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Radiogenomic landscape: Assessment of specific phagocytosis regulators in lower-grade gliomas.
Maimaiti, Aierpati; Abulaiti, Aimitaji; Tang, Bin; Dilixiati, Yilidanna; Li, Xueqi; Yakufu, Suobinuer; Wang, Yongxin; Jiang, Lei; Shao, Hua.
Afiliación
  • Maimaiti A; Department of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China.
  • Abulaiti A; Department of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China.
  • Tang B; Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China.
  • Dilixiati Y; Xinjiang Medical University, Urumqi 830054, China.
  • Li X; Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China.
  • Yakufu S; Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China.
  • Wang Y; Department of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China.
  • Jiang L; Department of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China.
  • Shao H; Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China.
Exp Biol Med (Maywood) ; 248(23): 2289-2303, 2023 Dec.
Article en En | MEDLINE | ID: mdl-38062999
Genome-wide CRISPR-Cas9 knockout screens have emerged as a powerful method for identifying key genes driving tumor growth. The aim of this study was to explore the phagocytosis regulators (PRs) specifically associated with lower-grade glioma (LGG) using the CRISPR-Cas9 screening database. Identifying these core PRs could lead to novel therapeutic targets and pave the way for a non-invasive radiogenomics approach to assess LGG patients' prognosis and treatment response. We selected 24 PRs that were overexpressed and lethal in LGG for analysis. The identified PR subtypes (PRsClusters, geneClusters, and PRs-score models) effectively predicted clinical outcomes in LGG patients. Immune response markers, such as CTLA4, were found to be significantly associated with PR-score. Nine radiogenomics models using various machine learning classifiers were constructed to uncover survival risk. The area under the curve (AUC) values for these models in the test and training datasets were 0.686 and 0.868, respectively. The CRISPR-Cas9 screen identified novel prognostic radiogenomics biomarkers that correlated well with the expression status of specific PR-related genes in LGG patients. These biomarkers successfully stratified patient survival outcomes and treatment response using The Cancer Genome Atlas (TCGA) database. This study has important implications for the development of precise clinical treatment strategies and holds promise for more accurate therapeutic approaches for LGG patients in the future.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Encefálicas / Glioma Límite: Humans Idioma: En Revista: Exp Biol Med (Maywood) Asunto de la revista: BIOLOGIA / FISIOLOGIA / MEDICINA Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Encefálicas / Glioma Límite: Humans Idioma: En Revista: Exp Biol Med (Maywood) Asunto de la revista: BIOLOGIA / FISIOLOGIA / MEDICINA Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza