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

doi:10.21203/rs.3.rs-4897362/v1

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Research Article

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

https://doi.org/10.21203/rs.3.rs-4897362/v1

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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 major gastrointestinal cancers has not been reported before. We aimed to identify cancer-specific serum N-glycan signatures and introduce a discriminative model between cancers in the same system.

Methods

The study population was initially screened according to the exclusion criteria process. Serum N-glycan 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 was differentially distributed among those three cancer groups, and several altered N-glycans were unique to the specific cancer type.

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 cancer-specific molecular signatures.

The gastrointestinal cancers including colorectal cancer (CRC), gastric cancer (GC) and esophageal cancer (EC), are three of the most common cancers worldwide [1]. In China, esophageal squamous cell carcinoma (ESCC) accounts for nearly 90% of all EC cases [2]. Those three major gastrointestinal cancers have considerable social, psychological and financial impacts on the patients’ life and pose heavy global burden to public health [3], necessitating the efficient biomarker discovery and increasing the uptake rate of cancer screening. Several tumor markers for these cancers have been established with clinical usefulness, such as carcinoembryonic antigen (CEA) and carbohydrate antigen 19 − 9 (CA 19 − 9), but their diagnostic accuracy are limited [4]. Besides, the absence of non-invasive and reliable molecular indicator significantly impedes the generalization of early screening to a wider population. Although particular molecular subtypes could stratify the gastrointestinal cancer in clinical settings, such molecular panels still require refinement [5]. Therefore, it is crucial to find novel non-invasive biomarker for diagnosis and stratification of those three gastrointestinal cancers.

As one of the most common post-translational modifications (PTM) in mammalian, glycosylation could contribute to disease initiation and progression due to its involvement in many key physiological and pathological processes, such as cell adhesion, molecular trafficking and clearance [6]. Glycans regulate protein and cell functions, and alterations in glycan structure cause pathologic events, suggesting the potential of specific glycan biomarkers for diagnosis and prediction of disease progression [7]. Uncovering the glycome would not only contribute to reveal the underlying molecular mechanism of disease, but also to spur development of a new generation of therapeutics targeting glycans [8]. Emerging evidences have demonstrated the profound effect of intestinal epithelial glycosylation on host genetics and gut microbiota that interface with the gastrointestinal pathogenesis [9]. Hence, characterization of glycome profile in gastrointestinal cancers promotes to explore the hallmark of cancer progression.

Aberrant N-glycosylation plays an important role in cancer development, and understanding the tumor-associated N-glycosylation signatures is essential for discovering anti-cancer targets and indicators of therapy monitoring and diagnosis [10]. Previously, it was reported that fucosylated N-glycans on haptoglobin in the sera of five types of gastroenterological cancer, including esophageal, gastric, colon, gallbladder and pancreatic cancer [11]. Additionally, serological IgG N-glycosylation was regarded as a potential candidate for noninvasive diagnosis of GI cancers [12], particularly for IgG galactosylation [13]. For colorectal cancer, Chun-Fang Gao and co-workers constructed two diagnostic models (CRCglycoA and CRCglycoB) based on the serum N-glycan markers [14]. Shifang Ren et al reported that specific serum N-glycan signatures have valuable potential in the clinical application of early detection and early relapse prediction of CRC [15, 16]. Comparative glycomic profiling of esophageal cancer revealed the capability of a subset of serum N-glycans to distinguish disease-free from different stages of EC patients [17]. Furthermore, serum IgG N-glycans featured with fucosylation and mannosylation may contribute to the early prevention of EC [18]. Gastric cancer cases could be differentiated from nonatrophic gastritis with glycan profiles of serum or tissue [19, 20]. Besides, a nomogram based on glycomic biomarkers in serum and clinicopathological characteristics has valuable potential to assist clinical decision-making before surgery [21]. However, most of these findings were derived from a single cancer group or based on the altered glycans in one specific glycoprotein. Hence, a full picture of the changes in N-glycan profile and differential distribution of glycome between those three gastrointestinal cancers have not been investigated.

