AUTHOR=Guo Xiang , Ji Jinyu , Jose Kumar Sreena Goutham Sanker , Hou Xiaoqiang , Luo Yanan , Fu Xianyun , Mei Zhigang , Feng Zhitao 

TITLE=Computational Prediction of Antiangiogenesis Synergistic Mechanisms of Total Saponins of Panax japonicus Against Rheumatoid Arthritis

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 11 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2020.566129

DOI=10.3389/fphar.2020.566129

ISSN=1663-9812

ABSTRACT=Objective: To investigate the anti-angiogenesis mechanisms and key targets of total saponins of Panax japonicus (TSPJ) in the treatment of rheumatoid arthritis (RA).
Methods: RStudio3.6.1 software was used to obtain differentially expressed genes (DEGs) by analyzing the difference of gene expression in the synovial tissue of RA and predict the potential targets of active compounds from TSPJ by Pharmmapper and SwissTargetPrediction databases. We received the overlapping genes by inter-sectional analysis of DEGs and drug targets. Based on the overlapping genes, we used Cytoscape 3.7.2 software to construct the protein-protein interactions (PPI) network and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to find out the mechanisms of the treatment. Finally, the correlation of angiogenesis-related genes was explored.
Results: Altogether 2670 DEGs were obtained by differential analysis, and 371 drug targets were predicted for 4 active components (Araloside A, Chikusetsusaponin IVa, Ginsenoside Rg2, and Ginsenoside Ro). A total of 52 overlapping genes were constructed for the PPI network and the KEGG analysis. However, only 41 genes in the PPI network have protein interaction. The results of the KEGG enrichment analysis were all related to angiogenesis, including the vascular endothelial growth factor (VEGF) and the Hypoxia-inducible factor 1 (HIF-1) signaling pathway. 7 negative correlation genes and 16 positive correlation genes were obtained by correlational analysis of DEGs in VEGF and HIF-1 signaling pathway. SRC proto-oncogene, non-receptor tyrosine kinase (SRC) and the signal transducer and the activator of transcription 3 (STAT 3) are with a higher value of the degree and show a significant correlation in pathways were regarded as key targets.
Conclusion: In the current study, we found that anti-angiogenesis is one of the effective strategies of TSPJ against RA; SRC and STAT 3 may be the key targets of TSPJ acting on VEGF and HIF-1 signaling pathways, which will provide new insight for the treatment of RA from angiogenesis.