Exploring the Therapeutic Mechanisms and Prognostic Targets of Biochanin A in Glioblastoma via Network Pharmacology and Bioinformatics Analysis

Abstract

Abstract Purpose Glioblastoma (GBM) is the most malignant type of brain tumor, characterized by a poor prognosis and high recurrence and mortality rates. Biochanin A (BCA) has demonstrated promising clinical antitumor effects. This study aimed to explore the pharmacological mechanisms by which BCA acts against GBM. Methods Network pharmacology was employed to identify overlapping target genes between BCA and GBM. Differentially expressed genes were extracted from the Gene Expression Profiling Interactive Analysis 2 database and visualized using VolcaNose. The STRING database was used to analyze interactions among these overlapping genes. Protein–protein interaction networks were constructed using Cytoscape 3.8.1 software. Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology enrichment analyses were conducted using DAVID. Survival analyses for these genes were performed using the GEPIA2 database. The Chinese Glioma Genome Atlas database was employed to analyze correlations between key prognostic genes. Molecular docking was verified using the DockThor database and visualized with PyMol software. Results A total of 63 target genes were initially identified as potential targets for BCA in the treatment of GBM. Enrichment analysis results suggested that the pharmacological mechanisms of BCA primarily involved inhibition of the cell cycle, induction of cell apoptosis, and regulation of immunity. Based on these findings, AKT1, EGFR, CASP3, and MMP9 were preliminarily predicted as key prognostic target genes for BCA in treating GBM. Conclusion In this study, target prediction based on network pharmacology and bioinformatics analyses offered a novel research approach for the multi-target treatment of GBM using BCA.