Abstract
BackgroundPreviously 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 AS in glioma has not yet been conducted. In this study, we aimed at classifying glioma based on prognostic AS.MethodsUsing the TCGA glioblastoma (GBM) and low-grade glioma (LGG) datasets, we analyzed prognostic splicing events. Consensus clustering analysis was conducted to classified glioma samples and correlation analysis was conducted to characterize regulatory network of splicing factors and splicing events.ResultsWe 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.ConclusionA comprehensive analysis of AS in glioma was conducted in this study, shedding new light on glioma heterogeneity and providing new insights into glioma diagnosis and treatment.
Highlights
Developed classifications of glioma have provided enormous advantages for the diagnosis and treatment of glioma
We focused on a novel approach, classification of glioma based on alternative splicing (AS) event profiles, to understand glioma more comprehensively and explore new ideas for its diagnosis and treatment
To conduct further clustering analysis, which was needed to ensure that the data were not null and exclude the splicing events affected by outliers, splicing events that met the following conditions were included: 1) the Percent Spliced In (PSI) value was not missing for any samples; and 2) the sample size of each group was higher than 5% of the total size (n ≥ 8 in GBM, n ≥ 33 in glioma)
Summary
Developed classifications of glioma have provided enormous advantages for the diagnosis and treatment of glioma. The role of alternative splicing (AS) in cancer, especially in glioma, has been validated, a comprehensive analysis of AS in glioma has not yet been conducted. Glioma classification based on molecular characteristics plays an increasingly important role in diagnosis and treatment of glioma. AS of ANXA7, MARK4, MAX, USP5, WWOX, BIN, RON, and CCND1 were reported to affect critical biological functions of glioma, resulting in altered prognosis [13,14,15]; a systematic analysis of glioma splicing profiles has not been performed. Building on the availability of RNA-seq data of TCGA, we performed a comprehensive analysis of splicing events associated with prognosis in patients with glioma. Based on prognosis-related splicing events, we identified novel classification of glioma with distinct splicing characteristics and clinical features, shedding light on the new ideas for glioma research
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