Use SRWR algorithm and network pharmacology to predicting the mechanism of the action of Lithospermum erythrorhizon Sieb on atopic dermatitis

Abstract The application of network analysis algorithms promoted the development of network pharmacology. This study aimed to combine network pharmacology and signed random walk with restart (SRWR) to reveal the mechanism by which Lithospermum erythrorhizon Sieb (LES) exerts effects on atopic dermatitis (AD), and illustrate our approach and as a proof of principle for the method. We retrieved the compounds and targets of LES from TCMID and TCMSP and identified important compounds and targets by intersection analysis and PPI network. Then we firstly constructed a human genomic signaling network, which containing KEGG signaling network(including proteins/genes, interactions and signaling pathways in KEGG database), and STRING database(covers of the correlation scores between genes), and ran SRWR and KEGG based on this network, we got the conclusion that the active LES-derived compounds caffeic acid, Isovaleric acid, Arnebinol and Alannan may inhibite PTGS2, HSP90AA1, MAPK14, which are critical mediators involved in PI3K-Akt pathway, Fc epsilon RI signaling pathway, VEGF signaling pathway, Calcium signaling pathway. The application of SRWR could obtain putative LES targets with a lower false-positive rate, and lead to a more reliable foundation and a more suitable TCM herb study. more verification will further illustrate the advantage of this approach in the study of TCM herb.