ArticleBioMed research international2019
A 6-Gene Risk Signature Predicts Survival of Glioblastoma Multiforme.
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 13 papers.
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Who cites it
13 citing papers in PubMed, 20 citations in OpenAlex.
- Characterization of Exhausted T Cell Signatures in Pan-Cancer Settings.International journal of molecular sciences · 2025Article
- Cross-Talks between RKIP and YY1 through a Multilevel Bioinformatics Pan-Cancer Analysis.Cancers · 2023Article
- Brain radiotoxicity-related 15CAcBRT gene expression signature predicts survival prognosis of glioblastoma patients.Neuro-oncology · 2023Article
- Integrative genomic analysis facilitates precision strategies for glioblastoma treatment.iScience · 2022Article
- Development of an immune-related gene pairs signature for predicting clinical outcome in lung adenocarcinoma.Scientific reports · 2021Article
- Integrated Analysis to Obtain Potential Prognostic Signature in Glioblastoma.Frontiers in integrative neuroscience · 2021Article
- Identification of the Prognostic Signatures of Glioma With DifferentFrontiers in oncology · 2021Article
- The clinical significance of glutathione peroxidase 2 in glioblastoma multiforme.Translational neuroscience · 2021Article
- An Immune-Related Gene Pairs Signature for Predicting Survival in Glioblastoma.Frontiers in oncology · 2021Article
- Integrative Analysis of DNA Methylation and Transcriptome Identifies a Predictive Epigenetic Signature Associated With Immune Infiltration in Gliomas.Frontiers in cell and developmental biology · 2021Article
- Promoting Prognostic Model Application: A Review Based on Gliomas.Journal of oncology · 2021Review
- Identification of the potential biomarkers in patients with glioma: a weighted gene co-expression network analysis.Carcinogenesis · 2020Article
- High Levels of HIST1H2BK in Low-Grade Glioma Predicts Poor Prognosis: A Study Using CGGA and TCGA Data.Frontiers in oncology · 2020Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aims to develop novel signatures for glioblastoma multiforme (GBM).
methodsGBM expression profiles from The Cancer Genome Atlas (TCGA) were downloaded and DEGs between tumor and normal samples were identified by differential expression analysis (DEA). A risk signature was developed by applying weighted gene coexpression network analysis (WGCNA) and Cox regression analysis. Patients were divided into high and low risk group, followed by evaluating the performance of the signature via Kaplan-Meier curve analysis. In addition, the prognostic significance of the signature was further validated using an independent validation dataset from Chinese Glioma Genome Atlas (CGGA). DEGs between high and low risk group were subjected to functional annotation.
resultsA total of 748 DEGs were identified between primary tumor and normal samples. Following WGCNA and Cox regression analysis, 6 DEGs were identified and used to construct a risk signature. The signature showed high performance in both training and validation dataset. Subsequently, 397 DEGs were identified between high and low risk group. These DEGs were mainly enriched in terms related to calcium signaling, cAMP-mediated signaling, and synaptic transmission.
conclusionsThe risk signature may contribute to GBM diagnosis in future clinical practice.
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