Screening of the Key Genes and Signalling Pathways for Diabetic Nephropathy Using Bioinformatics Analysis

Abstract

Background: This study aimed to identify biological markers for diabetic nephropathy (DN) and explore their underlying mechanisms.

Methods: Four datasets, GSE30528, GSE47183, GSE104948, and GSE96804, were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using the "limma" package, and the "RobustRankAggreg" package was used to screen the overlapping DEGs. The hub genes were identified using cytoHubba of Cytoscape. Logistic regression analysis was used to further analyse the hub genes, followed by receiver operating characteristic (ROC) curve analysis to predict the diagnostic effectiveness of the hub genes. Correlation analysis and enrichment analysis of the hub genes were performed to identify the potential functions of the hub genes involved in DN.

Results: In total, 55 DEGs, including 38 upregulated and 17 downregulated genes, were identified from the three datasets. Four hub genes (FN1, CD44, C1QB, and C1QA) were screened out by the "UpSetR" package, and FN1 was identified as a key gene for DN by logistic regression analysis. Correlation analysis and enrichment analysis showed that FN1 was positively correlated with four genes (COL6A3, COL1A2, THBS2, and CD44) and with the development of DN through the extracellular matrix (ECM)-receptor interaction pathway.

Conclusions: We identified four candidate genes: FN1, C1QA, C1QB, and CD44. On further investigating the biological functions of FN1, we showed that FN1 was positively correlated with THBS2, COL1A2, COL6A3, and CD44 and involved in the development of DN through the ECM-receptor interaction pathway. THBS2, COL1A2, COL6A3, and CD44 may be novel biomarkers and target therapeutic candidates for DN.

Keywords: FN1; bioinformatic analysis; biomarkers; diabetic nephropathy; differentially expressed genes.