stable biomarker for predicting CKD progression, and the CCR7-CCL19/CCL21 axis may be a therapeutic target for end-stage renal disease. However, further experiments are needed to explore the relationship between these genes and CKD.