Original Article
Identification of CD2, CCL5 and CCR5 as potential therapeutic target genes for renal interstitial fibrosis
Abstract
Background: We aimed to explore potential gene biomarkers of renal interstitial fibrosis (RIF) due to a lack of effective and non-invasive methods for diagnosis.
Methods: Three data sets (GSE22459, GSE76882 and GSE57731) including 350 samples were acquired from Gene Expression Omnibus (GEO) database. We used bioconductor limma package to perform background adjustment. Cluster analysis was conducted by ‘edgeR’ package to identify the differentially expressed genes (DEGs). We generated heat maps with using heatmap package in R software. Function annotation of genes was performed by Gene Ontology (GO) enrichment analysis. STRING (Search Tool for the Retrieval of Interacting Genes) database was employed to construct the protein-protein interaction (PPI) network and the results were visualized by Cytoscape 3.6.1. At last, we applied Graphpad Prism 7.0. to explore the correlation between three hub genes and pathological degrees of RIF.
Results: By applying the “edgeR” package in R, we detected 116 DEGs with three data sets. These genes were enriched in 19 GO biological process categories. Three main hub genes (CD2, CCL5 and CCR5) were identified after construction of PPI network. In Pearson correlation coefficient, CD2, CCL5 and CCR5 was found to hold higher expression patterns in RIF samples based on independent data set GSE57731. Besides, their gene expression levels were found significantly positive correlation with the degree of RIF (CD2: P<0.05, r=0.29; CCL5: P<0.05, r=0.31; CCR5: P<0.05, r=0.38).
Conclusions: CD2, CCL5 and CCR5 might serve as potential early biomarkers of RIF. The mechanism between these genes and RIF remains to be further studied.
Methods: Three data sets (GSE22459, GSE76882 and GSE57731) including 350 samples were acquired from Gene Expression Omnibus (GEO) database. We used bioconductor limma package to perform background adjustment. Cluster analysis was conducted by ‘edgeR’ package to identify the differentially expressed genes (DEGs). We generated heat maps with using heatmap package in R software. Function annotation of genes was performed by Gene Ontology (GO) enrichment analysis. STRING (Search Tool for the Retrieval of Interacting Genes) database was employed to construct the protein-protein interaction (PPI) network and the results were visualized by Cytoscape 3.6.1. At last, we applied Graphpad Prism 7.0. to explore the correlation between three hub genes and pathological degrees of RIF.
Results: By applying the “edgeR” package in R, we detected 116 DEGs with three data sets. These genes were enriched in 19 GO biological process categories. Three main hub genes (CD2, CCL5 and CCR5) were identified after construction of PPI network. In Pearson correlation coefficient, CD2, CCL5 and CCR5 was found to hold higher expression patterns in RIF samples based on independent data set GSE57731. Besides, their gene expression levels were found significantly positive correlation with the degree of RIF (CD2: P<0.05, r=0.29; CCL5: P<0.05, r=0.31; CCR5: P<0.05, r=0.38).
Conclusions: CD2, CCL5 and CCR5 might serve as potential early biomarkers of RIF. The mechanism between these genes and RIF remains to be further studied.
