Integrating transcriptomics, glycomics and glycoproteomics to characterize hepatitis B virus-associated hepatocellular carcinoma

doi:10.21203/rs.3.rs-3341499/v1

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Integrating transcriptomics, glycomics and glycoproteomics to characterize hepatitis B virus-associated hepatocellular carcinoma

https://doi.org/10.21203/rs.3.rs-3341499/v1

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Background

Hepatocellular carcinoma (HCC) ranks as the third most common cause of cancer related death globally, representing a substantial challenge to global healthcare systems. In China, the primary risk factor for HCC is the hepatitis B virus (HBV). Aberrant serum glycoconjugate levels have long been linked to the progression of HBV-associated HCC (HBV-HCC). Nevertheless, few study systematically explored the dysregulation of glycoconjugates in the progression of HBV-associated HCC and their potency as the diagnostic and prognostic biomarker.

Methods

An integrated strategy that combined transcriptomics, glycomics, and glycoproteomics was employed to comprehensively investigate the dynamic alterations in glyco-genes, N-glycans, and glycoproteins in the initiation and development of HBV- HCC.

Results

Bioinformatic analysis of Gene Expression Omnibus (GEO) datasets uncovered dysregulation of fucosyltransferases (FUTs) in liver tissues from HCC patients compared to adjacent tissues. Glycomic analysis indicated an elevated level of fucosylated N-glycans, especially a progressive increase in fucosylation levels on IGHA1 and IGHG2 determined by glycoproteomic analysis.

Conclusions

The findings indicate that the abnormal fucosylation plays a pivotal role in the progression of HBV-HCC. Systematic and integrative multi-omic analysis is anticipated to facilitate the discovery of aberrant glycoconjugates in tumor progression.

HBV-associated HCC

transcriptomics

glycomics

glycoproteomics

fucosylation

Hepatocellular carcinoma (HCC) is a primary liver malignancy and ranks as the third leading cause of cancer-related death worldwide [1]. Hepatitis B and C viral infections are the two major etiologies of HCC [2, 3], with chronic HBV being predominating in China due to the high prevalence of HBV infection [4, 5]. The relative risk of HCC in HBV-positive patients compared to HBV-negative controls is much higher [6]. Most HBV-HCC patients experience a trilogy of hepatitis-cirrhosis-HCC [7, 8]. Alpha-fetoprotein (AFP) is the most commonly used serum biomarker for HCC since it was discovered in 1964 [9]. However, approximately 30% of patients with early-stage HCC are AFP negative [10], and elevated AFP levels are also detected in patients with cirrhosis or chronic HCV exacerbations [11]. Indeed, most cancer markers used in clinics are glycoproteins [12], and their glycan moieties may be structurally different in cancer [13]. Therefore, a global analysis of the precise variation of glycoconjugates in HBV- HCC is urgently needed.

Glycosylation is one of the most abundant and diverse protein post-translational modifications in eukaryotes, with profound effects on protein function, stability, subcellular localization and other traits [14]. N-glycosylation, the main form of protein glycosylation, was dysregulated in common diseases such as cancer [15–17], inflammation [18], Alzheimer’s disease [19, 20] and diabetes [21–23]. Connor A West et al reported that the levels of tetra-antennary and fucosylation was elevated in HCC tissues compared to either cirrhotic tissue or adjacent untransformed tissue [24]. N-glycans with β1,6-N-acetylglucosamine (β1,6-GlcNAc) branching and sialylated GlcNAc structures were increased in HCC cell line, Huh7 [25].

As one of the terminal modifications, fucosylation is the process of transferring fucose from GDP-fucose to their substrates by fucosyltransferases in all mammalian cells [26]. Recent studies revealed that levels of core and outer-armer fucosylation were increased in the serum of patients with HCC [27]. The cancer-promoting capacity of cancer-associated fibroblasts was facilitated by modifying EGFR core fucosylation in non-small cell lung cancer [28]. Some highly abundant serum proteins such as transferrin (TF) and alpha-1-anti-trypsin (A1AT) were found to be decorated with increased levels of fucosylation in HCC [29]. Together, accumulated studies demonstrated the essential role of glycosylation in HBV-HCC, and it is urgent to explore the association between HBV-HCC and glycosylation at RNA, glycan and glycopeptides levels.

In this study, multi-omics including transcriptomics, glycomics, and glycoproteomics were integrated to profile the aberrant levels of glycan-related gene, glycan pattern, site-specific glycoproteins in HBV-HCC process.

