Comprehensive analysis of human IgG Fc N-glycopeptides and construction of a screening model for colorectal cancer
Graphical abstract
Introduction
Colorectal cancer (CRC) currently ranks as one of the most fatal types of cancer worldwide [1]. The CRC prevalence rate differs geographically, with the highest incidence in Australia, New Zealand, Europe and North America, and the relatively low incidence in Africa and South-Central Asia. Additionally, in most parts of the world, men have higher CRC incidence rate than women [2]. The CRC incidence rate is significantly high in people over the age of 50 [3]. It is hence of clinical and public importance for targeted prevention and early detection in the control of CRC. At present, early screening methods include screening colonoscopy, fecal occult blood testing, sigmoidoscopy, computed tomography colonography and double cecum barium enema [2,[4], [5], [6]]. Except for colonoscopy, the misdiagnosis rates of these screening methods are high [2,7,8]. Even if colonoscopy is a highly sensitive screening method, it is invasive, complex and costly [4]. Therefore, it is essential to find an accurate and feasible method for the early screening of CRC.
Glycosylation is an important post-translational modification of proteins in normal physiological process. It is well reported that changes in protein glycosylation are associated with the occurrence of several diseases, as well as invasion and metastasis of malignant cells [9,10]. Glycoproteins with specific N-glycan epitopes have been treated as cancer biomarkers, which act as criteria for diagnosing and prognosing malignant tumors [[11], [12], [13]]. For instance, ɑ-fetoprotein (AFP), prostate-specific antigen (PSA) and carbohydrate antigen (CA)15–3 are currently used as the biomarkers of hepatocellular carcinoma, prostate cancer and breast cancer respectively [[14], [15], [16]]. Recently, a number of studies have focused on analyzing glycosylation characteristics of individual proteins [[17], [18], [19]], especially immunoglobulin G (IgG) [[20], [21], [22], [23]].
IgG is a prominent glycoprotein in circulation that plays a key role in humoral immune response [24]. The conformation of the Fc region is altered significantly by the variation in IgG glycosylation, which influences the interaction with receptors and modulates the biological functions of IgG [9,25,26]. As reported by some previous studies, certain changes of IgG glycosylation may be correlated with CRC and its progression using glycan analysis [[27], [28], [29]]. With the development of glycopeptide enrichment technology, research on IgG N-glycopeptides has gained much attention. Subtle differences in IgG glycopeptides have been found to reflect different physiological and pathological states, along with pathogenesis and progression of diseases, including cancers [[30], [31], [32], [33], [34], [35]]. Compared with glycan analysis, the glycopeptide analysis method could provide information about the amino acid sequence besides glycan information. Therefore, it could distinguish between Fab and Fc glycans. The glycans derived from different IgG subclasses could also be discriminated.
Recently, our group has designed the chitosan@poly (glycidyl methacrylate) @iminodiacetic acid (CS@PGMA@IDA) nanomaterial, which was previously demonstrated able to recognize the glycosylation site of IgG and enrich glycopeptides efficiently [36]. Compared with other glycopeptide enrichment methods, this nanomaterial showed great capacity to purify N-glycopeptide from a complex plasma system and could bind N-glycopeptides from digested proteins within 20 min, which could greatly shorten the enrichment time with extremely high sensitivity and specificity. In this study, we designed a workflow based on this nanomaterial to assess the differences in IgG Fc N-glycopeptides in plasma under different physiological states and further developed multivariate logistic regression models that could identify CRC patients in an easy and efficient way.
