Bioinformatics analysis reveals CCR7 as a potential biomarker for predicting CKD progression

Area of Science:

• Genomics and Bioinformatics
• Renal Medicine
• Immunology

Background:

• Chronic kidney disease (CKD) often progresses to end-stage renal disease without timely intervention.
• Current methods for predicting CKD progression based solely on renal function changes have limitations.
• There is a need for sensitive and specific biomarkers for early prediction of CKD progression.

Purpose of the Study:

• To identify differentially expressed genes (DEGs) associated with progressive versus stable CKD.
• To identify key genes and pathways involved in CKD progression using bioinformatics analysis.
• To validate the predictive ability of identified key genes for CKD progression.

Main Methods:

• Utilized Gene Expression Omnibus (GEO) microarray datasets (GSE45980, GSE60860) for differential gene expression analysis.
• Performed functional enrichment and protein-protein interaction network analysis to identify hub genes.
• Validated gene expression using real-time PCR and Western blot in an in vitro renal interstitial fibrosis model.
• Assessed predictive ability using receiver operating characteristic (ROC) curve analysis.

Main Results:

• Identified 50 upregulated and 47 downregulated DEGs between progressive and stable CKD.
• Upregulated DEGs were enriched in immune and inflammatory responses (e.g., NF-κB pathway).
• Downregulated DEGs were associated with angiogenesis and the extracellular environment.
• CCR7 was identified as a key gene, with its receptors CCL19 and CCL21 also being DEGs.
• ROC analysis indicated that CCR7 and CCL19 have good predictive ability for progressive CKD.

Conclusions:

• CCR7 shows potential as a stable biomarker for predicting CKD progression.
• The CCR7-CCL19/CCL21 axis may represent a therapeutic target for end-stage renal disease.
• Further research is warranted to elucidate the precise role of these genes in CKD pathogenesis.