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Publicly Available Published by De Gruyter August 23, 2018

Aberrant glycosylation and cancer biomarker discovery: a promising and thorny journey

  • Mengmeng Wang , Jianhui Zhu , David M. Lubman and Chunfang Gao EMAIL logo


Glycosylation is among the most important post-translational modifications for proteins and is of intrinsic complex character compared with DNAs and naked proteins. Indeed, over 50%–70% of proteins in circulation are glycosylated, and the “sweet attachments” have versatile structural and functional implications. Both the configuration and composition of the attached glycans affect the biological activities of consensus proteins significantly. Glycosylation is generated by complex biosynthetic pathways comprising hundreds of glycosyltransferases, glycosidases, transcriptional factors, transporters and the protein backbone. In addition, lack of direct genetic templates and glyco-specific antibodies such as those commonly used in DNA amplification and protein capture makes research on glycans and glycoproteins even more difficult, thus resulting in sparse knowledge on the pathophysiological implications of glycosylation. Fortunately, cutting-edge technologies have afforded new opportunities and approaches for investigating cancer-related glycosylation. Thus, glycans as well as aberrantly glycosylated protein-based cancer biomarkers have been increasingly recognized. This mini-review highlights the most recent developments in glyco-biomarker studies in an effort to discover clinically relevant cancer biomarkers using advanced analytical methodologies such as mass spectrometry, high-performance liquid chromatographic/ultra-performance liquid chromatography, capillary electrophoresis, and lectin-based technologies. Recent clinical-centered glycobiological studies focused on determining the regulatory mechanisms and the relation with diagnostics, prognostics and even therapeutics are also summarized. These studies indicate that glycomics is a treasure waiting to be mined where the growth of cancer-related glycomics and glycoproteomics is the next great challenge after genomics and proteomics.


Glycosylation, a fundamental and prominent post-translational modification of proteins, plays crucial roles in a variety of cellular activities such as cell growth, differentiation, transformation, adhesion, and immune surveillance of tumors [1], [2]. Two main types of glycosylation, N-glycosylation and mucin-type O-glycosylation, are a series of enzymatic reactions occurring in the endoplasmic reticulum and Golgi complex [1]. Apart from the rare congenital disorders of glycosylation (CDG), aberrant glycosylation has reportedly permeated through every aspect of human diseases including autoimmune diseases [3], [4], infection with bacteria [5], [6], viruses [7], [8] and parasites [9], [10], and especially cancer [11], [12].

Aberrant glycosylation has been pinpointed as a hallmark of cancer [13] due to its contributions in carcinogenesis, cancer progression and metastasis, conferring a new perspective for cancer research involving underlying mechanisms investigation and clinical translation and application [14], [15], [16], [17]. The majority of tumor biomarkers currently used in the clinic are glycoproteins (e.g. AFP for liver cancer, CA125 for ovarian cancer, CEA for colon cancer, and PSA for prostate cancer) or are glycan-related such as CA19-9, also known as sialyl-Lewis A for gastrointestinal and pancreatic cancer [11]. Currently used glyco-biomarkers and recent (in the past 5 years) findings of potential cancer glyco-biomarkers are summarized in Table 1. Here, “glyco-biomarker discovery” refers in particular to detect the specific glycosylation changes.

Table 1:

Glyco-biomarkers in clinical practice and novel glyco-biomarker discovery in recent 5 years.

Glycomic study targetCancerSignificant glycosylation alterationsRole
AFP-L3 (used in clinic)HepaticCore-fucosylation [18], [19]Diagnosis, prognosis
CA19-9 (used in clinic)PancreaticSialyl-Lewis A structure [20], [21]Diagnosis, prognosis
Total serum/plasma profilesHepaticIncreased multi-antennary glycans with fucose residues [22]Diagnosis
GastricDecreased core-fucosylated glycans [23], [24]; increased hybrid and multi-branched structures and decreased monoantennary, galactose, bisecting type and core fucose structures [25]Diagnosis; monitoring progression
PancreaticIncreased core-fucose residues [26]Diagnosis
BreastIncreased sialylation, branching, and outer-arm fucosylation and decreased high-mannosylated and biantennary core-fucosylated glycans [27]Diagnosis
Immunoglobulin G (IgG)HepaticDecreased galactosylation [28]; increased core-fucosylation [29]Diagnosis; prognosis
ColorectalDecreased galactosylation [28]; decreased galactosylation and sialylation; increased core-fucosylation of neutral glycans and decreased core-fucosylation of sialylated glycans [30]Diagnosis
GastricDecreased galactosylation [28]Diagnosis
LungDecreased galactosylation [28]Diagnosis
OvarianDecreased galactosylation [28]; decreased galactosylation [31]Diagnosis
Haptoglobin (Hp)HepaticIncreased bi-fucosylation [32]Diagnosis
OvarianIncreased fucosylation [33]Diagnosis
α1-Antitrypsin (A1AT)LungIncreased galactosylation, fucosylation and poly-LacNAc structures [34]Diagnosis
HepaticIncreased fucosylation [35]Diagnosis
α1-Acid glycoprotein (AGP)HepaticIncreased multifucosylated tetraantennary structures [36]; increased fucosylation [37]; increased fucosylation and sialylation [38]Diagnosis
PancreaticIncreased α-1,3 fucosylation [39]Diagnosis
OvarianIncreased core-fucosylated bi-antennary structures [40]Diagnosis
Ceruloplasmin (CP)HepaticIncreased core-fucosylation [41]Diagnosis
OvarianIncreased LacdiNAc motifs [42]Diagnosis
PancreaticIncreased sialyl-Lewis X levels [43]Diagnosis
Fetuin AHepaticIncreased fucosylation [35], [44]Diagnosis; prognosis
CholangioIncreased fucosylation [45]Diagnosis

Enormous efforts have been devoted to identify specific tumor-associated alterations in glycosylation which could serve as a distinct feature of cancer in cancer diagnostics and prognostics [12], [46]. This review highlights the most recent advances in glycomics-based biomarker studies, including the state-of-the-art analytical methodologies (e.g. mass spectrometry [MS], high-performance liquid chromatographic [HPLC]/ultra-performance liquid chromatography [UPLC], capillary electrophoresis [CE], and lectin-based methods) and their application in biomarker discovery. Additionally, as glycosylation is a complex and non-template driven process, several clinical-centered glycobiological studies which were devoted to unveil regulatory mechanisms and interplay with other pathways are also briefly refined.

Recent advances in glycomics-based biomarker discovery

Mass spectrometry (MS)

MS-based approaches have the advantages of high sensitivity, high mass accuracy, and most importantly, detailed structural information, which provides a major advance in detecting changes, especially subtle changes, in glycan structures that can be used as markers for cancer. Derivatization of glycans by permethylation [47], [48] and reductive amination [49], [50] is often applied prior to MS analysis to improve the ionization efficiency and intensity, and ultimately the sensitivity of detection. Improvements in ion mobility-MS for the structural characterization of glycans have been reviewed recently [51]. This section focuses on discussing two main MS methods, liquid chromatography (LC)-ESI-MS and MALDI-MS, in terms of their recent advances in characterization and quantitation of glycan changes derived from target serum glycoproteins or global serum/plasma/tissue proteins for glyco-biomarker discovery.


LC-MS has been widely employed to determine glycosylation changes in medium-abundance glycoproteins in patient serum, such as α1-antitrypsin (A1AT) [52], α1-acid glycoprotein [36], haptoglobin (Hp) [53], [54], [55], and immunoglobulin G (IgG) [28], whose aberrant glycosylations are highly associated with cancer progression. Due to the immense structural complexity and heterogeneity of glycans, glycan separation is required prior to MS detection, where LC is extensively coupled with MS to achieve a more complete characterization of the glycome, which includes reverse phase (RP)-LC, hydrophilic interaction chromatography (HILIC)-LC, and porous graphitized carbon (PGC)-LC.

