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Iran J Neurosurg 2025, 11 - Continuous Publishing: 0-0 |
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Ethics code: IR.GUMS.REC.1403.037
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Abbasi H, Morovat S, Yousefzadeh-Chabok S, Norollahi S E, Nejatifar F, Delpasand K, et al . Bioinformatics-based Study of Cancer Stem Cell Function as Diagnostic Biomarkers in Glioblastoma Multiforme. Iran J Neurosurg 2025; 11 : 22
URL: http://irjns.org/article-1-500-en.html
URL: http://irjns.org/article-1-500-en.html
Haniye Abbasi 
, Saman Morovat , Shahrokh Yousefzadeh-Chabok1 , Seyedeh Elham Norollahi , Fatemeh Nejatifar , Kourosh Delpasand2 , Kosar Babaei , Afshin Dalili3 , Ali Akbar Samadani *4
, Saman Morovat , Shahrokh Yousefzadeh-Chabok1 , Seyedeh Elham Norollahi , Fatemeh Nejatifar , Kourosh Delpasand2 , Kosar Babaei , Afshin Dalili3 , Ali Akbar Samadani *4
1- Guilan Road Trauma Research Center, Trauma Institute, Guilan University of Medical Sciences, Rasht, Iran. & Neuroscience Research Center, Trauma Institute, Guilan University of Medical Sciences, Rasht, Iran.
2- Department of Medical Ethics, Guilan University of Medical Sciences, Rasht, Iran.
3- Guilan Road Trauma Research Center, Trauma Institute, Guilan University of Medical Sciences, Rasht, Iran
4- Guilan Road Trauma Research Center, Trauma Institute, Guilan University of Medical Sciences, Rasht, Iran ,a.a.hormoz@gmail.com
2- Department of Medical Ethics, Guilan University of Medical Sciences, Rasht, Iran.
3- Guilan Road Trauma Research Center, Trauma Institute, Guilan University of Medical Sciences, Rasht, Iran
4- Guilan Road Trauma Research Center, Trauma Institute, Guilan University of Medical Sciences, Rasht, Iran ,
Abstract: (112 Views)
Background and Aim: Glioblastoma multiforme (GBM) is a highly aggressive primary brain tumor with poor prognosis, largely driven by glioma stem-like cells (GSCs). Novel biomarkers that both characterize GSCs and predict patient survival are urgently needed. This study aims to identify GSCrelated genes with prognostic value in GBM through integrated bioinformatics analyses.
Methods and Materials/Patients: Microarray data (GSE23806) comparing glioma cell lines and GSCs were downloaded via GEOquery in R and normalized. Differentially expressed genes (DEGs) were identified using the limma package (log₂ fold change ≥1, adjusted P<0.01). DEGs were crossreferenced with TCGA GBM tumor data via GEPIA2 to find common dysregulated genes. Kaplan– Meier survival analyses were performed using GEPIA2 to identify genes associated with survival. Protein–protein interaction (PPI) networks were built using GeneMANIA in cytoscape. Functional enrichment analyses (GO and KEGG) were conducted using clusterProfiler, with q<0.01.
Results: A total of 1212 DEGs were i SMAD13 dentified in GSCs vs glioma cell lines and 5224 DEGs in GBM tumors vs normal tissues; 355 genes were commonly differentially expressed. Survival analysis revealed six genes (APLP1, CA14, PTPRN2, POR, ARMC10, and SMAD13) significantly associated with overall survival. Among these, CA14 and SMAD13 appeared protective (Hazzard ratio [R]~0.53–0.56), while APLP1, PTPRN2, POR, and ARMC10 were associated with risk (HR 1.6– 1.7). A PPI network with 26 nodes and 256 edges underscored extensive functional interactions. Enrichment analyses highlighted dysregulation in pathways including hypoxia response, metabolic processes, TGFβ signaling, and stem-like phenotypes.
Conclusion: Our integrated bioinformatics framework identified six novel GSC-related genes with prognostic significance in GBM, representing diverse biological roles that range from tumor suppression to oncogenic signaling. These genes hold promise as diagnostic or therapeutic targets, but require experimental validation, including gene expression assays and functional studies to substantiate their roles in GBM pathogenesis and therapy resistance.
Methods and Materials/Patients: Microarray data (GSE23806) comparing glioma cell lines and GSCs were downloaded via GEOquery in R and normalized. Differentially expressed genes (DEGs) were identified using the limma package (log₂ fold change ≥1, adjusted P<0.01). DEGs were crossreferenced with TCGA GBM tumor data via GEPIA2 to find common dysregulated genes. Kaplan– Meier survival analyses were performed using GEPIA2 to identify genes associated with survival. Protein–protein interaction (PPI) networks were built using GeneMANIA in cytoscape. Functional enrichment analyses (GO and KEGG) were conducted using clusterProfiler, with q<0.01.
Results: A total of 1212 DEGs were i SMAD13 dentified in GSCs vs glioma cell lines and 5224 DEGs in GBM tumors vs normal tissues; 355 genes were commonly differentially expressed. Survival analysis revealed six genes (APLP1, CA14, PTPRN2, POR, ARMC10, and SMAD13) significantly associated with overall survival. Among these, CA14 and SMAD13 appeared protective (Hazzard ratio [R]~0.53–0.56), while APLP1, PTPRN2, POR, and ARMC10 were associated with risk (HR 1.6– 1.7). A PPI network with 26 nodes and 256 edges underscored extensive functional interactions. Enrichment analyses highlighted dysregulation in pathways including hypoxia response, metabolic processes, TGFβ signaling, and stem-like phenotypes.
Conclusion: Our integrated bioinformatics framework identified six novel GSC-related genes with prognostic significance in GBM, representing diverse biological roles that range from tumor suppression to oncogenic signaling. These genes hold promise as diagnostic or therapeutic targets, but require experimental validation, including gene expression assays and functional studies to substantiate their roles in GBM pathogenesis and therapy resistance.
Article number: 22
Keywords: Glioblastoma multiforme, bioinformatics, cancer stem cells, glioma cancer cells, signaling pathway
Type of Study: Research |
Subject:
Brain Tumors
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This is an open access article distributed under the terms of the Creative Commons Attribution License (CC-By-NC), which permits use, distribution, and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
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