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Improved analytical workflow towards machine learning supported N-glycomics-based biomarker discovery.

Agnes Vathy-Fogarassy1, Veronika Gombas1, Rebeka Torok2

  • 1Department of Computer Science and Systems Technology, University of Pannonia, Egyetem u 10., Veszprem, H-8200, Hungary.

Talanta
|May 31, 2025
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Summary
This summary is machine-generated.

This study developed an automated workflow for analyzing N-glycans using capillary electrophoresis and machine learning. This method accurately predicts lung cancer patient response to chemotherapy, aiding disease management.

Keywords:
Capillary electrophoresisFeature selectionLung cancerMachine learningN-glycome

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Area of Science:

  • Glycomics
  • Analytical Chemistry
  • Computational Biology

Background:

  • Glycan analysis is complex, hindering manual interpretation.
  • Capillary electrophoresis (CE) is a key separation technique for glycans.
  • Integrating CE with machine learning (ML) offers enhanced glycomic data interpretation.

Purpose of the Study:

  • To develop an automated sample preparation method for reproducible N-glycome profiling using CE.
  • To enable ML-supported data interpretation for glycan analysis.
  • To apply this workflow to predict lung cancer patient response to chemotherapy.

Main Methods:

  • Development of an automated, liquid-handling robot-based sample preparation system.
  • Optimization of the system for N-glycome profiling via capillary electrophoresis.
  • Application of a machine learning-supported data interpretation pipeline.

Main Results:

  • Achieved reproducible N-glycome profiles suitable for ML analysis.
  • Demonstrated the workflow's ability to predict chemotherapy effectiveness in lung cancer.
  • Obtained high predictive performance with AUC values ranging from 0.8290 to 0.8410.

Conclusions:

  • The developed automated glycoanalytical workflow enhances N-glycan data acquisition and interpretation.
  • N-glycan profiles contain significant clinical information for predicting chemotherapy response.
  • This approach supports personalized medicine and effective lung cancer treatment management.