The Lubman group monitored fucosylated N-glycan structures at low levels in serum Hp by a quantitative HILIC-LC-MS method, which revealed that the fucosylated N-glycan structures in serum Hp were significantly up-regulated in hepatocellular carcinoma (HCC) compared to cirrhosis [56]. The Lubman group also detected the site-specific core-fucosylation (CF) level in serum α2-macroglobulin (A2MG) [57] and serum ceruloplasmin (CP) [41] by RP-LC-MS/MS, which determined that CF levels at site 138 of CP significantly increased in HCC compared to cirrhosis patients. Similarly, using RP-LC-MS/MS, the Hancock group characterized site-specific N-glycan changes of clusterin in the plasma of clear cell renal cell carcinoma patients before and after curative nephrectomy [58].

Using a PGC-LC-MS/MS approach, the Lebrilla group identified the global serum N-glycome and determined relative abundances of N-glycans released from human serum [59]. Interestingly, the most abundant N-glycan, disialylated biantennary species, is not from the most abundant glycoprotein, IgG, but originated from several glycoproteins including A2MG, transferrin, and A1AT [59]. By utilizing this method, the Fanayan group identified a strong representation of high mannose and α-2,6-linked sialylated complex N-glycans on the cell membrane in colorectal cancer (CRC) tissues, which was distinctly associated with CRC [60]. The Rudd group profiled the total serum N-glycans from advanced breast cancer patients and healthy individuals by HPLC coupled with MS, demonstrating a significant increase in the sialyl-Lewis X epitope, which harbors potential to be a better indicator to monitor breast cancer progression than the currently used biomarker, CA15-3 [61]. Most recently, the Li group optimized the HILIC-LC-MS parameters of the targeted MultiNotch MS3 method for quantifying TMT labeled N-glycans derived from human serum proteins, which significantly improves the quantification performance of the N-glycome compared to traditional quantification methods and has great potential to be widely applied in quantitative glycomics analysis for biomarker discovery [62].


For analysis of clinical samples in limited quantities, MALDI-TOF MS is a premier approach for glycan profiling [63] except that it has difficulties in determining the structural isomers with different branching patterns and linkage positions [64]. With a quantitative MALDI-QIT-MS/MS approach using only 10 μL of serum in individual patients, the Lubman group identified that fucosylated N-glycans in Hp were significantly elevated in pancreatic cancer compared to chronic pancreatitis [65] and a unique pattern of bi-fucosylated tetra-antennary N-glycan was identified in HCC patients, which can outperform the clinically used AFP in discriminating early stage HCC from cirrhosis [32]. Nakagawa et al. utilized MALDI-TOF-MS to determine N-glycan structures of AFP from HCC patients and demonstrated these glycans were affected by specific glycosyltransferases in HCC [66]. Palmigiano et al. utilized MALDI-MS for a high-throughput screening of differential serum O-glycans and N-glycans in five patients with genetic defects of COG5-CDG, showing a significant increase of apoC-III0a (aglycosylated glycoform) and decrease of apoC-III1 (mono-sialylated glycoform) in support of clinical diagnosis of COG5-CDG patients [67].


UPLC has been extensively used for N-glycan analysis, and it has advantages of high-throughput, speed and capability to provide branch specific information, which holds great promise for biomarkers related to immune responses [68], [69].

The Rudd group combined HILIC-UPLC with CE-LIF for characterization of N-glycans released from IgG in human serum samples, demonstrating a complete structural annotation of IgG N-glycans within 20 min, which provides a powerful platform for rapid IgG N-glycan structural elucidation of therapeutic antibodies with great potential for clinical use [70]. The group further profiled human serum N-glycome using HILIC-UPLC, with more than 140 N-glycans assigned, where an increase in sialylation, branching, and outer-arm fucosylation and a decrease in high-mannosylated and bi-antennary core-fucosylated glycans are the most significant alterations between breast cancer patients and healthy controls [27]. Knezevic et al. developed a rapid UPLC method to profile N-glycans from plasma samples where N-glycans were labeled with aniline, 2-aminobenzamide, and 2-aminoacridone individually and then co-injected for UPLC-fluorescence detection (UPLC-FLD), permitting a superior throughput analysis of 864 plasma samples per day that can greatly facilitate serum-based glyco-biomarker discovery [71].

