Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India.
Department of Physics, Chuo University, Bunkyo-ku, Tokyo 112-8551, Japan.
Genes (Basel). 2022 Feb 25;13(3):428. doi: 10.3390/genes13030428.
Glioblastoma multiforme (GBM) is the most common infiltrating lethal tumor of the brain. Tumor heterogeneity and the precise characterization of GBM remain challenging, and the disease-specific and effective biomarkers are not available at present. To understand GBM heterogeneity and the disease prognosis mechanism, we carried out a single-cell transcriptome data analysis of 3389 cells from four primary IDH-WT (isocitrate dehydrogenase wild type) glioblastoma patients and compared the characteristic features of the tumor and periphery cells. We observed that the marker gene expression profiles of different cell types and the copy number variations (CNVs) are heterogeneous in the GBM samples. Further, we have identified 94 differentially expressed genes (DEGs) between tumor and periphery cells. We constructed a tissue-specific co-expression network and protein-protein interaction network for the DEGs and identified several hub genes, including , and . The DEGs were significantly enriched with proliferation and migration pathways related to glioblastoma. Additionally, we were able to identify the differentiation state of microglia and changes in the transcriptome in the presence of glioblastoma that might support tumor growth. This study provides insights into GBM heterogeneity and suggests novel potential disease-specific biomarkers which could help to identify the therapeutic targets in GBM.
多形性胶质母细胞瘤(GBM)是最常见的浸润性致命脑肿瘤。肿瘤异质性和 GBM 的精确特征仍然具有挑战性,目前还没有特异性和有效的疾病生物标志物。为了了解 GBM 的异质性和疾病预后机制,我们对来自 4 名 IDH-WT(异柠檬酸脱氢酶野生型)胶质母细胞瘤患者的 3389 个细胞进行了单细胞转录组数据分析,并比较了肿瘤和周围细胞的特征。我们观察到 GBM 样本中不同细胞类型的标记基因表达谱和拷贝数变异(CNVs)存在异质性。此外,我们鉴定了肿瘤和周围细胞之间的 94 个差异表达基因(DEGs)。我们为 DEGs 构建了组织特异性共表达网络和蛋白质-蛋白质相互作用网络,并鉴定了几个枢纽基因,包括、和。DEGs 显著富集了与胶质母细胞瘤相关的增殖和迁移途径。此外,我们能够识别存在胶质母细胞瘤时小胶质细胞的分化状态和转录组的变化,这可能支持肿瘤生长。这项研究深入了解了 GBM 的异质性,并提出了新的潜在疾病特异性生物标志物,有助于确定 GBM 的治疗靶点。
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