BV, H and Jolly, MK (2024) Proneural-mesenchymal antagonism dominates the patterns of phenotypic heterogeneity in glioblastoma. In: iScience, 27 (3).
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Abstract
The aggressive nature of glioblastoma (GBM) � one of the deadliest forms of brain tumors � is majorly attributed to underlying phenotypic heterogeneity. Early attempts to classify this heterogeneity at a transcriptomic level in TCGA GBM cohort proposed the existence of four distinct molecular subtypes: Proneural, Neural, Classical, and Mesenchymal. Further, a single-cell RNA sequencing (scRNA-seq) analysis of primary tumors also reported similar four subtypes mimicking neurodevelopmental lineages. However, it remains unclear whether these four subtypes identified via bulk and single-cell transcriptomics are mutually exclusive or not. Here, we perform pairwise correlations among individual genes and gene signatures corresponding to these proposed subtypes and show that the subtypes are not distinctly mutually antagonistic in either TCGA or scRNA-seq data. We observed that the proneural (or neural progenitor-like)-mesenchymal axis is the most prominent antagonistic pair, with the other two subtypes lying on this spectrum. These results are reinforced through a meta-analysis of over 100 single-cell and bulk transcriptomic datasets as well as in terms of functional association with metabolic switching, cell cycle, and immune evasion pathways. Finally, this proneural-mesenchymal antagonistic trend percolates to the association of relevant transcription factors with patient survival. These results suggest rethinking GBM phenotypic characterization for more effective therapeutic targeting efforts. © 2024 The Authors
| Item Type: | Journal Article |
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| Publication: | iScience |
| Publisher: | Elsevier Inc. |
| Additional Information: | The copyright for this article belongs to the Authors. |
| Department/Centre: | Division of Interdisciplinary Sciences > Centre for Biosystems Science and Engineering Division of Physical & Mathematical Sciences > Mathematics |
| Date Deposited: | 23 Apr 2024 07:17 |
| Last Modified: | 23 Apr 2024 07:18 |
| URI: | https://eprints.iisc.ac.in/id/eprint/84638 |
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