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RNA splicing analysis deciphers developmental hierarchies and reveals therapeutic targets in adult glioma
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
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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Research Article Cell biology Oncology

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 3

Intratumoral AS heterogeneity is associated with the developmental hierarchy in glioma.

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Intratumoral AS heterogeneity is associated with the developmental hiera...
(A) Computational pipeline of AS analysis using a cell-state based pseudobulk strategy in scRNA-Seq data of gliomas. (B) Hierarchical clustering analysis with the PSI data of events in pseudobulks. The heatmap on the right illustrates the PSI data of events at the same order in TCGA samples. (C) Expression of neural lineage markers in each cell state. Dot sizes indicate the percentage of cells in each group expressing the gene, and colors indicate average expression levels. NEC, neuroepithelial cells; RG, radial glia; AC, astrocyte; OPC, oligodendrocyte progenitors; OC, oligodendrocytes. (D) Box plots showing the AS score and PSI distribution of representative AS events in each cell state. The box representing the interquartile range of the data, the line within the box representing the median, and the whiskers extending to the most extreme data points within 1.5 times the interquartile range. Individual data points beyond this range are shown as dots. The color of the dots represents the patient.

Copyright © 2026 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)

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