Abstract
Background: Randall's plaque is regarded as the precursor lesion of lithiasis. However, traditional bioinformatic analysis is limited and ignores the relationship with immune response. To investigate the underlying calculi formation mechanism, we introduced innovative algorithms to expand our understanding of kidney stone disease. Methods: We downloaded the GSE73680 series matrix from the Gene Expression Omnibus (GEO) related to CaOx formation and excluded one patient, GSE116860. In the RStudio (R version 4.1.1) platform, the differentially expressed genes (DEGs) were identified with the limma package for GO/KEGG/GSEA analysis in the clusterProfiler package. Furthermore, high-correlated gene co-expression modules were confirmed by the WGCNA package to establish a protein-protein interaction (PPI) network. Finally, the CaOx samples were processed by the CIBERSORT algorithm to anchor the key immune cells group and verified in the validation series matrix GSE117518. Results: The study identified 840 upregulated and 1065 downregulated genes. The GO/KEGG results revealed fiber-related or adhesion-related terms and several pathways in addition to various diseases identified from the DO analysis. Moreover, WGCNA selected highly correlated modules to construct a PPI network. Finally, 16 types of immune cells are thought to participate in urolithiasis pathology and are related to hub genes in the PPI network that are proven significant in the validation series matrix GSE117518. Conclusion: Randall's plaque may relate to genes DCN, LUM, and P4HA2 and M2 macrophages and resting mast immune cells. These findings could serve as potential biomarkers and provide new research directions.