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Updated: Jan 11, 2026

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures

Published on: January 9, 2019

Multi-Omics Integration for Advancing Glioma Precision Medicine.

Maria Guarnaccia¹, Valentina La Cognata¹, Giulia Gentile¹

¹Institute for Biomedical Research and Innovation (IRIB), National Research Council (CNR), Catania, Italy.

Annals of Clinical and Translational Neurology

|November 18, 2025

Summary

This summary is machine-generated.

Integrating multiple omics data with AI can improve glioma diagnosis and treatment. This approach enhances understanding of tumor complexity for better patient outcomes and personalized therapies.

Keywords:

artificial intelligence gliomas multi‐omics strategies personalized medicine therapeutic interventions

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Last Updated: Jan 11, 2026

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures

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Published on: January 9, 2019

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

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

Published on: September 13, 2022

Area of Science:

Neuro-oncology
Computational Biology
Genomics

Background:

Gliomas are aggressive central nervous system tumors with poor prognosis and limited effective treatments.
Current diagnosis and management, often based on single genetic markers, struggle to capture tumor complexity.
High-throughput technologies have enabled molecular classification but challenges remain in clinical application.

Purpose of the Study:

To provide an overview of multi-omics strategies for adult-type diffuse glioma molecular taxonomy.
To highlight the potential of integrating diverse omics data for improved glioma understanding.
To explore how computational methods enhance diagnostic precision, prognostic accuracy, and therapeutic development.

Main Methods:

Comprehensive review of multi-omics data integration strategies (genomics, transcriptomics, epigenomics, proteomics, metabolomics, radiomics, single-cell, spatial omics).
Focus on computational methodologies and artificial intelligence (machine learning algorithms).
Analysis of sex-dependent differential gene expression patterns.

Main Results:

Multi-omics integration offers a deeper understanding of glioma biology.
Combined data and machine learning improve diagnostic precision and prognostic accuracy.
This integrated approach facilitates personalized and targeted therapeutic interventions.

Conclusions:

Multi-omics data integration is crucial for deciphering glioma molecular taxonomy.
Machine learning-based analysis of multilayer data advances glioma patient prognosis.
The future of glioma treatment lies in personalized, targeted therapies informed by comprehensive molecular insights.