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

Area of Science:

• Oncology
• Immunology
• Bioinformatics

Background:

• The CHI3L1 signaling pathway impacts glioma angiogenesis, but its role in the tumor microenvironment (TME) is not fully understood.
• Understanding glioma heterogeneity is crucial for developing effective therapeutic strategies.

Purpose of the Study:

• To classify glioma vascular phenotypes using CHI3L1-associated signatures.
• To investigate the biological characteristics, genomic alterations, therapeutic vulnerabilities, and immune profiles within these phenotypes.
• To develop a vascular-related risk (VR) score for predicting glioma prognosis and treatment response.

Main Methods:

• Integrative analysis of multi-omics datasets (transcriptome, genomics, digital pathology, clinical data).
• Machine learning algorithms to identify CHI3L1-associated vascular signatures (CAVS).
• Unsupervised consensus clustering to stratify gliomas into distinct vascular phenotypes.
• Single-cell RNA sequencing to analyze immune cell infiltration and function.

Main Results:

• Three distinct glioma vascular phenotypes were identified: Cluster A (high vascularization, low TILs), Cluster B (moderate vascularization, high TILs), and Cluster C (low vascularization, sparse immune infiltration).
• The CAVS effectively indicated glioma-associated angiogenesis and immune suppression.
• A high VR score correlated with enhanced angiogenesis, reduced immune response, immunotherapy resistance, and poorer clinical outcomes.
• The VR score independently predicted glioma prognosis.

Conclusions:

• CHI3L1-associated vascular phenotypes define distinct immune landscapes in gliomas.
• The VR score serves as a robust tool for clinical decision-making and prognosis prediction in glioma.
• These findings offer insights for optimizing therapeutic strategies targeting the glioma TME.