Profiling of isomer-specific IgG N-glycosylation in cohort of Chinese colorectal cancer patients

Immunoglobulin G N-glycan markers of mild cognitive impairment in a Chinese population with cerebrovascular stenosis: A case-control study

BACKGROUND

Immunoglobulin G (IgG) N-glycans have been shown to regulate the inflammatory response in the context of disease. In recent years, it has been found to be associated with several neurodegenerative disorders. In this study, we examined the relationship between IgG N-glycans and mild cognitive impairment (MCI) in a high-risk population for MCI, specifically patients with cerebrovascular stenosis.

METHODS

In a case-control study, we investigated IgG N-glycans and cytokines in MCI and non-MCI patients in a population with cerebrovascular stenosis. A multifactorial logistic regression analysis was employed to investigate the potential association between IgG N-glycoprotein and MCI, with familial error rates being corrected for using the Benjamin-Hochberg method. To construct discriminatory models, logistic stepwise regression was employed and evaluated for their diagnostic efficacy.

RESULTS

A statistically significant difference was found in eight of the IgG-GPs between the two groups. Three IgG-GPs were correlated with MCI, with an overall false discovery rate <0.05. Specifically, IgG-GP7 (non-sialylated glycan) was positively correlated with MCI, while IgG-GP14 (digalactosylated glycans) and IgG-GP18 (bis-sialylated glycan) were negatively correlated with MCI. The model constructed by combining IgG N-glycans (IgG-GP7, IgG-GP14, IgG-GP18) and cytokines (IL-1β, IL-10, BDNF and VEGF) demonstrated the highest diagnostic efficacy [AUC: 0.939, 95 % CI: (0.910-0.967)].

DISCUSSION

In the present study, we observed that agalactosylation and no-sialylation play a role in the progression of MCI by influencing the pro-inflammatory impact of IgG. The integration of IgG N-glycan and cytokines into a discriminative model demonstrated strong diagnostic efficacy, suggesting its potential use as a screening tool for early prediction of MCI in patients with cerebrovascular stenosis.

Recent advances in N-glycan biomarker discovery among human diseases

N-glycans play important roles in a variety of biological processes. In recent years, analytical technologies with high resolution and sensitivity have advanced exponentially, enabling analysts to investigate N-glycomic changes in different states. Specific glycan and glycosylation signatures have been identified in multiple diseases, including cancer, autoimmune diseases, nervous system disorders, and metabolic and cardiovascular diseases. These glycans demonstrate comparable or superior indicating capability in disease diagnosis and prognosis over routine biomarkers. Moreover, synchronous glycan alterations concurrent with disease initiation and progression provide novel insights into pathogenetic mechanisms and potential treatment targets. This review elucidates the biological significance of N-glycans, compares the existing glycomic technologies, and delineates the clinical performance of N-glycans across a range of diseases.

Deep learning enhanced the diagnostic merit of serum glycome for multiple cancers

Protein glycosylation is associated with the pathogenesis of various cancers. The utilization of certain glycans in cancer diagnosis models holds promise, yet their accuracy is not always guaranteed. Here, we investigated the utility of deep learning techniques, specifically random forests combined with transfer learning, in enhancing serum glycome's discriminative power for cancer diagnosis (including ovarian cancer, non-small cell lung cancer, gastric cancer, and esophageal cancer). We started with ovarian cancer and demonstrated that transfer learning can achieve superior performance in data-disadvantaged cohorts (AUROC >0.9), outperforming the approach of PLS-DA. We identified a serum glycan-biomarker panel including 18 serum N-glycans and 4 glycan derived traits, most of which were featured with sialylation. Furthermore, we validated advantage of the transfer learning scheme across other cancer groups. These findings highlighted the superiority of transfer learning in improving the performance of glycans-based cancer diagnosis model and identifying cancer biomarkers, providing a new high-fidelity cancer diagnosis venue.