Liquid biopsy is promising for precision medicine in cancer care, which has been expanded to profile the products of proteins modified by glycosylation [22]. Of note, blood test is one of the most accessible approach to evaluate the abnormal indicators for diseases. Glycans on secretory proteins are influenced by genetic, cellular and environmental factors, the signature of which probably indicates the transformation from normal to carcinoma [23]. Serum N-glycome profile could be used for cancer diagnosis, patients’ outcomes prediction or therapeutic response [24], which is regarded as an attractive source for biomarker discovery. In addition, particular glycan-derived traits enabled the stratification of patients with different subtypes of inflammatory bowel disease (IBD), showing disease course-specific glycan signatures [25]. However, little is known about the distribution of serum N-glycans between CRC, GC and ESCC. Furthermore, altered glycosylation is a universal feature in cancer progression [26], such as higher sialylation of serum proteins, and glycomics studies depicting the disease-specific glycan profile in the three gastrointestinal cancers have not reported.

In this study, we exploited the mass spectrometry based high-throughput assay to characterize serum N-glycome profile in the three major gastrointestinal cancers, including CRC, ESCC and GC. N-glycomic profile was analyzed through serum collection, N-glycan release, methylamidation of carboxyl group followed by matrix assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and data processing. Biomarker panels were established for each cancer type by random forest analysis. The distribution of glycome profile among the three cancers was evaluated by multivariate analysis.

Study populations. The samples involved in this study consist of healthy controls (n = 29), esophageal squamous cell carcinoma (n = 30), gastric cancer (n = 35) and colorectal cancer (n = 34). The age and sex were matched between those four groups. Healthy controls were the volunteers identified as disease-free individual. All the samples were collected from Tongji Medical College of Huazhong University of Science and Technology. The study was performed in accordance with the principles of the Declaration of Helsinki criteria, and was approved by the Ethics Committee of Huazhong University of Science and Technology. The clinical and histological characteristics of the cohorts are shown in Table S1.

N-glycan release and derivatization. The N-glycans were enzymatically released from serum as previously described [27]. Briefly, the samples were processed by dissolution and denaturation with Protein Deglycosylation Mix (New England Biolabs), which was subsequently added with 5 mU PNGaseF and incubated overnight at 37 ℃. We removed deglycosylated peptides from the reaction mixture using HyperSep™ Hypercarb™ SPE columns (ThermoFisher). To avoid sialic acid dissociation when directly ionized by MALDI-MS, methylamidation of carboxyl group was conducted according to our previous study [28]. Following the facile derivatization, serum N-glycans were purified using self-packed column contained microcrystalline cellulose.

MALDI-MS analysis. Chemically derivatized glycans were resuspended in 5 µL acetonitrile: water solution (50:50 ratio), of which 0.5 µL sample aliquots were directly spotted on MALDI plate. After air-dried, an equal volume of 2,5-DHB matrix solution prepared with 10 mg/mL DHB in 50% ACN containing 10 mM sodium acetate was loaded onto the plate in triplicate. The mixture was dried at room temperature prior to MALDI-MS analysis. Mass spectra were acquired in the positive mode by 5800 MALDI-MS equipped with a 355 nm Nd: YAG laser, with the range of m/z at 1000–4000 Da. The acquired spectra were the average of 1000 laser shots. MS data were processed by advanced baseline correction, noise filter, and peak deisotoping via Data Explorer 4.0.

Data processing and statistical analysis. The MALDI-MS data for targeted glycans were exported as text files, which were further directly imported into MATLAB and processed with self-developed code (Mass_Master) [29]. Each dataset for one group can be accomplished within 5 seconds, resulting in the means of relative abundance of serum N-glycans. The distribution of variables was initially tested by Kolmogorov-Smirnov (K-S) test. Data passing the K-S test were further analyzed by parameter test, whereas the group of non-normal distribution was analyzed by nonparametric test. One-way analysis of variance (ANOVA) was performed to examine the difference between three or more groups when the residues conform to Gaussian distribution. On the contrary, Kruskal-Wallis test was performed to test the data with non-Gaussian distribution, and the multiple correction is conducted by Tukey’s multiple comparison test. Multivariate partial least squares discriminant analysis (PLS-DA) was conducted to visualize the classification between normal group and three cancers in this study. Random forest (RF) was used to build the three-classification model of ROC curve for distinguishing those three GI cancer groups as described previously [30]. RF was performed by R 4.2.1 with ranger package (version 0.14.1), using the default parameter of ntree = 500. To elucidate the accuracy, 5-fold cross validation was subsequently conducted. The performance of the model was assessed by several commonly used multi-classification evaluation indicators: binary AUC for each category, including CRC_ROC, ESCC_ROC, GC_ROC, MacroROC and MicroROC.

