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Related Experiment Video

Updated: Jul 12, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Machine-Learning-Based Prediction of Clinical Outcomes in Gliomas Using Glycomic Features.

Xin Ma1,2, Derek Allison3,4, Jessica K A Macedo5

  • 1Department of Biostatistics, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA.

Computational and Structural Biotechnology Journal
|July 10, 2026
PubMed
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Glycomics analysis shows promise for predicting glioma patient outcomes. Specific glycan patterns can effectively classify mortality and stratify patients, offering potential new prognostic biomarkers for brain tumors.

Area of Science:

  • Neuro-oncology
  • Biomarker discovery
  • Glycomics

Background:

  • Gliomas are aggressive primary malignant brain tumors with poor prognosis.
  • Grade IV astrocytomas represent the most lethal form, necessitating improved prognostic tools.
  • Reliable biomarkers are crucial for predicting patient outcomes in glioma.

Purpose of the Study:

  • To investigate the prognostic value of N-linked glycomics in predicting outcomes for glioma patients.
  • To evaluate the performance of glycomic features in classifying mortality and seizure status.
  • To identify potential glycan-based biomarkers for glioma prognosis.

Main Methods:

  • N-linked glycomics analysis using matrix-assisted laser desorption/ionization mass spectrometry on 78 gliomas and 8 controls.

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Published on: January 9, 2019

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Last Updated: Jul 12, 2026

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  • Survival analyses and machine-learning predictive modeling were employed.
  • A cohort of 86 patients was analyzed using tissue microarrays.

    • Main Results:

    • Glycomic features demonstrated strong predictive performance for mortality (AUROC 0.880, AUPRC 0.925) and seizure status (AUROC 0.778, AUPRC 0.766).
    • Prognostic value was consistent in survival analyses (concordance index 0.759).
    • Machine learning identified top-ranked glycans capable of stratifying patients by survival outcomes.

    Conclusions:

    • N-linked glycomic features are potent predictors of glioma patient outcomes.
    • Glycans hold significant potential as novel prognostic biomarkers for glioma.
    • This glycomics approach offers a promising avenue for improving glioma patient management.