ArticleTranslational cancer research2022
Analysis of the key prognostic genes and potential traditional Chinese medicine therapeutic targets in glioblastoma based on bioinformatics and network pharmacology methods.
Article in Translational cancer research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 6 citations in OpenAlex.
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- Analysis of transcription profiles for the identification of master regulators as the key players in glioblastoma.Computational and structural biotechnology journal · 2024Article
- Elucidation of the mechanism of action of ailanthone in the treatment of colorectal cancer: integration of network pharmacology, bioinformatics analysis and experimental validation.Frontiers in pharmacology · 2024Article
- Small molecule drug discovery for glioblastoma treatment based on bioinformatics and cheminformatics approaches.Frontiers in pharmacology · 2024Article
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9 authors at 3 institutions in 1 country.
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Abstract
Background: To analyze the key prognostic genes and potential traditional Chinese medicine targets in glioblastoma (GBM) by bioinformatics and network pharmacology. Methods: GBM datasets were obtained from the Gene Expression Omnibus (GEO) database to clarify the differentially-expressed genes (DEGs) in the carcinoma and paracancerous tissues. The molecular functions (MF) and signaling pathways of enriched DEGs were analyzed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The STRING database and Cytoscape software were used to construct the protein-protein interaction (PPI) network and screen hub genes to focus on genes with greater clinical significance. The transcription expression and prognosis of hub genes were analyzed using the Gene Expression Profiling Interactive Analysis 2 (GEPIA 2) database. The important compounds and target molecules were obtained via the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) database. We identified the active ingredients by setting the property values of pharmacokinetic attribute values. We constructed the network of "Chinese medicine ingredients-DEGs target" and screened out the target genes and active ingredients with high correlation scores. Finally, molecular docking verification was carried out using AutoDock Tools and PyMOL. Results: We obtained 271 DEGs, including 212 up-regulated genes and 59 down-regulated genes and screened ten hub genes. GO and KEGG analyses suggested that the hub genes were mainly involved in the following biological processes: the cell cycle, cell division, and cell adhesion, as well as extracellular matrix adhesion-related pathways, the p53 signaling pathways, and cadherin binding involved in cell-cell adhesion. We established the interaction network between the components and DEGs to screen out the traditional Chinese medicine active component (luteolin) and target genes (BIRC5 and CCNB1) for the treatment of GBM. The molecular docking results showed that the bindings of protein receptors, BIRC5 and CCNB1, with the compound ligand, luteolin, were stable and formed by hydrogen bonding interaction. Conclusions: In this study, we determined that luteolin potentially inhibits glioblastoma proliferation and migration through key target genes, BIRC5 and CCNB1, via bioinformatics and network pharmacology analysis, and affects the prognosis of GBM patients, providing new ideas for clinical targeted therapy and new drug development.
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