文献类型：期刊文献

英文题名：Characterization and Clinical Diagnostic Potential of IHRDEGs in Renal Interstitial Fibrosis: An Integrative Data Analysis and Model Construction Study

作者：Zhang, Jie[1,2];Dang, Xinyu[1];Dai, Enlai[1]

第一作者：张杰

通信作者：Dai, EL[1]

机构：[1]Gansu Univ Chinese Med, Sch Tradit Chinese & Western Med, 35 Dingxi Rd, Lanzhou 730000, Gansu, Peoples R China;[2]Gansu Univ Chinese Med, Affiliated Hosp, Dept Nephrol, Lanzhou 730020, Gansu, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Gansu Univ Chinese Med, Sch Tradit Chinese & Western Med, 35 Dingxi Rd, Lanzhou 730000, Gansu, Peoples R China.|[10735]甘肃中医药大学;

年份：2026

卷号：19

外文期刊名：JOURNAL OF INFLAMMATION RESEARCH

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001844435600001)】；

基金：This work was supported by the National Natural Science Foundation of China (Grant No. 82160852) and the Natural Science Foundation of Gansu Province, China (Grant No. 26JRRA673) .

语种：英文

外文关键词：hypoxia signaling; immune cell infiltration; bioinformatics analysis; biomarker discovery; machine learning model; fibrotic progression

摘要：Purpose: Renal interstitial fibrosis (RIF) is a critical pathological process in the progression of chronic kidney disease (CKD). This study aimed to identify and validate inflammation- and hypoxia-related differentially expressed genes (IHRDEGs) associated with RIF and to construct a robust diagnostic model with potential clinical applications. Patients and Methods: Three public GEO datasets (GSE22459, GSE76882, GSE53605) comprising 76 RIF and 142 control samples were integrated following batch correction and normalization. Differentially expressed IHRDEGs were screened and analyzed using GO and KEGG pathway enrichment. A diagnostic model was constructed using logistic regression and optimized through SVM and LASSO algorithms. Immune infiltration was evaluated using ssGSEA, and consensus clustering was used to define molecular subtypes. Experimental validation was conducted in a rat model of RIF using RT-qPCR, Western blotting, and immunohistochemistry. Results: A total of five hub IHRDEGs (EDN1, HLA-G, MYC, HIF1A, and TLR2) were identified and incorporated into a diagnostic model that demonstrated strong predictive ability (AUC 0.7-0.9; sensitivity and specificity > 70-90%). These genes were significantly correlated with immune cell infiltration patterns. Subtype analysis revealed two distinct molecular clusters of RIF with different immunopathological features. Co-expression and regulatory interaction analyses further elucidated the involvement of hub genes in fibrotic mechanisms. Experimental validation confirmed the upregulation of hub genes at both mRNA and protein levels in the RIF model. Conclusion: This study uncovers the diagnostic and mechanistic significance of inflammation- and hypoxia-related genes in RIF. The five identified hub genes may serve as promising biomarkers and therapeutic targets. These findings provide novel insights into the immunehypoxia interplay in renal fibrosis and offer a potential framework for early diagnosis and targeted treatment of CKD-related fibrosis.