ArticleBioMed research international2024
Integrated Gene Expression Data-Driven Identification of Molecular Signatures, Prognostic Biomarkers, and Drug Targets for Glioblastoma.
Article in BioMed research international, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Vitiligo Information Resource database v3.NPJ systems biology and applications · 2026Article
- In-silico analysis of deleterious non-synonymous SNPs in the human AVPR1a gene linked to autism.BMC genomics · 2025Article
- Current Research Trends in Glioblastoma: Focus on Receptor Tyrosine Kinases.International journal of molecular sciences · 2025Review
- Transitioning from molecular methods to therapeutic methods: An in‑depth analysis of glioblastoma (Review).Oncology reports · 2025Review
- The Role of the Dysregulation of circRNAs Expression in Glioblastoma Multiforme.Journal of molecular neuroscience : MN · 2025Review
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Authors and funding
7 authors.
Funding
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Abstract
Glioblastoma (GBM) is a highly prevalent and deadly brain tumor with high mortality rates, especially among adults. Despite extensive research, the underlying mechanisms driving its progression remain poorly understood. Computational analysis offers a powerful approach to explore potential prognostic biomarkers, drug targets, and therapeutic agents for GBM. In this study, we utilized three gene expression datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) associated with GBM progression. Our goal was to uncover key molecular players implicated in GBM pathogenesis and potential avenues for targeted therapy. Analysis of the gene expression datasets revealed a total of 78 common DEGs that are potentially involved in GBM progression. Through further investigation, we identified nine hub DEGs that are highly interconnected in protein-protein interaction (PPI) networks, indicating their central role in GBM biology. Gene Ontology (GO) and pathway enrichment analyses provided insights into the biological processes and immunological pathways influenced by these DEGs. Among the nine identified DEGs, survival analysis demonstrated that increased expression of GMFG correlated with decreased patient survival rates in GBM, suggesting its potential as a prognostic biomarker and preventive target for GBM. Furthermore, molecular docking and ADMET analysis identified two compounds from the NIH clinical collection that showed promising interactions with the GMFG protein. Besides, a 100 nanosecond molecular dynamics (MD) simulation evaluated the conformational changes and the binding strength. Our study highlights the potential of GMFG as both a prognostic biomarker and a therapeutic target for GBM. The identification of GMFG and its associated pathways provides valuable insights into the molecular mechanisms driving GBM progression. Moreover, the identification of candidate compounds with potential interactions with GMFG offers exciting possibilities for targeted therapy development. However, further laboratory experiments are required to validate the role of GMFG in GBM pathogenesis and to assess the efficacy of potential therapeutic agents targeting this molecule.
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