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Integrating Network Pharmacology, Bioinformatics, and Mendelian Randomization Analysis to Identify Hub Targets and Mechanisms of Kunkui Baoshen Decoction in Treating Diabetic Kidney Disease

孟德尔随机化 汤剂 计算生物学 系统药理学 生物 生物信息学 糖尿病 疾病 医学 药理学 传统医学 药品 内分泌学 内科学 基因 生物化学 遗传变异 基因型
作者
Siyuan Song,Jiangyi Yu
出处
期刊:Current Pharmaceutical Design [Bentham Science]
卷期号:30 (42): 3367-3393 被引量:3
标识
DOI:10.2174/0113816128331463240816145054
摘要

Objective: To uncover the potential hub targets of Kunkui Baoshen Decoction (KKBS) in alleviating Diabetic Kidney Disease (DKD). Methods: Targets associated with KKBS and DKD were curated from TCMSP, GeneCards, OMIM, and Dis- GeNET databases. Common targets were identified through intersection analysis using a Venn diagram. Employing the "Drug-component-target" approach and constructing a Protein-protein Interaction (PPI) network, pivotal components and hub targets involved in KKBS's therapeutic action against DKD were identified. Functional enrichment and Gene Set Enrichment Analysis (GSEA) elucidated the potential mechanisms of these hub targets. Molecular docking simulations validated binding interactions. Subsequently, hub targets were validated using independent cohorts and clinical datasets. Immune cell infiltration in DKD samples was assessed using ESTIMATE, CIBERSORT, and IPS algorithms. A nomogram was developed to predict DKD prevalence. Finally, causal relationships between hub targets and DKD were explored through Mendelian randomization (MR) analysis at the genetic level. Results: Jaranol, isorhamnetin, nobiletin, calycosin, and quercetin emerged as principal effective components in KKBS, with predicted modulation of the PI3K/Akt, MAPK, HIF-1, NF-kB, and IL-17 signaling pathways. The hub targets in the PPI network include proteins involved in regulating podocyte autophagy and apoptosis, managing antioxidant stress, contributing to insulin resistance, and participating in extracellular matrix deposition in DKD. Molecular docking affirmed favorable binding interactions between principal components and hub targets. Validation efforts across cohorts and databases underscored the potential of hub targets as DKD biomarkers. Among 20 model algorithms, the Extra Tree model yielded the largest Area Under the Curve (AUC) in receiver operating characteristic (ROC) analysis. MR analysis elucidated that the targets related to antioxidant stress had a positive impact on DKD, while the target associated with renal tubular basement membrane degradation had a negative impact. Conclusion: Integration of Network Pharmacology, Bioinformatics, and MR analysis unveiled the capacity of KKBS to modulate pivotal targets in the treatment of DKD.
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