The Repertoire of Glycan Alterations and Glycoproteins in Human Cancers

Abstract

Cancer as the leading cause of death worldwide has many issues that still need to be addressed. Since the alterations on the glycan compositions or/and structures (i.e., glycosylation, sialylation, and fucosylation) are common features of tumorigenesis, glycomics becomes an emerging field examining the structure and function of glycans. In the past, cancer studies heavily relied on genomics and transcriptomics with relatively little exploration of the glycan alterations and glycoprotein biomarkers among individuals and populations. Since glycosylation of proteins increases their structural complexity by several orders of magnitude, glycome studies resulted in highly dynamic biomarkers that can be evaluated for cancer diagnosis, prognosis, and therapy. Glycome not only integrates our genetic background with past and present environmental factors but also offers a promise of more efficient patient stratification compared with genetic variations. Therefore, studying glycans holds great potential for better diagnostic markers as well as developing more efficient treatment strategies in human cancers. While recent developments in glycomics and associated technologies now offer new possibilities to achieve a high-throughput profiling of glycan diversity, we aim to give an overview of the current status of glycan research and the potential applications of the glycans in the scope of the personalized medicine strategies for cancer.

Introduction

Cancer is the overriding cause of morbidity and mortality worldwide and represents a global health burden. In cancer, in addition to alterations in RNA, protein, and metabolite levels due to genome reprogramming, alterations in glycan compositions and/or structures such as glycosylation, fucosylation, and sialylation are also widely observed. In accordance with the development of glycan profiling approaches, implementation of glycan studies for cancer to determine potential cancer glycan biomarkers has increased day by day (Wang, 2013).

Glycosylation is the enzyme-catalyzed chemical reaction formed by the covalent attachment of a carbohydrate to a polypeptide, lipid, polynucleotide, or carbohydrate, or other organic compounds (Kunej, 2019). Glycosylation is a regular process and every enzyme is encoded by glycogens (the storage form of glucose in cells) (Wang and Jia, 1987). Glycosylation is a co-translational or posttranslational modification process and may result in different classes of glycans through their specific glycosylation process regarding the alterations on glycan molecules (Ohtsubo and Marth, 2006).

N-glycosylation is a process in which glycans attach to nitrogen of asparagine or arginine side chains. A 14-sugar precursor is first added to the asparagine in the target protein. The structure of this precursor is the same in most eukaryotes, containing three glucose, nine mannose, and two N-acetylglucosamine molecules. Through a complex sequence of reactions, this branched chain is attached to a carrier molecule called dolichol and then transferred to the appropriate location on the polypeptide as the target protein passes into the endoplasmic reticulum (ER) lumen to form N-linked glycans (Wang et al., 2013). O-linked glycans are attached to the hydroxyl oxygen of serine and threonine in general. O-glycosylation also occurs due to the binding of sugar residues to amino acids like tyrosine, hydroxylysine, or hydroxyproline side chains, as well as serine and threonine. It is one of the common modifications in mammalian cells.

In addition, C-glycosylation, glypiation, phosphoglycosylation result in other forms of glycans. C-linked glycans are rare glycans with a carbon added on a tryptophan side-chain as a result of C-glycosylation. Phosphoglycosylation process forms phosphoglycans that are linked through the phosphate of a phosphoserine. Finally, glypiation is one of the common posttranslational modifications of eukaryotic proteins involving the addition of glycosylphosphatidylinositol (GPI) anchor proteins to lipids through glycan linkages. GPI contains a conserved phosphoglycan that is modified in a cell- and tissue-specific manner (Thomas et al., 2020).

The glycosylation of glycoproteins can be significantly altered in cancer due to changes in the expression levels of glycosyltransferases as a result of oncogene-activated signaling pathways coupled with gain or loss in chromosome copy number (Abbott and Pierce, 2010). Collectively, these changes result in glycoproteins exported to the cell surface and extracellular region with altered glycan structures that can lead to significant changes in cell phenotype. Therefore, glycoproteins with altered glycan expression are highly likely biomarker candidates in cancer.

Fucosylation is the addition of fucose sugar to a molecule by fucosyltransferase enzymes (Costa et al., 2018; Shu et al., 2011), and sialylation is the covalent adjoining of sialic acid to the terminal end of glycoproteins by sialyltransferase enzymes (Mereiter et al., 2016; Vajaria et al., 2016). Elevated levels of fucosylation and sialylation have been studied in various pathological cases, including inflammation and cancer (Costa et al., 2018; Shu et al., 2011). Moreover, specific types of fucosylated glycoproteins, which distinguish fucosylated glycans such as sialyl Lewis a/x, have been suggested as cancer biomarkers (Saldova et al., 2008).

Tumors may have different underlying genetic causes and can be expressed differently in one patient versus another. This natural variability of cancer contributes to the growing sensitivity and field of personalized medicine. The password of personalized medicine stands for the right drug, for the right patient, at the right time, and at the right dose (Sadée and Dai, 2005). Since the glycans and their alterations can be observed in cancer cases, they are expected to be used in personalized medical approaches. Glycans are reported as dynamic biomarkers for predictive, preventive, and personalized medicine for several disorders, including cardiometabolic diseases (Wang et al., 2019), Type 2 diabetes (Adua, 2018), and chronic diseases (Adua et al., 2017; Wang et al., 2020).

