ArticleBioMed research international2019
Identification of Potential Biomarkers in Glioblastoma through Bioinformatic Analysis and Evaluating Their Prognostic Value.
Article in BioMed research international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 4 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 4 syntheses or guidelines pooled it, 77 citations in OpenAlex.
- mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses.BMC cancer · 2024Pooled it
- Identification of Paxillin as a Prognostic Factor for Glioblastoma via Integrated Bioinformatics Analysis.BioMed research international · 2022Pooled it
- Glioblastoma multiforme (GBM): An overview of current therapies and mechanisms of resistance.Pharmacological research · 2021Pooled it
- Analysis of the Efficacy and Mechanism of Action of Xuebijing Injection on ARDS Using Meta-Analysis and Network Pharmacology.BioMed research international · 2021Pooled it
- Integrated 2D-3D Proteomic Profiling Identifies MLK4 as a Microenvironment-Responsive Regulator of Chemotherapeutic Resistance in Human Glioblastoma Cells.Biology of the cell · 2026Article
- Bioinformatics analysis of DLGAP5 as a biomarker in lung adenocarcinoma: implications for immune infiltration and targeted therapy.Discover oncology · 2025Article
- Key genes altered in glioblastoma based on bioinformatics (Review).Oncology letters · 2025Review
- Liquid Biopsy as a Diagnostic and Monitoring Tool in Glioblastoma.Medicina (Kaunas, Lithuania) · 2025Review
- A novel golgi related genes based correlation prognostic index can better predict the prognosis of glioma and responses to immunotherapy.Discover oncology · 2025Article
- Cancer/testis antigens FBXO39 and CEP55 expression correlates with survival in GBM patients.PloS one · 2025Article
- Identification of MAD2L1 as a novel biomarker for hepatoblastoma through bioinformatics and machine learning approaches.Frontiers in oncology · 2025Article
- Integration of transcriptomics, proteomics and loss-of-function screening reveals WEE1 as a target for combination with dasatinib against proneural glioblastoma.Cancer letters · 2024Article
- Bioinformatics Analysis and Experimental Validation for Exploring Key Molecular Markers for Glioblastoma.Applied biochemistry and biotechnology · 2024Article
- Circulating Liquid Biopsy Biomarkers in Glioblastoma: Advances and Challenges.International journal of molecular sciences · 2024Review
- SV2B/miR-34a/miR-128 axis as prognostic biomarker in glioblastoma multiforme.Scientific reports · 2024Article
- Identification of genomic biomarkers and their pathway crosstalks for deciphering mechanistic links in glioblastoma.IET systems biology · 2023Article
- CEP55: an immune-related predictive and prognostic molecular biomarker for multiple cancers.BMC pulmonary medicine · 2023Article
- Drugging Hijacked Kinase Pathways in Pediatric Oncology: Opportunities and Current Scenario.Pharmaceutics · 2023Review
- Article
- The identification of key genes and pathways in glioblastoma by bioinformatics analysis.Molecular & cellular oncology · 2023Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glioblastoma is a common malignant tumor in the central nervous system with an extremely poor outcome; understanding the mechanisms of glioblastoma at the molecular level is essential for clinical treatment. In the present study, we used bioinformatics analysis to identify potential biomarkers associated with prognosis in glioblastoma and elucidate the underlying mechanisms. The result revealed that 552 common genes were differentially expressed between glioblastoma and normal tissues based on TCGA, GSE4290, and GSE 50161 datasets. Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction (PPI) network were carried out to gain insight into the actions of differentially expressed genes (DEGs). As a result, 20 genes (CALB1, CDC20, CDCA8, CDK1, CEP55, DLGAP5, KIF20A, KIF4A, NDC80, PBK, RRM2, SYN1, SYP, SYT1, TPX2, TTK, VEGFA, BDNF, GNG3, and TOP2A) were found as hub genes via CytoHubba in Cytoscape and functioned mainly by participating in cell cycle and p53 signaling pathway; among them, RRM2 and CEP55 were considered to have relationship with the prognosis of glioblastoma, especially RRM2. High expression of RRM2 was consistent with shorter overall survival time. In conclusion, our study displayed the bioinformatic analysis methods in screening potential oncogenes in glioblastoma and underlying mechanisms. What is more is that we successfully identified RRM2 as a novel biomarker linked with prognosis, which might be expected to be a promising target for the therapy of glioblastoma.
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Registered trials
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