Cao Lei, Lu Xu, Wang Xia, Wu Hao, Miao Xiaye
Department of Oncology, The Affiliated Suqian First People's Hospital of Nanjing Medical University, Suqian, China.
Department of Oncology, The Affiliated Huai'an Hospital of Xuzhou Medical University and the Second People's Hospital of Huai'an, Huai'an, China.
Front Immunol. 2024 Sep 17;15:1475235. doi: 10.3389/fimmu.2024.1475235. eCollection 2024.
Gliomas are aggressive brain tumors associated with a poor prognosis. Cancer stem cells (CSCs) play a significant role in tumor recurrence and resistance to therapy. This study aimed to identify and characterize glioma stem cells (GSCs), analyze their interactions with various cell types, and develop a prognostic signature.
Single-cell RNA sequencing data from 44 primary glioma samples were analyzed to identify GSC populations. Spatial transcriptomics and gene regulatory network analyses were performed to investigate GSC localization and transcription factor activity. CellChat analysis was conducted to infer cell-cell communication patterns. A GSC signature (GSCS) was developed using machine learning algorithms applied to bulk RNA sequencing data from multiple cohorts. and experiments were conducted to validate the role of TUBA1C, a key gene within the signature.
A distinct GSC population was identified, characterized by high proliferative potential and an enrichment of E2F1, E2F2, E2F7, and BRCA1 regulons. GSCs exhibited spatial proximity to myeloid-derived suppressor cells (MDSCs). CellChat analysis revealed an active MIF signaling pathway between GSCs and MDSCs. A 26-gene GSCS demonstrated superior performance compared to existing prognostic models. Knockdown of TUBA1C significantly inhibited glioma cell migration, and invasion , and reduced tumor growth .
This study offers a comprehensive characterization of GSCs and their interactions with MDSCs, while presenting a robust GSCS. The findings offer new insights into glioma biology and identify potential therapeutic targets, particularly TUBA1C, aimed at improving patient outcomes.
神经胶质瘤是侵袭性脑肿瘤，预后较差。癌症干细胞（CSCs）在肿瘤复发和治疗抵抗中起重要作用。本研究旨在鉴定和表征神经胶质瘤干细胞（GSCs），分析它们与各种细胞类型的相互作用，并建立一种预后特征。
分析来自44个原发性神经胶质瘤样本的单细胞RNA测序数据，以鉴定GSC群体。进行空间转录组学和基因调控网络分析，以研究GSC的定位和转录因子活性。进行CellChat分析，以推断细胞间通讯模式。使用应用于来自多个队列的批量RNA测序数据的机器学习算法建立GSC特征（GSCS）。并进行实验以验证特征内关键基因TUBA1C的作用。
鉴定出一个独特的GSC群体，其特征是具有高增殖潜力以及E2F1、E2F2、E2F7和BRCA1调控子的富集。GSCs与髓源性抑制细胞（MDSCs）在空间上接近。CellChat分析揭示了GSCs和MDSCs之间活跃的MIF信号通路。与现有的预后模型相比，一个包含26个基因的GSCS表现出更好的性能。敲低TUBA1C可显著抑制神经胶质瘤细胞的迁移和侵袭，并减少肿瘤生长。
本研究全面表征了GSCs及其与MDSCs的相互作用，同时提出了一个强大的GSCS。这些发现为神经胶质瘤生物学提供了新的见解，并确定了潜在的治疗靶点，特别是TUBA1C，旨在改善患者预后。
