Guangqi Li1, Yuanjun Jiang2, Xintong Lyu1, Yiru Cai1, Miao Zhang1, Zuoyuan Wang3, Guang Li1, Qiao Qiao1. 1. Department of Radiation Oncology, the First Hospital of China Medical University, Shenyang 110001, Liaoning, China. 2. Department of Urology, the First Hospital of China Medical University, Shenyang 110001, Liaoning, China. 3. The First Clinical Medical College of China Medical University, Shenyang 110001, Liaoning, China.
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.
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.