Also, glycans are potential biomarkers of suboptimal health status studies recently covered for some of the population-based researches (Adua et al., 2019; Wang and Yan, 2012). Plus, the defects and/or alterations in protein N-glycosylation, protein O-glycosylation, combined glycosylation processes (multiple pathway involvement), and glycosphingolipid and GPI anchor synthesis could cause congenital disorders of glycosylation, which are clinically and genetically heterogeneous metabolic disorders (Grünewald et al., 2002; Wang et al., 2016a; Wang, 2019). Therefore, developments on glycan research might shed light on various diseases and are urgently needed. Researchers consider that to study just two biomolecules (nucleic acids and proteins) is mostly adequate in personalized medicine research and therefore tended to underappreciate carbohydrates (i.e., glycans), which are one of the building blocks of life (Özdemir et al., 2020).

However, it should be kept in mind that diverse glycans have long been known to play major metabolic, structural, and physical roles in biological systems. They appear to be ubiquitous to all cells in nature, and glycans are not different from other major macromolecular building blocks of life (nucleic acids, proteins, and lipids), simply more rapidly evolving and complex (Varki, 2017). Well, then why was the deserved attention not given to the glycans?

Although a variety of glycoprotein markers are routinely used in clinical laboratories, the general opinion is that most of the glycan markers do not still have enough sensitivity and specificity for early detection of cancers (except for well-known and widely used markers like CA125, PSA, and CA15–3), and are not recommended for clinical routine (Duffy, 1999; Meany et al., 2009; Wong et al., 2003). This is primarily because of the lower abundance of these glycoproteins. Since it is particularly challenging to detect lower abundant glycoproteins, it makes the situation more difficult and new avenues to overcome this pitfall are urgently needed. Moreover, the limited technology to detect and analyze glycans brings another bottleneck.

Besides their advantages, some tools can reduce the performance and limit analytical uses. Lectin-based assays, Western blotting (WB), mass spectrometry (MS), and high-performance liquid chromatography (HPLC) are the main technologies used in glycan analysis. Lectin-based assays have two main limitations that need to be overcome: (i) the weak affinity for their target glycan and (ii) the availability of lectins with uncommon/less-studied glycan structures (Haab, 2012). Difficulty in finding the primary antibody against the glycoprotein of interest is the biggest limitation for WB.

Moreover, many antibodies interact with other proteins and show off-target effects (Ghosh et al., 2014). For MS analysis, indispensable pretreatment operations (i.e., glycans that need to be separated and derived from proteins and lipids) prevent the direct application of glycan samples. Also, it is not possible to identify isomers and precise structural assignments based on the acquired mass spectra with MS. Similar to MS, in HPLC also, sample pretreatment is required and it is a relatively time-consuming method (Svarovsky and Joshi, 2014). Finally, the DNA and glycans are further complex and open ended than the sequence of a finite-length genome due to the reason that they do not synthesize by a template; that is why glycans are evaluated with different perspectives from other biomolecules (Özdemir, 2020).

Despite these limitations, it should not be forgotten that life needs more than nucleic acids and proteins, which can be completed by the integration of the glycans. In that regard, in this study, we aimed to depict hitherto reported glycan alterations in human cancers and present a catalog of clinically validated glycoproteins as cancer biomarkers. For this purpose, a detailed literature survey was performed for reported glycan alterations in human cancers and proposed glycoproteins as potential cancer biomarkers through electronic databases (i.e., Science Direct, Scopus, PubMed, and Web of Science).

The survey was based on several keywords such as “glycan biomarkers,” “O-glycans,” “N-glycans,” “glycosylation,” “sialylation,” and “fucosylation” and repeated independently by six researchers. In this review, the studies to be included were selected under the following criteria: (1) the articles should have been published between the years of 1985 and 2020, (2) the studies must be published as a review or an original research article, (3) the findings must be confirmed by wet-laboratory experimentation, (4) and the experiments should have been performed on human samples (tumor biopsy, body fluids, or cell lines). The literature survey resulted in 205 studies reporting 142 glycan alterations and 88 glycoprotein biomarkers associated with 26 human cancers.

Overview of Reported Glycan Alterations in Cancer

So far, a total of 142 glycan alterations (fucosylation, sialylation, and glycosylation) have been reported in human cancers (Tables 1–3). A summary of the reported alterations in human cancers and the experimental methodology employed during the studies is presented in Figure 1.

FIG. 1.

The conceptual summary of the conducted studies about glycan alterations for the 20 different types of cancers. (A) Bar graph indicating the numbers of reported glycan alterations observed in all cancer types. (B) Pie chart providing the portion of glycan alterations studied in the various sample origins. (C) Pie chart exhibiting the distribution of three major alterations of glycan on all cancers types. (D) Pie chart indicating changes in the expression patterns of reported glycan alterations. (E) Pie chart showing the distribution of major methods that were used to detect glycan alterations.