Serum IgG N-glycans enable early detection and early relapse prediction of colorectal cancer

Colorectal cancer (CRC) develops mainly from colorectal advanced adenomas (AA), which are considered precancerous lesions. Novel early diagnostic biomarkers are urgently needed to distinguish CRC and AA from healthy control (HC). Alternative glycosylation of serum IgG has been shown to be closely associated with CRC. We aimed to explore the potential of IgG N-glycan as biomarkers in the early differential diagnosis of CRC. The study population was strictly matched to the exclusion criteria process. Serum IgG N-glycan profiles were analyzed by a robust and reliable relative quantitative method based on ultra-performance liquid chromatography (UPLC). Relative quantification and classification performance of IgG N-glycans were evaluated by Mann-Whitney U tests and ROC curve based on directly detected and derived glycan traits, respectively. Six and 14 directly detected glycan traits were significantly changed in AA and CRC, respectively, compared with HC. GP1 and GP3 were able to accurately distinguish AA from HC for early precancerous lesions screening. GP4 and GP14 provided a high value in discriminating CRC from HC. A novel combined index named GlycoF, including GP1, GP3, GP4, GP14 and CEA was developed to provide a potential early diagnostic biomarker in discriminating simultaneously AA (AUC = 0.847) and CRC (AUC = 0.844) from HC. GlycoF also demonstrated a superior CRC detection rate across CRC all stages and conspicuous prediction ability of risk of relapse. Serum IgG N-glycans analysis provided powerful early screening biomarkers that can efficiently differentiate CRC and AA from HC.

Three Major Gastrointestinal Cancers Could Be Distinguished through Subclass-Specific IgG Glycosylation

Esophageal cancer (EC), gastric cancer (GC), and colorectal cancer (CRC) are three major digestive tract tumors with higher morbidity and mortality due to significant molecular heterogeneity. Altered IgG glycosylation has been observed in inflammatory activities and disease progression, and the IgG glycome profile could be used for disease stratification. However, IgG N-glycome profiles in these three cancers have not been systematically investigated. Herein, subclass-specific IgG glycosylation in CRC, GC, and EC was comprehensively characterized by liquid chromatography-tandem mass spectrometry. It was found that IgG1 sialylation was decreased in all three cancers, and the alterations in CRC and EC may be subclass-specific. IgG4 mono-galactosylation was increased in all three cancers, which was a subclass-specific change in all of them. Additionally, glycopeptides of IgG1-H5N5, IgG2-H4N3F1, and IgG4-H4N4F1 could distinguish all three cancer groups from controls with fair diagnostic performance. Furthermore, bioinformatics verified the differential expression of relevant glycosyltransferase genes in cancer progression. Significantly, those three gastrointestinal cancers could be distinguished from each other using subclass-specific IgG glycans. These findings demonstrated the spatial and temporal diversity of IgG N-glycome among digestive cancers, increasing our understanding of the molecular mechanisms of EC, GC, and CRC pathogenesis.

Glycosylation of immunoglobin G in tumors: Function, regulation and clinical implications

Immunoglobulin G (IgG) is the predominant component in humoral immunity and the major effector of neutralizing heterogeneous antigens. Glycosylation, as excessive posttranscriptional modification, can modulate IgG immune function. Glycosylated IgG has been reported to correlate with tumor progression, presenting several characteristic modifications, including the core fucose, galactose, sialic acid, and the bisect N-acetylglucosamine (GlcNAc). Meanwhile, IgG glycosylation regulates tumor immunity involved in tumor progression and is thus a potential target. Herein, we summarized the research progression to provide novel insight into the application of IgG glycosylation in tumor diagnosis and treatment.

IgG N-glycans

Glycosylation, one of the most common post-translational modifications in mammalian cells, impacts many biological processes such as cell adhesion, proliferation and differentiation. As the most abundant glycoprotein in human serum, immunoglobulin G (IgG) plays a vital role in immune response and protection. There is a growing body of evidence suggests that IgG structure and function are modulated by attached glycans, especially N-glycans, and aberrant glycosylation is associated with disease states. In this chapter, we review IgG glycan repertoire and function, strategies for profiling IgG N-glycome and recent studies. Mass spectrometry (MS) based techniques are the most powerful tools for profiling IgG glycome. IgG glycans can be divided into high-mannose, biantennary complex and hybrid types, modified with mannosylation, core-fucosylation, galactosylation, bisecting GlcNAcylation, or sialylation. Glycosylation of IgG affects antibody half-life and their affinity and avidity for antigens, regulates crystallizable fragment (Fc) structure and Fcγ receptor signaling, as well as antibody effector function. Because of their critical roles, IgG N-glycans appear to be promising biomarkers for various disease states. Specific IgG glycosylation can convert a pro-inflammatory response to an anti-inflammatory activity. Accordingly, IgG glycoengineering provides a powerful approach to potentially develop effective drugs and treat disease. Based on the understanding of the functional role of IgG glycans, the development of vaccines with enhanced capacity and long-term protection are possible in the near future.

