Lung cancer is the leading cause of cancer-related death, with high morbidity and mortality rates due to the lack of reliable methods for diagnosing lung cancer at an early stage. Low-dose computed tomography can help detect abnormal areas in the lungs, but only 16% of cases are diagnosed early. Tests for lung cancer markers are often employed to determine genetic expression or mutations in lung carcinogenesis. Serum glycome analysis is a promising new method for early lung cancer diagnosis as glycopatterns exhibit significant differences in lung cancer patients. In this study, we employed a solid-phase chemoenzymatic method to systematically compare glycopatterns in benign cases, adenocarcinoma before and after surgery, and advanced stages of adenocarcinoma. Our findings indicate that serum high-mannose levels are elevated in both benign cases and adenocarcinoma, while complex N-glycans, including fucose and 2,6-linked sialic acid, are downregulated in the serum. Subsequently, we developed an algorithm that utilizes 16 altered N-glycans, 7 upregulated and 9 downregulated, to generate a score based on their intensity. This score can predict the stages of cancer progression in patients through glycan characterization. This methodology offers a potential means of diagnosing lung cancer through serum glycome analysis.