ArticleTechnology in cancer research & treatment
Hub biomarkers for the diagnosis and treatment of glioblastoma based on microarray technology.
Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 14 citations in OpenAlex.
- KIF23 in disease pathogenesis and therapeutics : from molecular mechanisms to clinical translation.Cancer cell international · 2026Review
- Decoding the Role of Kinesin Superfamily Proteins in Glioma Progression.Journal of molecular neuroscience : MN · 2025Review
- Bioinformatics Analysis Screening and Identification of Key Biomarkers and Drug Targets in Human Glioblastoma.Current medicinal chemistry · 2025Article
- Discovery of key molecular signatures for diagnosis and therapies of glioblastoma by combining supervised and unsupervised learning approaches.Scientific reports · 2024Article
- Signature Genes Selection and Functional Analysis of Astrocytoma Phenotypes: A Comparative Study.Cancers · 2024Article
- Understanding the immunosuppressive microenvironment of glioma: mechanistic insights and clinical perspectives.Journal of hematology & oncology · 2024Review
- The identification of key genes and pathways in glioblastoma by bioinformatics analysis.Molecular & cellular oncology · 2023Article
- An update on the molecular biology of glioblastoma, with clinical implications and progress in its treatment.Cancer communications (London, England) · 2022Review
- HSA-miR-34a-5p regulates the SIRT1/TP53 axis in prostate cancer.American journal of translational research · 2022Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGlioblastoma (GBM) is the most common clinical intracranial malignancy worldwide, and the most common supratentorial tumor in adults. GBM mainly causes damage to the brain tissue, which can be fatal. This research explored potential gene targets for the diagnosis and treatment of GBM using bioinformatic technology.
methodsPublic data from patients with GBM and controls were downloaded from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) were identified by Gene Expression Profiling Interactive Analysis (GEPIA) and Gene Expression Omnibus 2R (GEO2R). Construction of the protein-protein interaction network and the identification of a significant module were performed. Subsequently, hub genes were identified, and their expression was examined and compared by real-time quantitative (RT-q)PCR between patients with GBM and controls.
resultsGSE122498 (GPL570 platform), GSE104291 (GPL570 platform), GSE78703_DMSO (GPL15207 platform), and GSE78703_LXR (GPL15207 platform) datasets were obtained from the GEO. A total of 130 DEGs and 10 hub genes were identified by GEPIA and GEO2R between patients with GBM and controls. Of these, strong connections were identified in correlation analysis between
conclusionsThe hub genes
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.