in Macrophages: A Potential Biomarker for Predicting the Progression of Diabetic Nephropathy based on Bioinformatics Analysis.

OBJECTIVE

This study aimed to identify the potential biomarkers in DN.

METHODS

DN datasets GSE30528 and GSE47183 were downloaded from the Gene Expression Omnibus database. Immune cell infiltration was analyzed using CIBERSORT. Weighted gene co-expression network analysis (WGCNA) was performed to obtain the module genes specific to DN. The relevant genes were identified intersecting the module genes and differentially expressed genes (DEGs). The core genes were identified using the MCC algorithm in Cytoscape software. ROC and Pearson analyses alongside gene set enrichment analysis (GSEA) were performed to identify the key gene for the core genes. Finally, we performed the Spearman to analyze the correlation between key gene and glomerular filtration rate (GFR), serum creatinine (Scr), age and sex in DN.

RESULTS

CIBERSORT analysis revealed the immune cell infiltration in the DN renal tissue and Venn identified 12 relevant genes. Among these, 5 core genes, namely TYROBP, C1QA, C1QB, CD163 and MS4A6A, were identified. Pearson analyses revealed that immune cell infiltration and expression of core genes are related. The key genes with high diagnostic values for DN were identified to be CD163 via ROC analyses. After Spearman correlation analysis, the expression level of CD163 was correlated with GFR (r =0.27), a difference that nearly reached statistical significance (P =0.058). However, there was no correlation between the level of CD163 and age (r =-0.24, P =0.09), sex (r =-0.11, P=0.32) and Scr (r=0.15, P=0.4).

CONCLUSION

We found that CD163 in macrophages may be a potential biomarker in predicting and treating DN.