机构：[1]Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.四川大学华西医院[2]Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.首都医科大学附属天坛医院[3]Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.首都医科大学附属天坛医院[4]Department of Vascular Surgery, Fuwai Yunnan Cardiovascular Hospital, Affiliated Cardiovascular Hospital of Kunming Medical University, Kunming, China.[5]Department of Neurosurgery, The Third Hospital of Jilin University, Changchun, China.[6]Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.[7]Department of Neurosurgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.[8]Department of Neurosurgery, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China.
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.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: Metabolismhigh/tumor microenvironment (TME)high, Metabolismlow/TMEhigh, Metabolismhigh/TMElow, and Metabolismlow/TMElow. 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.The Metabolismlow/TMElow subgroup showed the best prognosis, whereas the Metabolismhigh/TMEhigh 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.This study demonstrates that integrating multi-omics data with MRI radiomics provides a robust framework for glioma classification, enabling more precise and personalized treatment strategies. The findings highlight the critical role of metabolic and immune profiling in understanding glioma heterogeneity and improving clinical outcomes.
基金：
the Beijing Municipal Natural Science Foundation (No.
JQ23040) and the Major Scientific and Technological Innovation
Project (2021ZD0201605).
语种：
外文
PubmedID：
中科院(CAS)分区：
出版当年[2025]版：
大类|2 区医学
小类|2 区肿瘤学
最新[2025]版：
大类|2 区医学
小类|2 区肿瘤学
第一作者：
第一作者机构：[1]Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.[2]Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.[3]Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
共同第一作者：
通讯作者：
通讯机构：[2]Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.[3]Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
推荐引用方式(GB/T 7714)：
Li Jinwei,Yan Zeya,Zhang Yang,et al.Integrative Multi-Omics Features Stratify Metabolic and Immune Subtypes in Glioma[J].JCO Precision Oncology.2025,9:e2400928.doi:10.1200/PO-24-00928.
APA：
Li Jinwei,Yan Zeya,Zhang Yang,Hu Jie,Hui Xuhui...&Wang Yinyan.(2025).Integrative Multi-Omics Features Stratify Metabolic and Immune Subtypes in Glioma.JCO Precision Oncology,9,
MLA：
Li Jinwei,et al."Integrative Multi-Omics Features Stratify Metabolic and Immune Subtypes in Glioma".JCO Precision Oncology 9.(2025):e2400928
