Identification of common biomarkers in diabetic kidney disease and cognitive dysfunction using machine learning algorithms

Abstract

Abstract Cognitive dysfunction caused by diabetes has become a serious global medical issue. Diabetic kidney disease (DKD) exacerbates cognitive dysfunction in patients, although the precise mechanism behind this remains unclear. Here, we conducted an investigation using RNA sequencing data from the Gene Expression Omnibus (GEO) database. We analyzed the differentially expressed genes in DKD and three types of neurons in the temporal cortex (TC) of diabetic patients with cognitive dysfunction. Through our analysis, we identified a total of 133 differentially expressed genes (DEGs) shared between DKD and TC neurons (62 up-regulated and 71 down-regulated). To identify potential common biomarkers, we employed machine learning algorithms (LASSO and SVM-RFE) and Venn diagram analysis. Ultimately, we identified 8 overlapping marker genes (ZNF564, VPS11, YPEL4, VWA5B1, A2ML1, KRT6A, SEC14L1P1, SH3RF1) as potential biomarkers, which exhibited high sensitivity and specificity in ROC curve analysis. Functional analysis using Gene Ontology (GO) revealed that these genes were primarily enriched in autophagy, ubiquitin/ubiquitin-like protein ligase activity, MAP-kinase scaffold activity, and syntaxin binding. Further enrichment analysis using Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) indicates that these biomarkers may play a crucial role in the development of cognitive dysfunction and diabetic nephropathy. Building upon these biomarkers, we developed a diagnostic model with a reliable predictive ability for DKD complicated by cognitive dysfunction. To validate the 8 biomarkers, we conducted RT-PCR analysis in the cortex, hippocampus and kidney of animal models. The results demonstrated the up-regulation of SH3RF1 in the cortex, hippocampus and kidney of mice, which was further confirmed by immunofluorescence and Western blot validation. Notably, SH3RF1 is a scaffold protein involved in cell survival in the JNK signaling pathway. Based on these findings, we support that SH3RF1 may be a common gene expression feature that influences DKD and cognitive dysfunction through the apoptotic pathway.

Keywords

• Diabetic kidney disease
• Cognitive dysfunction
• Machine learning algorithms
• RNA sequencing
• SH3RF1