Classification of Glioma Based on Prognostic Alternative Splicing
44 Pages Posted: 22 Feb 2019More...
Background: Previously developed classifications of glioma have provided enormous advantages for the diagnosis and treatment of glioma. Although the role of alternative splicing (AS) in cancer, especially in glioma, has been validated, a comprehensive analysis of prognostic AS in glioma has not yet been conducted.
Methods: TCGA glioblastoma (GBM) and low-grade glioma (LGG) datasets were downloaded from TCGA data portal and used to analyzed the prognostic splicing events. Consensus clustering analysis were used to classified glioma samples and correlation analysis was used to characterize regulatory network of splicing factors and splicing events.
Results: We analyzed prognostic splicing events and proposed novel splicing classifications across pan-glioma samples (labeled pST1-7) and across GBM samples (labeled ST1-3). Distinct splicing profiles between GBM and LGG were observed, and the primary discriminator for the pan-glioma splicing classification was tumor grade. Subtype-specific splicing events were identified; one example is AS of zinc finger proteins, which is involved in glioma prognosis. Furthermore, correlation analysis of splicing factors and splicing events identified SNRPB and CELF2 as hub splicing factors that upregulated and downregulated oncogenic AS, respectively.
Conclusion: A comprehensive analysis of prognostic AS in glioma was conducted in this study, shedding new light on glioma heterogeneity and providing new insights into glioma diagnosis and treatment.
Funding: This study was supported by the National Natural Science Foundation of China (Grant nos. 81773290, 81472315, and 81872442), Guangdong Science and Technology Department (2017A030313497, 2016A040403053).
Declaration of Interest: The authors report no competing interests.
Ethical Approval: MIssing
Keywords: Glioma, Glioblastoma, Alternative splicing, Molecular pathology, Prognosis, Classification
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