mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses

Abstract Background Glioblastoma multiforme (GBM) is a type of fast-growing brain glioma associated with a very poor prognosis. This study aims to identify key genes whose expression is associated with the overall survival (OS) in patients with GBM. Methods A systematic review was performed using Pu...

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Published in:BMC Cancer
Main Authors: Parisa Azimi, Taravat Yazdanian, Abolhassan Ahmadiani
Format: Article
Language:English
Published: BMC 2024-05-01
Subjects:
Online Access:https://doi.org/10.1186/s12885-024-12345-z
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author Parisa Azimi
Taravat Yazdanian
Abolhassan Ahmadiani
author_facet Parisa Azimi
Taravat Yazdanian
Abolhassan Ahmadiani
author_sort Parisa Azimi
collection DOAJ
container_title BMC Cancer
description Abstract Background Glioblastoma multiforme (GBM) is a type of fast-growing brain glioma associated with a very poor prognosis. This study aims to identify key genes whose expression is associated with the overall survival (OS) in patients with GBM. Methods A systematic review was performed using PubMed, Scopus, Cochrane, and Web of Science up to Journey 2024. Two researchers independently extracted the data and assessed the study quality according to the New Castle Ottawa scale (NOS). The genes whose expression was found to be associated with survival were identified and considered in a subsequent bioinformatic study. The products of these genes were also analyzed considering protein-protein interaction (PPI) relationship analysis using STRING. Additionally, the most important genes associated with GBM patients’ survival were also identified using the Cytoscape 3.9.0 software. For final validation, GEPIA and CGGA (mRNAseq_325 and mRNAseq_693) databases were used to conduct OS analyses. Gene set enrichment analysis was performed with GO Biological Process 2023. Results From an initial search of 4104 articles, 255 studies were included from 24 countries. Studies described 613 unique genes whose mRNAs were significantly associated with OS in GBM patients, of which 107 were described in 2 or more studies. Based on the NOS, 131 studies were of high quality, while 124 were considered as low-quality studies. According to the PPI network, 31 key target genes were identified. Pathway analysis revealed five hub genes (IL6, NOTCH1, TGFB1, EGFR, and KDR). However, in the validation study, only, the FN1 gene was significant in three cohorts. Conclusion We successfully identified the most important 31 genes whose products may be considered as potential prognosis biomarkers as well as candidate target genes for innovative therapy of GBM tumors.
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spelling doaj-art-d2822cfa59cc46eb9901d7330d82104f2025-08-19T23:06:02ZengBMCBMC Cancer1471-24072024-05-0124111610.1186/s12885-024-12345-zmRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analysesParisa Azimi0Taravat Yazdanian1Abolhassan Ahmadiani2Neurosurgeon, Neuroscience Research Center, Shahid Beheshti University of Medical SciencesSchool of Medicine, Capital Medical UniversityNeurosurgeon, Neuroscience Research Center, Shahid Beheshti University of Medical SciencesAbstract Background Glioblastoma multiforme (GBM) is a type of fast-growing brain glioma associated with a very poor prognosis. This study aims to identify key genes whose expression is associated with the overall survival (OS) in patients with GBM. Methods A systematic review was performed using PubMed, Scopus, Cochrane, and Web of Science up to Journey 2024. Two researchers independently extracted the data and assessed the study quality according to the New Castle Ottawa scale (NOS). The genes whose expression was found to be associated with survival were identified and considered in a subsequent bioinformatic study. The products of these genes were also analyzed considering protein-protein interaction (PPI) relationship analysis using STRING. Additionally, the most important genes associated with GBM patients’ survival were also identified using the Cytoscape 3.9.0 software. For final validation, GEPIA and CGGA (mRNAseq_325 and mRNAseq_693) databases were used to conduct OS analyses. Gene set enrichment analysis was performed with GO Biological Process 2023. Results From an initial search of 4104 articles, 255 studies were included from 24 countries. Studies described 613 unique genes whose mRNAs were significantly associated with OS in GBM patients, of which 107 were described in 2 or more studies. Based on the NOS, 131 studies were of high quality, while 124 were considered as low-quality studies. According to the PPI network, 31 key target genes were identified. Pathway analysis revealed five hub genes (IL6, NOTCH1, TGFB1, EGFR, and KDR). However, in the validation study, only, the FN1 gene was significant in three cohorts. Conclusion We successfully identified the most important 31 genes whose products may be considered as potential prognosis biomarkers as well as candidate target genes for innovative therapy of GBM tumors.https://doi.org/10.1186/s12885-024-12345-zGlioblastomamRNASystematic reviewOverall survivalBioinformatics
spellingShingle Parisa Azimi
Taravat Yazdanian
Abolhassan Ahmadiani
mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses
Glioblastoma
mRNA
Systematic review
Overall survival
Bioinformatics
title mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses
title_full mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses
title_fullStr mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses
title_full_unstemmed mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses
title_short mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses
title_sort mrna markers for survival prediction in glioblastoma multiforme patients a systematic review with bioinformatic analyses
topic Glioblastoma
mRNA
Systematic review
Overall survival
Bioinformatics
url https://doi.org/10.1186/s12885-024-12345-z
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AT taravatyazdanian mrnamarkersforsurvivalpredictioninglioblastomamultiformepatientsasystematicreviewwithbioinformaticanalyses
AT abolhassanahmadiani mrnamarkersforsurvivalpredictioninglioblastomamultiformepatientsasystematicreviewwithbioinformaticanalyses