In:
Epigenomics, Future Medicine Ltd, Vol. 11, No. 11 ( 2019-08), p. 1323-1333
Abstract:
Aim: IDH-mutant lower grade glioma (LGG) has been proven to have a good prognosis. However, its high recurrence rate has become a major therapeutic difficulty. Materials & methods: We combined epigenomic deconvolution and a network analysis on The Cancer Genome Atlas IDH-mutant LGG data. Results: Cell type compositions between recurrent and primary gliomas are significantly different, and the key cell type that determines the prognosis and recurrence risk was identified. A scoring model consisting of four gene expression levels predicts the recurrence risk (area under the receiver operating characteristic curve = 0.84). Transcription factor PPAR-α explains the difference between recurrent and primary gliomas. A cell cycle-related module controls prognosis in recurrent tumors. Conclusion: Comprehensive deconvolution and network analysis predict the recurrence risk and reveal therapeutic targets for recurrent IDH-mutant LGG.
Type of Medium:
Online Resource
ISSN:
1750-1911
,
1750-192X
DOI:
10.2217/epi-2019-0137
Language:
English
Publisher:
Future Medicine Ltd
Publication Date:
2019
SSG:
12
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