Table 1.

Altered Glycosylations in Various Tumors

Tumor type	Sample origin	Alteration	Method(s)	Reference study
Adrenocortical carcinoma	Tumor biopsy	Decreased N-glycosylation in NOVH	IHC, NB	Martinerie et al. (2001)
	Cell line	Increased glycosylation in ST8SIA4	LA, microarray	Montgomery and Hull (2019)
	Cell line	Increased glycosylation in B4GALT2 after HDAC inhibition	LA, microarray	Montgomery and Hull (2019)
Bladder cancer	Tumor biopsy	Glycosylation of α-1B-glycoprotein	AC with nano LC/MS/MS	Kreunin et al. (2007)
Breast cancer	Tumor biopsy	Increased glycosylation in serotransferrin, filamin A, collagen alpha-3 (VI) chain, fibrinogen beta chain, immunoglobulin lambda-like polypeptide 5, Ig gamma-2 chain C region, gelsolin, tropomyosin alpha-4 chain, tropomyosin alpha-3 chain, tropomyosin beta chain	LC-MS/MS	Al-wajeeh et al. (2007)
	Serum	Increased high-mannose glycans (Man₉)	MALDI FT-ICR MS, MS/MS, HPLC Chip/TOF-MS	de Leoz et al. (2011)
	Cell line and tumor biopsy	Decreased biantennary N-glycans (NA2 and NA2FB), triantennary glycan (NA3FB)	DNA sequencing	Liu et al. (2013)
	Serum Increased of high-mannose glycans (Man9)	Increased N-glycans	MALDI-TOF/MS	Gebrehiwot et al. (2019)
Cervical cancer	Cell line	Altered O-GlcNAcylationandhypo-O-GlcNAcylation of K8/18 and β-actin and their decreased expression	IB	Jaskiewicz et al. (2017)
Cervical cancer	Cell line and tumor biopsy	Increased O-linked GlcNAcylation	IHC	Zeng et al. (2016)
Colorectal/colon cancer	Tumor biopsy	Increased GlcNAcylation of Annexin A1 and HSP90b	LA, nano LC-MS/MS	Li et al. (2013)
Glioblastoma	Cell line and tumor biopsy	Increased Galectin-3 levels	IHC	Das et al. (2003)
	Tumor biopsy and serum	Increased glycosomal glycoproteins/hydrolysis of increased glycosyl residues of PTPRZ1, EGFR	WB, LA, LC-MS/MS	He et al. (2011)
	Tumor biopsy	Increased expression of galactosylated glycoproteins of overexpressed receptor-type tyrosine protein phosphatase zeta, tenascin C, chondroitin sulfate proteoglycan NG2, podocalyxin-like protein 1 and CD90, and the downregulated CD44	LA, LC-MS/MS	He et al. (2011)
	Cell line and tumor biopsy	GalNAc and GlcNAc of CD133	LA	Tucker-Burden et al. (2012)
Glioma	Tumor biopsy	Increased β 1,6-N-acetylglucosamine (β1,6-GlcNAc)-bearing N-glycans	WB, LA	Yamamoto et al. (2000)
Glioma	Cell line	Decreased glycosylation associated with BEHAB/brevican	WB, IHC	Viapiano et al. (2005)
Hepatocellular cancer	Serum	Decreased levels of (β-1,4) triantennary N-linked glycan of Apo-J	LA, LC-MS/MS	Comunale et al. (2011)
	Cell line	Increased glycosylation and increased CD82, N-cadherin levels	FC, IHC	Marjon et al. (2016)
	Cell line	Decreased α-Dg glycosylation and dystroglycan	WB, qRT-PCR	Alonso-Rangel et al. (2017)
	Cell line	Glycosylation of Wt-FLT3 and decreased FLT3	IP, IB	Chen et al. (2016)
	Cell line	Glycosylation of ceramides/glycosyl ceramide and increased P-glycoprotein	TLC	Turzanski et al. (2005)
	Cell line	Increased glycosylation of NOTCH receptors and increased levels of POFUT1, LFNG, MFNG, RFNG, GXYLT1, GXYLT2, and XXYLT1 in THP-1 cells, RFNG and GXYLT1 in TMD7 cells	IB, qRT-PCR	Wang et al. (2018)
	Cell line	Ceramide glycosylation and blocked ceramide glycosylation	MS	Morad et al. (2015)
	Cell line	Altered regulation of protein-glycosylation pathways and differential expression of TNM	LA, PCR	Pavlikova et al. (2016)
	Cell line	Increased mannosylation in unmutated CLL surface proteins	FC, SDS-PAGE	Krysov et al. (2010)
	Cell line	Alteration of high-mannose N-glycan and increased expression of ALG family (FTX/miR-342/ALG3)	MALDI-TOF MS, LA	Liu et al. (2018a)
	Peripheral blood	Impaired glycosylation and decreased sIgM- μ and CD79a	FC, IPREC, WB	Vuillier et al. (2005)
Lung cancer	Serum	Altered glycosylation and affected α-1-antitrypsin (A1AT) protein	LA, ELISA	Liang et al. (2015)
Lymphoma	Cell line and serum	Presence of oligomannose glycans Ig variable (V) regions	HPLC	Radcliffe et al. (2007)
Meningioma	Tumor biopsy	Detection of glycosylphosphatidylinositol-anchored protein cell surface protein of mesothelin	WB	Johnson et al. (2008)
Ovarian cancer	Tumor biopsy	Increased high-mannose glycans	MALDI-TOF MS, ESI-QIT	Everest-Dass et al. (2016)
Pancreatic cancer	Tumor biopsy	Increased N-glycosylation level and affected proteins such as MUC5AC, CEACAM5, IGFBP3, and LGALS3BP	HPLC-MS	Pan et al. (2014)
	Serum	Increased α-linked mannose and glycan elevations involved the Thomsen-Friedenreich antigen, fucose, and Lewis antigens affected MUC1 and MUC5AC	Microarray, WB	Yue et al. (2009)
	Serum	Increase Asn-88 N-glycosylation and differential RNase-1expression	ELISA, WB	Nakata (2014)
Pituitary neuroendocrine tumor	Tumor biopsy	Glycosylation of clusterin protein in mature and presecretory form and increased glycosylated form of clusterin	WB, SDS-PAGE	Chesnokova et al. (2011)
Renal cell cancer/Kidney cancer	Serum	Alteration of N-glycan profiles (increased triantennary N-glycan (P40)) affected b1,4-N-acetylglucosaminyltransferase-IV	MALDI-TOF MS	Hatekayema et al. (2014)
Stomach/gastric cancer	Tumor biopsy	Increased sialylation and aberrant glycosylation and differential RON receptor tyrosine kinase levels	WB, RT-PCR, HPLC	Mereiter et al. (2016)
	Tumor biopsy	Altered N-glycosylation of FAM5C and differential FAM5C and UGGT1 levels	WB, IPREC	Terao et al. (2017)
	Tumor biopsy	β1,6 GlcNAc-branched N-glycans/aberrant N-glycosylation at Asn-554 and affected E-cadherin	LC-ESI-MS/MS, WB, LA	Carvalho et al. (2016)
Uterine sarcoma	Cell line	Glycosylation of N-glycan and increased P-glycoprotein	MALDI-MS/MS	Greer and Ivey (2007)

