Identification of the underlying mechanisms of pathogenesis in chronic kidney disease based on bioinformatics analysis

Abstract

Abstract

Background：Chronic Kidney Disease (CKD) presents with a poor prognosis and limited treatment options. This study aimed to explore the key genes expressed in CKD, to identify new pathways and drug targets and to provide insight for subsequent molecular studies in terms of the potential mechanisms of CKD.

Methods: Four microarray data sets GSE15072, GSE41030, GSE66494, and GSE98603 were analyzed using the Expression Omnibus database (GEO). The SVA package in the R software was used to merge and correct batch effects in the four datasets and the limma package was used to screen the differentially expressed genes (DEGs) between CKD and normal samples. There were instances where the Gene Ontology (GO) and pathways indicated that the DEGs were associated with different pathways such as “platelet activation”, “regulation of wound healing”, “platelet degranulation”, “focal adhesion”, and the “PI3K-Akt signaling pathway”. The GO plot package was used to perform the GO analysis of the DEGs and to generate a volcano map for DEGs and the Heml software was used to illustrate a heat map of the DEGs. The cluster Profiler package and the Kyoto Encyclopedia of Genes and Genomes（KEGG) website was used to perform pathway enrichment analysis and data on the differential genes were used to construct a protein-protein interaction (PPI) network to identify the central gene.

Results：Following removal of the batch effect, data on 10937 genes and 88 DEGs was obtained. A total of 53 differential genes were screened and the expression levels of 28 genes were upregulated and those of 25 genes were downregulated.

Discussions: GO analysis revealed that the response to potassium ion was most significant. KEGG pathway analysis helped identify four important pathways, namely the chemokine signaling pathway, pancreatic secretion, protein digestion and absorption and the regulation of actin cytoskeleton pathway. with the establishment of the PPI network, a central gene with high connectivity was selected, namely the Serum Amyloid A1 (SAA1) gene.

Conclusions：This study indicated that the SAA1 gene plays an extremely important role in the pathogenesis of CKD. The new pathway identified in this study may provide new insights into the underlying mechanism of CKD at the molecular level. [Ethiop. J. Health Dev. 2022; 36(1):000-000]

Keywords：Chronic Kidney Disease (CKD), Bioinformatics Analysis, Central Gene, Molecular Pathway, Pathogenesis.