Diagnostic potential of site-specific serotransferrin N-glycosylation in discriminating different liver diseases

Background: Altered glycosylation modulates the structure and function of disease-related proteins. The associ-ations between serotransferrin (STF) N-glycosylation and liver diseases (LDs) have been revealed. However, how intact N-glycopeptides vary among different types of liver diseases remains unclear.Methods: Intact STF N-glycopeptides from patients with chronic liver disease (CLD, n = 92), primary liver cancer (PLC, n = 123), metastatic liver cancer (MLC, n = 57), and healthy controls (HCs, n = 59) were determined using high-resolution mass spectrometry.Results: Significant changes were displayed in STF glycosylation among 4 groups. The LD screening model, including Asn432 G1S/G2S, Asn432 G2S/G2S2, and Asn630 G2NS2/G2FNS2, was constructed to differentiate LDs from HCs, with a AUC of 0.92. The liver cancer (LC) diagnostic model, a combination of Asn432 G1-N/G1S-N, Asn432 G1/G2, Asn432 G2FS/G2FS2, and Asn630 G1S-N /G1S, showed good performance in discriminating LC from CLD (AUC = 0.93). Moreover, AFP-negative LC patients (93 %) were successfully predicted by the LC diagnostic model. Furthermore, the MLC triage model, composed of Asn432 G1/G2, Asn432 G3F/G3FS, Asn630 G2/G2S, Asn630 G2S2/G2NS2, and Asn630 G3FS/G3FS2, yielded an AUC of 0.98 between PLC and MLC.Conclusions: STF N-glycosylation is a potential biomarker for the accurate classification of different LDs.

Automated summary

The glycosylation of serotransferrin (STF) has been found to vary significantly among different liver diseases. Models for screening liver diseases and diagnosing liver cancer have been developed based on N-glycosylation patterns of STF. Additionally, a triage model has been created to discriminate between primary liver cancer and metastatic liver cancer.