Identification and Validation of Potential Biomarkers and Their Functions in Acute Kidney Injury.

Jianwen Chen,Xiangmei Chen,Alberto Olivero,Yalei Chen

doi:10.3389/fgene.2020.00411

Abstract

Acute kidney injury (AKI) is a global public health concern associated with high morbidity, mortality, and health-care costs, and the therapeutic measures are still limited. This study aims to investigate crucial genes correlated with AKI, and their potential functions, which might contribute to a better understanding of AKI pathogenesis. The high-throughput data GSE52004 and GSE98622 were downloaded from Gene Expression Omnibus; four group sets were extracted and integrated. Differentially expressed genes (DEGs) in the four group sets were identified by limma package in R software. The overlapping DEGs among four group sets were further analyzed by the VennDiagram package, and their potential functions were analyzed by the GO and KEGG pathway enrichment analyses using the DAVID database. Furthermore, the protein–protein interaction (PPI) network was constructed by STRING, and the functional modules of the PPI network were filtered by MCODE and ClusterOne in Cytoscape. Hub genes of overlapping DEGs were identified by Cyto-Hubba and cytoNCA. The expression of 35 key genes was validated by quantitative real-time PCR (qRT-PCR). Western blot and immunofluorescence were performed to validate an important gene Egr1. A total of 722 overlapping DEGs were differentially expressed in at least three group sets. These genes mainly enriched in cell proliferation and fibroblast proliferation. Additionally, 5 significant modules and 21 hub genes, such as Havcr1, Krt20, Sox9, Egr1, Timp1, Serpine1, Edn1, and Apln were screened by analyzing the PPI networks. The 5 significant modules were mainly enriched in complement and coagulation cascades and Metabolic pathways, and the top 21 hub genes were mainly enriched in positive regulation of cell proliferation. Through validation, Krt20 were identified as the top 1 upregulated genes with a log2 (fold change) larger than 10 in all these 35 genes, and 21 genes were validated as significantly upregulated; Egr1 was validated as an upregulated gene in AKI in both RNA and protein level. In conclusion, by integrated analysis of different high-throughput data and validation by experiment, several crucial genes were identified in AKI, such as Havcr1, Krt20, Sox9, Egr1, Timp1, Serpine1, Edn1, and Apln. These genes were very important in the process of AKI, which could be further utilized to explore novel diagnostic and therapeutic strategies.

Highlights

Acute kidney injury (AKI) is a syndrome characterized by the rapid loss of the kidney’s excretory function and is typically diagnosed by the decreased urine output or accumulation of end products of nitrogen metabolism, or both (Bellomo et al, 2012)
Several crucial genes were identified in AKI, such as Havcr1, Krt20, Sex-determining region Y-box 9 (Sox9), Early growth response 1 (Egr1), tissue inhibitor of metalloproteinase 1 (Timp1), Serpine1, Edn1, and Apln
The preprocessing of the gene expression profile data, which includes the background correction, the quantile normalization, the median polish summarization, and the log2 transformation, was performed by R software2 and RStudio software3 using the Robust Multichip Average (RMA) algorithmin affy package, which can be downloaded on the Bioconductor website4 (Lin and Lin, 2017)

Summary

Introduction

Acute kidney injury (AKI) is a syndrome characterized by the rapid loss of the kidney’s excretory function and is typically diagnosed by the decreased urine output or accumulation of end products of nitrogen metabolism (urea and creatinine), or both (Bellomo et al, 2012). AKI is a global public health concern associated with high morbidity, mortality, and health-care costs (Zuk and Bonventre, 2016). It occurs in about 13.3 million people per year and is thought to contribute to about 1.7 million deaths every year (Mehta et al, 2015). Data show that 21% of hospital admissions were affected with AKI at least one time, and patients who require dialysis or those with Kidney Disease: Improving Global Outcomes (KDIGO) stage 3 had a high mortality rate (42 and 46%) (Mehta et al, 2015). The experimental mouse ischemia–reperfusion injury (IRI) model of AKI has been widely applied to study the pathogenesis and injury outcome of ischemic AKI (Hu et al, 2013), while the underlying mechanisms of AKI remain largely unclear

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