IHC, immunohistochemistry; LA, lectin-based methods; FC, flow cytometry; HPLC, high-performance liquid chromatography; IB, immunoblotting; RT-PCR, reverse transcription polymerase chain reaction; IPREC, immunoprecipitation; WB, Western blot.

Table 3.

Altered Fucosylations (and Core Fucosylations) in Various Tumors

Tumor type	Sample origin	Alteration	Method(s)	Reference study
Breast cancer	Serum	Fucosylated N-glycan structures	MALDI-MS	Kyselova et al. (2008)
	Serum	Increase sialylation and fucosylation and elevated levels of the sLex-carrying triantennary structure (CA 15-3)	HPLC, MS	Abd et al. (2008)
	Serum	Core-fucosylated, multiply branched and sialylated N-glycans	MALDI-TOF-MS	Gebrehiwot et al. (2019)
	Tumor biopsy	High-mannose, branched, fucosylated and polylactosamine glycans	MALDI-IMS	Scott et al. (2019)
	Serum	Increased bifucosylation degree of N-glycans	LTQ-ESI-MS	Ju et al. (2016)
	Serum	Core-fucosylated, multiply branched and sialylated N-glycans	MALDI-TOF-MS	Gebrehiwot et al. (2019)
Cervical cancer	Tumor biopsy	Increased sialylation and reduced fucosylation	ELLA	Jin et al. (2016)
	Tumor biopsy	Decreased sialylation and fucosylation of intracellular proteins	ELLA	Kim et al. (2014)
	Cell line	Increased fucosylation, medium degree of sialylation, and increased high-mannose glycans	Nano-UHPLC-MS/MS	Turiák et al. (2019)
Colorectal/Colon cancer	Tumor biopsy	Decrease in galactosylation, sialylation and increase in core fucosylation of neutral glycans with concurrent decrease of core fucosylation of sialylated glycans (IgG)	HILIC-UPLC	Vuckovic et al. (2016)
	Tumor biopsy	Decreased bisecting GlcNAc, sulfated glycans, increased paucimannosidic glycans, and glycans containing a sialylated Lewis-type epitope and core-fucosylated high-mannose N-glycans (Man5–Man9)	HILIC-HPLC/MS, MALDI-TOF-MS	Balog et al. (2012)
	Tumor biopsy	Decreased core a1,6-fucose residues of fucosyltransferase	qRT-PCR, LA	Zhao et al. (2012)
	Serum	Leucine-rich alpha-2-glycoprotein-1 with fucosylated triantennary N-glycan	LC-TOF-MS	Shinozaki et al. (2018)
	Tumor biopsy	1-3/4 fucosylation at Asn 241 of β-haptoglobin	LA, microarray	Park et al. (2011)
Glioma	Tumor biopsy	Tetra-antennary proximally fucosylated with α2,3-linked N-acetylneuraminic acid N-glycan	HPLC, MALDI-TOF-MS	Costa et al. (2018)
Hepatocellular cancer	Plasma	Increased fucosylation in hemopexin and complement factor H	Nano-LC-MS/MS	Benicky et al. (2014)
	Serum	Increased core (α-1,6) fucosylation in A1AT	MALDI-TOF-MS, LA	Comunale et al. (2010)
	Serum	Increased fucosylation	IGOT-LC-MS, LA	Kaji et al. (2013)
	Serum	Increased core fucosylation in AFP, IgM, Kininogen,HBsAg, GP73, hemopexin, fibrinogen gamma chain precursor	LC-MS/MS, MALDI, IB, LA	Comunale et al. (2006)
Hepatocellular cancer	Serum	Increased Lewis X-type fucosylation in Hp-β	LA, HPLC, WB	Shu et al. (2011)
Hepatocellular cancer	Tumor biopsy and serum	Aberrant increase of Core α-1, 6-fucosylated Triantennary Glycan (NA3Fb)	DSA-FACE	Nie et al. (2015)
Lung cancer	Serum	Increased Sialyl Lewis X, monoantennary glycans, Trisialylated Glycans, decreased in disialylated glycans, core-fucosylated biantennary glycans	HILIC, HPLC	Arnold et al. (2011)
	Serum	Altered sialylation and fucosylation in Haptoglobin	2D-DiGE, IB, LA, ELISA	Hoagland et al. (2007)
	Serum	Altered glycosylation and increased fucosylation in α-1-antitrypsin	LA, ELISA	Liang et al. (2015)
	Serum	Increased fucosylation in haptoglobin	MS	Tsai et al. (2011)
	Serum	Increased core fucosylation	LA	Liang et al. (2019)
	Tumor biopsy	Increased core fucosylation, decreased hybrid-type glycans (MGAT1)	Nano-LC Chip TOF-MS	Ruhaak et al. (2015b)
	Tumor biopsy	Increased fucosylation in C9	LC-ESI-MS/MS, LA	Narayanasamy et al. (2011)
	Serum	Increased fucosylation and decreased PON1	LC-MS/MS	Ahn et al. (2014)
Oral cancer	Cell line	Increased fucosylated N-glycans, especially core-fucosylated N-glycans in CD147	MALDI-MS	Chen et al. (2013)
	Saliva	Increase high-mannose glycans, hybrid and complex structures, highly fucosylated structures	UPLC-MS	Sinevici et al. (2019)
	Serum	Increase of some triantennary and tetra-antennary glycans with varying degrees of fucosylation and sialylation	MALDI-TOF-MS	Guu et al. (2017)
	Plasma	Increase core fucosylation	LC-MS/MS	Chang et al. (2019)
	Serum	Increase fucosylation in alpha-L-fucosidase	LA	Shah et al. (2008)
	Cell line and tumor biopsy	Increased fucosylated glycans in B7-H3	LC-MS/MS	Chen et al. (2015)
Ovarian cancer	Serum	Core-fucosylated biantennary N-glycans, higher antennarity and increased amounts of LewisX motif (1-acid glycoprotein)	MALDI-TOF/TOF-MS	Weiz et al. (2016)
	Serum	Increased SLex structure, core fucosylation, decreased galactosylation and sialylation	MALDI-TOF-MS, ESI-QTOF	Saldova et al. (2008)
	Cell line	Increased high mannose-type glycans and core-fucosylated tetra-antennary structures	MALDI-TOF-MS	Machado et al. (2011)
	Serum	Increased fucosylated, decreased mannosylated and highly sialylated glycans	ESI-chip-TOF	Hua et al. (2013)
	Tumor biopsy	Increased high mannose-type glycans, fucosylated and sialylated complex structures	MALDI-TOF-MS	Chen et al. (2017)
Ovarian cancer	Serum	Increased abundances of tribranched and tetrabranched structures with varying degrees of sialylation and fucosylation and an apparent decrease in the levels of bisecting glycans (CA-125)	MALDI-TOF-MS	Alley et al. (2012)
Ovarian cancer	Serum	Decrease of monosialylated monoantennary N-glycan structures, increased triantennary, tetra-antennary fucosylated structures, afucosylated, and fucosylated triantennary structures (CA-125)	MALDI-TOF-MS	Dědová et al. (2019)
Pancreatic cancer	Serum	Increased α1-3 fucosylation in α-1-acid glycoprotein	ELLA, HILIC-MS	Balmaña et al. (2016)
Renal cell cancer/kidney cancer	Plasma	Increased core-fucosylated biantennary digalactosyl disialylated and biantennary digalactosyl disialylated glycans (clusterin)	Nano-LC-MS/MS, LA, HPLC	Gbormittah et al. (2015)
Renal cell cancer/kidney cancer	Plasma	Decrease in level of biantennary digalactosyl disialylated glycans and increased core fucosylated biantennary digalactosyl disialylated glycan and a triantennary trigalactosyl disialylated glycan	HPLC, HILIC-UPLC, LC-MS	Tousi et al. (2012)
Stomach/Gastric cancer	Tumor biopsy	Decreased core fucosylated glycans in FUT8	MALDI-TOF-MS	Qin et al. (2017)
Stomach/Gastric cancer	Tumor biopsy	Increased levels of core fucosylated agalactosyl biantennary glycans in IgG	2D-DiGE	Bones et al. (2011)
Thyroid carcinoma	Urine and plasma	Increased fucosylated desialo-N-glycopeptides	LC-MS/MS, WB	Zhang et al. (2020)

