Construction and validation of a hypertensive nephropathy diagnostic model based on mitochondrial-related genes using machine learning combined with WGCNA
- 04.11.2025
- Nephrology – Original Paper
- Verfasst von
- Gonglin Tang
- Guixin Ding
- Li Xie
- Hongwei Zhao
- Jitao Wu
- Erschienen in
- International Urology and Nephrology
Abstract
Background
Hypertensive nephropathy (HTN) arises from chronic hypertension and may potentially result in severe renal failure. Due to the absence of reliable and efficient biomarkers, the diagnosis of HTN is constrained. Mitochondrial-associated genes are closely associated with the pathogenesis of HTN. Our study aims to find new reliable diagnostic and therapeutic targets for HTN.
Methods
We obtained the dataset GSE37460 from the GEO database and performed Weighted Gene Co-expression Network Analysis (WGCNA) to analyze the expression modules of differentially expressed genes (DEGs) identified from GSE37460, in conjunction with mitochondrial-related genes (MRGs) for hub gene selection. We employed KEGG, GO, and GSEA enrichment analyses to elucidate the differential genes. Subsequently, diagnostic models were developed using LASSO, SVM, and RF algorithms. Furthermore, functional analysis between the disease group and control group in GSE37460 was conducted using ssGSEA, and the diagnostic performance was evaluated through Receiver Operating Characteristic (ROC) curve manipulation.
Results
Through differential analysis and WGCNA analysis, we have identified a total of 695 DEGs. Cross-analysis revealed that among these genes, 14 are MRGs. These genes are primarily enriched in biological processes and pathways related to NOD-like receptor signaling, coronavirus disease—COVID-19, necroptosis, tumor necrosis factor production, nuclear envelope, transmembrane transporter binding, and protein tyrosine kinase activity. Furthermore, utilizing LASSO, SVM-REF, and Random Forest algorithms, we constructed a diagnostic model consisting of three genes (PYCARD, NRP1, and IFI27). The resulting ROC curve demonstrated high diagnostic accuracy, with an area under the curve (AUC) value of 0.998. In addition, ssGSEA analysis revealed significant correlations between these three hub genes and heme metabolism, inflammatory response, cholesterol homeostasis, interferon response, Wnt/β-catenin pathway, and epithelial mesenchymal transition.
Conclusion
We have elucidated the role of MRGs in the pathogenesis of HTN and developed a diagnostic model with high diagnostic potential.
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- Titel
- Construction and validation of a hypertensive nephropathy diagnostic model based on mitochondrial-related genes using machine learning combined with WGCNA
- Verfasst von
-
Gonglin Tang
Guixin Ding
Li Xie
Hongwei Zhao
Jitao Wu
- Publikationsdatum
- 04.11.2025
- Verlag
- Springer Netherlands
- Erschienen in
-
International Urology and Nephrology
Print ISSN: 0301-1623
Elektronische ISSN: 1573-2584 - DOI
- https://doi.org/10.1007/s11255-025-04887-3
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