Revealing the changes of IgG subclass-specific N-glycosylation in colorectal cancer progression by high-throughput assay

PURPOSE

The changes of glycosylation of different IgG subclass in colorectal cancer (CRC) were rarely investigated. The authors aimed to use a simple and high-throughput analytical method to explore the changes of subclass-specific IgG glycosylation in CRC, and to find the specific glyco-biomarkers for early detection of this disease.

EXPERIMENTAL DESIGN

Serum samples from 71 cancer patients and 22 benign patients with 50 age- and sex-matched healthy controls were collected from two independent cohorts. Subclass-specific IgG glycosylation was profiled by MALDI-MS followed by the structural identification through MALDI-MS/MS. The exported MS data was automatically and rapidly processed by the self-developed MATLAB code.

RESULTS

Statistical analysis suggested the significantly decreased galactosylation and remarkably increased agalactosylation of IgG1 or IgG2 in the malignant transformation of CRC, which enables the differentiation between cancer patients and healthy controls. The changes of glycan features were elucidated by the exploration of individual glycopeptides, showing the biantennary fucosylated glycan without galactose (H3N4F1) or with two galactose (H5N4F1) of IgG1 and IgG2 could distinguish cancer group from both benign and control groups.

CONCLUSIONS AND CLINICAL RELEVANCE

Through the simple and high-throughput procedures, this study revealed the important role of IgG glycopeptides in the premature pathology of CRC.

Artificial Intelligence in Colorectal Cancer Diagnosis Using Clinical Data: Non-Invasive Approach

Colorectal cancer is the third most common and second most lethal tumor globally, causing 900,000 deaths annually. In this research, a computer aided diagnosis system was designed that detects colorectal cancer, using an innovative dataset composing of both numeric (blood and urine analysis) and qualitative data (living environment of the patient, tumor position, T, N, M, Dukes classification, associated pathology, technical approach, complications, incidents, ultrasonography-dimensions as well as localization). The intelligent computer aided colorectal cancer diagnosis system was designed using different machine learning techniques, such as classification and shallow and deep neural networks. The maximum accuracy obtained from solving the binary classification problem with traditional machine learning algorithms was 77.8%. However, the regression problem solved with deep neural networks yielded with significantly better performance in terms of mean squared error minimization, reaching the value of 0.0000529.

Immunoglobulin G Glycosylation Changes in Aging and Other Inflammatory Conditions

Among the multiple roles played by protein glycosylation, the fine regulation of biological interactions is one of the most important. The asparagine 297 (Asn₂₉₇) of IgG heavy chains is decorated by a diantennary glycan bearing a number of galactose and sialic acid residues on the branches ranging from 0 to 2. In addition, the structure can present core-linked fucose and/or a bisecting GlcNAc. In many inflammatory and autoimmune conditions, as well as in metabolic, cardiovascular, infectious, and neoplastic diseases, the IgG Asn₂₉₇-linked glycan becomes less sialylated and less galactosylated, leading to increased expression of glycans terminating with GlcNAc. These conditions alter also the presence of core-fucose and bisecting GlcNAc. Importantly, similar glycomic alterations are observed in aging. The common condition, shared by the above-mentioned pathological conditions and aging, is a low-grade, chronic, asymptomatic inflammatory state which, in the case of aging, is known as inflammaging. Glycomic alterations associated with inflammatory diseases often precede disease onset and follow remission. The aberrantly glycosylated IgG glycans associated with inflammation and aging can sustain inflammation through different mechanisms, fueling a vicious loop. These include complement activation, Fcγ receptor binding, binding to lectin receptors on antigen-presenting cells, and autoantibody reactivity. The complex molecular bases of the glycomic changes associated with inflammation and aging are still poorly understood.