Capillary electrophoresis (CE)

Over the past years, CE has been used more as a means for oligosaccharide separation, usually coupling with LC and MS for glycomic analysis due to the high properties of separation, resolving and sample processing [72], [73]. However, combined with laser-induced FLD (LIF), CE-based methods have become a powerful analytic technique, not merely a separation method, for carbohydrate characterization particularly in the field of glycan-based biomarker identification [74], [75], [76], [77]. Furthermore, an integrated multiplexing CE method has been developed on the basis of different separation mechanisms and evaluated for analyzing complicated samples [78].

When utilizing CE-LIF for glycan profiling, the derivatization of glycans is needed to facilitate the separation and enhance the sensitivity. Among different kinds of tags, the 8-aminopyrene-1,3,6-trisulfonic acid (APTS) is the most commonly used. An N-glycomic profiling method using a DNA sequencer was established by Callewaert et al. [79], [80] which has been widely applied to characterize APTS labeled N-glycome in bio-fluids [81], [82], [83] and purified proteins [84]. The Gao group has adopted and modified this method to investigate the N-glycan-based biomarkers and thus constructed the diagnostic models for various types of cancer including HCC, CRC, multiple myeloma, gastric cancer and extrahepatic cholangiocarcinoma [23], [85], [86], [87], [88]. Recently, several high-throughput variants using a fragment analysis module with clinical application potential have been reported [89], [90]. Using an artificial intelligence (AI) algorithm, the predictive diagnostic model might be developed based on the CE pattern.

Lectin-based technologies

Lectins are a group of proteins or glycoproteins derived from plants, animals (including human beings) or microorganisms, which can recognize and reversibly bind to specific free glycans or oligosaccharide moieties on various glycoconjugates [91], [92].

Lack of glyco-specific antibodies has become the major obstacle for the detection of specifically glycosylated proteins. Fortunately, lectins have been considered as an extremely useful tool for glycomic and glycoproteomic study that can be used alone or in conjunction with other methods such as MS and CE because of the wide availability, affinity and relative specificity [93]. Several approaches based on lectins including lectin blot [94], [95], lectin histochemistry/cytochemisty [29], [96], lectin-antibody sandwich enzyme-linked immunosorbent assay (ELISA) [33], [44], [97], lectin affinity chromatography [98], [99], lectin microarray [100], [101], [102] and lectin microfluidics [103] have been applied to capture the specific glycan of the glycoproteins [102], [104], [105], [106], enhancing the understanding of aberrant glycosylation and carcinogenesis, accelerating biomarkers recognition and quantitative detection. The Gao group has investigated the diagnostic and prognostic value of fucosylated fetuin A in HCC patients by AAL-based ELISA [107]. The Lubman group identified and validated the abnormal sialylation levels of clusterin, complement factor H, hemopexin in the serum of ovarian cancer patients compared to benign diseases using an SNA-based ELISA [108]. With the development of a reverse AAL-based ELISA, the Lubman group also evaluated fucosylated Hp in ovarian cancers, which, in combination with CA125, showed improved performance to distinguish early-stage ovarian cancer from benign diseases compared to CA125 alone [33].

More typical examples of cancer glyco-biomarker discovery using the aforementioned technologies are summarized in Table 1.


The databases, softwares, and bioinformatic tools developed to interpret glycomics data as glycan structural information in different glyco-analysis methods including MS (GlycoWorkbench, UniCarb-DB, GlycoDigest) and HPLC/UPLC (GlycoBase) have been recently reviewed [109], [110]. The information on how to utilize glycan structural information to query databases that associate glycans with proteins (UniCarbKB) and with interactions with pathogens (SugarBind) has also been included [110].

