Department of Digestive Internal Medicine, Harbin Medical University Cancer Hospital, Harbin, China.
Cancer Center, Department of Neurosurgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Zhejiang, Hangzhou, China.
Funct Integr Genomics. 2023 Aug 31;23(3):286. doi: 10.1007/s10142-023-01217-7.
Glioblastoma (GBM) is an aggressive and unstoppable malignancy. Natural killer T (NKT) cells, characterized by specific markers, play pivotal roles in many tumor-associated pathophysiological processes. Therefore, investigating the functions and complex interactions of NKT cells is great interest for exploring GBM.
We acquired a single-cell RNA-sequencing (scRNA-seq) dataset of GBM from Gene Expression Omnibus (GEO) database. The weighted correlation network analysis (WGCNA) was employed to further screen genes subpopulations. Subsequently, we integrated the GBM cohorts from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases to describe different subtypes by consensus clustering and developed a prognostic model by least absolute selection and shrinkage operator (LASSO) and multivariate Cox regression analysis. We further investigated differences in survival rates and clinical characteristics among different risk groups. Furthermore, a nomogram was developed by combining riskscore with the clinical characteristics. We investigated the abundance of immune cells in the tumor microenvironment (TME) by CIBERSORT and single sample gene set enrichment analysis (ssGSEA) algorithms. Immunotherapy efficacy assessment was done with the assistance of Tumor Immune Dysfunction and Exclusion (TIDE) and The Cancer Immunome Atlas (TCIA) databases. Real-time quantitative polymerase chain reaction (RT-qPCR) experiments and immunohistochemical profiles of tissues were utilized to validate model genes.
We identified 945 NKT cells marker genes from scRNA-seq data. Through further screening, 107 genes were accurately identified, of which 15 were significantly correlated with prognosis. We distinguished GBM samples into two distinct subtypes and successfully developed a robust prognostic prediction model. Survival analysis indicated that high expression of NKT cell marker genes was significantly associated with poor prognosis in GBM patients. Riskscore can be used as an independent prognostic factor. The nomogram was demonstrated remarkable utility in aiding clinical decision making. Tumor immune microenvironment analysis revealed significant differences of immune infiltration characteristics between different risk groups. In addition, the expression levels of immune checkpoint-associated genes were consistently elevated in the high-risk group, suggesting more prominent immune escape but also a stronger response to immune checkpoint inhibitors.
By integrating scRNA-seq and bulk RNA-seq data analysis, we successfully developed a prognostic prediction model that incorporates two pivotal NKT cells marker genes, namely, CD44 and TNFSF14. This model has exhibited outstanding performance in assessing the prognosis of GBM patients. Furthermore, we conducted a preliminary investigation into the immune microenvironment across various risk groups that contributes to uncover promising immunotherapeutic targets specific to GBM.
胶质母细胞瘤（GBM）是一种侵袭性和不可阻挡的恶性肿瘤。自然杀伤 T（NKT）细胞具有特定的标记物，在许多与肿瘤相关的病理生理过程中发挥关键作用。因此，研究 NKT 细胞的功能和复杂相互作用对于探索 GBM 具有重要意义。
我们从基因表达综合数据库（GEO）数据库中获取了胶质母细胞瘤的单细胞 RNA 测序（scRNA-seq）数据集。采用加权相关网络分析（WGCNA）进一步筛选基因亚群。随后，我们整合了来自癌症基因组图谱（TCGA）和中国脑胶质瘤基因组图谱（CGGA）数据库的 GBM 队列，通过共识聚类描述不同亚型，并通过最小绝对收缩和选择算子（LASSO）和多变量 Cox 回归分析开发预后模型。我们进一步研究了不同风险组之间生存率和临床特征的差异。此外，通过结合风险评分和临床特征，开发了一个列线图。我们通过 CIBERSORT 和单样本基因集富集分析（ssGSEA）算法研究了肿瘤微环境（TME）中免疫细胞的丰度。利用肿瘤免疫功能障碍和排除（TIDE）和癌症免疫图谱（TCIA）数据库评估免疫治疗疗效。利用实时定量聚合酶链反应（RT-qPCR）实验和组织免疫组织化学谱验证模型基因。
我们从 scRNA-seq 数据中鉴定出 945 个 NKT 细胞标记基因。通过进一步筛选，准确鉴定出 107 个基因，其中 15 个与预后显著相关。我们将 GBM 样本分为两个不同的亚型，并成功建立了一个稳健的预后预测模型。生存分析表明，NKT 细胞标记基因的高表达与 GBM 患者的不良预后显著相关。风险评分可作为独立的预后因素。列线图在辅助临床决策方面表现出显著的实用性。肿瘤免疫微环境分析显示，不同风险组之间的免疫浸润特征存在显著差异。此外，高风险组中免疫检查点相关基因的表达水平持续升高，提示更明显的免疫逃逸，但对免疫检查点抑制剂的反应也更强。
通过整合 scRNA-seq 和批量 RNA-seq 数据分析，我们成功建立了一个预后预测模型，该模型包含两个关键的 NKT 细胞标记基因，即 CD44 和 TNFSF14。该模型在评估 GBM 患者的预后方面表现出优异的性能。此外，我们对不同风险组的免疫微环境进行了初步研究，为揭示特定于 GBM 的有前途的免疫治疗靶点提供了线索。
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