In terms of the reported number of glycan alterations, breast, hepatocellular, ovarian, stomach, and lung carcinomas were the leading types of cancer (Fig. 1A). In general, it has been observed that studies were focused on cancers more frequently observed in society. Either no study has been performed or very few studies have been reported on glycan alterations in tumors with low observation frequency or rare tumors.

In the detection of glycan alterations and preclinic and clinical validation studies, tumor biopsies, cell lines, and serum samples were preferred sample types in almost 90% of reports. A limited number of studies were carried out by the use of urine, saliva, ascites, bone marrow mononuclear cells, and blood constitutes such as plasma, peripheral blood mononuclear cells, or peripheral blood (Fig. 1B). Considering the frequent secretion of glycoproteins to the extracellular region, future studies should be extended to body fluids, which have been neglected so far.

The glycan alterations were primarily observed in terms of sialylation (37%). Further alterations in fucosylation and core fucosylation (34%) and glycosylation (29%) were observed (Fig. 1C). Although the expression patterns of the glycan alterations were varying, in general, the increased levels of fucosylation, sialylation, and glycosylation were encountered in cancers (Fig. 1D).

Numerous methods have been applied to the analysis of glycan profiles (Fig. 1E). However, a common strategy to identify glycan alterations was to use methods employing lectins, which are carbohydrate-binding proteins with variable affinities for specific glycan structures, and further separations using HPLC- and MS (MALDI-TOF-MS, and LC-MS/MS)-based technologies and validations using WB, polymerase chain reaction (PCR) and real-time quantitative reverse transcription PCR, enzyme-linked immunosorbent assay and enzyme-linked lectin assay, capillary electrophoresis, or immunohistochemistry.

When the altered glycosylations in human cancers were considered (Table 1), these alterations were primarily reported in hepatocellular carcinoma, which was commonly studied using cell lines, and the expression patterns (increased/decreased) were varying. No reported studies were found for leukemia, oral, and thyroid cancers. Out of 41 glycosylation studies, 12 of them used the LA technique. Moreover, MUC5AC, CD44, and CD133 were some of the affected proteins in terms of glycosylation alterations.

Breast and ovarian cancer were the two cancers in which sialylation alteration was mostly observed (Table 2). In both cancers, among reported expression patterns, all alterations were increased. No reported studies were found for adrenocortical, hepatocellular, pancreatic, and uterine carcinoma. Forty-seven percent of the sialylation studies were based on MS or MS-based (i.e., MALDI-MS, MALDI-TOF-MS, or nano-LC−MS) methods. ST3GAL, CA-125, and ST6GAL1 were some of the affected proteins in terms of sialylation alterations.

Table 2.

