Comprehensive analysis of the relationship between xanthine oxidoreductase activity and chronic kidney disease
Yiyuan Zhang,
Xiaobao Ding,
Lihao Guo,
Yanan Zhong,
Juan Xie,
Yong Xu,
Hailun Li,
Donghui Zheng
Affiliations
Yiyuan Zhang
Department of Nephrology, The Affiliated Huai’an Hospital of Xuzhou Medical University and Huai’an Second People’s Hospital, Huai’an, China
Xiaobao Ding
Department of Nephrology, The Affiliated Huai’an Hospital of Xuzhou Medical University and Huai’an Second People’s Hospital, Huai’an, China; Department of Pharmacology, Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
Lihao Guo
Department of Nephrology, The Affiliated Huai’an Hospital of Xuzhou Medical University and Huai’an Second People’s Hospital, Huai’an, China
Yanan Zhong
Department of Nephrology, The Affiliated Huai’an Hospital of Xuzhou Medical University and Huai’an Second People’s Hospital, Huai’an, China
Juan Xie
Department of Nephrology, The Affiliated Huai’an Hospital of Xuzhou Medical University and Huai’an Second People’s Hospital, Huai’an, China
Yong Xu
Department of Nephrology, The Affiliated Huai’an Hospital of Xuzhou Medical University and Huai’an Second People’s Hospital, Huai’an, China; Corresponding author
Hailun Li
Department of Nephrology, The Affiliated Huai’an Hospital of Xuzhou Medical University and Huai’an Second People’s Hospital, Huai’an, China; Corresponding author
Donghui Zheng
Department of Nephrology, The Affiliated Huai’an Hospital of Xuzhou Medical University and Huai’an Second People’s Hospital, Huai’an, China; Corresponding author
Summary: Chronic kidney disease (CKD) is a common disease that seriously endangers human health. However, the potential relationship between xanthine oxidoreductase (XOR) activity and CKD remains unclear. In this study, we used clinical data, CKD datasets from the Gene Expression Omnibus database, and untargeted metabolomics to explain the relationship between XOR activity and CKD. First, XOR activity showed high correlation with the biomarkers of CKD, such as serum creatinine, blood urea nitrogen, uric acid, and estimated glomerular filtration rate. Then, we used least absolute shrinkage and selection operator logical regression algorithm and random forest algorithm to screen CKD molecular markers from differentially expressed genes, and the results of qRT-PCR of XDH, KOX-1, and ROMO1 were in accordance with the results of bioinformatics analyses. In addition, untargeted metabolomics analysis revealed that the purine metabolism pathway was significantly enriched in CKD patients in the simulated models of kidney fibrosis.
