Novel CHI3L1 ‐Associated Angiogenic Phenotypes Define Glioma Microenvironments: Insights From Multi‐Omics Integration

作者：Y. Zhao, Yuxiang Cai, Zhi‐Yong Pan, Feng Tang, Chao Ma, Ze‐Fen Wang, Gang Li, Hang Chang, Su‐Fang Tian, Zhiqiang Li · 发表于：Cancer Science · 年份：2025 · DOI：10.1111/cas.70028 · 被引用次数：5 · 研究领域：Cancer Mechanisms and Therapy、Peptidase Inhibition and Analysis、Extracellular vesicles in disease

The CHI3L1 signaling pathway significantly influences glioma angiogenesis, but its role in the tumor microenvironment (TME) remains elusive. We propose a novel CHI3L1-associated vascular phenotype classification for glioma through integrative analyses of multiple datasets with bulk and single-cell transcriptome, genomics, digital pathology, and clinical data. We investigated the biological characteristics, genomic alterations, therapeutic vulnerabilities, and immune profiles within these phenotypes through a comprehensive multi-omics approach. We constructed the vascular-related risk (VR) score based on CHI3L1-associated vascular signatures (CAVS) identified by machine learning algorithms. Utilizing unsupervised consensus clustering, gliomas were stratified into three distinct vascular phenotypes: Cluster A, marked by high vascularization and stromal activation with a relatively low levels of tumor-infiltrating lymphocytes (TILs); Cluster B, characterized by moderate vascularization and stromal activity, coupled with a high density of TILs; and Cluster C, defined by low vascularization and sparse immune cell infiltration. We observed that the CAVS effectively indicated glioma-associated angiogenesis and immune suppression by single-cell RNA-seq analysis. Moreover, the high-VR-score group exhibited enhanced angiogenic activity, reduced immune response, resistance to immunotherapy, and poorer clinical outcomes. The VR score independently predicted glioma prognosis and, combined...