Exploration of the shared genetic biomarkers in Alzheimer’s disease and chronic kidney disease using integrated bioinformatics analysis

Abstract

In order to investigate the potential link between Alzheimer’s disease (AD) and chronic kidney disease (CKD), we conducted a comprehensive analysis using a bioinformatics approach. We downloaded AD and CKD datasets from the Gene Expression Omnibus database and analyzed differentially expressed genes and weighted gene co-expression networks to identify candidate genes for AD and CKD. We used a combination of the least absolute shrinkage and selection operator and random forest algorithms to select the shared genes. Subsequently, we shared genes and performed an immune infiltration analysis to investigate the association between different immune cell types and shared genes. Finally, we elucidated the relationship between the expression levels of the shared genes in disease samples and cells using single-cell analysis. Our analysis identified 150 candidate genes that may be primarily involved in immune inflammatory responses and energy metabolism pathways. We found that JunD Proto-Oncogene, ALF transcription elongation factor 1, and ZFP36 Ring Finger Protein Like 1 were the best co-diagnostic markers for AD and CKD based on the results of Least Absolute Shrinkage Selection Operator analysis and the random forest algorithm. Based on the results of immune infiltration analysis, macrophages and T-cells play a significant role in the progression of AD and CKD. Our scRNA-sequencing data showed that the 3 shared genes in AD were significantly expressed in astrocytes, excitatory neurons, oligodendrocytes, and MAIT cells. The 3 shared genes in CKD were significantly expressed in oligodendrocytes, neutrophils, fibroblasts, astrocytes, and T-cells. JunD Proto-Oncogene, ALF transcription elongation factor 1, and ZFP36 Ring Finger Protein Like 1 genes are the best diagnostic markers for AD and CKD.