Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

High-Density Electroencephalography Detects Spatiotemporal Abnormalities in Brain Networks in Patients With Glioma-Related Epilepsy.

CNS neuroscience & therapeutics·2025
Same author

Radiomics in glioma: emerging trends and challenges.

Annals of clinical and translational neurology·2025
Same author

Molecular mechanisms and diagnostic model of glioma-related epilepsy.

NPJ precision oncology·2024
Same author

Molecular subtyping of diffuse gliomas using magnetic resonance imaging: comparison and correlation between radiomics and deep learning.

European radiology·2021
Same author

Adjuvant Transarterial Chemoembolization for HBV-Related Hepatocellular Carcinoma After Resection: A Randomized Controlled Study.

Clinical cancer research : an official journal of the American Association for Cancer Research·2018
Same author

A New Real-Time Cycle Slip Detection and Repair Method under High Ionospheric Activity for a Triple-Frequency GPS/BDS Receiver.

Sensors (Basel, Switzerland)·2018

Related Experiment Video

Updated: Jan 16, 2026

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma
09:17

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

Published on: September 13, 2022

2.7K

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

Jinwei Li1,2,3, Zeya Yan2,3, Yang Zhang4

  • 1Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.

JCO Precision Oncology
|September 26, 2025
PubMed
Summary

This study classifies gliomas using multi-omics and MRI radiomics, identifying metabolic and immune subtypes. The Metabolism-low/tumor microenvironment-low subtype shows the best prognosis, guiding personalized treatment strategies.

More Related Videos

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis
06:48

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis

Published on: May 31, 2020

6.2K
Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections
05:45

Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections

Published on: July 31, 2017

10.1K

Related Experiment Videos

Last Updated: Jan 16, 2026

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma
09:17

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

Published on: September 13, 2022

2.7K
On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis
06:48

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis

Published on: May 31, 2020

6.2K
Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections
05:45

Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections

Published on: July 31, 2017

10.1K

Area of Science:

  • Neuro-oncology
  • Genomics
  • Radiomics
  • Immunology
  • Metabolic profiling

Background:

  • Gliomas are aggressive central nervous system (CNS) tumors characterized by significant heterogeneity, complicating treatment strategies.
  • Accurate classification of glioma subtypes is crucial for developing effective and personalized therapeutic approaches.

Purpose of the Study:

  • To enhance glioma classification by integrating multi-omics data (genomics) with magnetic resonance imaging (MRI)-based radiomics.
  • To focus on identifying and characterizing metabolic and immune subtypes within gliomas.
  • To develop a robust framework for noninvasive glioma subtyping.

Main Methods:

  • Analysis of transcriptome data from 1,720 glioma patients, focusing on metabolism-related genes and immune cells.
  • Development of a metabolism-immune classifier to define four glioma subgroups: Metabolismhigh/tumor microenvironment (TME)high, Metabolismlow/TMEhigh, Metabolismhigh/TMElow, and Metabolismlow/TMElow.
  • Application of multicohort MRI radiomics and machine learning algorithms for noninvasive subtype prediction.
  • Validation of subgroup metabolic and immunological characteristics using single-cell RNA and spatial transcriptome sequencing.

Main Results:

  • The Metabolismlow/TMElow subgroup exhibited the most favorable prognosis, while the Metabolismhigh/TMEhigh subgroup displayed the poorest outcomes.
  • Machine learning models successfully predicted glioma subtypes noninvasively using MRI radiomics data.
  • Single-cell RNA sequencing confirmed distinct metabolic and immune profiles across the identified glioma subgroups, highlighting significant tumor microenvironment (TME) heterogeneity.

Conclusions:

  • Integrating multi-omics data with MRI radiomics offers a powerful approach for precise glioma classification.
  • The study underscores the importance of metabolic and immune profiling in understanding glioma heterogeneity.
  • Findings support the development of more personalized treatment strategies for glioma patients based on their specific subtype.