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Predicting the effectiveness of chemotherapy treatment in lung cancer utilizing artificial intelligence-supported serum N-glycome analysis

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Computers in Biology and Medicine 2025年 186卷 109681-109681页

作者： Torok, Rebeka Meszaros, Brigitta Gombas, Veronika Vathy-Fogarassy, Agnes Szabo, Miklos Csanky, Eszter Jarvas, Gabor Guttman, Andras Research Institute of Biomolecular and Chemical Engineering University of Pannonia Veszprem Hungary Horváth Csaba Memorial Laboratory of Bioseparation Sciences Research Center for Molecular Medicine Doctoral School of Molecular Medicine Faculty of Medicine University of Debrecen Debrecen Hungary Department of Computer Science and Systems Technology University of Pannonia Veszprem Hungary Department of Pulmonology Borsod Academic County Hospital Miskolc Hungary

An efficient novel approach is introduced to predict the effectiveness of chemotherapy treatment in lung cancer by monitoring the serum N-glycome of patients combined with artificial intelligence-based data analysis. The study involved thirty-three lung cancer patients undergoing chemotherapy treatments. Serum samples were taken before and after the treatment. The N-linked oligosaccharides were enzymatically released, fluorophore-labeled, and analyzed by capillary electrophoresis with laser-induced fluorescence detection. The resulting electropherograms were thoroughly processed and evaluated by artificial intelligence-based classifiers, i.e., utilizing a machine learning algorithm to categorize the data into two (binary) classes. The classifier analysis method revealed a strong association between the structural changes in the N-glycans and the outcomes of the chemotherapy treatments (ROC >0.9). This novel combination of bioanalytical and AI methods provided a precise and rapid tool for predicting the effectiveness of chemotherapy. © 2025 The Authors

关键词： Lung cancer

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