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

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2021-2022

The use of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry for the analysis of carbohydrates and glycoconjugates is a well-established technique and this review is the 12th update of the original article published in 1999 and brings coverage of the literature to the end of 2022. As with previous review, this review also includes a few papers that describe methods appropriate to analysis by MALDI, such as sample preparation, even though the ionization method is not MALDI. The review follows the same format as previous reviews. It is divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of computer software for structural identification. (2) Applications to various structural types such as oligo- and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other general areas such as medicine, industrial processes, natural products and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. MALDI is still an ideal technique for carbohydrate analysis, particularly in its ability to produce single ions from each analyte and advancements in the technique and range of applications show little sign of diminishing.

Site-specific immunoglobulin G N-glycosylation is associated with gastric cancer progression

BACKGROUND

The relationship between cancer development and alterations in IgG N-glycosylation has been well-established. However, comprehensive profiling of the N-glycome and N-glycoproteome in gastric cancer (GC) remains limited. Furthermore, the prognostic potential of IgG N-glycan patterns in identifying precursors to GC has yet to be fully elucidated.

METHODS

The IgG N-glycome in GC was characterized using a custom high-throughput orthogonal mass spectrometry approach. Multivariate analysis was employed to identify and assess glycomic alterations. A comprehensive bioinformatics analysis was also conducted to investigate the differential expression of N-glycosylation-related genes and their potential roles in GC pathogenesis. Additionally, interleukin-11 (IL-11) levels were quantified using a standardized enzyme-linked immunosorbent assay (ELISA).

RESULTS

Galactosylation and sialylation of IgG decreased mainly in the IgG1 and IgG2 subclasses in GC, with subclass-specific changes in IgG3 and IgG4 galactosylation. These glycan modifications were represented by unique glycopeptides (IgG1_H5N5, IgG2_H4N3F1, IgG2_H4N4, IgG2_H4N4F1S1, IgG3/4_H4N4F1, IgG3/4_H4N4F1S1), which outperformed CA72-4 for GC diagnosis. Analysis of key glycogenes revealed differential expression patterns, implicating a functional role for IgG N-glycosylation in GC. Notably, the abundance of specific IgG glycosylation exhibited a significant correlation with serum level of IL-11.

CONCLUSIONS

Alterations in subclass-specific IgG N-glycosylation represent promising biomarkers for the detection and monitoring of GC progression, potentially influenced by cytokine-driven inflammation. Understanding these changes could improve our knowledge of molecular mechanisms, aiding in diagnostic improvements and therapeutic development.

Identification of serum N-glycans signatures in three major gastrointestinal cancers by high-throughput N-glycome profiling

BACKGROUND

Alternative N-glycosylation of serum proteins has been observed in colorectal cancer (CRC), esophageal squamous cell carcinoma (ESCC) and gastric cancer (GC), while comparative study among those three cancers has not been reported before. We aimed to identify serum N-glycans signatures and introduce a discriminative model across the gastrointestinal cancers.

METHODS

The study population was initially screened according to the exclusion criteria process. Serum N-glycans profiling was characterized by a high-throughput assay based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Diagnostic model was built by random forest, and unsupervised machine learning was performed to illustrate the differentiation between the three major gastrointestinal (GI) cancers.

RESULTS

We have found that three major gastrointestinal cancers strongly associated with significantly decreased mannosylation and mono-galactosylation, as well as increased sialylation of serum glycoproteins. A highly accurate discriminative power (> 0.90) for those gastrointestinal cancers was obtained with serum N-glycome based predictive model. Additionally, serum N-glycome profile exhibited distinct distributions across GI cancers, and several altered N-glycans were hyper-regulated in each specific disease.

CONCLUSIONS

Serum N-glycome profile was differentially expressed in three major gastrointestinal cancers, providing a new clinical tool for cancer diagnosis and throwing a light upon the disease-specific molecular signatures.

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.

IgG glycans in health and disease: Prediction, intervention, prognosis, and therapy

Immunoglobulin (IgG) glycosylation is a complex enzymatically controlled process, essential for the structure and function of IgG. IgG glycome is relatively stable in the state of homeostasis, yet its alterations have been associated with aging, pollution and toxic exposure, as well as various diseases, including autoimmune and inflammatory diseases, cardiometabolic diseases, infectious diseases and cancer. IgG is also an effector molecule directly involved in the inflammation processes included in the pathogenesis of many diseases. Numerous recently published studies support the idea that IgG N-glycosylation fine-tunes the immune response and plays a significant role in chronic inflammation. This makes it a promising novel biomarker of biological age, and a prognostic, diagnostic and treatment evaluation tool. Here we provide an overview of the current state of knowledge regarding the IgG glycosylation in health and disease, and its potential applications in pro-active prevention and monitoring of various health interventions.

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.

Proteomics of post-translational modifications in colorectal cancer: Discovery of new biomarkers

Colorectal cancer (CRC) is one of the costliest health problems and ranks second in cancer-related mortality in developed countries. With the aid of proteomics, many protein biomarkers for the diagnosis, prognosis, and precise management of CRC have been identified. Furthermore, some protein biomarkers exhibit structural diversity after modifications. Post-translational modifications (PTMs), most of which are catalyzed by a variety of enzymes, extensively increase protein diversity and are involved in many complex and dynamic cellular processes through the regulation of protein function. Accumulating evidence suggests that abnormal PTM events are associated with a variety of human diseases, such as CRC, thus highlighting the need for studying PTMs to discover both the molecular mechanisms and therapeutic targets of CRC. In this review, we begin with a brief overview of the importance of protein PTMs, discuss the general strategies for proteomic profiling of several key PTMs (including phosphorylation, acetylation, glycosylation, ubiquitination, methylation, and citrullination), shift the emphasis to describing the specific methods used for delineating the global landscapes of each of these PTMs, and summarize the recent applications of these methods to explore the potential roles of the PTMs in CRC. Finally, we discuss the current status of PTM research on CRC and provide future perspectives on how PTM regulation can play an essential role in translational medicine for early diagnosis, prognosis stratification, and therapeutic intervention in CRC.