Investigating the therapeutic mechanism of Xiaotan Sanjie Formula for gastric cancer via network pharmacology and molecular docking: A review

Abstract

Xiaotan Sanjie Formula (XTSJF), a traditional Chinese prescription, holds promising potential in addressing gastric cancer (GC). Despite this, the fundamental constituents and underlying mechanisms that define XTSJF’s attributes remain enigmatic. Against this backdrop, this study endeavors to unravel the latent mechanisms driving XTSJF’s impact on GC, leveraging the synergistic prowess of network pharmacology and molecular docking methodologies. To understand the potential mechanism of XTSJF against GC, this study used network pharmacology, molecular docking, and bioinformatics analytic methodologies. There are 135 active components where the active ingredients with a higher degree value are quercetin, β-sitosterol, naringenin, nobiletin, and kaempferol and 167 intersecting targets in which TP53, MAPK3, MAPK1, STAT3, and AKT1 were key targets were identified in XTSJF in the treatment of GC. According to GO and KEGG analyses, XTSJF is mostly involved in the positive control of transcription from the RNA polymerase II promoter, enzyme interaction, and other biological processes in GC. KEGG analysis shows that XTSJF treated GC primarily by regulating signaling pathways including the TNF, PI3K-Akt, and MAPK signaling pathways. According to the results of the PPI network and molecular docking, quercetin, β-sitosterol, naringenin, nobiletin, and kaempferol exhibit stronger affinity with TP53, MAPK3, MAPK1, STAT3, and AKT1. This study indicates the active components of XTSJF as well as its possible molecular mechanism against GC, and it serves as a foundation for future fundamental research.

The therapeutic effect and targets of herba Sarcandrae on breast cancer and the construction of a prognostic signature consisting of inflammation-related genes

“…In cancer research, network pharmacology is helpful for comprehensively understanding the role of drugs in complex biological systems and for predicting and analyzing the pharmacological effects and potential mechanisms of drugs [ 22 ]. Combining these two technologies gives full play to their advantages [ 23 ]. Molecular docking can provide detailed molecular interaction data for network pharmacology to more accurately construct interaction networks between drugs and cancer-related target proteins.…”