• Uncovering Molecular Pathways of Down-regulated Genes in Glioblastoma Using Bioinformatics Analysis
  • Sareh Ranjbar Karim Abadi,1 Ehsan Arefian,2,*
    1. Department of Microbiology, School of Biology, College of Science, University of Tehran, Tehran, Iran.
    2. Pediatric Cell and Gene Therapy Research Center, Tehran University of Medical Sciences. Department of Microbiology, School of Biology, College of Science, University of Tehran, Tehran, Iran.


  • Introduction: Glioblastoma is an extremely aggressive and malignant type of brain cancer that begins in the glial cells of the brain. This cancer is known for its swift tumor growth, ability to infiltrate adjacent brain tissues, and resistance to various treatments. Multiple signaling pathways are integral to the onset and advancement of glioblastoma. This study aims to explore the GEO dataset to gather valuable information for a bioinformatics analysis focused on genes associated with glioblastoma.
  • Methods: Seven microarray expression datasets, namely GSE226990, GSE196694, GSE192710, GSE184859, GSE120627, GSE130857, and GSE115397, were gathered and subjected to statistical analysis to identify genes that are down-regulated (with a Log2FC threshold of ≤- 1) in relation to glioblastoma.
  • Results: The pathway enrichment analysis revealed 93 genes associated with cancer signaling pathways that exhibit significant dysregulation in glioblastoma. This process includes aligning differentially expressed genes with established biological pathways, highlighting genes such as E2F2, CAMK2A, CAMK2D, CDKN1A, EGF, GADD45G, IGF1, PIK3R3, and PDGFRA.
  • Conclusion: Combining multiple omics datasets, including genomics, transcriptomics, and proteomics, can offer a holistic perspective on the molecular changes occurring in glioblastoma. This approach can shed light on the critical molecular pathways that drive glioblastoma progression and potentially identify novel therapeutic targets. Considering the gene pathways and their functions, the most related to the processes of cell proliferation, the inhibition of programmed cell death and the invasion of cancer cells. Among the important pathways and target genes in this research, the following can be mentioned. The insulin-like growth factor 1 (IGF1) signaling pathway plays a crucial role in glioblastoma through multiple mechanisms. A significant factor is the binding of IGF1 to its receptor, IGF1R, which stimulates cell growth and migration by activating downstream signaling pathways, including PI3K/AKT and ERK1/2. This activation is associated with unfavorable outcomes and a decreased response to standard treatments such as Temozolomide in patients with glioblastoma.PIK3R3 is capable of activating the MAPK/ERK pathway in glioblastoma stem-like cells, which enhances their ability to migrate, invade, and resist chemotherapy. Suppressing PIK3R3 or the downstream ERK signaling can hinder these aggressive characteristics. Elucidating the signaling cascades implicated in glioblastoma is essential for designing targeted interventions capable of curbing tumor progression and enhancing patient prognosis. The bioinformatics investigation of genes associated with glioblastoma can shed light on the fundamental molecular processes driving this malignancy and pinpoint promising therapeutic targets for tailored treatment approaches. In summary, a collaborative strategy that merges sophisticated bioinformatics analysis with thorough cellular and clinical research is essential for thoroughly understanding the molecular foundations of glioblastoma and creating effective targeted treatments. The integration of these complementary approaches offers significant potential for enhancing patient outcomes in the face of this challenging disease.
  • Keywords: Glioblastoma multiform, In silico analysis, Biomarker genes, Diagnosis.