基于单细胞转录组的多级别胶质瘤异质性及免疫微环境分析揭示了潜在的预后生物标志物

Single-cell transcriptome analysis of multigrade glioma heterogeneity and immune microenvironment revealed potential prognostic biomarkers

摘要:

脑胶质瘤（glioma）是中枢神经系统最常见的内在肿瘤，具有发病率高、预后较差等特点。本研究旨在鉴定多形性胶质母细胞瘤（glioblastoma multiforme，GBM）和低级别胶质瘤（lower-grade gliomas，LGG）之间的差异表达基因（differentially expressed genes，DEGs），以探讨不同级别胶质瘤的预后影响因素。从NCBI基因表达综合数据库中收集了胶质瘤的单细胞转录组测序数据，其中包括来自3个数据集的共29 097个细胞样本。对于不同分级的人脑胶质瘤进行分析，经过滤得到21 071个细胞，通过基因本体分析、京都基因与基因组百科全书途径分析，从差异表达基因中筛选出70个基因，我们通过查阅文献，聚焦到delta样典型Notch配体3（delta like canonical Notch ligand 3，DLL3）这个基因。基于TCGA的基因表达谱交互分析（gene expression profiling interactive analysis，GEPIA）数据库用于探索LGG和GBM中DLL3基因的表达差异，采用基因表达谱交互式分析和肿瘤免疫学估计资源（tumor immune estimation resource，TIMER）数据库，研究关键基因在不同分级的脑胶质瘤中的表达，预测了与免疫治疗密切相关的生物标志物。cBioPortal数据库用于探索DLL3表达与25个免疫检查点之间的关系。基因集富集分析（gene set enrichment analysis，GSEA）进一步确定了与中心基因相关的途径。最后，在中国胶质瘤基因组图谱（Chinese glioma genome atlas，CGGA）中验证了生物标志物在预后和预测中的疗效。这些结果发现，预后基因与肿瘤增殖和进展有关，通过生物学信息和生存分析，表明这些基因可能作为一种有前途的预后生物标志物，并作为选择治疗策略的新靶点。

Abstract:

Glioma, the most common intrinsic tumor of the central nervous system, is characterized by its high incidence and poor prognosis. The aim of this study was to identify differentially expressed genes (DEGs) between glioblastoma multiforme (GBM) and low-grade glioma (LGG) to explore prognostic factors of different grades of gliomas. Single-cell transcriptome sequencing data of gliomas were collected from the NCBI Gene Expression Omnibus (GEO), which included a total of 29 097 cell samples from three datasets. For the analysis of human gliomas of different grades, 21 071 cells were obtained by filtering, and 70 genes were screened from differentially expressed genes by gene ontology (GO) analysis, Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, from which the gene DLL3 was focused by reviewing the literature. The TCGA-based gene expression profiling interactive analysis (GEPIA) database was used to explore the survival curves of genes in LGG and GBM, and the gene expression profiling interactive analysis and tumor immune estimation resource (TIMER) database was used to study the expression of key genes in gliomas of different grades, predicting biomarkers that were closely related to immunotherapy. The cBioPortal database was used to explore the relationship between DLL3 expression and 25 immune checkpoints. Gene set enrichment analysis (GSEA) further identified pathways associated with central genes. Finally, the efficacy of biomarkers in prognosis and prediction was validated in the Chinese glioma genome atlas (CGGA). These results demonstrated that prognostic genes are associated with tumor proliferation and progression. Analysis of biological information and survival suggested that these genes might serve as a promising prognostic biomarker and as new targets for selecting therapeutic strategies.