Berberine (BBR) is an active component of Phellodendri Cortex (PC), which is a traditional Chinese medicine that has been prescribed clinically for hyperuricemia (HUA) for hundreds of years. Many studies reported the anti-inflammatory and nephroprotective properties of BBR and PC; however, the therapeutic effects of BBR on HUA have not been explored. This study aims to investigate the efficacy and mechanism of BBR for treating HUA. Methods: The mechanism of BBR in the treatment of HUA were predicted by network pharmacology. A mouse model of HUA established by potassium oxonate and hypoxanthine was used to verify the prediction. The levels of serum uric acid (UA), urea nitrogen (BUN) and creatinine (CRE) were determined by biochemical test kits. Hematoxylin and eosin staining of kidney tissues was used to observe the kidney damage. ELISA kits were applied to detect the levels of interleukin (IL)-1β and IL-18 in serum and kidney tissues. Quantitative real-time PCR and Western blotting were adopted to analyze the expression of NLRP3, ASC, Caspase1, IL-1β and URAT1. The expressions of URAT1 in the kidney tubules were visualized by immunohistochemical staining. Molecular docking was used to assess the interaction between URAT1 and BBR. Results: The network pharmacology screened out 82 genes and several inflammation-related signaling pathways related to the anti-hyperuricemia effect of BBR. In the in vivo experiment, BBR substantially decreased the level of UA, BUN and CRE, and alleviated the kidney damage in mice with HUA. BBR reduced IL-1β and IL-18, and downregulated expressions of NLRP3, ASC, Caspase1 and IL-1β. BBR also inhibited expression of URAT1 and exhibited strong affinity with this target in silico docking. Conclusion: BBR exerts anti-HUA and nephroprotective effects via inhibiting activation of NLRP3 inflammasome and correcting the aberrant expression of URAT1 in kidney. BBR might be a novel therapeutic agent for treating HUA.

Background. The aim of this study is to explore the interactions between effective monomers of herbal formulas and their therapeutic targets using systems biology approaches which may be a promising approach to unraveling their underlying mechanisms. Shentao Ruangan decoction (STRGD), which has been experimentally, clinically demonstrated to be effective in treating liver hepatocellular carcinoma (LIHC), was selected. Methods. Bioactive ingredients and drug targets of STRGD were retrieved from the traditional Chinese medicine systems pharmacology database and analysis platform and BATMAN-TCM databases. LIHC-related differentially expressed genes (DEGs) and key modules were identified by a weighted gene coexpression network analysis using The Cancer Genome Atlas data. The Kaplan–Meier analysis was used to investigate the relationship between STRGD tumor targets and patients survival. The CIBERSORT deconvolution algorithm was used to analyze the correlation between STRGD tumor targets and infiltrating immune cells. Enrichment analysis was used to analyze biological functions. Interactions between STRGD compounds and LIHC-immune-related genes were investigated using molecular docking and MDS. Results. We identified 24 STRGD tumor targets, which were found to be correlated with survival and the level of immune cell infiltration in LIHC patients. Immune infiltration, gene set enrichment, and Kyoto Encyclopedia of Genes and Genomes analyses highlighted the roles of T and B cell subsets, which were both related to activator protein 1 (AP1), in STRGD action. Docking studies and HPLC indicated that tanshinone IIA is the main compound of STRGD in LIHC treatment, and MDS showed that the potential LIHC-immune-related targets 1FOS and 1JUN firmly bind to tanshinone IIA. Conclusions. The mechanisms of STRGD in improving the immune and survival status of LIHC patients include interactions between STRGD compounds and LIHC-immune-related targets. The findings of this study can guide research studies on the potential usefulness of tanshinone IIA in the development of drugs targeting 1JUN and 1FOS for the treatment of LIHC.

