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Updated: Jun 26, 2025

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Machine Learning-Based Integrated Multiomics Characterization of Colorectal Cancer Reveals Distinctive Metabolic

Ran Zheng1, Rui Su1, Yusi Fan2

  • 1State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130021, China.

Analytical Chemistry
|May 14, 2024
PubMed
Summary
This summary is machine-generated.

Identifying a metabolic signature is key for early colorectal cancer diagnosis and treatment. This study found seven key metabolic signatures with high accuracy, including PC (30:0), aiding in early detection and understanding cancer progression.

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Area of Science:

  • Oncology
  • Metabolomics
  • Bioinformatics

Background:

  • Colorectal cancer (CRC) diagnosis and treatment require identifying reliable biomarkers.
  • Metabolic alterations are increasingly recognized as hallmarks of cancer progression.

Purpose of the Study:

  • To develop a robust metabolic signature for early colorectal cancer diagnosis.
  • To identify specific metabolites and associated genes involved in CRC pathogenesis.

Main Methods:

  • Untargeted metabolomic analysis using extractive electrospray ionization mass spectrometry (EESI-MS).
  • Machine learning approaches (Logistic Regression_L1 and eXtreme Gradient Boosting) for signature development and validation.
  • Multi-omics analysis integrating metabolic and transcriptomic data.

Main Results:

  • A panel of seven metabolic signatures demonstrated high diagnostic performance (87.74% accuracy, 85.82% sensitivity, 89.66% specificity).
  • The metabolite PC (30:0) showed significant diagnostic potential and was linked to colorectal cancer cell proliferation.
  • Multi-omics analysis revealed correlations between genetic changes and metabolic dysregulation in CRC.

Conclusions:

  • The identified metabolic signatures offer a promising tool for early colorectal cancer detection.
  • PC (30:0) may play a role in CRC progression, highlighting potential therapeutic targets.
  • Understanding metabolic dysregulation provides insights into CRC mechanisms and informs intervention strategies.