2.1. Patients and samples

Three pairs of HBV-HCC and adjacent tissues, and serum samples from 27 chronic hepatitis B (CHB), 22 HBV-related liver cirrhosis (LC), 31 HBV- HCC patients and 20 healthy controls (HC) were collected from The First Affiliated Hospital of Xi’an Medical University. Informed consent was conducted in accordance with Declaration of Helsinki and experiments were approved by The First Affiliated Hospital of Xi’an Medical University Ethics Committee. Characteristic of samples are listed in Tab. S1&2.

2.2. GEO data analysis

The gene expression profile data GSE135631 and GSE94660 was downloaded from GEO of the National Center for Biotechnology Information (NCBI). Gene expression data deposited as the Series Matrix data was used for analysis of the fold change of gene expression and p value.

2.3. Quantitative reverse transcription PCR (RT-qPCR)

RNA from the indicated tissues was extracted using Trizol (ABclonal; Wuhan, Hubei, China) and reversed transcribed using the cDNA Reverse Transcription Kit (ABclonal). RT-qPCR was performed using SYBR Green Master Mix using Real-Time PCR system (TIANLONG; Xi’an, Shaanxi, China). Primer sequences were provided in Tab. S3.

2.4. Analysis of N-glycans

Glycomic analysis was performed as previously described [30]. A volume of 10 µL serum was added onto a size-exclusion spin ultrafiltration unit (Millipore; Billerica, MA, USA). Proteins were denatured with 8 M urea/ 50 mM NH₄HCO₃, reduced with dithiothreitol (DTT), alkylated with iodoacetamide (IAM), and digested with PNGase F overnight at 37°C. Released N-glycans were collected by centrifugation, lyophilized, and desalted using HyperSep Hypercarb solid phase extraction (SPE) cartridge (ThermoFisher Scientific; Waltham, Massachusetts, US).

Lyophilized N-glycans were dissolved and spotted onto an MTP AnchorChip sample target with 20 mg/mL 2,5-dihydroxybenzoic acid (DHB) as the matrix. Measurements were taken in positive-ion mode. Each full-mass scan was performed with following conditions: acceleration voltage, 24.59 kV; reflector voltage, 26.6 kV; pulsed ion extraction, 100 ns; mass range, 1000–3500 m/z. For the data analysis, the peaks were smoothed and baseline subtraction performed three times. Relative intensity was analyzed and generated using FlexAnalysis software (Bruker Daltonics; Bremen, Germany) based on MALDI-TOF/TOF-MS intensity. m/z data were obtained and annotated using GlycoWorkbench software program [31].

2.5. Glycoproteomics

Protein extraction and digestion was performed as previously described [30]. In brief, the serum protein was denatured in 8 M urea/ 50 mM NH₄HCO₃, then reduced with DTT, alkylated with IAM, digested with lysyl endopeptidase (Promega; Madison, WI, USA) for 4 h at 37°C, and then incubated with trypsin (Promega) overnight at 37°C with shaking. The digested peptides were acidified with 10% trifluoroacetic acid to pH < 3, collected by centrifugation and purified using Oasis HLB cartridges (Waters; Milford, MA, USA). N-glycopeptides were enriched by MAX column (Waters) using digested peptides as described previously [32].

2.6. Enzyme-Linked Immunosorbent Assay (ELISA)

ELISA plates were coated with antibodies against IGHA1 or IGHG2 (Abcam; Cambridge, MA, USA) overnight at 4°C, rinsed with PBST, blocked with 5% BSA, incubated with serum samples for 2 h at 37°C, rinsed with PBS, incubated with biotinylated Lens Culinaries Agglutinin (LCA; Vector Laboratories; Burlingame, CA, USA) overnight at 4°C, incubated with VECTASTAIN ABC reagent (Vector Labs) for 30 min at room temperature, and visualized with TMB substrate solution (Beyotime Institute of Biotechonology; Haimen, China). The absorbance values were determined at 450 nm with a plate reader.

2.7. Data analysis

All experiments were reproduced at least three times. All values were presented as mean ± standard deviation (SD). Two-tailed Student’s t-test was used for comparison of data sets between two groups and differences with p < 0.05 were considered statistically significant. Statistical analysis was performed using GraphPad Prism 9.3.1 software program.

