Affiliation:
1. Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College Huazhong University of Science and Technology Wuhan China
2. The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics‐Hubei Bioinformatics & Molecular Imaging Key Laboratory Systems Biology Theme Department of Biomedical Engineering College of Life Science and Technology Huazhong University of Science and Technology Wuhan China
Abstract
AbstractAberrant serum N‐glycan profiles have been observed in multiple cancers including non‐small‐cell lung cancer (NSCLC), yet the potential of N‐glycans in the early diagnosis of NSCLC remains to be determined. In this study, serum N‐glycan profiles of 275 NSCLC patients and 309 healthy controls were characterized by MALDI‐TOF‐MS. The levels of serum N‐glycans and N‐glycosylation patterns were compared between NSCLC and control groups. In addition, a panel of N‐glycan biomarkers for NSCLC diagnosis was established and validated using machine learning algorithms. As a result, a total of 54 N‐glycan structures were identified in human serum. Compared with healthy controls, 29 serum N‐glycans were increased or decreased in NSCLC patients. N‐glycan abundance in different histological types or clinical stages of NSCLC presented differentiated changes. Furthermore, an optimal biomarker panel of eight N‐glycans was constructed based on logistic regression, with an AUC of 0.86 in the validation set. Notably, this model also showed a desirable capacity in distinguishing early‐stage patients from healthy controls (AUC = 0.88). In conclusion, our work highlights the abnormal N‐glycan profiles in NSCLC and provides supports potential application of N‐glycan biomarker panel in clinical NSCLC detection.
Funder
National Natural Science Foundation of China
Subject
Molecular Biology,Biochemistry
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献