Integrative bioinformatic analysis to identify potential phytochemical candidates for glioblastoma

Glioblastoma (GBM) is one of the most malignant forms of cancer with the lowest survival ratio. Our study aims to utilize an integrated bioinformatic analysis to identify hub genes against GBM and explore the active phytochemicals with drug-like properties in treating GBM. The study employed databases of DisGenet, GeneCards, and Gene Expression Omnibus to retrieve GBM-associated genes, revealing 142 overlapping genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were used to analyze the role of these genes, which were involved in cancer-associated cell signaling pathways with tyrosine kinase activities and mainly enriched in the Nucleus. Furthermore, the hub genes identification through Cytoscape identified the top 10 ranked genes in a network, which were used as targets to dock against phytochemicals retrieved from the NPACT database having the ability to pass the blood-brain barrier and drug-likeness properties. The molecular docking and dynamics simulation studies predicted the binding of Isochaihulactone and VismioneB to the active site residues of EGFR and SRC genes. In contrast, Resveratrol binds to key residues of PIK3CA. Further, the binding free energy of the docked complex was calculated by performing MM-GBSA analysis, providing a detailed understanding of the underlying molecular interactions. The results offer interactional and structural insights into candidate phytochemicals towards GBM-associated top-ranked proteins. However, validation studies must be done through both in vitro and in vivo disease models to strengthen our computational results.