IDENTIFICATION OF DIFFERENTIALLY EXPRESSED GENES AND SIGNALING PATHWAYS IN DIABETIC NEPHROPATHY BY BIOINFORMATICS ANALYSIS

Abstract Background and Aims Diabetes has considerable negative impact on morbidity and mortality and causes huge social and economic burden. As one of the most serious microvascular complication of diabetes, diabetic nephropathy (DN) leads to a large population of end-stage renal disease in many countries. The pathogenesis of DN is always a hot topic and the underlying molecular events are not completely clear. Tubular injury plays an important role and may be the initial event. Although few therapeutic treatments could postpone the onset and development, the morbidity of DN remains high. More available therapeutic treatments are urgently needed as well as early stage diagnostic markers and more credible prognostic molecular markers. As a wide range application of high-throughput omics technology, various public network database platforms have included extensive transcriptomics data for deeper bioinformatics analysis. Integrating these data provides better understandings of molecular functions and biological processes. We performed integrated bioinformatics to recognize differentially expressed genes and discussed potential molecular mechanisms in DN. Method The expression profiles of GSE30529, GSE47184, GSE99325 and GSE104954 were downloaded from the Gene Expression Omnibus database. The four microarray datasets were centralized, integrated and performed a difference analysis. Next, differentially expressed genes (DEGs) were deeply analyzed by gene ontology annotation and enrichment analysis. STRING database was used to conducted a PPI network and Molecular Complex Detection (MCODE) software was used to identify central genes. Results The four files contain 63 tubular biopsy samples from patients with DN and 41 control tubule samples. We identified 18 target DEGs, C3, PROM1, LUM, CPA3, SERPINA3, ANXA1, CX3CR1, AGR2, CD48, REG1A, RARRES1, CYP24A1, C1R, CFB, CDH6, PVALB, GADD45B and KLK1. GO analysis indicated that biological processes of DEGs concentrate on proteolysis, inflammatory response, complement activation and regulation of complement activation. Main cellular components include extracellular exosome, extracellular region, extracellular space, blood microparticle, protein complex and plasma membrane. Molecular functions include calcium ion binding and serine-type endopeptidase activity. DEGs were found that maybe mainly involved in staphylococcus aureus infection, renin-angiotensin system, and complement and coagulation cascades by KEGG pathway analysis. The PPI network of DEGs were established by STRING database and one significant modules were identified by MCODE software. In addition, 3 hub genes, C3, CX3CR1 and ANXA1, were discerned from the PPI network. Conclusion To better clarify the underlying molecular mechanisms and provide more effective targets, this study screened DEGs and pathways in DN using bioinformatics analyses.