Section snippets
Study cohort
In this study, a total of 113 subjects were enrolled, including 46 CRC patients and 67 normal subjects (Table 1). After the patients were diagnosed with CRC by pathological biopsy, the plasma was centrifuged for 10 min, and the supernatant was collected and placed in a test tube and inverted several times. The protease inhibitor (cOmplete, Mini, EDTA-free, EASYpa; Roche, Basel, Switzerland) was added to the supernatant in a ratio of 1:500 (mass ratio), mixed and transferred to a − 80 °C freezer
Strategy for IgG N-glycopeptide analysis with MALDI-TOF-MS
Purifying IgG with protein G beads from plasma is a pretreatment step in analyzing the glycosylation features of IgG. After purification, a huge number of the IgG complexes were detected, among which 9 glycoproteins seriously interfered with the IgG N-glycosylation analysis (Fig. S1, Table S1). In the glycopeptide analysis method developed by Li et al., to obtain pure IgG N-glycopeptides, polyacrylamide gel electrophoresis was performed and the bands of interest were then excised [33]. The
Discussion
IgG N-glycan analysis methods and IgG N-glycopeptide analysis methods, which were used to discover the glycosylation profiling of CRC patients, vary in many aspects. First, the experimental workflows were different. In the glycan analysis workflow, after the common IgG enrichment step, IgGs were digested by PNGase F. Then the released glycans were labeled and purified by HILIC-SPE, followed by detection via HILIC-UPLC (Fig. 1a). In contrast, the glycopeptide analysis workflow was much simpler.
Conclusion
In this study, we attempted to uncover the characteristics of IgG Fc N-glycopeptides in the plasma of CRC patients. By analyzing IgG N-glycosylation in the plasma of CRC patients and healthy controls, we found that the levels of 11 N-glycopeptides such as IgG1 G0N in CRC patients significantly varied (p < .01). With machine learning, we used 11 N-glycopeptides as features to distinguish between CRC patients and healthy controls and the average AUC value of the ROC curves reached 0.893, which is
Declaration of Competing Interest
The authors declare that they have no conflicts of interest.
Acknowledgements
This work was supported by the Seeding Grant for Medicine and Information Sciences of Peking University (Grant number: 2014-MI-21), the National Natural Science Foundation of China (Grant numbers: 21572010 and 21772005), and the Qinghai Department of Science & Technology (Grant number: 2015-ZJ-742).
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2022, Cancer LettersCitation Excerpt :Up and down glycosylation in a sense projects the immune status in the progression of colorectal cancer, with its implicated clinical value. A multivariate logistic regression model was constructed to distinguish colorectal cancer patients from healthy individuals, and the prediction performance was excellent by virtue of an average area under the curve (AUC) of 0.893 from receiver operator characteristic (ROC) curve analysis [128]. Five characteristic IgG N-glycans out of the glycosylation profile in CRC patients were demonstrated to be correlated with CRC progression [129], and changed Fc glycosylation was identical to early diagnosis in the azoxymethane (AOM)/dextran sodium sulfate (DSS)-induced colorectal cancer mouse model [130].
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2022, Biochimica et Biophysica Acta - Reviews on CancerCitation Excerpt :Plasma glycoproteins with increased C9 and decreased FN1 levels are biomarkers of metastatic CRC, and the combined application of CEA, C9, and FN1 has better diagnostic value [80]. A growing number of studies have shown that the glycosylation pattern of immunoglobulins in the serum of patients with CRC has changed specifically [81–83]. Immunoglobulin glycosylomics based on MS shows that IgG glycosylation modification modes and glycosylation levels of the IgG Fc region distinguish CRC patients from healthy controls [81,82].
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2022, International Journal of Mass SpectrometryCitation Excerpt :In terms of MS detection method, ESI-MS has proven to be a powerful tool for detecting subclass-specific glycosylated IgG Fc fragments [82–85]. In addition, MALDI-MS, with its high throughput and universality, has been widely used for the detection of glycosylated IgG peptides [31,86]. Optimization of the matrix and detection mode of MALDI-MS, such as the use of 4-chloro-α-cyanocinnamic acid (CI-CCA) as a matrix combined with negative ion mode for detection, allowed high-throughput identification of glycosylation sites and glycoforms on subclass-specific IgG Fc fragments without complex sample processing and purification steps [87].
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2020, Biochimica et Biophysica Acta - General SubjectsCitation Excerpt :Immunoglobulin G (IgG) is a glycosylated protein with high abundance in serum that undertakes most of the functions of immunity and cancer immunosurveillance [7–10]. Significantly, alternative glycosylation of serum IgG has been reported to be closely associated with human colorectal cancer (CRC) [11–15]. These studies focused on the difference in IgG N-glycans between colorectal cancer patients and healthy people and suggested potential indicators, including significantly altered sialylation, galactosylation, and core-fucosylated glycans.
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These authors contributed equally.