Department of Neurosurgery, Neurosurgery Centre, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China.
Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China.
Exp Biol Med (Maywood). 2023 Dec;248(23):2289-2303. doi: 10.1177/15353702231211939. Epub 2023 Dec 8.
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.
全基因组 CRISPR-Cas9 敲除筛选已成为鉴定驱动肿瘤生长的关键基因的强大方法。本研究旨在利用 CRISPR-Cas9 筛选数据库探索与低级别胶质瘤(LGG)特异性相关的吞噬调节因子(PRs)。确定这些核心 PRs 可能为新的治疗靶点开辟道路,并为非侵入性放射基因组学方法评估 LGG 患者的预后和治疗反应铺平道路。我们选择了 24 个在 LGG 中过度表达和致死的 PR 进行分析。鉴定的 PR 亚型(PRsClusters、geneClusters 和 PRs-score 模型)有效地预测了 LGG 患者的临床结局。发现免疫反应标志物,如 CTLA4,与 PR-score 显著相关。使用各种机器学习分类器构建了九个放射基因组学模型来揭示生存风险。这些模型在测试和训练数据集中的曲线下面积(AUC)值分别为 0.686 和 0.868。CRISPR-Cas9 筛选确定了新的预后放射基因组学生物标志物,与 LGG 患者特定 PR 相关基因的表达状态密切相关。这些生物标志物使用癌症基因组图谱(TCGA)数据库成功地对患者的生存结局和治疗反应进行分层。这项研究对制定精确的临床治疗策略具有重要意义,并有望为未来的 LGG 患者提供更准确的治疗方法。