Basic glycobiological studies contribute to development of clinical glycomics

Cancer-specific glycosylation changes are mainly based on the aberrant expression and activity of relevant glycosyltransferases and glycosidases. Sialyltransferases (STs) [111], fucosyltransferases (FUTs) [112], [113] and N-acetylglucosaminyltransferases (GnTs) [114], [115] represent a part of glycosyltransferases responsible for the formation of a manifold of glyco-phenotypes, and are currently the most studied molecules in glycobiology [116], [117]. Below, we briefly discuss relevant background and recent progress of cancer glycobiology.

The sugar chains of glycoconjugates are often terminated by diverse sialic acids, which play critical roles in cellular interaction, cancer cell dissociation and metastasis, and also affords another view of biomarker research [118]. Generally, total serum sialylation levels appear to be increased in various malignancies and present application potentials for cancer diagnosis, monitoring and prognosis [119]. Given aberrant sialylation favors cancer initiation and progression via multilevel effects, mediated by more than 20 distinct STs [120], [121], [122], [123], inhibitors of STs are of pharmaceutical interest, in particular, for cancer therapy [124], [125], [126].

As for FUTs, a systematic, multidisciplinary strategy has been recently employed to confirm FUT8 as a pro-metastatic regulator of melanoma and potential therapeutic target [127]. In addition, FUT8 also has tight connections with lung cancer [112], liver cancer [128], [129], prostate cancer [130], etc. For instance, Tu et al. found that FUT8 could stimulate breast cancer cell invasion and metastasis by remodeling TGF-β receptor CF [94]. A new study has reported that inhibition of FUT8 and blockage of CF enhances T cell anti-tumor immune responses [131]. Additionally, use of FUTs (FUT2 and FUT3) genotype-dependent cut-off values for CA19-9 could improve sensitivity and reduce false-positive results [132], shedding light on the personalized application for glycomics, especially when combined with genetic and epigenetic analysis [133], [134].

GnTs is another family of key enzymes involved in the maturation and alteration of oligosaccharides. The clinical significance of GnTs presents relevance and specificity of cancer type to a certain degree [135], [136], [137]. For instance, GnT-V and β1-6 branching N-linked glycans are associated with good prognosis of bladder cancer [136], whereas GnT-V in gastric cancer correlates with metastasis and poor prognosis [138]. The underlying causes and mechanisms still need to be elucidated. Interestingly, there are interactions not only within different GnTs, but also with other glycosyltransferases [114], [139], [140].

Additionally, it should be mentioned that the altered glycan repertoire of some key regulators such as cadherins [141] in carcinogenesis may lead to dysfunction and abnormal turnover of these molecules, which is associated with the acquisition of cancerous phenotypes. There is growing evidence that glycosylation is not a bystander in the complicated network of malignant transformation, it is indeed a determinant in a series of molecular events during carcinogenesis and can also be a potent immune modulatory or therapeutic target [12], [141], [142]. An intriguing study has demonstrated that the sugar moiety is critical for PD-L1 and PD-1 interaction and targeting glycosylation is a promising immunotherapy strategy for triple-negative breast cancer [143]. An opinion article has attached more importance to deciphering cancer “glyco-code” which modifies immunity and suggested that targeting glycans could offer new therapeutic opportunities [144].

Conclusions and prospects

Glycoproteins are one of the most important glycoconjugates in addition to proteoglycans and glycolipids. Glycans, which covalently attach to the protein backbone, are variant and versatile due the variant sugar type, number, linkage site, enzyme activity, donor sugar-nucleotide availability as well as target protein conformation. Glycans greatly enrich the biological information of glycoproteins and thus enhance their interaction in cell behaviors. The complicated features of glycans as well as the limitation of research methodology have made the investigation of glycomics severely lag behind genomics and proteomics. Sialylation, fucosylation and complex branching structures have been revealed to be among the most commonly observed structural patterns in malignancies. The cutting-edge achievements of techniques including MS, CE, UPLC, which focus on glycomics as well as lectin based glyco-capture, microarray and microfluidics which focus on aberrantly glycosylated glycoproteins have greatly accelerated the discovery and application potentialities of glycomics and glycoprotein markers in various cancers.

