AUTHOR=Yu Wei , Wang Ting , Wu Feng , Zhang Yiding , Shang Jin , Zhao Zhanzheng 

TITLE=Identification and validation of key biomarkers for the early diagnosis of diabetic kidney disease

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2022.931282

DOI=10.3389/fphar.2022.931282

ISSN=1663-9812

ABSTRACT=Background: Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. This study aimed to explore the core genes and pathways associated with DKD to identify potential diagnostic and therapeutic targets.
Methods: We downloaded microarray datasets GSE96804 and GSE104948 from the Gene Expression Omnibus (GEO) database. The dataset includes a total of 53 DKD samples and 41 normal samples. Differentially expressed genes (DEGs) were identified using the R package "limma". The Metascape database was subjected to Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to identify pathway and functional annotations of DEGs. The WGCAN network was constructed, the hub genes in the turquoise module were screened, and the core genes were selected using LASSO regression to construct a diagnostic model and validated using an independent dataset. The core genes were verified by a several of experimental techniques in vitro and in vivo.
Results: A total of 430 DEGs were identified in the GSE96804 dataset, including 285 up-regulated and 145 down-regulated DEGs. WGCNA screened out 128 modeled candidate gene sets. A total of 8 characteristic genes of DKD were identified by LASSO regression to build a prediction model. The results showed that the accuracy of the training set (GSE96804) was 99.15%, while the accuracy of the test set (GSE104948-GPL22945 and GSE104948-GPL24120) was 94.44% and 100%, respectively. Three core genes (OAS1, SECTM1 and SNW1) with high connectivity were selected among the modeled genes. In vitro and in vivo experiments confirmed the up-regulation of them.
Conclusions: Through bioinformatics analysis combined with experimental validation, three novel DKD specific genes were identified, which may advance our understanding of the molecular basis of DKD and provide potential therapeutic targets for clinical management.