Open Access

Transcriptome analysis and prognostic model construction based on splicing profiling in glioblastoma

  • Authors:
    • Jiting Qiu
    • Chunhui Wang
    • Hongkang Hu
    • Sarah Chen
    • Xuehua Ding
    • Yu Cai
  • View Affiliations

  • Published online on: December 20, 2020     https://doi.org/10.3892/ol.2020.12399
  • Article Number: 138
  • Copyright: © Qiu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Glioblastoma (GBM) is the most aggressive malignant brain tumour, with high morbidity and mortality rates. Currently, there is a lack of systematic and comprehensive analysis on the prognostic significance of alternative splicing (AS) profiling for GBM. The GBM data, including RNA‑sequencing, corresponding clinical information and the expression levels of splicing factor genes, were downloaded from The Cancer Genome Atlas and the SpliceAid2 database. The prognostic models were assessed by the least absolute shrinkage and selection operator Cox regression analysis. The correlation network between survival‑associated AS events and splicing factors was plotted. Prognostic models were built for every AS event type and performed well for risk stratification in patients with GBM. The final prognostic signature served as an independent prognostic factor [hazard ratio (HR), 4.61; 95% confidence interval (CI), 2.97‑7.16; P=9.66x10‑12] for several clinical parameters, including age, sex, isocitrate dehydrogenase mutation, O6‑methylguanine‑DNA methyltransferase promoter methylation and risk score. The HR for risk score with GBM was 1.0063 (95% CI, 1.0024‑1.0103). The splicing regulatory network indicated that heat shock protein b‑1, protein arginine N‑methyltransferase 5, protein FAM50B and endoplasmic reticulum chaperone BiP genes were independent prognostic factors for GBM. The results of the present study support the ongoing effort in developing novel genomic models and providing potentially more effective treatment options for patients with GBM.
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February-2021
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Spandidos Publications style
Qiu J, Wang C, Hu H, Chen S, Ding X and Cai Y: Transcriptome analysis and prognostic model construction based on splicing profiling in glioblastoma. Oncol Lett 21: 138, 2021
APA
Qiu, J., Wang, C., Hu, H., Chen, S., Ding, X., & Cai, Y. (2021). Transcriptome analysis and prognostic model construction based on splicing profiling in glioblastoma. Oncology Letters, 21, 138. https://doi.org/10.3892/ol.2020.12399
MLA
Qiu, J., Wang, C., Hu, H., Chen, S., Ding, X., Cai, Y."Transcriptome analysis and prognostic model construction based on splicing profiling in glioblastoma". Oncology Letters 21.2 (2021): 138.
Chicago
Qiu, J., Wang, C., Hu, H., Chen, S., Ding, X., Cai, Y."Transcriptome analysis and prognostic model construction based on splicing profiling in glioblastoma". Oncology Letters 21, no. 2 (2021): 138. https://doi.org/10.3892/ol.2020.12399