Altered Sialylations in Various Tumors

Tumor type	Sample origin	Alteration	Method(s)	Reference study
Bladder cancer	Cell line	Increased sialyl Lewis X (sLex), terminal GalNAc and Gal, and high mannose-type N-glycans	MALDI-TOF/TOF-MS, LA, SILAC	Yang et al. (2015)
	Tumor biopsy	Increased O-6 sialylation in the sTn antigen	Nano-LC-ESI-MS/MS	Cotton et al. (2017)
	Tumor biopsy	Increased sialylations in sTn and s6T antigens	IHC	Lima et al. (2013)
	Tumor biopsy	Increased sialylations in Tn antigen (ST6GalNAc-I)	qRT-PCR	Ferreira et al. (2013)
Breast cancer	Serum	Increased levels of glycans containing sLex epitopes	HPLC	Saldova et al. (2011)
	Serum	Sialylated N-glycan structures	MALDI-MS	Kyselova et al. (2008)
	Serum	Increased levels of agalactosyl biantennary glycans and glycans containing the sLeX epitope	HPLC	Pierce et al. (2010)
	Serum	Increase sialylation and elevated levels of the sLex-carrying triantennary structure (CA 15-3)	HPLC, MS	Abd et al. (2008)
	Cell line and tumor biopsy	Increased expression of sLex structure (E-selectin)	LC-MS/MS	Julien et al. (2011)
	Serum	Sialylated N-glycans	MALDI-TOF-MS	Gebrehiwot et al. (2019)
	Tumor biopsy	Increased sialylation in versican	MS/MS	Tian et al. (2012)
Cervical cancer	Tumor biopsy	Increased sialylation	ELLA	Jin et al. (2016)
	Tumor biopsy	Decreased sialylation of intracellular proteins	ELLA	Kim et al. (2014)
	Cell line	Medium degree of sialylation of TFR1, CBPM, LAMP1/2, FOLR1, DAF, NICA	Nano-UHPLC-MS/MS	Turiák et al. (2019)
Colorectal/Colon cancer	Cell line	Increased α2,6-sialylation, reduced α2,3-sialylation of ST6GAL1 with decreased expression	LC-MS/MS/ESI/MALDI	Sethi et al. (2016)
	Tumor biopsy	Decrease in sialylation of IgG	HILIC-UPLC	Vuckovic et al. (2016)
	Tumor biopsy	Sialylated glycans with Lewis-type epitope	HILIC-HPLC/MS, MALDI-TOF-MS	Balog et al. (2012)
	Tumor biopsy	Low O-acetylation of sialyl-Le(x)	WB, HPLC	Mann et al. (1997)
Glioblastoma	Tumor biopsy	Increased sialoglycoproteins ANGPTL2, ICAM1, IL1RAP, ITGB8, LGALS3BP, SLC1A3, and THBD	Nano-LC-MS/MS, Microarray, qRT-PCR	Autelitano et al. (2014)
Glioblastoma	Tumor biopsy	Increase of sialidase NEU4 expression	WB, qRT-PCR	Silvestri et al. (2014)
Glioma	Cell line	Increased levels of α2,3-linked sialic acid	Normal phase-HPLC-MALDI-TOF	Costa et al. (2018)
	Cell line	Increased sialylated of α3β1 integrin N-glycans	WB, IPREC	Yamamoto et al. (2001)
	Cell line	Increased sialylation of BEHAB/Brevican	WB, IHC	Viapiano et al. (2005)
Leukemia	PBMC	Increase of 9-O-acetylated sialoglycoconjugates affected, increased expression of IFN-γ	qRT-PCR, ELISA	Ghosh et al. (2005)
	Cell line and BMMC	Sialylated N-glycan, altered α2, 3- and α2, 8-linked sialylation increases ST3GAL5 and ST8SIA4 expression	MALDI–TOF-MS, WB	Ma et al. (2015)
	Cell line	Increased sialylation by downregulation of mir-181c yielded increased ST8SIA4	WB	Zhao et al. (2016)
	Cell line and BMMC	Increased α2–3 sialylation increases ST3GAL IV expression	MALDI-TOF-MS, qRT-PCR, WB	Zhou et al. (2016)
	Cell line and PBMC	Altered expression of 2,8-sialyltransferases and increased expression of ST8SIAL4 and ST8SIA6	qRT-PCR, WB	Zhang et al. (2015)
