Related Experiment Video
Updated: May 8, 2025
Tumor Treating Field Therapy in Combination with Bevacizumab for the Treatment of Recurrent Glioblastoma
Published on: October 27, 2014
FN1 and VEGFA Are Potential Therapeutic Targets in Glioblastoma as Determined by Bioinformatics Analysis
Mijung Im1, Jungwook Roh2, Wonyi Jang1
1Department of Science Education, Korea National University of Education, Cheongju-si, Republic of Korea.
Background/Aim:
Glioblastoma is the most malignant brain tumor, and despite advances in treatment, survival rates are still dismal. Therefore, a comprehensive understanding of the underlying molecular mechanisms of glioblastoma is needed. This study suggests potential therapeutic targets in glioblastoma that may provide new therapeutic insights.
Materials And Methods:
To identify hub genes in glioblastoma, three datasets were selected from the GEO database. After screening DEGs using GEO2R, GO and KEGG analyses were performed using DAVID. The PPI network was visualized using Cytoscape and 7 hub genes were extracted. The prognostic potential of 7 hub genes was investigated using the Gliovis and GEPIA2 databases.
Results:
In total, 176 up-regulated and 263 down-regulated genes were identified. From the PPI network, 7 hub genes were identified including CAMK2A, DLG4, SNAP25, SYT1, MYC, FN1, and VEGFA. Out of the 7 hub genes identified, FN1 and VEGFA have been associated with a poor prognosis in glioblastoma based on the survival analysis.
Conclusion:
This study suggests that high levels of FN1 and VEGFA expression are associated with a poor prognosis in glioblastoma and that both genes are promising targets for glioblastoma therapy. Bioinformatics analysis of DEGs revealed putative targets that might reveal the molecular mechanisms underlying glioblastoma.
Insights
Identifying key genes in glioblastoma is crucial for improving survival rates. This study highlights FN1 and VEGFA as promising therapeutic targets, as their high expression correlates with a poor prognosis in glioblastoma patients.
Area of Science:
- Oncology
- Molecular Biology
- Bioinformatics
Background:
- Glioblastoma is a highly aggressive brain tumor with poor patient survival rates.
- Understanding the molecular mechanisms of glioblastoma is essential for developing effective therapies.
Purpose of the Study:
- To identify novel therapeutic targets for glioblastoma.
- To elucidate the molecular mechanisms underlying glioblastoma progression.
Main Methods:
- Utilized GEO database datasets and GEO2R for differential gene expression analysis.
- Performed Gene Ontology (GO) and KEGG pathway analyses.
- Constructed a protein-protein interaction (PPI) network using Cytoscape to identify hub genes.
- Investigated the prognostic value of hub genes using Gliovis and GEPIA2.
Main Results:
- Identified 176 upregulated and 263 downregulated genes.
- Extracted 7 hub genes from the PPI network: CAMK2A, DLG4, SNAP25, SYT1, MYC, FN1, and VEGFA.
- Survival analysis indicated that high expression of FN1 and VEGFA is associated with a poor prognosis in glioblastoma.
Conclusions:
- FN1 and VEGFA are identified as promising therapeutic targets for glioblastoma.
- High expression levels of FN1 and VEGFA correlate with unfavorable patient prognosis.
- Bioinformatic analysis revealed potential molecular targets for glioblastoma treatment.
