Volume 11, Issue 2 (9-2024)
jhbmi 2024, 11(2): 131-148 |
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Nizamali M, Mohammadi M, Nezhad Ali Lampajani M. Bioinformatics Analysis of Metabolic Signaling Pathways in Glioblastoma Cancer Stem Cells. jhbmi 2024; 11 (2) :131-148
URL: http://jhbmi.ir/article-1-871-en.html
URL: http://jhbmi.ir/article-1-871-en.html
Assistant Professor, Ph D. in Biology, Department of Biology, Islamshahr branch of Islamic Azad University, Islamshahr, Tehran, Iran
Abstract: (1015 Views)
Introduction: Glioblastoma is one of the common brain cancers that has a high mortality rate. In this study, the genes present in the metabolic pathways of glioblastoma stem cells were examined and nominated using bioinformatics analysis.
Method: In this study, an appropriate dataset was selected for analysis by referring to GEO database. This dataset included gene expression profiles in stem cells isolated from glioblastoma patients. Gene clusters with high and low expression were categorized. Rich databases such as Enrichr, STRING, and GEPIA were used for more accurate data evaluation. Finally, candidate genes were isolated.
Results: 1250 genes were highly expressed in cholesterol biosynthesis, inositol triphosphate metabolism, geranyl diphosphate metabolism, zymosterol biosynthesis, and phosphatidylinositol metabolism, and 1030 genes were low in chondroitin sulfate, dermatan sulfate, N acetyl glucosamine, and glycolysis pathways. After evaluating the relationship between protein networks, genes with high and low expression were selected. All these genes were observed in the survival curve, in about 20 months. The survival rate of patients was less than 10%.
Conclusion: The results of this study showed that the DHCR7 gene had a significant increase in expression. However, the ENO2 gene had a significant decrease in expression. The rest of the genes had a relative increase and decrease in expression.
Method: In this study, an appropriate dataset was selected for analysis by referring to GEO database. This dataset included gene expression profiles in stem cells isolated from glioblastoma patients. Gene clusters with high and low expression were categorized. Rich databases such as Enrichr, STRING, and GEPIA were used for more accurate data evaluation. Finally, candidate genes were isolated.
Results: 1250 genes were highly expressed in cholesterol biosynthesis, inositol triphosphate metabolism, geranyl diphosphate metabolism, zymosterol biosynthesis, and phosphatidylinositol metabolism, and 1030 genes were low in chondroitin sulfate, dermatan sulfate, N acetyl glucosamine, and glycolysis pathways. After evaluating the relationship between protein networks, genes with high and low expression were selected. All these genes were observed in the survival curve, in about 20 months. The survival rate of patients was less than 10%.
Conclusion: The results of this study showed that the DHCR7 gene had a significant increase in expression. However, the ENO2 gene had a significant decrease in expression. The rest of the genes had a relative increase and decrease in expression.
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