Hong Liu,1,* Manxue Mei,1,* Hua Zhong,2 Shuyin Lin,1 Jiahui Luo,1 Sirong Huang,1 Jiuyao Zhou1 

1Department of Pharmacology, School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China; 2Department of Gerontology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Jiuyao Zhou, Department of Pharmacology, School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China, Email yaoli@gzucm.edu.cn, zhoujiuyao@vip.tom.com

Background: Chronic kidney disease (CKD) is a progressive condition that arises from diverse etiological factors, resulting in structural alterations and functional impairment of the kidneys. We aimed to establish the Anoikis-related gene signature in CKD by bioinformatics analysis.
Methods: We retrieved 3 datasets from the Gene Expression Omnibus (GEO) database to obtain differentially expressed genes (DEGs), followed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) of them, which were intersected with Anoikis-related genes (ARGs) to derive Anoikis-related differentially expressed genes (ARDEGs). Besides, we conducted weighted gene co-expression network analysis (WGCNA) to identify hub genes. And then, we adopted the quantitative real-time PCR (RT-qPCR) assay to validate the hub genes among several CKD animal models. Furthermore, we constructed a competitive endogenous RNA (ceRNA) network for the hub genes utilizing the ENCORI and miRDB databases, while also calculating Spearman correlation coefficients. Ultimately, we applied the CIBERSORTx algorithm to conduct immune infiltration analysis, classifying immune characteristics based on the abundance of 22 immune cell types.
Results: To summarize, we identified 13 ARDEGs. WGCNA yielded 6 hub genes, all of which demonstrated significant diagnostic potential in univariate logistic regression analysis (P< 0.05). The principal pathways enriched were involved in cell cycle progression Toxoplasmosis, Cell adhesion molecules, Influenza A, Pathogenic Escherichia coli infection, Small cell lung cancer, Amoebiasis, TNF signaling pathway, and Leukocyte transendothelial migration. Notably, 6 immune cell types exhibited significant differences (P< 0.05) across subgroups with distinct immune characteristics. Moreover, 2 hub genes showed significant variations (P< 0.05) across these immune characteristic subtypes. Among the 4 types of CKD mouse models, the mRNA expression levels of LAMB3 and CDH3 were significantly (P< 0.05) up-regulated in the model group.
Conclusion: We identified 6 hub genes that may serve as potential key biomarkers of Anoikis-related progression in CKD.

Keywords: chronic kidney disease, bioinformatics analysis, anoikis, biomarkers