Human immunoglobulin G (IgG), especially autoantibodies, has major implications for the diagnosis and management of a wide range of autoimmune diseases. However, some healthy individuals also have autoantibodies, while a portion of patients with autoimmune diseases test negative for serologic autoantibodies. Recent advances in glycomics have shown that IgG Fc
N
-glycosylations are more reliable diagnostic and monitoring biomarkers than total IgG autoantibodies in a wide variety of autoimmune diseases. Furthermore, these
N
-glycosylations of IgG Fc, particularly sialylation, have been reported to exert significant anti-inflammatory effects by upregulating inhibitory FcγRIIb on effector macrophages and reducing the affinity of IgG for either complement protein or activating Fc gamma receptors. Therefore, sialylated IgG is a potential therapeutic strategy for attenuating pathogenic autoimmunity. IgG sialylation-based therapies for autoimmune diseases generated through genetic, metabolic or chemoenzymatic modifications have made some advances in both preclinical studies and clinical trials.
Aberrant 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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.