Bioinformatics analysis of the molecular network of diabetic nephropathy

Abstract

Objective To investigate the potential pathogenesis and therapeutic targets for diabetic kidney disease (DKD) using renal genomic expression profile and the bioinformatics methods. Methods Genomic data of kidney from diabetic kidney disease patients and diabetic kidney disease mice model were selected from NCBI-GEO database. The data was analyzed by bioinformatics software to find differentially expressed genes (DEG). Gene ontology and ingenuity pathway analysis (IPA) were performed to find potential targets for DKD for elucidating the possible molecular mechanisms in DKD. Results Eighty-nine common DEG were identified from the human and mouse diabetic kidney disease database. Most of the DEG were mainly enriched in metabolism-related enzymes, transporters, nucleic acid binding proteins, and cell proliferation and extracellular matrix. The bioinformatics analysis of IPA showed that the common differentially expressed genes were mainly concentrated in the complement system, the primary immunodeficiency signaling and the B cell development, among them, the complement system is one of important regulatory pathways in diabetic kidney disease. Disease and biological function enrichment analysis showed that DKD was closely related to diabetes mellitu, glucose metabolism disorder, cell movement of blood cells, activation of blood cells and leukocyte migration. IPA regulatory network analysis revealed that proto-oncogene ETS1 and integrin β2 (ITGB) were two crucial sites identified by IPA bioinformatics analysis. Conclusion The potential site of diabetic kidney disease can be screened by using different species genomic data of diabetic kidney disease. Bioinformatics analysis shows that transcription factor ETS1 and ITGB might play a role in the pathogenesis of diabetic kidney disease. Key words: Diabetic kidney disease; Genome expression profiling; ETS Proto-Oncogene 1; Integrin β2