Objective. To analyze the target and potential mechanism of Scutellaria baicalensis (SB) in the treatment of HCC based on bioinformatics, so as to provide suggestions for the diagnosis, treatment, and drug development of hepatocellular carcinoma (HCC). Methods. The regulated gene targets of SB were screened by gene expression pattern clustering and differential analysis of gene expression data of HepG2 cells treated with SB at 0 h, 1 h, 3 h, 6 h, 12 h, and 24 h. The module genes related to HCC were identified by the weighted gene coexpression network analysis (WGCNA). KEGG and GO enrichment were used to analyze the molecular function and structure of the module, and GSEA was used to evaluate the different functional pathways between normal people and patients with HCC. Then, the module gene was used for univariate Cox proportional hazard analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to build a prognostic model. The protein-protein interaction network (PPI) was used to analyze the core genes regulated by SB (CGRSB) of the module, and the survival curve revealed the CGRSB impact on patient survival. The CIBERSORT algorithm combined with correlation analysis to explore the relationship between CGRSB and immune infiltration. Finally, the single-cell sequencing technique was used to analyze the distribution of CGRSB at the cellular level. Results. SB could regulate 903 genes, of which 234 were related to the occurrence of HCC. The prognosis model constructed by these genes has a good effect in evaluating the survival of patients. KEGG and GO enrichment analysis showed that the regulation of SB on HCC mainly focused on some cell proliferation, apoptosis, and immune-related functions. GSEA enrichment analysis showed that these functions are related to the occurrence of HCC. A total of 24 CGRSB were obtained after screening, of which 13 were significantly related to survival, and most of them were unfavorable factors for patient survival. The correlation analysis of gene expression showed that most of CGRSB was significantly correlated with T cells, macrophages, and other functions. The results of single-cell analysis showed that the distribution of CGRSB in macrophages was the most. Conclusion. SB has high credibility in the treatment of HCC, such as CDK2, AURKB, RRM2, CENPE, ESR1, and PRIM2. These targets can be used as potential biomarkers for clinical diagnosis. The research also shows that the p53 signal pathway, MAPK signal pathway, apoptosis pathway, T cell receptor pathway, and macrophage-mediated tumor immunity play the most important role in the mechanism of SB in treating HCC.

Tumor-associated macrophages (TAMs) participate in and shape the tumor microenvironment of hepatocellular carcinoma (HCC), which is closely related to the formation of tumor heterogeneity. The aim of this study is to distinguish different subtypes of patients according to the activity level of macrophage functional gene set in HCC. We collected 1203 tissue samples from TCGA, ICGC and GEO databases. Using macrophage-associated gene set (MRRGS) from GSEA database, the score of MRRGS was calculated based on gene set variation analysis (GSVA). The key MRRGS was screened by univariate COX regression analysis and LASSO regression. Finally, non-negative matrix factorization (NMF) was used to classify HCC subtypes. Six immune cell infiltration algorithms, immune checkpoint expression differences, tumor immunity and rejection (TIDE) analysis, mutation data analysis, stem cell index based on mRNA expression (mRNAsi) were used to evaluate and reveal the differences of immunity, mutation and tumor cell malignancy among different HCC subtypes. Weighted gene coexpression network (WGCNA) is used to analyze the functional mechanism involved in MRRGS. CAMP and drug sensitivity analysis are used to explore drugs for different HCC subtypes. Two machine learning algorithms assist in screening characteristic genes among subtypes to facilitate subtype discrimination. Our study divides patients into two subtype (C1 and C2) by defining 12 MRRGS, which are similar to hot and cold tumors mentioned in previous studies. The stability of the macrophage functional classifier was validated in two independent HCC cohorts and this classifier can well predict the ability of patients to respond to immunotherapy, TACE treatment and various drug. Based on the above results, we built a bioinformatics tool to help users quickly distinguish patient subtypes and prognosis. In addition, immune signals (such as PD1-PDL1 signals), mutations, metabolic abnormalities, viral infection and chemical erosion in the environment are important upstream foundations of HCC heterogeneity caused by macrophages. This provides insights into the clinical treatment and management of HCC.