ArticleFrontiers in genetics2022
Comprehensive development and validation of gene signature for predicting survival in patients with glioblastoma.
Article in Frontiers in genetics, 2022. 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.
- mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses.BMC cancer · 2024Pooled it
- Comprehensive analysis of mitochondria-associated genes in glioblastoma via single-cell and bulk RNA sequencing.Discover oncology · 2025Article
- Assessing the Causal Effect of Circulating Protein-To-Protein Ratio on the Risk of Morbidity of Hepatocellular Carcinoma.Cancer medicine · 2025Article
- Systematic integration of m6A regulators and autophagy-related genes in combination with long non-coding RNAs predicts survival in glioblastoma multiforme.Scientific reports · 2023Article
- Genomic analysis of immunogenic cell death-related subtypes for predicting prognosis and immunotherapy outcomes in glioblastoma multiforme.Open medicine (Warsaw, Poland) · 2023Article
- Molecular and clinical characterization of ANG expression in gliomas and its association with tumor-related immune response.Frontiers in medicine · 2023Article
- Article
- Advancing precision prognostication in neuro-oncology: Machine learning models for data-driven personalized survival predictions in IDH-wildtype glioblastoma.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glioblastoma (GBM) is the most common brain tumor, with rapid proliferation and fatal invasiveness. Large-scale genetic and epigenetic profiling studies have identified targets among molecular subgroups, yet agents developed against these targets have failed in late clinical development. We obtained the genomic and clinical data of GBM patients from the Chinese Glioma Genome Atlas (CGGA) and performed the least absolute shrinkage and selection operator (LASSO) Cox analysis to establish a risk model incorporating 17 genes in the CGGA693 RNA-seq cohort. This risk model was successfully validated using the CGGA325 validation set. Based on Cox regression analysis, this risk model may be an independent indicator of clinical efficacy. We also developed a survival nomogram prediction model that combines the clinical features of OS. To determine the novel classification based on the risk model, we classified the patients into two clusters using ConsensusClusterPlus, and evaluated the tumor immune environment with ESTIMATE and CIBERSORT. We also constructed clinical traits-related and co-expression modules through WGCNA analysis. We identified eight genes (
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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.