Identification of biomarkers and potential therapeutic targets of kidney stone disease using bioinformatics.

Abstract

Kidney stone disease (KSD) is a common urological disease, but its pathogenesis remains unclear. In this study, we screened KSD-related hub genes using bioinformatic methods and predicted the related pathways and potential drug targets. The GSE75542 and GSE18160 datasets in the Gene Expression Omnibus (GEO) were selected to identify common differentially expressed genes (DEGs). We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to identify enriched pathways. Finally, we constructed a hub gene-miRNA network and drug-DEG interaction network. In total, 44 upregulated DEGs and 1 downregulated DEG were selected from the GEO datasets. Signaling pathways, such as leukocyte migration, chemokine activity, NF-κB, TNF, and IL-17, were identified in GO and KEGG. We identified 10 hub genes using Cytohubba. In addition, 21 miRNAs were predicted to regulate 4 or more hub genes, and 10 drugs targeted 2 or more DEGs. LCN2 expression was significantly different between the GEO datasets. Quantitative real-time polymerase chain reaction (qRT-PCR) analyses showed that seven hub gene expressions in HK-2 cells with CaOx treatment were significantly higher than those in the control group. The 10 hub genes identified, especially LCN2, may be involved in kidney stone occurrence and development, and may provide new research targets for KSD diagnosis. Furthermore, KSD-related miRNAs may be targeted for the development of novel drugs for KSD treatment.