3.1. Glycan-related genes expression of HBV-HCC and adjacent tissues

A better understanding of glyco-gene expression changes may facilitate the exploration of association between glycosylation and HBV-HCC. The differences in glyco-gene expression between HBV-HCC and adjacent samples were investigated using an integrated strategy with a combination of transcriptomics, glycomics and glycoproteomics (Fig. 1A). First, GSE135631 and GSE94660 gene expression profiles, including 15 and 21 pairs of HBV-HCC and adjacent tissues respectively, were retrieved from the GEO database. A total of 7502 and 7839 genes were differentially expressed in GSE135631 and GSE94660, as shown in Fig. 1B, in which top 100 differentially expressed glyco-genes (DEGGs) were highlighted (Fig. 1B, Tab. S4). Partial Least Squares Discrimination Analysis (PLS-DA) of these DEGGs exhibited a clear separation between HBV-HCC and adjacent tissues (Fig. 1C). The expression patterns of all DEGGs across HBV-HCC and adjacent tissues from the combined two GSE study were shown as a heatmap (Fig. 1D). Next, functional enrichment analysis revealed that the main molecular function categories were carbohydrate binding, transferring glycosyl and hexosyl group activity (Fig. 1E).

Protein-protein interaction (PPI) network of these DEGGs was constructed in an attempt to explicit the molecular mechanisms in the progression of HBV-HCC. Clustering analysis revealed ten major modules, in which protein glycosylation, GPI anchor biosynthetic process and glycosylation processes were mostly enriched (Fig. 1F; S1). Hub genes with a high degree of connectivity in the PPI network are significantly enriched in the process of fucosylation (FUT4, FUT6, FUT2 and FUT1), GPI anchor biosynthesis (PIGM, PIGV, PIGT and GPAA1), galactosylation (B4GALT3) and galNAcylation (B3GNT3).

Furthermore, FUTs family expression at mRNA levels were determined by RT-qPCR in HBV-HCC and adjacent tissues (Fig. 1G), revealed an elevated level of FUT1, FUT2, FUT4, FUT5, FUT8 and FUT10, as well as a reduced level of FUT3, FUT6, FUT7, FUT9, FUT11 and FUT12 in HBV-HCC tissues. Collectively, these data indicated that changes in glycosylation especially fucosylation were closely correlated to HBV-HCC.

3.2. N-glycan profiles of normal, hepatitis, cirrhosis and HCC serum samples

In our studies, N-glycans in HC, CHB, LC and HBV-HCC serum samples (Tab. S1) were profiled by MALDI-TOF/TOF-MS to reveal abnormal N-glycosylation. Representative MS spectra of N-glycans with signal-to-noise ratios > 5 were displayed and annotated (Fig. 2; Tab. S5). A total of 36 distinct m/z N-glycan structures were identified, with 31, 32, 25 and 25 N-glycans present in HC, CHB, LC and HBV-HCC samples, respectively. There were 21 N-glycans found in all groups but with different intensities.

The expression pattern of identified N-glycans in HC, CHB, LC and HBV-HCC were exhibited in Fig. 3A. Hierarchical clustering revealed that LC/HBV-HCC were clearly separated from HC and CHB, however, LC/HBV-HCC could not be separated from each other. Furthermore, quantitative comparison analysis revealed that 25, 24, and 22 N-glycan structures were differentially expressed in CHB, LC and HBV-HCC versus HC. Of these N-glycans, 11 N-glycans were concertedly down-regulated, and 7 N-glycans were concertedly up-regulated in the progression of HBV-HCC. Notably, 6 of 7 up-regulated N-glycans were fucosylated (Fig. 3A).

N-glycans were classified into three types, including high mannose, complex and hybrid. Relative abundance of hybrid and complex N-glycans were increased in CHB, LC and HBV-HCC versus HC, while which of high mannose N-glycans were decreased (Fig. 3B). Consistent with elevated FUTs expression at mRNA levels, relative abundance of total fucosylation were up-regulated, of which mono-fucosylation levels were increased, while bi-fucosylation levels were decreased (Fig. 3C). Other terminal modification sialylation levels were increased, followed by a decrease in the progression of HBV-HCC (Fig. 3D). Significantly higher levels of bi-antennary structures were found in CHB, LC and HBV-HCC versus HC, while mono-antennary structures was progressively reduced (Fig. 3E). In combination of transcriptomic and glycomic analysis revealed that fucosylation levels were up-regulated in the progression of HBV-HCC.

