S100 Calcium-Binding Protein A8 Functions as a Tumor-Promoting Factor in Renal Cell Carcinoma via Activating NF-κB Signaling Pathway.

BACKGROUND: Renal cell carcinoma (RCC), arising from the renal tubular epithelium, is one of the most common types of genitourinary malignancies. Based on the Gene Expression Omnibus (GEO) database (GSE100666), S100 calcium-binding protein A8 (S100A8) was highly expressed in RCC tissues. S100A8, an inflammatory regulatory factor, has emerged as an important mediator associated with the occurrence and development of cancer. MATERIALS AND METHODS: The Gene Expression Omnibus (GEO) database was used to identify the key genes and investigate the main signaling pathways in RCC. Human RCC samples and corresponding adjacent normal tissues were collected in our hospital. The expression of S100A8 in human RCC samples was detected using western blotting and immunohistochemical analysis. S100A8 overexpression or knockdown was mediated by using Lipofectamine 3000 in human renal cell carcinoma cell line 786-O and ACHN cells. Basic experiments, including MTT and cell apoptosis assays, were utilized for investigating the function of S100A8 in RCC. Furthermore, the levels of inflammation were also evaluated in 786-O and ACHN cells. RESULTS: In the current study, we found that downregulation of S100A8 inhibited proliferation and promoted apoptosis in 786-O and ACHN RCC cells. Of note, S100A8 silencing downregulated the phosphorylation of NF-κB p65, thereby decreasing the levels of TNF-α, cleaved caspase1, and MMP9. By contrast, S100A8 upregulation could increase these expressions. CONCLUSION: Overall, S100A8 knockdown restrained RCC malignant biological properties, which was associated with the deactivation of the NF-κB signaling pathway. This present study demonstrates new insights that S100A8 may be a potential therapeutic target in RCC.

Exploration of the mechanism of Zisheng Shenqi decoction against gout arthritis using network pharmacology.

BACKGROUND: In this study, the network pharmacological methods were used to predict the target of effective components of compounds in Zisheng Shenqi Decoction (ZSD, or Nourishing Kidney Qi Decoction) in the treatment of gouty arthritis (GA). METHOD: The main effective components and corresponding key targets of herbs in the ZSD were discerned through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis (TCMSP), Bioinformatics Analysis Tool for Molecular mechanism of Traditional Chinese Medicine (BATMAN-TCM) database. UniProt database and Swiss Target Prediction (STP) database was used to rectify and unify the target names and supply the target information. The targets related to GA were obtained by using GeneCards database. After we discovered the potential common targets between ZSD and GA, the interaction network diagram of "ZSD-component-GA-target" was constructed by Cytoscape software (Version 3.7.1). Subsequently, the Protein-protein interaction (PPI) network of ZSD effective components-targets and GA-related targets was constructed by Search Tool for the Retrieval of Interacting Genes Database (STRING). Bioconductor package "org.Hs.eg.db" and "cluster profiler" package were installed in R software (Version 3.6.0) which used for Gene Ontology analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis. RESULTS: 146 components and 613 targets of 11 herbal medicines in the ZSD were got from TCMSP database and BATMAN-TCM database. 987 targets of GA were obtained from GeneCards database. After intersected and removed duplications, 132 common targets between ZSD and GA were screened out by Cytoscape software (Version 3.7.1). These common targets derived from 81 effective components of 146 components, such as quercetin, stigmasterol and kaempferol. They were closely related to anti-inflammatory, analgesic and anti oxidative stress and the principal targets comprised of Purinergic receptor P2X, ligand-gated ion channel 7 (P2x7R), Nod-like receptor protein 3 (NLRP3) and IL-1ß. GO enrichment analysis and KEGG pathway enrichment analysis by R software (Version 3.6.0) showed that the key target genes had close relationship with oxidative stress, reactive oxygen species (ROS) metabolic process and leukocyte migration in aspects of biological process, cell components and molecular function. It also indicated that ZSD could decrease inflammatory reaction, alleviate ROS accumulation and attenuate pain by regulating P2 × 7R and NOD like receptor signaling pathway of inflammatory reaction. CONCLUSION: A total of 81 effective components and 132 common target genes between ZSD and GA were screened by network pharmacology. The PPI network, GO enrichment analysis and KEGG pathway enrichment analysis suggested that ZSD can exerte anti-inflammatory and analgesic effects on the treatment of GA by reducing decreasing inflammatory reaction, alleviating ROS accumulation, and attenuating pain. The possible molecular mechanism of it mainly involved multiple components, multiple targets and multiple signaling pathways, which provided a comprehensive understanding for further study. In general, the network pharmacological method applied in this study provides an alternative strategy for the mechanism of ZSD in the treatment of GA.