| Autor: |
Danyang Chen, Zhicheng Liu, Jingxuan Wang, Chen Yang, Chao Pan, Yingxin Tang, Ping Zhang, Na Liu, Gaigai Li, Yan Li, Zhuojin Wu, Feng Xia, Cuntai Zhang, Hao Nie, Zhouping Tang |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
iScience, Vol 25, Iss 11, Pp 105276- (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2589-0042 |
| DOI: |
10.1016/j.isci.2022.105276 |
| Popis: |
Summary: Glioblastoma (GBM) is the most common form of malignant primary brain tumor with a dismal prognosis. Currently, the standard treatments for GBM rarely achieve satisfactory results, which means that current treatments are not individualized and precise enough. In this study, a multiomics-based GBM classification was established and three subclasses (GPA, GPB, and GPC) were identified, which have different molecular features both in bulk samples and at single-cell resolution. A robust GBM poor prognostic signature (GPS) score model was then developed using machine learning method, manifesting an excellent ability to predict the survival of GBM. NVP−BEZ235, GDC−0980, dasatinib and XL765 were ultimately identified to have subclass-specific efficacy targeting patients with a high risk of poor prognosis. Furthermore, the GBM classification and GPS score model could be considered as potential biomarkers for immunotherapy response. In summary, an integrative genomic analysis was conducted to advance individual-based therapies in GBM. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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