Dissection of action mechanisms of Zuogui Pill in the treatment of liver cancer based on machine learning and network pharmacology: A review

Abstract

This study aimed to investigate the underlying mechanism of Zuogui Pill in its efficacy against liver cancer, employing a combination of data mining approaches and network pharmacology methods. A novel clustering analysis algorithm was proposed to identify the core gene modules of Zuogui Pill. This algorithm successfully identified 5 core modules, with the first large module comprised of twelve proteins forming a 12-clique, representing the strongest connections among them. By utilizing GEO platform, ten key target proteins were detected, including FOS, PTGS2, and MYC. According to the GO annotation and KEGG analysis, desired target proteins were significantly enriched in various biological processes (BP). The analysis showed that ten key targets were strongly associated with signaling pathways mainly centered on MAPK and PI3K-Akt pathway. Additionally, molecular docking revealed strong binding affinities between core active ingredients of Zuogui Pill and these key targets, and the best affinity modes were observed for PTGS2-Sesamin, PRKCA-Sesamin, FOS-delta-Carotene. In order to establish the relationships between clinical symptoms and drug targets, a heterogeneous targets-related network was constructed. A total of 60 key target-symptom association pairs were detected, exemplified by the strongly association between fever and PTGS2 through the intermediary of Shu Di Huang. In summary, symptom-target associations are valuable in uncovering the underlying molecular mechanisms of Zuogui Pill. Our work reinforced the notion that Zuogui pill exhibits therapeutic potential on liver cancer through network targets, as well as synergistic effects of multi-component and multi-pathway. This study provided specific references for future experiments at the cost of less time.