Decoding the Mechanism behind the Pathogenesis of the Focal Segmental Glomerulosclerosis

Focal segmental glomerulosclerosis (FSGS) is a chronic glomerular disease associated with podocyte injury which is named after the pathologic features of the kidney. The aim of this study is to decode the key changes in gene expression and regulatory network involved in the formation of FSGS. Integrated network analysis included Gene Expression Omnibus (GEO) datasets to identify differentially expressed genes (DEGs) between FSGS patients and healthy donors. Bioinformatics analysis was used to identify the roles of the DEGs and included the development of protein-protein interaction (PPI) networks, Gene Ontology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and the key modules were assured. The expression levels of DEGs were validated using the additional dataset. Eventually, transcription factors and ceRNA networks were established to illuminate the regulatory relationships in the formation of FSGS. 1130 DEGs including 475 upregulated genes and 655 downregulated genes with functional enrichment analysis were determined. Further analysis uncovered that the validated hub genes were defined as candidate genes, including Complement C3a Receptor 1 (C3AR1), C-C Motif Chemokine Receptor 1(CCR1), C-X3-C Motif Chemokine Ligand 1 (CX3CL1), Melatonin Receptor 1A (MTNR1A), and Purinergic Receptor P2Y13 (P2RY13). More importantly, we identified transcription factors and mRNA-miRNA-lncRNA regulatory networks associated with the candidate genes. The candidate genes and regulatory networks discovered in this study can help to comprehend the molecular mechanism of FSGS and supply potential targets for the diagnosis and therapy of FSGS.

Automated summary

This study analyzed gene expression data and conducted bioinformatics analysis to identify differentially expressed genes (DEGs) associated with the formation of Focal segmental glomerulosclerosis (FSGS). The functional enrichment, protein-protein interaction networks, and regulatory networks of the DEGs were investigated. The study identified candidate genes and their associated transcription factors and regulatory networks.