A spatial transcriptomics study of MES-like and mono/macro cells in gliomas

作者：Boyan Zhao, Jianing Wu, Tiehui Zhang, Mingyang Han, Cheng Zhang, Rong Xuan, Ruotian Zhang, Xin Chen, Peng Fei, Jin Y. Jin, Shiya Liu, Xingli Dong, Shiguang Zhao · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-95277-3 · 被引用次数：5 · 研究领域：Chemokine receptors and signaling、interferon and immune responses、Immune cells in cancer

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 t...