3.3 Site-specific glycoproteomic profiling in HBV-HCC serum samples

To decode the biological function of specific N-glycosylation especially fucosylation in the progression of HBV-HCC, intact glycoproteomic analysis of HC, CHB, LC and HBV-HCC serum samples (Tab. S2) were performed. A total of 1114 glycopeptides were identified (Fig. 4A; Tab. S6), of which, 1019, 1011, 999 and 982 glycopeptides, including 864 in common were found in HC, CHB, LC and HBV-HCC serum samples, respectively. The identified glycopeptides represented 129 glycosites were modified with 102 glycan structures. These N-glycans contained 8 high mannose, 13 hybrid, 77 complex and 4 paucimannose subtypes (Fig. 4B).

Quantitative comparison analysis revealed that a total of 288 unique glycopeptides from 73 glycoproteins were differentially expressed using the cutoff of fold change > 2 or < 0.5 and p value < 0.05 in CHB, LC and HBV-HCC versus HC (Fig. 4C). Differentially expressed glycopeptides (DEGPeps) were mainly decorated with complex type N-glycans, of which sialylated N-glycans accounted for the largest proportion, followed by fucosylated N-glycans and bi-antennary structures (Fig. 4D).

To understand the association between glycoprotein trajectories and the progression of HBV-HCC, hierarchical clustering for all DEGPeps was performed, with which three cluster (I-IV) were generated (Fig. 5A&B; Tab. S7). Glycopeptides in “cluster I” were continually increased in the progression of HBV-HCC, encompassing 27 proteins. These DEGPeps were significantly enriched in endopeptidase inhibitor activity, acute-phase response, blood microparticle and complement and coagulation cascades by functional enrichment analysis (Fig. 5C). Glycopeptides in “cluster II” were significantly decreased, followed by an increase (39 proteins), and mainly involved in the peptidase regulator activity, complement activation, blood microparticle and complement and coagulation cascades. Glycopeptides in “cluster III” were slightly decreased, followed by an increase (25 proteins), and enriched in regulation of humoral immune response, blood microparticle and complement and coagulation cascades. Glycopeptides in “cluster IV” (31 proteins) were decreased and primarily connected with peptidase regulator activity, acute-phase response, blood microparticle, complement and coagulation cascades. (Fig. S2).

Taking into account the escalating malignancy of HBV-HCC progression, 49 glycopeptides with fucosylation attached in “cluster I” were screened, which were consisted of 23 fucosylated N-glycans and 25 peptides from 21 protiens (Fig. 5D). These fucosylated N-glycans contained three to seven HexNAc, three to eight hexose, up to four fucose, and two sialic acid. Among 21 identified glycoproteins, 16 contained 1 glycosite, 3 contained 2 glycosites, and 1 contained 3 glycosites. Notably, among these glycopeptides, IGHA1-340-N5H5F1S2 was identified with highest PSM score.

3.4 Site-specific fucosylation on IGHA1 and IGHG2

We further mapped N-glycan structures on each glycosite of IGHA1 and IGHG2 based on our glycoproteomics data. A total of 30 and 5 unique intact glycopeptides were identified from IGHA1 and IGHG2, which were comprised 3 glycosites (¹⁴⁴N^#LT and ³⁴⁰N^#VS for IGHA1, ¹⁷⁶N^#ST for IGHG2) and 31 glycans (Fig. 6A). Among these glycopeptides, fucosylation was accounted for 46% of all glycans on glycopeptides, and the majority of fucosylated intact glycopeptides were up-regulated in the progression of HBV-HCC.

Next, we screened out 8 and 3 site-specific target glycopeptides from IGHA1 and IGHG2 respectively, which were all fucosylated and gradually elevated in the progression of HBV-HCC (Fig. 7A). Consistent with the glycoproteomic results, LCA-based ELISA analysis confirmed the elevated levels of fucosylated IGHA1 and IGHG2 in HBV-HCC serum (Fig. 7B). In summary, combined results of glycoproteomic and ELISA analysis revealed that IGHA1 and IGHG2 are highly fucosylated and fucosylation on IGHA1 and IGHG2 were up-regulated in the progression of HBV-HCC, implied that fucosylated of IGHA1 and IGHG2 might be involved in the initiation and development of HBV-HCC.

