In recent years, the emerging network pharmacology has been extensively applied to the field of traditional Chinese medicine and has made great contributions to the modernization of TCM. Therefore, this paper provides an overview of the progress of research ideas and methods in the network pharmacology in the last few years in the field of traditional Chinese medicine and presents insights into future research methods and ideas in the network pharmacology. Problems with the current network pharmacology are discussed and prospects of its future development are put forward.

Diabetic kidney disease (DKD) is a common complication of diabetes and has become the leading cause of end-stage kidney disease. The pathogenesis of DKD is complicated, and oxidative stress is considered as a core of DKD onset. High glucose can lead to increased production of reactive oxygen species (ROS) via the polyol, PKC, AGE/RAGE and hexosamine pathways, resulting in enhanced oxidative stress response. In this way, pathways such as PI3K/Akt, TGF-β1/p38-MAPK and NF-κB are activated, inducing endothelial cell apoptosis, inflammation, autophagy and fibrosis that cause histologic and functional abnormalities of the kidney and finally result in kidney injury. Presently, the treatment for DKD remains an unresolved issue. Traditional Chinese medicine (TCM) has unique advantages for DKD prevention and treatment attributed to its multi-target, multi-component, and multi-pathway characteristics. Numerous studies have proved that Chinese herbs (e.g., Golden Thread, Kudzuvine Root, Tripterygium glycosides, and Ginseng) and patent medicines (e.g., Shenshuaining Tablet, Compound Rhizoma Coptidis Capsule, and Zishen Tongluo Granule) are effective for DKD treatment. The present review described the role of oxidative stress in DKD pathogenesis and the effect of TCM intervention for DKD prevention and treatment, in an attempt to provide evidence for clinical practice.

Background. Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease throughout the world. In kidney disease, oxidative stress has been linked to both antioxidant depletions and increased reactive oxygen species (ROS) production. Thus, the objective of this study was to identify biomarkers related to oxidative stress in DKD. Methods. The gene expression profile of the DKD was extracted from the Gene Expression Omnibus (GEO) database. The identification of the differentially expressed genes (DEGs) was performed using the “limma” R package, and weighted gene coexpression network analysis (WGCNA) was used to find the gene modules that were most related to DKD. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using “Org.Hs.eg.db” R package. The protein-protein interaction (PPI) network was constructed using the STRING database. The hub genes were identified by the Molecular Complex Detection (MCODE) plug-in of Cytoscape software. The diagnostic capacity of hub genes was verified using the receiver operating characteristic (ROC) curve. Correlations between diagnostic genes were analyzed using the “corrplot” package. In addition, the miRNA gene transcription factor (TF) network was used to explain the regulatory mechanism of hub genes in DKD. Results. DEGs analysis and WGCNA-identified 160 key genes were identified in DKD patients. Among them, nine oxidative stress-related genes were identified as candidate hub genes for DKD. Using the PPI network, five hub genes, NR4A2, DUSP1, FOS, JUN, and PTGS2, were subsequently identified. All the hub genes were downregulated in DKD and had a high diagnostic value of DKD. The regulatory mechanism of hub genes was analyzed from the miRNA gene-TF network. Conclusion. Our study identified NR4A2, DUSP1, FOS, JUN, and PTGS2 as hub genes of DKD. These genes may serve as potential therapeutic targets for DKD patients.