Identification of , and As Potential Therapeutic Target Genes for Renal Interstitial Fibrosis

Overview

Journal Ann Transl Med

Publisher AME Publishing Company

Date 2019 Nov 9

PMID 31700890

Citations 6

Authors

Chuanjie Zhang

Xin Hu

Feng Qi

Jun Luo

Xiao Li

Affiliations

Soon will be listed here.

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 (, and ) were identified after construction of PPI network. In Pearson correlation coefficient, , and 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: , and might serve as potential early biomarkers of RIF. The mechanism between these genes and RIF remains to be further studied.

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