Identification of key genes in DN based on lipid metabolism
Authors: Yang Meng , Wang Jian , Meng Hu , Xu Jian , Xie Yu , Kong Weiying 
Published: March 2023

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