Glycomics and glycoproteomics: Approaches to address isomeric separation of glycans and glycopeptides

Changes in the glycome of human proteins and cells are associated with the progression of multiple diseases such as Alzheimer's, diabetes mellitus, many types of cancer, and those caused by viruses. Consequently, several studies have shown essential modifications to the isomeric glycan moieties for diseases in different stages. However, the elucidation of extensive isomeric glycan profiles remains challenging because of the lack of analytical techniques with sufficient resolution power to separate all glycan and glycopeptide iso-forms. Therefore, the development of sensitive and accurate approaches for the characterization of all the isomeric forms of glycans and glycopeptides is essential to tracking the progression of pathology in glycoprotein-related diseases. This review describes the isomeric separation achievements reported in glycomics and glycoproteomics in the last decade. It focuses on the mass spectrometry-based analytical strategies, stationary phases, and derivatization techniques that have been developed to enhance the separation mechanisms in liquid chromatography systems and the detection capabilities of mass spectrometry systems.

Associations between the serum levels of PFOS/PFOA and IgG N-glycosylation in adult or children

BACKGROUND

Perfluorooctanoic acid (PFOA) and perfluorooctanoic sulfonate (PFOS) have been shown to be associated with disease development. Immunoglobulin G (IgG) N-glycosylation plays a vital role in human immune system and inflammatory activities. Altered IgG glycosylation was one of the molecular markers of various disorders. However, whether the chemicals affect IgG glycosylation has not been investigated.

METHODS

Serum samples of 190 individuals including 95 adults and 95 children were selected based on the sex, age and PFOA/PFOS concentration. IgG N-glycome profile was obtained from glycan release, derivatization, and MALDI-MS analysis. One-factor ANOVA test was performed to analyze the association between different levels of PFOS/PFOA and IgG glycosylation changes. Evaluation of the diagnostic performance of significantly changed IgG glycosylation was performed by receiver operating characteristic curve. PFOS/PFOA concentrations were studied in relation to IgG glycosylation by 3D-nonlinear regression analysis.

RESULTS

10 of the 28 individual IgG glycans were significantly altered between different levels of PFOS/PFOA in adult serum. Among children with high serum levels of PFOS or PFOA, a total of 12 IgG N-glycans were markedly different from those with lower serum PFOS/PFOA. The glycan derived traits for adults with higher serum PFOS or PFOA were marked by significant alterations in IgG digalactosylation, agalactosylation, fucosylation, fucosylated sialylation, and disialylation. Similarly, pronounced changes in agalactosylation, digalactosylation, mono-sialylation and total sialylation, as well as neutral and sialo bisection, were associated with elevated serum PFOS or PFOA in children. Several glycans gained moderately accurate scores of area under the curve for diagnosis of PFOS or PFOA pollution. Nonlinear surface fitting showed the independent or coordinate effect of PFOS or PFOA on the expression of IgG glycosylation.

CONCLUSIONS

High levels of PFOS or PFOA in human serum were strongly associated with altered IgG glycosylation and therefore are a potential risk factor for the development of diseases.

Dynamic alterations in serum IgG N-glycan profiles in the development of colitis-associated colon Cancer in mouse model

BACKGROUND

Alternative glycosylation of serum IgG has been shown to be closely associated with colorectal cancer (CRC). Currently, a dynamic study which can not only minimize the influence of genetic background, environment and other interfering factors during cancer development, but also focus on investigating carcinogenic characteristics of IgG glycan is lacking.

METHODS

Serum IgG N-glycans were characterized at four stages of CRC development by ultra-performance liquid chromatography in a typical colitis-related CRC mouse model induced by azoxymethane-dextran sodium sulfate. Furthermore, the expression of related glycosyltransferases in splenic B lymphocytes at the corresponding time was also assessed.

RESULTS

The relative abundance of seven IgG glycans, which can be classified as monoantennary, core fucose, sialic acid, galactose and bisecting, was changed during tumor growth. The abundance of some glycans was altered during the first stage of cancer induction. Correspondingly, the expression of glycosyltransferases in splenic B lymphocytes and different tissues in cancer groups was also decreased compared to that in controls.

CONCLUSIONS

This study represents the comprehensive analysis of IgG glycosylation in the dynamic process of colitis-associated CRC. To our knowledge, this is the first report that the expression of glycosyltransferases in mouse splenic B lymphocytes is consistent or inconsistent with the alterations of IgG N-glycans, and the variation tendency is tissue nonspecific.

GENERAL SIGNIFICANCE

Providing a novel approach to identify the IgG glycans related to the development of CRC and laying a foundation for research on structure and function of glycans using mouse.

A comprehensive analysis of subclass-specific IgG glycosylation in colorectal cancer progression by nanoLC-MS/MS

Colorectal cancer is associated with changed IgG glycosylation, but the alteration in specific subclasses of IgG is unknown.