Characterization of N-glycosylation profile by MALDI-MS

The schematic workflow of this study is presented in Fig. 1.a, and the MALDI-MS spectra of serum N-glycome is displayed in Fig. 1.b. Prior to characterizing the profile of healthy controls and gastrointestinal patients, quality control was performed to evaluate the intraday repeatability for the quantitation of serum protein glycosylation. Relative abundance of five representative N-glycans was highly repeatable with the relative standard deviation (RSD) of both intraday and interday less than 20% (Table S2), suggesting the good robustness and repeatability of the method. Total 54 N-glycans were identified and 16 of glycan derived traits were also calculated as reported in our previous study [31], such as mannosylation, sialylation, galactosylation and fucosylation (Table S3). Representative MALDI-MS spectra was shown from individuals with healthy controls and patients with CRC, ESCC or GC (Fig S1).

Changes of serum N-glycans in three major gastrointestinal cancers

Significant differences were observed in four types of glycan features, including mannosylation, galactosylation, bisection and sialylation (Fig. 2, Table S3). Specifically, mannosylation (Man) was lower in all three gastrointestinal cancers compared with controls (Fig. 2.a). Further individual glycan analysis showed that the differential expression of mannosylation in GI cancer patients was primarily reflected by remarkable decrease in glycan compositions of H5N2, H8N2 and H9N2 (Table S4). Concurrently, mono-galactosylation (G1 total) was substantially decreased in all three cancer patients (Fig. 2.c), which were consistent with alterations in glycans of H4N3F1, H4N4 and H6N3 (Table S4). Additionally, total sialylation was elevated in cancer groups compared with controls (Fig. 2.o), especially for ESCC and CRC. Sialylated glycans of H5N4S1, H5N4S2, H7N6S1, H6N5F1S2, H6N5F1S3 and H7N6F1S2 were likely to contribute to the differential sialylation due to their remarkably increase in gastrointestinal cancers (Table S4).

Identification of dysregulated serum N-glycans for three major gastrointestinal cancers

Considering the importance of disease stratification for the therapeutic intervention, we dissected the molecular diversity of three major gastrointestinal cancers at serum N-glycome level. It was observed that 8 of serum N-glycans (H4N4, H3N5, H8N2, H5N3S1, H9N2, H5N4S1, H5N5S1 and H5N4S2) were significantly changed in the three cancers in the same direction (Fig. 3. a), spanning high mannosylation, sialylation and fucosylation and bisection. Further evaluation of diagnostic performance showed that most of those eight N-glycans gained moderately accurate AUCs for distinguishing GI cancers from healthy controls (Table S5). Significantly, the other particular serum N-glycans were changed distinctively in those three cancers. CRC progression was closely associated with increased neutral mono-antennary glycan of fucosylation (H3N3F1) and mono-sialylated glycan of bisection (H4N5S1), concurrently with decreased biantennary mono-sialylated glycan with core-fucose (H4N4F1S1), and tri-antennary bi-sialylated glycan (H6N5S2) (Fig. 3. b). Serum N-glycans of M6 (H6N2), mono-antennary glycan of mono-sialylation (H4N3S1), neutral biantennary bisected glycan (H5N5), neutral triantennary galactosylated glycan (H6N5) and biantennary bi-sialylated glycan of fucosylation (H5N4F1S1) were uniformly increased in GC (Fig. 3. c). For ESCC, two neutral biantennary glycans (H5N4F1 and H5N5F1) were decreased, as opposed to the mono-antennary sialylated glycan (H4N3F1S1) (Fig. 3. d). Further ROC analysis showed most of specific N-glycans gained an AUC lower than 0.70 or higher than 0.30, while combination of them with biantennary logistic regression significantly improved the diagnostic performance (Table S6).