An alteration in glycosylation have long been observed with the initiation and development of HBV-HCC [33, 34]. Our study comprehensively characterizes the transcriptomics, glycomics and glycoproteomics landscapes of HC, CHB, LC and HBV-HCC, which allows us to interrogate underlying mechanisms in HBV-HCC progression. Our transcriptomic analysis revealed that a variety of FUTs were dysregulated in HBV-HCC (Fig. 1F), consistently, total fucosylation on N-glycans were elevated by MALDI-TOF-MS/MS (Fig. 3C). Recent studies also observed an elevated serum core-fucosylation by HPLC [35], and an elevated saliva fucosylation by saliva microarrays with Lotus Tetragonolobus Lectin [36] in the progression of human HBV-HCC. Moreover, a serum-associated fucosylated glycoprotein (< 10 kD) fingerprint was established to assist the diagnosis of HBV-HCC by using MALDI-TOF-MS, which could distinguish HBV-HCC from healthy subjects, patients with HBV and HBV-associated cirrhosis [37].

Specifically, we found that mono-fucosylated rather than bi-fucosylated N-glycans were up-regulated in the progression of HBV-HCC. Xue-En Liu et al found that a branch α1,3-fucosylated triantennary N-glycan (NA3Fb) was elevated in patients with HBV-HCC compared to who with HBV-fibrosis or cirrhosis or healthy subjects by using a DNA sequencer [38], furthermore, the log ratio of NA3Fb/NG1A2F was elevated in HBV-HCC, which was promising as N-glycan markers [39]. While, we found that levels of N5H6F1 N-glycan (the same component as NA3Fb) were reduced in HBV-HCC. This discrepancy might arise from inefficient distinction between N-glycan isoforms of MALDI-TOF-MS.

Advances in glycomics, genomics and proteomics platforms over the last decade have discovered numerous novel biomarkers and have improved the diagnosis of HBV-HCC. In recent years, the glycoproteome mapping provides innovative insights into the both peptides and glycosylation status in diseases and contributes to biomarker discovery. The increased bi-fucosylation on serum haptoglobin (HP) was highly associated with HBV, HCV, and alcohol consumption-related HCC patients compared to cirrhosis patients [40]. Specifically, fucosylated serum HP glycoforms at Asn184, 207 and 211 were significantly increased in chronic, cirrhosis and HCC with HBV infection compared to healthy subjects [33]. Core-fucosylated serum IgG was significantly increased in HBV-HCC compared to HBV-related cirrhosis, HBV carriers and healthy subjects, and have general diagnostic performance than AFP in the training set and validation cohorts [41]. Similar research revealed that TPLTAN²⁰⁵ITK (H5N5S1F1) and (H5N4S2F1) of IgA2 were significantly elevated in serum from patients with HBV infection and even higher in HBV cirrhosis in comparison with healthy donor [42]. Consistently, in the present study, 30 N-glycan structures on 2 glycosites of IgA1 and 5 N-glycans on 1 glycosite of IgG2 were identified. Specifically, differentially expressed IgA1 with fucosylated glycan structures (IGHA1-144-N4H4F2/N5H4F2/N5H5F1S1/N5H5F2/N5H5F3 and IGHA1-340-N5H5F1S2/N5H5F3/N6H3F1S2) and IgG2 with fucosylated glycan structures (IGHG2-176-N4H3F1/N4H4F1/N5H3F1) were elevated in the progression of HBV-HCC. IgG and IgA are both N-glycosylated proteins, and N-glycans on IgG and IgA act crucial role in the structure and their biological function [43, 44]. Nathaniel Washburn et al reported that the abundant ratio α-2,6 sialylated IgG Fc domain increases its anti-inflammatory activity [45]. The N-glycan sialylation modification on IgA at glycosite 459 enhances its antiviral activity by interfering with virus binding to sialic acid receptors [46]. Therefore, we speculate that changes in serum IgG and IgA glycosylation may closely correlate to the initiation and development of HCC, and act as complementary techniques to improve diagnosis.

HCC

Hepatocellular carcinoma

HBV

Hepatitis B virus

GEO

Gene Expression Omnibus

FUTs

Fucosyltransferases

HBV-HCC

HBV-associated HCC

AFP

Alpha-fetoprotein

β1,6-GlcNAc

β1,6-N-acetylglucosamine

Transferrin

A1AT

Alpha-1-anti-trypsin

CHB

Chronic hepatitis B

Liver cirrhosis

Healthy controls

NCBI

National Center for Biotechnology Information

RT-qPCR

Quantitative reverse transcription PCR

DTT

Dithiothreitol

IAM

Iodoacetamide

SPE

Solid phase extraction

DHB

2,5-dihydroxybenzoic acid

LCA

Lens Culinaries Agglutinin

standard deviation

DEGGs

differentially expressed glyco-genes

PLS-DA

Partial Least Squares Discrimination Analysis

PPI

Protein-protein interaction

DEGPeps

Differentially expressed glycopeptides

Ethics approval and consent to participate

This study was approved by The First Affiliated Hospital of Xi’an Medical University Ethics Committee (approval No. XYLS2021046) and all subjects signed informed consent.

