ArticleBrain and behavior2019
A 5-gene prognostic nomogram predicting survival probability of glioblastoma patients.
Article in Brain and behavior, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Review the role of oxygen-delivering nanobubbles in stroke therapy: A novel approach.F1000Research · 2025Pooled it
- Multilayered insights into the full spectrum of diffuse gliomas: a novel prognostic evaluation model.Discover oncology · 2025Article
- Development of a web-based tool for estimating individualized survival curves in glioblastoma using clinical, mRNA, and tumor microenvironment features with fusion techniques.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- Integrated analysis of single-cell RNA-seq dataset and bulk RNA-seq dataset constructs a prognostic model for predicting survival in human glioblastoma.Brain and behavior · 2022Article
- A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma.BMC cancer · 2021Article
- Development of a nomogram for prognostic prediction of lower-grade glioma based on alternative splicing signatures.Cancer medicine · 2020Article
- Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma.Cancer cell international · 2020Article
- A 5-gene prognostic nomogram predicting survival probability of glioblastoma patients.Brain and behavior · 2019Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGlioblastoma (GBM) remains the most biologically aggressive subtype of gliomas with an average survival of 10 to 12 months. Considering that the overall survival (OS) of each GBM patient is a key factor in the treatment of individuals, it is meaningful to predict the survival probability for GBM patients newly diagnosed in clinical practice. MATERIAL AND
methodsUsing the TCGA dataset and two independent GEO datasets, we identified genes that are associated with the OS and differentially expressed between GBM tissues and the adjacent normal tissues. A robust likelihood-based survival modeling approach was applied to select the best genes for modeling. After the prognostic nomogram was generated, an independent dataset on different platform was used to evaluate its effectiveness.
resultsWe identified 168 differentially expressed genes associated with the OS. Five of these genes were selected to generate a gene prognostic nomogram. The external validation demonstrated that 5-gene prognostic nomogram has the capability of predicting the OS of GBM patients.
conclusionWe developed a novel and convenient prognostic tool based on five genes that exhibited clinical value in predicting the survival probability for newly diagnosed GBM patients, and all of these five genes could represent potential target genes for the treatment of GBM. The development of this model will provide a good reference for cancer researchers.
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