How far have we explored fungi to fight cancer?

Novel natural inhibitors targeting B-RAF(V600E) by computational study

The aim of this research was to screen the ZINC15 database to select lead compounds and drug candidates which can inhibit B-RAF (V600E). In order to identify drugs potentially inhibited B-RAF (V600E), numerous modules of Discovery Studio 4.5 were employed. Structure-based screening using LibDock was carried out followed by ADME (absorption, distribution, metabolism, excretion) and toxicity prediction. CDOCKER was performed to demonstrate the binding affinity and mechanism between ligands and B-RAF(V600E). To evaluate whether ligand-receptor complexes were stable, molecular dynamics were employed. Two novel natural compounds (ZINC000100168592 and ZINC000049784088) from ZINC15 database were found binding to B-RAF(V600E) with more favorable interaction energy in comparison with the reference drug Vemurafenib. Also, they were predicted with less ames mutagenicity, rodent carcinogenicity, non-developmental toxic potential and tolerance to cytochrome P450 2D6 (CYP2D6). The molecular dynamics simulation analysis indicated that the compound-B-RAF(V600E) complexes had more favorable potential energy compared with Vemurafenib and they can exist in natural environments stably. The result of this study shows that ZINC000100168592 and ZINC000049784088 are ideal leading potential compounds to inhibit B-RAF(V600E). The findings of this study and these selected drug candidates greatly contributed to the medication design and improvement of B-RAF(V600E) and other proteins.

Utilizing network pharmacology to explore potential mechanisms of YiSui NongJian formula in treating myelodysplastic syndrome

The study aims to explore potential mechanisms of YiSui NongJian formula (YSNJF) in treating myelodysplastic syndromes (MDS) by network pharmacology-based strategy. Active compounds and corresponding potential therapeutic targets of YSNJF were harvested by utilizing the database of TCMSP (Traditional Chinese Medicine Systems Pharmacology) and BATMAN-TCM (Bioinformatics Analysis Tool for Molecular mechanism of Traditional Chinese Medicine). MDS targets were adopted from GeneCard, KEGG (Kyoto Encyclopedia of Genes and Genomes), TTD (Therapeutic Target Database), DrugBank, and DisGeNet. Then a network of YSNJF- compounds-target-MDS network was harvested. The protein–protein interaction (PPI) network was then generated by the Sting database and subjected to Cytoscape software to harvest major and core targets network by topological analysis. Genes from the core targets network were further subjected to Gene Ontology (GO) and KEGG enrichment analysis to figure out potential targeting pathways. Finally, a compounds-targets-pathways network was generated by Cytoscape. A total of 210 active compounds and 768 corresponding potential therapeutic targets were harvested from ingredients of YSNJF. MDS was shown to have 772 potential treating targets with 98 intersected targets corresponding to 98 active compounds in YSNJF. Topological analysis revealed that 15 targets formed the core PPI network. Further, GO and KEGG enrichment analysis revealed that those core targets were mainly enriched on cell cycle- and immune-related pathways. The present study revealed that therapeutic effects of YSNJF on MDS might be achieved through regulating cell cycle- and immune-related pathways.

Prediction of clinical prognosis in cutaneous melanoma using an immune-related gene pair signature

Cutaneous melanoma (CM) is a malignant and aggressive skin cancer that is the leading cause of skin cancer-related deaths. Increasing evidence shows that immunity plays a vital role in the prognosis of CM. In this study, we developed an immune-related gene pair (IRGP) signature to predict the clinical prognosis of patients with CM. Immune-related genes from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were selected to construct the IRGPs, and patients with CM in these two cohorts were assigned to low- and high-risk subgroups. Moreover, we investigated the IRGPs and their individualized prognostic signatures using Kaplan-Meier survival analysis, univariate and multivariate Cox analyses, and analysis of immune cell infiltration in CM. A 41-IRGP signature was constructed from 2498 immune genes that could significantly predict the overall survival of patients with CM in both the TCGA and GEO cohorts. Immune infiltration analysis indicated that several immune cells, especially M1 macrophages and activated CD4 T cells, were significantly associated with the prognostic effect of the IRGP signature in patients with CM. Overall, the IRGP signature constructed in this study was useful for determining the prognosis of patients with CM and for providing further understanding of CM immunotherapy.