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Development and Validation a Prognostic Model Based on Natural Killer T Cells Marker Genes for Predicting Prognosis and Characterizing Immune Status in Glioblastoma Through Integrated Analysis of Single-Cell and Bulk RNA Sequencing.

Functional & Integrative Genomics(2023)

Harbin Medical University Cancer Hospital

Cited 1|Views20
Abstract
BACKGROUND: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.METHODS: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.RESULTS: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.CONCLUSIONS: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.
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Key words
ScRNA-seq,Bulk RNA-seq,NKT cells marker genes,Prognostic model,Immune status
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要点】:本研究通过整合单细胞和批量RNA测序数据,开发并验证了一种基于自然杀伤T细胞标记基因的预后模型,用于预测胶质oblastoma患者的预后和表征免疫状态,发现了两个关键的标记基因CD44和TNFSF14。

方法】:采用加权相关网络分析(WGCNA)筛选基因亚群,并整合了TCGA和CGGA数据库的GBM队列进行共识聚类,利用最小绝对选择和收缩操作(LASSO)及多变量Cox回归分析开发预后模型。

实验】:通过GEO数据库获取GBM的单细胞RNA测序数据,使用CIBERSORT和ssGSEA算法分析肿瘤微环境中的免疫细胞丰度,利用TIDE和TCIA数据库评估免疫治疗效果,并通过实时定量聚合酶链反应(RT-qPCR)实验和免疫组化组织谱验证模型基因。实验结果表明,高风险组的免疫浸润特征与低风险组有显著差异,且高风险组免疫检查点相关基因表达水平升高,预示更强的免疫逃避和免疫检查点抑制剂反应。