Identification and validation of energy metabolism-related genes in diabetic kidney disease through integrated bioinformatics and in vivo analysis
Hui Jiang, Ming-Hui Geng, Yue-Mei Zhan, Jin-Feng Shen, Fu-Zhen Wang, Sen-Qing Lin, Zhe Hong, Chun-Hua Guo, Jin-Xiu Deng, Sen-Chao Wu
TL;DR
This study identifies energy metabolism-related genes linked to diabetic kidney disease and validates their potential as diagnostic biomarkers.
Contribution
The study integrates bioinformatics and in vivo analysis to discover novel energy metabolism-related genes in diabetic kidney disease.
Findings
17 energy metabolism-related differentially expressed genes were identified in diabetic kidney disease.
CD36 and LPL showed high diagnostic accuracy for DKD.
CD36, IGF1, LPL, and UCP2 are potential biomarkers for DKD diagnosis and treatment.
Abstract
The primary renal complication of diabetes mellitus is diabetic kidney disease (DKD). The precise pathogenic mechanisms of DKD remain poorly elucidated. The aim of this study was to identify potential energy metabolism-related genes associated with DKD. The GSE30529 and GSE30528 datasets were retrieved from the Gene Expression Omnibus, and energy metabolism-related genes were obtained from the GeneCards database. Differentially expressed genes (DEGs) between DKD and control groups were analyzed. The biological functions and signaling pathways of these DEGs were evaluated using Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA). The diagnostic performance of hub genes for DKD was assessed using receiver operating characteristic (ROC) curve analysis. Expression levels of five significant energy metabolism-related genes were…
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Taxonomy
TopicsChronic Kidney Disease and Diabetes · Parathyroid Disorders and Treatments · GDF15 and Related Biomarkers