AFP-L3 for the detection of HCC had long been included in several clinical guidelines by APASL, Japanese and Chinese societies for the treatment of liver cancers. At a cut-off value of 10%, AFP-L3% has a sensitivity of 71% and specificity of 63% for diagnosis of HCC in patients with AFP of 10–200 ng/mL [18]. Serum CA19-9 has a sensitivity of 78.2% and specificity of 82.3% for the diagnosis of pancreatic cancer [20]. Together with the other clinical widely used glycoprotein tumor markers such as PSA, CA125, etc., the extended exploration of glycosylation for tumor diagnostics and even for targeted therapeutics is encouraging.

The circulating proteins at high or medium abundance provide ideal targets for glycosylation detection both individually (like AFP-L3) or as a panel. Panel detection helps to compensate the insufficiency of sensitivity and specificity of individual protein or glycosylated proteins in cancer identification. More well-designed clinical validation needs to be conducted before clinical application. Combined with advanced AI algorithms, the implementation of individual specific as well as panel structure abundance detection is feasible technically and clinically.

The target of pertinent cancer-specific glycosylation as well as lectin based capture is crucial for detection. Although robust lectin based glyco-capture together with immunological detection in microarrays and microfluidics make the determination of specifically glycosylated target proteins easier to standardize and less redundant, the standardization of detection, which should be adapted to clinical utility, is still on the way for glycomic analysis and glycoprotein detection except for AFP-L3. The interference from glycosylation structures of the detection antibody itself often makes the routine protein detection method such as lectin-antibody sandwich ELISA fail. Unfortunately, the antibody against an aberrantly glycosylated peptide site is difficult to obtain due to the low antigenicity of glycans. Very limited types of such antibodies are available. The detection antibodies derived from genetic engineering techniques might be an option in the future.

From the glycan point of view, with the wide application of MS in routine clinical chemistry analysis as well as the advance of MS, the quantitative MS analysis for glycopeptides with site information, which indicates the microheterogeneity information of glycosylation, might be an alternative and promising option in the future. However, it is too early now to expect that the aforementioned analytical methods can remarkably accelerate the anticipated glyco-biomarker revolution. After all, there is a long and thorny journey from the bench to the clinic for the glyco-biomarker discovery.

In addition, glycosylation is a template-free process and the expression of relevant genes involved are impacted by various transcription factors, epigenetic changes, macro- or microenvironment, etc. When thinking about glycomics and cancer biomarker discovery, a perspective of molecular pathological epidemiology (MPE) is of great reference value which provides transdisciplinary insights to decipher disease at multilevels integrating bioinformatics, in vivo pathology and omics technologies [145], [146], [147]. In turn, glycomics has also augmented the scope and connotation of MPE. Recent findings have also highlighted the relationship between glycosylation and immunity, and pinpointed the novel therapeutic strategies based on deciphering cancer “glyco-code” [144].

Thus, although the “sweet” molecules, with complicated physiological and pathological implications, belong to an “old” and “narrow” story, they surely will be uncovered with new promising diagnostic, prognostic, and even therapeutic applications in the future.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This work was funded by the Science and Technology Commission of Shanghai Municipality (no. 15JC1404100; no. 17JC1404500), the National Natural Science Foundation of China (funder ID: 10.13039/501100001809, no. 81271925), the National Cancer Institute under grants 1R01 CA160254 (D.M.L.), and 1R01 CA154455 (D.M.L.), funder ID: 10.13039/100000054, and U01CA225753 and the National Institutes of Health under grant R01 GM 49500 (D.M.L.), funder ID: 10.13039/100000002.

  3. Employment or leadership: None declared.

  4. Honorarium: None declared.

  5. Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication


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Received: 2018-04-14
Accepted: 2018-07-15
Published Online: 2018-08-23
Published in Print: 2019-03-26

©2019 Walter de Gruyter GmbH, Berlin/Boston

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