Consent for publication

Not applicable

Availability of data and materials

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors have declared that no competing interest exists.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 32271338 and 82102471), Shaanxi Provincial Health Research Fund Project (Grant No. 2022A019), and Shaanxi Fundamental Science Research Project for Chemistry & Biology (Grand No. 22JHQ077).

Authors' contributions

Zengqi Tan and Feng Guan designed the experiments. Zengqi Tan, Zhuo Li, Na Zhang, Zewen Dong, Yunyun Yang and Jing Li performed experiments and data analysis. Xin Wang, Jian Zhou and Juan Gao collected samples. Zengqi Tan, Zhuo Li and Na Zhang wrote the manuscript.

Acknowledgements

Not applicable

Corresponding authors

Zengqi Tan

Vogel A, Meyer T, Sapisochin G, Salem R, Saborowski A. Hepatocellular carcinoma. Lancet. 2022;400:1345–62.
Zhang CH, Cheng Y, Zhang S, Fan J, Gao Q. Changing epidemiology of hepatocellular carcinoma in Asia. Liver Int. 2022;42:2029–41.
Li TY, Yang Y, Zhou G, Tu ZK. Immune suppression in chronic hepatitis B infection associated liver disease: A review. World J Gastroenterol. 2019;25:3527–37.
Yue T, Zhang Q, Cai T, Xu M, Zhu H, Pourkarim MR, et al. Trends in the disease burden of HBV and HCV infection in China from 1990–2019. Int J Infect Dis. 2022;122:476–85.
Wang H, Men P, Xiao Y, Gao P, Lv M, Yuan Q, et al. Hepatitis B infection in the general population of China: a systematic review and meta-analysis. BMC Infect Dis. 2019;19:811.
Evans AA, Chen G, Ross EA, Shen FM, Lin WY, London WT. Eight-year follow-up of the 90,000-person Haimen City cohort: I. Hepatocellular carcinoma mortality, risk factors, and gender differences. Cancer Epidemiol Biomarkers Prev. 2002;11:369–76.
Kanda T, Goto T, Hirotsu Y, Moriyama M, Omata M. Molecular Mechanisms Driving Progression of Liver Cirrhosis towards Hepatocellular Carcinoma in Chronic Hepatitis B and C Infections: A Review. Int J Mol Sci 2019;20.
Seeger C, Mason WS. Molecular biology of hepatitis B virus infection. Virology 2015;479–80:672 – 86.
Hanif H, Ali MJ, Susheela AT, Khan IW, Luna-Cuadros MA, Khan MM, et al. Update on the applications and limitations of alpha-fetoprotein for hepatocellular carcinoma. World J Gastroenterol. 2022;28:216–29.
Luo P, Wu S, Yu Y, Ming X, Li S, Zuo X, et al. Current Status and Perspective Biomarkers in AFP Negative HCC: Towards Screening for and Diagnosing Hepatocellular Carcinoma at an Earlier Stage. Pathol Oncol Res. 2020;26:599–603.
Hu KQ, Kyulo NL, Lim N, Elhazin B, Hillebrand DJ, Bock T. Clinical significance of elevated alpha-fetoprotein (AFP) in patients with chronic hepatitis C, but not hepatocellular carcinoma. Am J Gastroenterol. 2004;99:860–5.
Silsirivanit A. Glycosylation markers in cancer. Adv Clin Chem. 2019;89:189–213.
Kailemia MJ, Park D, Lebrilla CB. Glycans and glycoproteins as specific biomarkers for cancer. Anal Bioanal Chem. 2017;409:395–410.
Hirata T, Kizuka Y. N-Glycosylation. Adv Exp Med Biol. 2021;1325:3–24.
Huang Y, Zhang HL, Li ZL, Du T, Chen YH, Wang Y, et al. FUT8-mediated aberrant N-glycosylation of B7H3 suppresses the immune response in triple-negative breast cancer. Nat Commun. 2021;12:2672.
Miao Z, Cao Q, Liao R, Chen X, Li X, Bai L, et al. Elevated transcription and glycosylation of B3GNT5 promotes breast cancer aggressiveness. J Exp Clin Cancer Res. 2022;41:169.
Scott DA, Drake RR. Glycosylation and its implications in breast cancer. Expert Rev Proteomics. 2019;16:665–80.
Radovani B, Gudelj I. N-Glycosylation and Inflammation; the Not-So-Sweet Relation. Front Immunol. 2022;13:893365.
Tena J, Tang X, Zhou Q, Harvey D, Barajas-Mendoza M, Jin LW, et al. Glycosylation alterations in serum of Alzheimer's disease patients show widespread changes in N-glycosylation of proteins related to immune function, inflammation, and lipoprotein metabolism. Alzheimers Dement (Amst). 2022;14:e12309.
Conroy LR, Hawkinson TR, Young LEA, Gentry MS, Sun RC. Emerging roles of N-linked glycosylation in brain physiology and disorders. Trends Endocrinol Metab. 2021;32:980–93.
Rudman N, Gornik O, Lauc G. Altered N-glycosylation profiles as potential biomarkers and drug targets in diabetes. FEBS Lett. 2019;593:1598–615.
Stambuk T, Gornik O. Protein Glycosylation in Diabetes. Adv Exp Med Biol. 2021;1325:285–305.
Rudman N, Kifer D, Kaur S, Simunovic V, Cvetko A, Pociot F, et al. Children at onset of type 1 diabetes show altered N-glycosylation of plasma proteins and IgG. Diabetologia. 2022;65:1315–27.
