Objective To explore the target and potential mechanism of Baijiang San in alleviating Crohn's disease (CD) by regulating oxidative stress (OS) based on bioinformatics analysis.
Methods The active ingredients of Baijiang San and their corresponding targets were obtained by using TCMSP database. CD related datasets (GSE36807, GSE59071 and GSE102133) were downloaded from GEO database, and the CD differential genes obtained by differential analysis were used as targets for disease treatment. CD differential genes obtained by differential analysis were used as therapeutic targets for diseases. OS-related genes were searched in Genecards database, and the common targets of drug active ingredients, diseases and OS-related genes were obtained. Cytoscape software was used to construct the "compound-target" regulatory network; the protein-protein interaction (PPI) network was made in String database, and the data were imported into Cytoscape software to screen the core targets using Cytohubba plug-in; R software was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis; microRNA (miRNA) was predicted by Mirtarbase database, Starbase database and Targetscan database; the Enrichr database was used to predict transcription factor (TF) and Cytoscape software was used to construct "miRNA-TF-mRNA" networks.
Results There were 175 differential CD genes, including 135 up-regulated genes and 40 down-regulated genes. In TCMSP database, Baijiang San included 9 effective ingredients in Yiyiren, 21 effective ingredients in Fuzi and 13 effective ingredients in Baijiangcao. The top 5 core genes screened by Cytohubba were interleukin-1β (IL-1β), prostaglandin-endoperoxide synthase 2 (PTGS2), C-X-C motif chemokine ligand 8 (CXCL8), intercellular adhesion molecule 1 (ICAM1), vascular endothelial cell growth factor-A receptor (KDR). Quercetin and kaempferol were the most common targets of drug components. The main pathways of KEGG enrichment analysis were advanced glycation end product (AGE)-receptor for AGE (AGE-RAGE) signaling pathway, nuclear factor-kappa B (NF-κB) signaling pathway and tumor necrosis factor (TNF) signaling pathway in diabetic complications; a total of 76 miRNAs and 5 transcription factors were predicted.
Conclusion The mechanisms of Baijiang San in alleviating CD by regulating OS are a multi-component, multi-target and multi-pathway biological process.
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