Integrative Multi-Omics Features Stratify Metabolic and Immune Subtypes in Glioma

作者：Jinwei Li, Zeya Yan, Yang Zhang, Jia Hu, Xuhui Hui, Jinnan Zhang, Rui Zhang, Tao Xin, Quan Liu, Yinyan Wang · 发表于：JCO Precision Oncology · 年份：2025 · DOI：10.1200/po-24-00928 · 被引用次数：4 · 研究领域：Glioma Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging、Ferroptosis and cancer prognosis

PURPOSE Gliomas are aggressive CNS tumors with significant heterogeneity, posing challenges for effective treatment. This study aims to enhance glioma classification by integrating multi-omics data, including genomics and magnetic resonance imaging (MRI)–based radiomics, focusing on metabolic and immune subtypes. METHODS Transcriptome data from 1,720 patients with glioma were analyzed to identify key prognostic factors, including 42 metabolism-related genes and 25 immune cells. A metabolism-immune classifier was developed to categorize gliomas into four subgroups: Metabolism high /tumor microenvironment (TME) high , Metabolism low /TME high , Metabolism high /TME low , and Metabolism low /TME low . Multicohort MRI radiomics combined with machine learning algorithms were used to predict these subtypes. Single-cell RNA and spatial transcriptome sequencing were used to validate subgroups' metabolic and immunological characterization. RESULTS The Metabolism low /TME low subgroup showed the best prognosis, whereas the Metabolism high /TME high subgroup had the worst. Machine learning models can predict glioma subtypes noninvasively based on MRI radiomics. Single-cell RNA sequencing confirmed the distinct metabolic and immune profiles of the glioma subgroups, revealing significant cellular heterogeneity within the TME. CONCLUSION This study demonstrates that integrating multi-omics data with MRI radiomics provides a robust framework for glioma classification, enabling more precise ...