West CA, Wang M, Herrera H, Liang H, Black A, Angel PM, et al. N-Linked Glycan Branching and Fucosylation Are Increased Directly in Hcc Tissue As Determined through in Situ Glycan Imaging. J Proteome Res. 2018;17:3454–62.
Kim KJ, Kim YW, Hwang CH, Park HG, Yang YH, Koo M, et al. A MALDI-MS-based quantitative targeted glycomics (MALDI-QTaG) for total N-glycan analysis. Biotechnol Lett. 2015;37:2019–25.
Shan M, Yang D, Dou H, Zhang L. Fucosylation in cancer biology and its clinical applications. Prog Mol Biol Transl Sci. 2019;162:93–119.
Kizuka Y, Taniguchi N. Enzymes for N-Glycan Branching and Their Genetic and Nongenetic Regulation in Cancer. Biomolecules 2016;6.
Li F, Zhao S, Cui Y, Guo T, Qiang J, Xie Q, et al. α1,6-Fucosyltransferase (FUT8) regulates the cancer-promoting capacity of cancer-associated fibroblasts (CAFs) by modifying EGFR core fucosylation (CF) in non-small cell lung cancer (NSCLC). Am J Cancer Res. 2020;10:816–37.
Ma J, Sanda M, Wei R, Zhang L, Goldman R. Quantitative analysis of core fucosylation of serum proteins in liver diseases by LC-MS-MRM. J Proteom. 2018;189:67–74.
Cao L, Zhou Y, Li X, Lin S, Tan Z, Guan F. Integrating transcriptomics, proteomics, glycomics and glycoproteomics to characterize paclitaxel resistance in breast cancer cells. J Proteom. 2021;243:104266.
Ceroni A, Maass K, Geyer H, Geyer R, Dell A, Haslam SM. GlycoWorkbench: a tool for the computer-assisted annotation of mass spectra of glycans. J Proteome Res. 2008;7:1650–9.
Zhou J, Yang W, Hu Y, Höti N, Liu Y, Shah P, et al. Site-Specific Fucosylation Analysis Identifying Glycoproteins Associated with Aggressive Prostate Cancer Cell Lines Using Tandem Affinity Enrichments of Intact Glycopeptides Followed by Mass Spectrometry. Anal Chem. 2017;89:7623–30.
Dalal K, Dalal B, Bhatia S, Shukla A, Shankarkumar A. Analysis of serum Haptoglobin using glycoproteomics and lectin immunoassay in liver diseases in Hepatitis B virus infection. Clin Chim Acta. 2019;495:309–17.
Guo L, Wan L, Hu Y, Huang H, He B, Wen Z. Serum N-glycan profiling as a diagnostic biomarker for the identification of hepatitis B virus-associated hepatocellular carcinoma. J Gastrointest Oncol. 2022;13:344–54.
Comunale MA, Lowman M, Long RE, Krakover J, Philip R, Seeholzer S, et al. Proteomic analysis of serum associated fucosylated glycoproteins in the development of primary hepatocellular carcinoma. J Proteome Res. 2006;5:308–15.
Block TM, Comunale MA, Lowman M, Steel LF, Romano PR, Fimmel C, et al. Use of targeted glycoproteomics to identify serum glycoproteins that correlate with liver cancer in woodchucks and humans. Proc Natl Acad Sci U S A. 2005;102:779–84.
Yao W, Wang K, Jiang Y, Huang Z, Huang Y, Yan H, et al. Serum profile of low molecular weight fucosylated glycoproteins for early diagnosis of hepatocellular carcinoma. Oncol Lett. 2020;20:1597–606.
Liu XE, Desmyter L, Gao CF, Laroy W, Dewaele S, Vanhooren V, et al. N-glycomic changes in hepatocellular carcinoma patients with liver cirrhosis induced by hepatitis B virus. Hepatology. 2007;46:1426–35.
Fang M, Zhao YP, Zhou FG, Lu LG, Qi P, Wang H, et al. N-glycan based models improve diagnostic efficacies in hepatitis B virus-related hepatocellular carcinoma. Int J Cancer. 2010;127:148–59.
Zhu J, Lin Z, Wu J, Yin H, Dai J, Feng Z, et al. Analysis of serum haptoglobin fucosylation in hepatocellular carcinoma and liver cirrhosis of different etiologies. J Proteome Res. 2014;13:2986–97.
Yi C-H, Weng H-L, Zhou F-G, Fang M, Ji J, Cheng C, et al. Elevated core-fucosylated IgG is a new marker for hepatitis B virus-related hepatocellular carcinoma. OncoImmunology. 2015;4:e1011503.
Zhang S, Cao X, Liu C, Li W, Zeng W, Li B, et al. N-glycopeptide Signatures of IgA(2) in Serum from Patients with Hepatitis B Virus-related Liver Diseases. Mol Cell Proteomics. 2019;18:2262–72.
Lux A, Nimmerjahn F. Impact of differential glycosylation on IgG activity. Adv Exp Med Biol. 2011;780:113–24.
Novak J, Barratt J, Julian BA, Renfrow MB. Aberrant Glycosylation of the IgA1 Molecule in IgA Nephropathy. Semin Nephrol. 2018;38:461–76.
Washburn N, Schwab I, Ortiz D, Bhatnagar N, Lansing JC, Medeiros A, et al. Controlled tetra-Fc sialylation of IVIg results in a drug candidate with consistent enhanced anti-inflammatory activity. Proc Natl Acad Sci U S A. 2015;112:E1297–306.
Maurer MA, Meyer L, Bianchi M, Turner HL, Le NPL, Steck M, et al. Glycosylation of Human IgA Directly Inhibits Influenza A and Other Sialic-Acid-Binding Viruses. Cell Rep. 2018;23:90–9.