Lung cancer	Serum	Increased sialyl Lewis X, monoantennary glycans, trisialylated glycans, decreased in disialylated glycans	HILIC, HPLC	Arnold et al. (2011)
Lung cancer	Serum	Altered sialylation of haptoglobin	2D-DiGE, IB, LA, ELISA	Hoagland et al. (2007)
Lymphoma	Cell Line	Cell surface sialylation of Gal-3, Rho GTPase family	CAIA	Suzuki et al. (2015)
Meningioma	Tumor biopsy	Gangliosides with reduced sialic acid content	NanoESIchip/QTOF-MS	Schiopu et al. (2012)
Meningioma	Cell line and tumor biopsy	Increased sialic acid and galectin-3 levels	IHC	Bresalier et al. (1997)
Oral cancer	Serum	Increase of some triantennary and tetra-antennary glycans with varying degrees of sialylation	MALDI-TOF-MS	Guu et al. (2017)
Ovarian cancer	Serum	Increased SLex structure and sialylation of CRP, serum amyloid A, haptoglobin, α1-acid glycoprotein, α1-anti-trypsin, α1-anti-chymotrypsin, and fibrinogen increase expressions	MALDI-TOF-MS, ESI-QTOF	Saldova et al. (2008)
	Serum	Increased amounts of LewisX motif of 1-acid glycoprotein	MALDI-TOF/TOF-MS	Weiz et al. (2016)
	Ascites	Increased antennarity, branching, sialylation, and LewisX motives	HPLC, MALDI-TOF-MS	Biskup et al. (2017)
	Serum	Highly sialylated glycans	ESI-chip-TOF	Hua et al. (2013)
	Serum	Increase of sialylated triantennary and tetra-antennary structures of CA-125	MALDI-TOF-MS	Biskup et al. (2014)
	Tumor biopsy	Sialylated complex structures	MALDI-TOF-MS	Chen et al. (2017)
	Serum	Increased abundances of tribranched and tetrabranched structures with varying degrees of sialylation of CA-125	MALDI-TOF-MS	Alley et al. (2012)
Pituitary neuroendocrine tumor	Tumor biopsy	Polysialylation of NCAM yielded increased expression	WB, ELISA	Trouillas et al. (2003)
Renal cell cancer/Kidney cancer	Cell line and tumor biopsy	Altered sialylation of β-dystroglycan	Nano-LC-MS/MS - HPLC	Aggelis et al. (2013)
	Plasma	Increased disialylated clusterin	Nano-LC−MS/MS, LA, HPLC	Gbormittah et al. (2015)
	Plasma	Decrease level of biantennary digalactosyldisialylated glycans and increased core fucosylated biantennary digalactosyldisialylated glycan and a triantennary trigalactosyldisialylated glycan effects clusterin expression	HPLC, HILIC-UPLC, LC-MS	Tousi et al. (2012)
	Cell line	Increase of sialic acid linked by a-2,6 bond with Gal/GalNAc and sialyl Lewis a/x antigens increase MUC1 expression	ELISA	Borzym-Kluczyk et al. (2015)
Stomach/Gastric cancer	Tumor biopsy	Lower sialylation	Nano-HPLC–Chip-TOF-MS	Ruhaak et al. (2015a)
	Tumor biopsy	Increased levels of sialyl Lewis X epitopes (SLeX) present on triantennary glycans	2D-DiGE	Bones et al. (2011)
	Tumor biopsy	Complete loss of cytosolic sialyl N-Glycans	WB, HPLC	Wang et al. (2015)
	Tumor biopsy	Increased sialylation of RON receptor tyrosine kinase	WB, RT-PCR, HPLC	Mereiter et al. (2016)
	Urine and plasma	Increased sialyl Lewis-A glycan antigen CA19-9	IA	Marrelli et al. (2001)
	Cell line	CD44 sialyl- Tn O-glycan	LA, UHPLC	Campos et al. (2015)
Thyroid carcinoma	Tumor biopsy	Increased sialylation (especially of alpha-2,3-linked sialic acid)	LA	Babal et al. (2006)

