Evidence map›Paper›PMID 42464365›Full record

ArticleClinical epigenetics2026

Aging-driven transcriptional programs in diabetic kidney disease: multi-omics discovery of diagnostic biomarkers and drug-repurposing targets.

Xue Hu, Yingzhuo Li, Yang Wang, Bosheng Luo, Chunjuan Xia, Jiaping Wang

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Article in Clinical epigenetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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Authors and funding

6 authors.

Xue Hu *Department of Radiology, Second Affiliated Hospital of Kunming Medical University, No. 783, Dianmei Avenue, Wuhua District, Kunming City, 650101, Yunnan Province, China.
Yingzhuo Li *Department of Radiology, Second Affiliated Hospital of Kunming Medical University, No. 783, Dianmei Avenue, Wuhua District, Kunming City, 650101, Yunnan Province, China.
Yang Wang *Department of Radiology, Second Affiliated Hospital of Kunming Medical University, No. 783, Dianmei Avenue, Wuhua District, Kunming City, 650101, Yunnan Province, China.
Bosheng LuoDepartment of Radiology, Second Affiliated Hospital of Kunming Medical University, No. 783, Dianmei Avenue, Wuhua District, Kunming City, 650101, Yunnan Province, China.
Chunjuan XiaDepartment of Ultrasound, Second Affiliated Hospital of Kunming Medical University, Kunming, 650101, Yunnan, China. 976106135@qq.com.
Jiaping WangDepartment of Radiology, Second Affiliated Hospital of Kunming Medical University, No. 783, Dianmei Avenue, Wuhua District, Kunming City, 650101, Yunnan Province, China. jiapingwang12@163.com.

Funding

science and technology plan project of Science and Technology Department of Yunnan Province 202401AY070001-005
6 · The paper itself

Abstract

backgroundDiabetic kidney disease (DKD) is the primary global cause of end-stage renal disease. However, the aging-related gene networks driving its progression remain unclear.

methodsIn this study, we integrated bioinformatics and experiments to screen for age-related hub genes in DKD and explore their diagnostic and therapeutic values. Transcriptomic datasets and aging-related gene databases were combined to obtain candidate differentially expressed genes enriched in the PI3K-Akt and JAK-STAT pathways. LASSO and Random Forest algorithms were applied to screen core hub genes, and a logistic diagnostic model was constructed for verification. Single-cell sequencing was utilized to clarify the main cell populations expressing key genes. Molecular docking and dynamics simulations were performed to analyze the binding stability of candidate drugs and target genes. High-glucose cell models and DKD rat models were established, and RT-qPCR, western blot, and pathological staining assays were used to validate the expression changes of key genes and the regulatory effects of drugs.

resultsNine hub genes (CLU, EGF, SLC16A7, MYC, RPA1, RB1, APOC3, SYK, and NR3C1) were finally screened out. The constructed logistic diagnostic model achieved an AUC of 0.881 in external verification. Glomerular endothelial cells and mesangial cells were identified as the main cell populations expressing these hub genes. Molecular simulation results confirmed the stable binding of fostamatinib to SYK and selexipag to APOC3. In vivo and in vitro validation experiments demonstrated that the two candidate agents could suppress NF-κB pathway activation, inflammatory cytokine release, and cellular senescence.

conclusionIn this study, we identified age-related hub genes, constructed a reliable diagnostic signature, and suggested SYK-fostamatinib and APOC3-selexipag as potential drug-repurposing candidates for DKD, which requires further experimental and clinical validation.

Indexed as

AgingDiabetic NephropathiesAnimalsBiomarkersComputational BiologyGene Expression ProfilingGene Regulatory NetworksHumansMaleMolecular Docking SimulationRatsTranscriptomeBiomarkersAge-related genesBiomarkersDiabetic kidney diseaseMachine learningMolecular dynamics simulationSingle-cell RNA sequencing

Identifiers

PMID42464365
PMCPMC13656577

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