Establishment of glycan-biomarker panels for each gastrointestinal cancer

To develop an efficient indicator for cancer screening, the diagnostic performance of serum N-glycome for the cancers was evaluated by discriminative model. It was shown that those three major gastrointestinal cancers could be respectively distinguished from controls using serum N-glycome (Fig S2. a-c). Interestingly, different glycan compositions occupied different proportion to discriminate cancer patients from healthy controls, as illustrated in the overall map (Fig. 4.a). Furthermore, feature selection through cross-validation showed the best diagnostic performance was obtained with primary 6 glycans for CRC (Fig S2. d), 4 glycans for ESCC (Fig S2. e), and 22 glycans for GC (Fig S2. f). Using the primary features as the glycan panels, we found GI patients could be well differentiated from controls with at least accurate AUC scores (Fig. 4. b-d).

Distribution of serum N-glycome among three major gastrointestinal cancers

Unsupervised machine learning was applied to explore the diversity of serum N-glycome profile among three major gastrointestinal cancers. It was found that those three groups were evidently distinguished from each other by serum N-glycome (Fig. 5.a), indicating the molecular diversity of those three gastrointestinal cancers at glycome level. Plot of VIP showed the importance of particular glycans for the contribution to the discriminative model, such as glycan composition of peak 33 (H6N5) and peak 48 (H7N6S2) (Fig. 5.b). Random forest of serum N-glycome identified the primary features for distinguishing those cancers from each other, with acceptable AUC scores (Fig S3). With the main glycans used in discriminative model, particular glycan signatures were shown to be disease-specific in the development of different cancers (Fig. 5.c).

The three cancer groups involved in this study, including CRC, ESCC and GC, dominated the top five fatal cancers in digestive system. Serum based test was mini-invasive approach to cancer screening, while clinically used criteria were lack of sensitivity or specificity. Glycome profile was significantly changed in the cancer initiation and tumor metastasis, suggesting the predictive role of particular glycan signatures for cancer progression. Importantly, some glycan patterns have been applied to the clinical trial for disease screening [32]. However, the efficient glycan-based biomarker panels for CRC, ESCC or GC have not been established. Additionally, the classification of those three cancers poses a great challenge due to the close intertwine even though they showed distinct etiology.

Currently, a high-throughput analytical assay was applied to dissect serum N-glycome profile among the three major gastrointestinal cancers. It was observed that those three gastrointestinal cancers share the similar alterations in significant decreased mannosylation and galactosylation, as well as increase in sialylation. Higher abundance of high-mannose-type N-glycans were observed in dysplatic region than in colorectal carcinoma[33, 34], implicating its role in cell proliferation. Decreased level of high-mannose type glycans of serum in GC was also found in previous study[19]. High mannose-binding lectin induces autophagy in GC cells via the downregulation of tumor cell surface integrin/EGFR[35], suggesting the inhibited function of mannosylation in GC. Notably, to our knowledge, this is the first study revealing the altered mannosylation in ESCC, and altered galactosylation in all three gastrointestinal cancers at serum N-glycome level. It has been established that sialylated glycans are fundamental in tumor growth and metastasis[36], suggesting the hallmark of upregulated hypersialylation among cancers. For the three gastrointestinal cancers, increased sialylation in CRC was consistent with the report form previous studies at both serum level[15] and cellular level[37]. Altered level of sialylation modulates CRC malignancy through the mediation of JAK2/STAT3 pathway[38]. Several sialylated N-glycans were significantly increased in esophageal adenocarcinoma[17]. Increased sialylation interfering with key signaling pathways and integrin glycosylation is one of the key mechanisms regulating GC malignant behavior[39]. Those findings show the contribution of sialylation to gastrointestinal cancers progression, indicating the value of altered sialylation for digestive diseases screening.

