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RNA splicing analysis deciphers developmental hierarchies and reveals therapeutic targets in adult glioma
Xiao Song, … , Bo Hu, Shi-Yuan Cheng
Xiao Song, … , Bo Hu, Shi-Yuan Cheng
Published April 25, 2024
Citation Information: J Clin Invest. 2024;134(11):e173789. https://doi.org/10.1172/JCI173789.
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Research Article Cell biology Oncology Article has an altmetric score of 25

RNA splicing analysis deciphers developmental hierarchies and reveals therapeutic targets in adult glioma

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Abstract

Widespread alterations in RNA alternative splicing (AS) have been identified in adult gliomas. However, their regulatory mechanism, biological significance, and therapeutic potential remain largely elusive. Here, using a computational approach with both bulk and single-cell RNA-Seq, we uncover a prognostic AS signature linked with neural developmental hierarchies. Using advanced iPSC glioma models driven by glioma driver mutations, we show that this AS signature could be enhanced by EGFRvIII and inhibited by in situ IDH1 mutation. Functional validations of 2 isoform switching events in CERS5 and MPZL1 show regulations of sphingolipid metabolism and SHP2 signaling, respectively. Analysis of upstream RNA binding proteins reveals PTBP1 as a key regulator of the AS signature where targeting of PTBP1 suppresses tumor growth and promotes the expression of a neuron marker TUJ1 in glioma stem-like cells. Overall, our data highlights the role of AS in affecting glioma malignancy and heterogeneity and its potential as a therapeutic vulnerability for treating adult gliomas.

Authors

Xiao Song, Deanna Tiek, Shunichiro Miki, Tianzhi Huang, Minghui Lu, Anshika Goenka, Rebeca Iglesia, Xiaozhou Yu, Runxin Wu, Maya Walker, Chang Zeng, Hardik Shah, Shao Huan Samuel Weng, Allen Huff, Wei Zhang, Tomoyuki Koga, Christopher Hubert, Craig M. Horbinski, Frank B. Furnari, Bo Hu, Shi-Yuan Cheng

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Figure 1

Unsupervised splicing analysis in bulk gliomas reveals a prognostic AS signature linked to neural lineage differentiation.

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Unsupervised splicing analysis in bulk gliomas reveals a prognostic AS s...
(A) Computational pipeline of AS analysis in gliomas. (B) Heatmaps showing the PSI values of the 200 AS events across 3 glioma samples. Samples were ordered based on their AS scores. Annotations on top show the association of AS landscape with IDH mutation, 1p/19q codeletion and predefined molecular subtyping. Pro, proneural; Mes, mesenchymal; Cla, classical; Mut, mutant; WT, wildtype. (C) AS scores of glioma samples in indicated groups from TCGA and CGGA data sets, analyzed using 1-way ANOVA multiple comparisons with correction by controlling the FDR. (D) Kaplan-Meier analyses in TCGA gliomas grouped by AS score. Log-rank test was used to compare between groups. (E) Multivariate cox regression analysis for overall survival in TCGA glioma samples. HR, hazard ratio; CI, confidence interval. (F) The correlation between AS score and the expression of neural lineage markers. Dot sizes indicate the P value from spearman correlation analysis, and colors indicate correlation coefficient value. The cartoon on left shows the neural differentiation trajectory. *P < 0.05; **P < 0.01; ***P < 0.001.

Copyright © 2025 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)

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