ArticleDiscover oncology2024
Unraveling molecular signatures and prognostic biomarkers in glioblastoma: a comprehensive study on treatment resistance and personalized strategies.
Article in Discover oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Sanggenol L Induces Endoplasmic Reticulum Stress and Triggers Cell Apoptosis in Glioblastoma by Binding to BiP.International journal of biological sciences · 2026Article
- Hippo pathway suppression reprograms TNFα-primed glioblastoma extracellular vesicles transcripts cargo to drive mesenchymal stem/stromal cells vasculogenic mimicry.Cell communication and signaling : CCS · 2025Article
- Integrative analysis of EMT-driving genes identifies a prognostic signature and GJB2 as a potential biomarker in glioblastoma.Frontiers in cell and developmental biology · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGlioblastoma (GBM) is a highly aggressive primary brain tumor with limited treatment success and poor prognosis. Despite surgical resection and adjuvant therapies, GBM often recurs, and resistance to radiotherapy and temozolomide presents significant challenges. This study aimed to elucidate molecular signatures associated with treatment responses, identify potential biomarkers, and enhance personalized treatment strategies for GBM.
methodsWe conducted a comprehensive analysis using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The GEO dataset (GSE206225) was used to identify differentially expressed genes (DEGs) between radiation-sensitive/resistant and temozolomide-sensitive/resistant GBM samples. TCGA data were utilized for subsequent analyses, including Lasso-Cox regression, risk score model construction, Kaplan-Meier survival analysis, and gene set enrichment analysis (GSEA). Hub genes were identified through survival analysis, and a gene prognostic nomogram was developed. Additionally, validation of the three-gene risk signature through multiple external cohorts and validation of protein expression levels were performed.
resultsDEG analysis identified 111 genes associated with chemoradiotherapy resistance, providing insights into the complex landscape of GBM treatment response. The risk score model effectively stratified patients, showing significant differences in overall survival and progression-free survival. GSEA offered a deeper understanding of pathway activities, emphasizing the intricate molecular mechanisms involved. NNAT, IGFBP6, and CYGB were identified as hub genes, and a gene prognostic nomogram demonstrated predictive accuracy.
conclusionThis study sheds light on the molecular intricacies governing GBM treatment response. The identified hub genes and the gene prognostic nomogram offer valuable tools for predicting patient outcomes and guiding personalized treatment strategies. These findings contribute to advancing our understanding of GBM biology and may pave the way for improved clinical management.
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