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This bioinformatics study evaluates secretome biomarkers and related pathways by integrating public datasets, identifying differentially expressed genes and signaling pathways, and linking hub genes to disease mechanisms and patient progression and survival.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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glioblastoma cancer stem cells: A bioinformatics analysis  \nEhsan Jangholi 1,2  | Hoda Ahmari Tehran 3 | Afsaneh Ghasemi 4 | Mohammad Hoseinian 1 | Sina Firoozi 5 | Seyed Mohammad Ghodsi 1,2 | Mona Tamaddon 6 | Ahmad Bereimipour 7 | Mahmoudreza Hadjighassem 1  \n1Brain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran 2Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran  \n3Department of Medical Education, Qom University of Medical Sciences, Qom, Iran 4Department of Public Health, School of Health, Fasa University of Medical Sciences, Fasa, Iran 5School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran  \n6Chronic Disease Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran 7Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, Texas, USA  \nCorrespondence  \nMahmoudreza Hadjighassem, Brain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran. [Email:](Email: mhadjighassem@tums.ac.ir)[ mhadjighassem@tums.ac.ir](Email: mhadjighassem@tums.ac.ir)  \nFunding information  \nBrain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Grant/Award Number: 57665  \nAbstract  \nBackground: Glioblastoma (GBM) is a malignant brain tumor that frequently occurs alongside other central nervous system (CNS) conditions. The secretome of GBM cells contains a diverse array of proteins released into the extracellular space, influencing the tumor microenvironment. These proteins can serve as potential biomarkers for GBM due to their involvement in key biological processes, exploring the secretome biomarkers in GBM research represents a cutting-edge strategy with significant potential for advancing diagnostic precision, treatment monitoring, and ultimately improving outcomes for patients with this challenging brain cancer.  \nAim: This study was aimed to investigate the roles of secretome biomarkers and their pathwayes in GBM through bioinformatics analysis.  \nMethods and Results: Using data from the Gene Expression Omnibus and the Cancer Genome Atlas datasets—where both healthy and cancerous samples were analyzed—we used a quantitative analytical framework to identify differentially expressed genes (DEGs) and cell signaling pathways that might be related to GBM. Then, we performed gene ontology studies and hub protein identifications to estimate the roles of these DEGs after finding disease-gene connection networks and signaling pathways. Using the GEPIA Proportional Hazard Model and the Kaplan–Meier estimator, we widened our analysis to identify the important genes that may play a role in both progression and the survival of patients with GBM. In total, 890 DEGs, including 475 and 415 upregulated and downregulated were identified, respectively. Our results revealed that SQLE, DHCR7, delta-1 phospholipase C (PLCD1), and MINPP1  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2024 The Authors. Cancer Reports published by Wiley Periodicals LLC.  \nCancer Reports. 2024;7:e2080 .  \n[https://doi.org/10.1002/cnr2.2080](https://doi.org/10.1002/cnr2.2080)  \n[wileyonlinelibrary.com/journal/cnr2](wileyonlinelibrary.com/journal/cnr2)  \n1 of 12  \n2 of 12  \nJANGHOLI  \nET AL.  \ngenes are highly expressed, and the Enolase 2 (ENO2) and hexokinase-1 (HK1) genes are low expressions.  \nConclusion: Hence, our findings suggest novel mechanisms that affect the occurrence of GBM development, growth, and/or establishment and may also serve as secretory biomarkers for GBM prognosis","cbCaippZLu07I3f2","https://ap.wps.com/l/cbCaippZLu07I3f2","pdf",9204479,12,"English","# Abstract\n## Background\n## Aim\n## Methods and Results\n## Conclusion\n# Introduction\n## Glioblastoma overview and treatment challenges\n## Secretome biomarkers and tumor microenvironment","[{\"question\":\"What is the goal of this bioinformatics study on GBM?\",\"answer\":\"To investigate the roles of secretome biomarkers and their pathways in glioblastoma (GBM) using bioinformatics analysis and integrated public datasets.\"},{\"question\":\"Which datasets were used to identify GBM-related genes and pathways?\",\"answer\":\"Gene Expression Omnibus and the Cancer Genome Atlas datasets were used to analyze healthy and cancerous samples and identify differentially expressed genes and related signaling pathways.\"},{\"question\":\"How were patient progression and survival-related genes assessed?\",\"answer\":\"The study used the GEPIA Proportional Hazard Model and Kaplan–Meier estimator to identify genes associated with GBM progression and patient survival.\"}]","Evaluation of secretome biomarkers in glioblastoma cancer stem cells - A bioinformatics analysis | PDF",1789990819]