Identification of potential biomarkers and therapeutic targets for antineutrophil cytoplasmic antibody-associated glomerulonephritis

Abstract

Summary: Exploring key genes for antineutrophil cytoplasmic antibody (ANCA)-associated glomerulonephritis (ANCA-GN) is of great significance. Through bioinformatics analysis, 79 immune protein-differentially expressed genes (IP-DEGs) were obtained. Six hub genes (PTPRC, CD86, TLR2, IL1B, CSF-1R, and CCL2) were identified and verified to be increased in ANCA-GN patients. Random forest algorithm and ROC analysis showed that CSF-1R was a potential biomarker. Plasma CSF-1R levels increased significantly in ANCA-GN-active patients compared with remission stage and control. Correlation analysis revealed that CSF-1R levels had positive relationship with serum creatinine and Birmingham scoring, while inversely correlated with eGFR. Multivariate analysis revealed that plasma CSF-1R were an independent poor prognostic variable for end-stage renal disease or death, after adjusting for age and gender (HR = 3.05, 95% CI = 1.45–6.43, p = 0.003). Overall, we revealed that the CSF-1R is related to disease activity and might be a vital gene associated with the pathogenesis of ANCA-GN.

Keywords

• Bioinformatics
• Expression study
• Machine learning
• Nephrology