Identification of Anoikis-Related Genes in Chronic Kidney Disease Based on Bioinformatics Analysis Combined with Experimental Validation

作     者：Liu, Hong Mei, Manxue Zhong, Hua Lin, Shuyin Luo, Jiahui Huang, Sirong Zhou, Jiuyao 

作者机构：Guangzhou Univ Chinese Med Sch Pharmaceut Sci Dept Pharmacol Guangzhou Peoples R China Shandong First Med Univ Affiliated Hosp 1 Dept Gerontol Jinan Peoples R China Shandong Prov Qianfoshan Hosp Jinan Peoples R China 

出 版 物：《JOURNAL OF INFLAMMATION RESEARCH》 (J. Inflamm. Res.)

年 卷 期：2025年第18卷

页      面：973-994页

核心收录：

基　　金：National Natural Science Foundation of China [82174061  GZC20230629] 

主　　题：chronic kidney disease bioinformatics analysis anoikis biomarkers 

摘      要：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 t