ArticleBioMed research international2019
Identifying Mutually Exclusive Gene Sets with Prognostic Value and Novel Potential Driver Genes in Patients with Glioblastoma.
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 5 papers.
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- Identification and validation of a seven-gene metastasis-associated prognostic model in breast cancer.Frontiers in genetics · 2026Article
- Comprehensive molecular profiling of urologic tumors presented in a molecular tumor board: insights from a real-world precision oncology cohort.Frontiers in oncology · 2025Article
- Prognostic and Therapeutic Implications of Cell Division Cycle 20 Homolog in Breast Cancer.Cancers · 2024Article
- Metabolic Rewiring in Glioblastoma Cancer:Frontiers in oncology · 2022Review
- A Five-Genes Based Diagnostic Signature for Sepsis-Induced ARDS.Pathology oncology research : POR · 2021Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The pathogenesis and prognosis of glioblastoma (GBM) remain poorly understood. Mutual exclusivity analysis can distinguish driver genes and pathways from passenger ones. The purpose of this study was to identify mutually exclusive gene sets (MEGSs) that have prognostic value and to detect novel driver genes in GBM. The genomic alteration profile and clinical information were derived from The Cancer Genome Atlas, and the MEGSA method was used to identify the MEGS. Next, we performed survival analysis and constructed a risk prediction model for prognostic stratification. Leave-one-out cross-validation and permutation test were used to evaluate its performance. Finally, we identified 21 statistically significant MEGSs. We found that the MEGS in the RB pathway was significantly associated with poor prognosis, after adjusting for age and gender (HR = 1.837, 95% CI: 1.192-2.831). Based on the risk prediction model, 208 (80.9%) and 49 (19.1%) patients were assigned to high- and low-risk groups, respectively (log-rank:
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Registered trials
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