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

SIMPLE SUMMARY: Globally, there were around 2.1 million lung cancer cases and 1.8 million deaths in 2018. Hungary—where this study was carried out—had the highest rate of lung cancer in the same year. We developed a new analytical method which can be readily used to follow up the tumor surgery by investigating the glycan (sugar) structures of proteins. As the results of such investigations are very complex, computer-assisted machine learning methods were utilized for data interpretation. ABSTRACT: The human serum N-glycome is a valuable source of biomarkers for malignant diseases, already utilized in multiple studies. In this paper, the N-glycosylation changes in human serum proteins were analyzed after surgical lung tumor resection. Seventeen lung cancer patients were involved in this study and the N-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 N-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 N-linked carbohydrate structures. The classification tree analysis resulted in a panel of N-glycans, which could be used to follow up on the effects of lung tumor surgical resection.