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: p < 0.001, adjusted p=0.001). Additionally, we found that SPTA1, a novel gene involved in the MEGS, was mutually exclusive with members of cell cycle, P53, and RB pathways. In conclusion, the MEGS in the RB pathway had considerable clinical value for GBM prognostic stratification. Mutated SPTA1 may be involved in GBM development.
Highlights
Glioblastoma (GBM) is the most common and biologically aggressive primary brain tumor [1, 2]
Other studies have identified mutual exclusivity (ME) patterns related to GBM; no study has analyzed their prognostic values [9, 10, 13, 15,16,17,18,19,20]. erefore, one purpose of this study is to identify mutually exclusive gene sets (MEGSs) and detect novel infrequently driver genes in GBM by integrating nonsynonymous single-nucleotide variants (SNVs) and copy number variations (CNVs) using Mutually exclusive gene set analysis (MEGSA)
Ese MEGSs involved 12 genetic abnormalities and a metagene, in which RB1, TP53, IDH1, PTEN, SPTA1, and NF1 occurred as single-nucleotide variants; CDK4, MDM2, EGFR, PDGFRA, and the metagene (MET, CAPZA2, ST7, ST7-AS1, ST7-OT4) possessed copy number amplification; CDKN2A and PTEN possessed copy number deletion
Summary
Glioblastoma (GBM) is the most common and biologically aggressive primary brain tumor [1, 2]. Each year, it affects over 10,000 new patients in the United States [3]. Mutual exclusivity of genomic alterations, indicating that genes belonging to the same functional pathway tend not to mutate simultaneously in the same patient, has been observed in various cancer types [11, 12]. Several methods based on mutual exclusivity have been proposed to uncover novel infrequent cancer drivers and investigate their functional relationship [9, 10, 13]. Simulation studies have indicated that MEGSA outperformed other methods, such as Dentrix, BioMed Research International
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