Zhao Boyan, Wu Jianing, Zhang Tiehui, Han Mingyang, Zhang Cheng, Rong Xuan, Zhang Ruotian, Chen Xin, Peng Fei, Jin Jin, Liu Shiya, Dong Xingli, Zhao Shiguang
Department of Neurosurgery, Shenzhen University General Hospital, Shenzhen, 518000, Guangdong, China.
Shenzhen University School of Medicine, Shenzhen, 518000, Guangdong, China.
Sci Rep. 2025 Apr 13;15(1):12730. doi: 10.1038/s41598-025-95277-3.
Gliomas, including both glioblastoma multiforme (GBM) and lower-grade glioma (LGG), present a substantial challenge in neuro-oncology because of genetic heterogeneity and unsatisfactory prognosis. This study aimed to conduct a comprehensive multi-omics analysis of gliomas using various bioinformatics approaches to identify potential therapeutic targets and prognostic markers. A comprehensive analysis was conducted on 1327 sequencing data samples alongside their relevant clinical information sourced from The Cancer Genome Atlas (TCGA) pertaining to glioblastoma (GBM), low-grade glioma (LGG), the Chinese Glioma Genome Atlas (CCGA) and University of California Santa Cruz Xena (UCSC Xena) datasets. These tools were employed for gene expression profiling, survival analysis, and cell communication mapping. Spatial transcriptomics revealed the localization of mesenchymal (MES)-like malignant tumors, and drug sensitivity analysis was performed to evaluate responses to quinpirole and meropenem. Additionally, the Tumor Immune Dysfunction and Exclusion (TIDE) framework was utilized to gauge the responsiveness to immunotherapy. The MES-like malignant and monocyte/macrophage (mono/macro) cell subsets showed high hallmark scores, playing key roles in the tumor microenvironment. MES-like malignant marker gene scores correlated with overall survival across datasets, whereas mono/macro marker gene scores were significant in the TCGA-LGG and CCGA datasets. Key interactions between these cell types were found, especially with CD14-ITGB2, LGALS1-CD69, and APOE-TREM2. The mono/macro cell subset demonstrated better immune therapy responsiveness, as indicated by lower TIDE scores. Spatial transcriptomics revealed that MES-like malignant tumors are predominantly localized in four distinct regions, with the marker genes CHI3L1 and ADM confirming these locations. Drug sensitivity analysis revealed differential responses of the MES-like malignant cell subset to quinpirole and meropenem. Our results offer fresh perspectives on the differential roles of MES-like malignant and monocyte/macrophage cell subsets in tumor progression and immune modulation, providing novel insights into glioma biology.
神经胶质瘤,包括多形性胶质母细胞瘤(GBM)和低级别胶质瘤(LGG),由于基因异质性和预后不理想,在神经肿瘤学中构成了重大挑战。本研究旨在使用各种生物信息学方法对神经胶质瘤进行全面的多组学分析,以确定潜在的治疗靶点和预后标志物。对1327个测序数据样本及其相关临床信息进行了全面分析,这些数据来自癌症基因组图谱(TCGA)中与胶质母细胞瘤(GBM)、低级别胶质瘤(LGG)相关的数据集,以及中国胶质瘤基因组图谱(CCGA)和加利福尼亚大学圣克鲁兹分校Xena(UCSC Xena)数据集。这些工具用于基因表达谱分析、生存分析和细胞通讯图谱绘制。空间转录组学揭示了间充质(MES)样恶性肿瘤的定位,并进行了药物敏感性分析以评估对喹吡罗和美罗培南的反应。此外,利用肿瘤免疫功能障碍与排除(TIDE)框架来评估对免疫治疗的反应性。MES样恶性和单核细胞/巨噬细胞(单核/巨噬)细胞亚群显示出高特征分数,在肿瘤微环境中起关键作用。MES样恶性标志物基因分数与各数据集中的总生存期相关,而单核/巨噬标志物基因分数在TCGA-LGG和CCGA数据集中具有显著性。发现了这些细胞类型之间的关键相互作用,特别是与CD14-ITGB2、LGALS1-CD69和APOE-TREM2的相互作用。单核/巨噬细胞亚群表现出更好的免疫治疗反应性,TIDE分数较低表明了这一点。空间转录组学显示,MES样恶性肿瘤主要定位于四个不同区域,标志物基因CHI3L1和ADM证实了这些位置。药物敏感性分析揭示了MES样恶性细胞亚群对喹吡罗和美罗培南的不同反应。我们的结果为MES样恶性和单核细胞/巨噬细胞亚群在肿瘤进展和免疫调节中的不同作用提供了新的视角,为胶质瘤生物学提供了新的见解。