Wangjing Hospital of China Academy of Chinese Medical Sciences, Beijing 100020, China.
Graduate College, Beijing University of Chinese Medicine, Beijing 100020, China.
Mediators Inflamm. 2023 May 31;2023:3648946. doi: 10.1155/2023/3648946. eCollection 2023.
The clinical outcomes of low-grade glioma (LGG) are associated with T cell infiltration, but the specific contribution of heterogeneous T cell types remains unclear.
To study the different functions of T cells in LGG, we mapped the single-cell RNA sequencing results of 10 LGG samples to obtain T cell marker genes. In addition, bulk RNA data of 975 LGG samples were collected for model construction. Algorithms such as TIMER, CIBERSORT, QUANTISEQ, MCPCOUTER, XCELL, and EPIC were used to depict the tumor microenvironment landscape. Subsequently, three immunotherapy cohorts, PRJEB23709, GSE78820, and IMvigor210, were used to explore the efficacy of immunotherapy.
The Human Primary Cell Atlas was used as a reference dataset to identify each cell cluster; a total of 15 cell clusters were defined and cells in cluster 12 were defined as T cells. According to the distribution of T cell subsets (CD4+ T cell, CD8+ T cell, Naïve T cell, and Treg cell), we selected the differentially expressed genes. Among the CD4+ T cell subsets, we screened 3 T cell-related genes, and the rest were 28, 4, and 13, respectively. Subsequently, according to the T cell marker genes, we screened six genes for constructing the model, namely, RTN1, HERPUD1, MX1, SEC61G, HOPX, and CHI3L1. The ROC curve showed that the predictive ability of the prognostic model for 1, 3, and 5 years was 0.881, 0.817, and 0.749 in the TCGA cohort, respectively. In addition, we found that risk scores were positively correlated with immune infiltration and immune checkpoints. To this end, we obtained three immunotherapy cohorts to verify their predictive ability of immunotherapy effects and found that high-risk patients had better clinical effects of immunotherapy.
This single-cell RNA sequencing combined with bulk RNA sequencing may elucidate the composition of the tumor microenvironment and pave the way for the treatment of low-grade gliomas.
低级别胶质瘤 (LGG) 的临床结果与 T 细胞浸润有关，但不同异质性 T 细胞类型的具体贡献尚不清楚。
为了研究 LGG 中 T 细胞的不同功能，我们将 10 个 LGG 样本的单细胞 RNA 测序结果映射到获得 T 细胞标记基因。此外，还收集了 975 个 LGG 样本的批量 RNA 数据用于模型构建。使用 TIMER、CIBERSORT、QUANTISEQ、MCPCOUTER、XCELL 和 EPIC 等算法描绘肿瘤微环境景观。随后，我们使用三个免疫治疗队列（PRJEB23709、GSE78820 和 IMvigor210）来探讨免疫治疗的疗效。
我们使用 Human Primary Cell Atlas 作为参考数据集来识别每个细胞簇；共定义了 15 个细胞簇，细胞簇 12 中的细胞被定义为 T 细胞。根据 T 细胞亚群（CD4+ T 细胞、CD8+ T 细胞、初始 T 细胞和 Treg 细胞）的分布，我们筛选了差异表达基因。在 CD4+ T 细胞亚群中，我们筛选了 3 个与 T 细胞相关的基因，其余分别为 28、4 和 13 个。随后，根据 T 细胞标记基因，我们筛选了 6 个基因用于构建模型，即 RTN1、HERPUD1、MX1、SEC61G、HOPX 和 CHI3L1。ROC 曲线显示，在 TCGA 队列中，该预后模型对 1、3 和 5 年的预测能力分别为 0.881、0.817 和 0.749。此外，我们发现风险评分与免疫浸润和免疫检查点呈正相关。为此，我们获得了三个免疫治疗队列来验证其对免疫治疗效果的预测能力，发现高风险患者的免疫治疗效果更好。
这项单细胞 RNA 测序结合批量 RNA 测序可能阐明肿瘤微环境的组成，并为低级别胶质瘤的治疗铺平道路。
Zotero 插件
