胶质母细胞瘤（GBM）是一种侵略性且无法阻止的恶性肿瘤。天然杀伤性T细胞（NKT细胞）通过特定标记物进行特征性描述， 在许多肿瘤相关的病理生理过程中起关键作用。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。我们从基因表达杂志（GEO）数据库中获取了一个单细胞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，通过共识聚类法将来自TCGA（The Cancer Genome Atlas）和CGGA（Chinese Glioma Genome Atlas）数据库的GBM队列整合，采用LASSO（最小绝对选择和收缩算子）和多变量Cox回归分析建立了预测模型。然后，我们进一步研究了不同风险组之间的生存率和临床特征差异。此外，我们通过将风险评分与临床特征相结合开发了一个预测图表。我们通过CIBERSORT和单样本基因集富集分析（ssGSEA）算法对肿瘤微环境（TME）中的免疫细胞丰度进行了研究，并通过TIDE（肿瘤免疫功能失调和排除）和TCIA（肿瘤免疫基因组图谱）数据库辅助进行了免疫治疗效果评估。我们利用实时定量PCR（RT-qPCR）实验和组织免疫组化分析验证了模型基因。我们从scRNA-seq数据中鉴定了945个NKT细胞标记基因。通过进一步筛选，我们准确鉴定了107个基因，其中15个与预后显著相关。我们将GBM样本划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子。预测图表在临床决策中展现了显著的实用性。肿瘤免疫微环境分析揭示了不同风险组之间免疫浸润特征的显著差异。此外，与高风险组相比，免疫检查点相关基因的表达水平持续升高，表明免疫逃避更加突出，但对免疫检查点抑制剂的反应更强。通过整合scRNA-seq和大规模RNA测序分析，我们成功开发了一个预后预测模型，该模型包含了两个重要的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色。此外，我们对不同风险组之间的免疫微环境进行了初步研究，有助于探索GBM特异性的有前景的免疫治疗靶点。© 2023年。作者，独家授权于Springer-Verlag GmbH德国部分，属于 Springer Nature。

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.© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.