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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
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Machine Learning-Driven Insights in Cancer Metabolomics: From Subtyping to Biomarker Discovery and Prognostic

Amr Elguoshy1, Hend Zedan2, Suguru Saito3

  • 1Biofluid Biomarker Center, Graduate School of Medical and Dental Sciences, Niigata University, Niigata 9502181, Japan.

Metabolites
|August 27, 2025
PubMed
Summary

Machine learning (ML) combined with metabolomics offers powerful insights into cancer biology. This integration aids in cancer subtyping, biomarker discovery, and prognostic modeling for precision oncology.

Keywords:
biomarker discoverycancer metabolomicsmachine learning (ML)prognostic modelingtumor subtyping

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Area of Science:

  • Oncology
  • Bioinformatics
  • Metabolomics

Background:

  • Cancer cells exhibit metabolic reprogramming crucial for tumor growth and treatment resistance.
  • Metabolomics, using mass spectrometry and NMR, provides a detailed view of cellular biochemistry.
  • Integrating metabolomics with machine learning (ML) enhances the analysis of complex cancer data.

Purpose of the Study:

  • To review the significant role of ML in advancing cancer metabolomics research.
  • To highlight ML applications in cancer subtyping, biomarker discovery, and prognostic modeling.
  • To discuss challenges and emerging solutions for clinical translation of ML-enhanced metabolomics.

Main Methods:

  • Systematic review of ML methodologies (supervised, unsupervised, deep learning) applied to cancer metabolomics.
  • Analysis of ML-metabolomics integration in cancer subtyping (e.g., triple-negative breast cancer).
  • Evaluation of ML models for biomarker discovery and prognostic modeling in various cancers.

Main Results:

  • ML-metabolomics successfully subtypes cancers, identifies biomarkers with >90% accuracy, and builds prognostic models.
  • Applications span breast, colorectal, lung, ovarian, prostate, thyroid, and pancreatic cancers.
  • ML aids in earlier detection, risk stratification, and personalized treatment planning.

Conclusions:

  • ML-enhanced metabolomics is pivotal for understanding cancer metabolism and driving precision oncology.
  • Addressing data quality and interpretability challenges is key for clinical implementation.
  • Emerging solutions like explainable AI (XAI) facilitate the translation of research findings to clinical practice.