Machine Learning-Driven Metabolomic Biomarker Discovery in Glioblastoma: Advances, Challenges, and Future Directions

Area of Science:

• Oncology
• Bioinformatics
• Metabolomics

Background:

• Glioblastoma (GBM) is an aggressive brain tumor with high recurrence rates after standard treatment.
• Intratumor heterogeneity complicates GBM management and treatment response prediction.
• Machine learning (ML) and metabolomics are emerging tools for understanding tumor biology and identifying biomarkers.

Purpose of the Study:

• To review recent advancements in applying ML to metabolomics for GBM biomarker discovery.
• To highlight the potential of these integrated approaches for improving GBM classification and predicting patient response to therapy.
• To discuss challenges and future directions for ML and metabolomics in clinical GBM management.

Main Methods:

• Review of current literature on machine learning applications in glioblastoma metabolomics.
• Analysis of metabolomic profiling data, particularly biogenic amines, in relation to GBM progression and treatment.
• Evaluation of machine learning models for GBM classification, staging, and biomarker identification.

Main Results:

• Machine learning models have demonstrated utility in GBM classification and biomarker discovery.
• Metabolomic profiling reveals links between metabolic pathways, tumor stage, and response to therapy.
• Integrated ML and metabolomics approaches offer potential for enhanced GBM management.

Conclusions:

• Machine learning combined with metabolomics represents a promising strategy for advancing glioblastoma biomarker discovery.
• These integrated methods can aid in classifying GBM and predicting patient response to treatments.
• Addressing challenges in data dimensionality and biomarker panel streamlining is crucial for clinical implementation.