ArticleBioinformatics and biology insights2024
Identification of Molecular Mechanisms of Ameloblastoma and Drug Repositioning by Integration of Bioinformatics Analysis and Molecular Docking Simulation.
Article in Bioinformatics and biology insights, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Comparative Performance Evaluation of GCN, GAT, and GraphSAGE Architectures for Drug-Gene Interaction Prediction in Ameloblastoma via MEK-Pathway Targeting.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- Unlocking Benzosampangine's Potential: A Computational Approach to Investigating, Its Role as a PD-L1 Inhibitor in Tumor Immune Evasion via Molecular Docking, Dynamic Simulation, and ADMET Profiling.Bioinformatics and biology insights · 2024Article
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4 authors.
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Abstract
Background: Ameloblastoma (AM) is a benign tumor locally originated from odontogenic epithelium that is commonly found in the jaw. This tumor makes aggressive invasions and has a high recurrence rate. This study aimed to investigate the differentially expressed genes (DEGs), biological function alterations, disease targets, and existing drugs for AM using bioinformatics analysis. Methods: The data set of AM was retrieved from the GEO database (GSE132474) and identified the DEGs using bioinformatics analysis. The biological alteration analysis was applied to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Protein-protein interaction (PPI) network analysis and hub gene identification were screened through NetworkAnalyst. The transcription factor-protein network was constructed via OmicsNet. We also identified candidate compounds from L1000CDS2 database. The target of AM and candidate compounds were verified using docking simulation. Results: Totally, 611 DEGs were identified. The biological function enrichment analysis revealed glycosaminoglycan and GABA (γ-aminobutyric acid) signaling were most significantly up-regulated and down-regulated in AM, respectively. Subsequently, hub genes and transcription factors were screened via the network and showed FOS protein was found in both networks. Furthermore, we evaluated FOS protein to be a therapeutic target in AMs. Candidate compounds were screened and verified using docking simulation. Tanespimycin showed the greatest affinity binding value to bind FOS protein. Conclusions: This study presented the underlying molecular mechanisms of disease pathogenesis, biological alteration, and important pathways of AMs and provided a candidate compound, Tanespimycin, targeting FOS protein for the treatment of AMs.
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