#6771 IDENTIFICATION OF KEY GENES IN DIABETIC NEPHROPATHY BASED ON LIPID METABOLISM

Abstract Background and Aims Diabetic nephropathy (DN), which is one of the most common systemic microvascular complications diabetes mellitus, extremely harmful to patients’ health. There were some studies had shown that disturbance lipid metabolism was connected with progression DN. Therefore, purpose our study find metabolism-related hub genes in DN provide a better reference for diagnosis Method The Gene Expression Omnibus (GEO) database used download gene expression profile data healthy samples (GSE142153), we obtained from Molecular Signatures Database (MSigDB). Differentially expressed (DEGs) between analyzed weighted co-expression network analysis (WGCNA) performed examine connection clinical traits screen key module Next, utilized Venn Diagram R package identify DN, Protein-Protein Interaction (PPI) these constructed. Then carried out Ontology (GO) Kyoto Encyclopedia Genes Genomes (KEGG) enrichment analyses. Moreover, identified using two machine learning algorithms, Set Enrichment Analysis (GSEA) analyze functions genes. Furthermore, immune infiltration discrepancies correlation cells estimated. Finally, quantitative reverse transcription-PCR (qRT-PCR) experiment verified Results A total 1445 DEGs found compared samples, 694 DN-related yellow turquoise modules by WGCNA. further 17 related constructed PPI network. GO revealed significantly correlated ‘phospholipid biosynthetic process’ ‘cholesterol process’, while KEGG showed enriched ‘glycerophospholipid metabolism’ ‘fatty acid degradation’. SAMD8 CYP51A1 through intersections algorithms. results GSEA ‘mitochondrial matrix’ ‘GTPase activity’ terms CYP51A1, pathways them mainly concentrated ‘pathways neurodegeneration - multiple diseases’. Immune suggested there 9 differently both activated B cell effector memory CD8 T cell. qRT-PCR confirmed high. Conclusion In summary, may play roles