No competing interests reported.

Additionalfile1.zip
Additional file 1 Fig. S1 PPI network of other DEGGs. The PPI network of DEGGs performed by the STRING database and cytoscape tools. Fig. S2 Functional enrichment analysis of glycopeptides.Functional enrichment analysis of glycopeptides from cluster II (A), III (B) and IV (C).
Additionalfile2.zip
Additional file 2 Table 1. Characteristics of 100 participants clincal data of glycomics. Table 2. Characteristics of 80 of 100 participants clincal data of glycoproteomics. Table 3. Primers of the 13 FUTs family genes. Table 4. Differentially expressed glyco-genes in GSE135631 and GSE94660. Table 5. Identified N-glycans in HC, CHB, LC and HCC serum samples. Table 6. Identified glycopeptides in HC, CHB, LC and HCC serum samples. Table 7. Differentially expressed intact glycopeptides from cluster I-IV.

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Integrating transcriptomics, glycomics and glycoproteomics to characterize hepatitis B virus-associated hepatocellular carcinoma

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1. Patients and samples

2.2. GEO data analysis

2.3. Quantitative reverse transcription PCR (RT-qPCR)

2.4. Analysis of N-glycans

2.5. Glycoproteomics

2.6. Enzyme-Linked Immunosorbent Assay (ELISA)

2.7. Data analysis

3. Results

3.1. Glycan-related genes expression of HBV-HCC and adjacent tissues

3.2. N-glycan profiles of normal, hepatitis, cirrhosis and HCC serum samples

3.3 Site-specific glycoproteomic profiling in HBV-HCC serum samples

3.4 Site-specific fucosylation on IGHA1 and IGHG2

4. Discussion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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