Additionally, we found bisection was slightly increased in GC, as opposed to the alteration in both CRC and ESCC. This finding is consistent to the previous report that bisecting GlcNAc N-glycans is involved in the suppression of cancer metastasis[40]. Overexpression of core-fucosylation is an important feature in several cancers[40], while slightly decreased core-fucosylation of total serum glycoproteins was found in those three gastrointestinal cancers. This may be explained by the interference of other factors in the blood system, or the heterogeneity of glycome profile in different organs. Considering the crucial role of glycosylation in digestive system, it is worthy of further investigations about the potential of N-glycan signatures.

Further individual N-glycans analysis showed the similar alteration in all three cancer groups, such as decreased biantennary mono-galactosylated glycan and increased biantennary di-sialylated glycan. This finding may elucidate the downexpression of galactosylation and overexpression of sialylation in those gastrointestinal cancers (GI) as described above. Indeed, clinical criteria for those three cancers share common features. Interestingly, several significantly changed N-glycans were unique to those three cancers, suggesting the differential expression of glycosylation in GI cancer patients. Further evidence showed that those three gastrointestinal cancers could be nearly distinguished by serum N-glycome profile, implicating the distinct molecular mechanism in the evolution of carcinogenesis. These data suggest that dysregulated N-glycosylation could be an underpinning of digestive cancers pathogenesis, warranting further studies of spatial glycomics.

Notably, it was found that particular N-glycan compositions, featuring with mannosylation, galactosylation or sialylation, were significantly changed between GC, CRC and ESCC, indicating the potential organ-specific glycan signatures. Different types of glycosylation have been observed in some major diseases[41], while it has not been investigated about the N-glycome in three major GI cancers. Given the impact of host genetics, environment and gut microbiota on intestinal epithelial glycosylation[9], in-depth understanding of the functional role of glycan signatures in cancer progression would be conducive to reveal the causality between altered glycosylation and cancer initiation.

However, some limitations existed in this study. First of all, it was conducted with relatively small sample size, warranting further investigations with a large-scale sample involvement. Besides, it lacks external validation of the N-glycan signatures based predictive model, using data from studies on other populations. The last but not least is that this study was performed at the serum level, further in vitro and in vivo studies will be necessary to advance our understanding of the pathogenetic mechanism of specific glycan signatures.

In conclusion, our observational study demonstrated the alterations in N-glycosylation of total serum glycoproteins were strongly associated with the pathogenesis of GI cancers. Three major gastrointestinal cancers could be differentiated from each other by serum N-glycome profile with fairly good diagnostic performance. Particular N-glycan signatures of serum were remarkably dysregulated among those three digestive cancers, implicating the cancer tissue-specific glycan code. Significantly, identification of cancer-specific biomarkers would immensely contribute to the personalized medicine.

Conflict of interest

The authors declare no conflict of interest.

Author Contribution

S. L., study concept and design, drafting of the manuscript, analysis and interpretation of data, statistical analysis; J.-M. H., research performance, administrative support, provision of study material or patients; Y.-Y. L., J.-J. L and H.-B. Z., bioinformatics analysis and data interpretation; L.-M. C., administrative support, provision of study material or patients; W.-M. Y, critical revision of the manuscript for important intellectual content, administrative support; X. L., study concept and design, critical revision of the manuscript for important intellectual content, administrative support. All authors read and approved the final manuscript.

Acknowledgement

We thank Professor Liming Cheng at Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, for providing the clinical samples.

Data Availability

The mass spectrometry glycomic data have been deposited to the GlycoPOST archive with the dataset identifier GPST000435.

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11 Sep, 2024
Reviewers agreed at journal
03 Sep, 2024
Reviewers agreed at journal
03 Sep, 2024
Reviewers agreed at journal
02 Sep, 2024
Reviewers agreed at journal
02 Sep, 2024
Reviewers invited by journal
28 Aug, 2024
Editor assigned by journal
15 Aug, 2024
Submission checks completed at journal
12 Aug, 2024
First submitted to journal
11 Aug, 2024

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

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Materials and Methods

Results

Characterization of N-glycosylation profile by MALDI-MS

Identification of dysregulated serum N-glycans for three major gastrointestinal cancers

Establishment of glycan-biomarker panels for each gastrointestinal cancer

Distribution of serum N-glycome among three major gastrointestinal cancers

Discussion

Declarations

Conflict of interest

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1