Machine Learning Based Analysis of Human Serum N- glycome Alterations to Follow up Lung Tumor Surgery

The human serum glycome is a valuable source of biomarkers for malignant diseases, already utilized in multiple studies. In this paper, the glycosylation changes in human serum proteins were analyzed after surgical lung tumor resection. Seventeen lung cancer patients were involved in this study and the glycosylation pattern of their serum samples was analyzed before and after the surgery using capillary electrophoresis separation with laser-induced fluorescent detection. The relative peak areas of 21 glycans were evaluated from the acquired electropherograms using machine learning-based data analysis. Individual glycans as well as their subclasses were taken into account during the course of evaluation. For the data analysis, both discrete (e.g., smoker or not) and continuous (e.g., age of the patient) clinical parameters were compared against the alterations in these 21 -linked carbohydrate structures. The classification tree analysis resulted in a panel of glycans, which could be used to follow up on the effects of lung tumor surgical resection.