Deconvolution and network analysis of IDH-mutant lower grade glioma predict recurrence and indicate therapeutic targets
| Autor: | Qiao Qiao, Miao Zhang, Guang Li, Guangqi Li, Xintong Lyu, Yiru Cai, Yuanjun Jiang, Zuoyuan Wang |
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| Rok vydání: | 2019 |
| Předmět: |
Oncology
Cancer Research medicine.medical_specialty Cell type medicine.medical_treatment Cell Mutant Biology Targeted therapy Glioma Internal medicine Databases Genetic Biomarkers Tumor Genetics medicine Humans Gene Regulatory Networks Molecular Targeted Therapy Epigenomics Receiver operating characteristic Brain Neoplasms medicine.disease Isocitrate Dehydrogenase Gene Expression Regulation Neoplastic Survival Rate medicine.anatomical_structure Mutation Deconvolution Neoplasm Grading Neoplasm Recurrence Local |
| Zdroj: | Epigenomics. 11:1323-1333 |
| ISSN: | 1750-192X 1750-1911 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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