UHPLC, ultra-high-performance liquid chromatography; UPLC, ultra-performance liquid chromatography; BMMC, bone marrow mononuclear cell; PBMC, peripheral blood mononuclear cell.

Lung cancer was the most reported cancer type when the altered fucosylations (and core fucosylations) in various tumors were investigated (Table 3). Almost all of them had an increased expression pattern in lung cancer and were studied only in serum and tumor biopsy samples. No reported studies were found for adrenocortical, bladder, glioblastoma, leukemia, lymphoma, meningioma, pituitary neuroendocrine tumor, and uterine cancers. Of the 48 studies reported, 30 had an increased fucosylation (and core fucosylation) pattern, while only 6 had a decreased pattern. MALDI-TOF-MS was the quarter of the techniques applied in the fucosylation (and core fucosylation) studies. Some of the affected proteins associated with altered fucosylations (and core fucosylations) include haptoglobin and CD147.

Glycoproteins as Cancer Biomarkers

Glycoproteins are frequently secreted from cells or released from cell surfaces by hydrolytic cleavage, and therefore, glycoproteins with altered glycan expressions are likely candidates as biomarkers that can be involved in the prediction or monitoring the states of various diseases, including cancer. As a result of a systematic survey, we end up with 88 different glycoproteins as clinically validated glycoprotein biomarkers in various tumors (Table 4).

Table 4.

Glycoprotein Biomarkers Clinically Validated in Various Tumors

Glycoprotein biomarker	Cancer type	Sample origin	Expression pattern	Clinical applications	Detection method	Reference study
A disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS5)	Glioblastoma	Tumor biopsy	Increase	Invasiveness biomarker	RT-PCR, hybridization	Nakada et al. (2005)
Adiponectin (ADIPOQ)	Lung cancer	Serum	Increase	Prognostic biomarker	MALDI-TOF MS, MS/MS, WB	Hongsachart et al. (2009)
ADP-ribosyl cyclase/cyclic ADP-ribose hydrolase 1 (CD38)	Leukemia	Blood	Increase	Prognostic biomarker	FC	Dürig et al. (2002)
Alpha-1,3-mannosyl-glycoprotein 2-beta-N-acetylglucosaminyltransferase (MGAT1)	Lung cancer	Tumor biopsy	Decrease	Diagnostic biomarker	Nano LC Chip TOF-MS	Ruhaak et al. (2015a)
Alpha-1,6-mannosyl-glycoprotein 2-beta-N-acetylglucosaminyltransferase (MGAT2)	Lung cancer	Tumor biopsy	Increase	Diagnostic biomarker	Nano LC Chip TOF-MS	Ruhaak et al. (2015a)
Alpha-1-acid glycoprotein (A1AG1)	Breast cancer	Plasma	Unstable pattern	Prognostic biomarker	Anion Exchange Chromatography	Smith et al. (2012)
	Lung cancer	Serum	Increase	Diagnostic biomarker	MALDI-TOF, Sandwich ELISA	Ayyub et al. (2016)
	Hepatocellular carcinoma	Plasma	Increase	Malignancy biomarker	MRM MS	Ahn et al. (2012)
Alpha-1-antichymotrypsin (AACT)	Hepatocellular carcinoma	Plasma	Increase	Malignancy biomarker	MRM MS	Ahn et al. (2012)
Alpha-1-antitrypsin (A1AT)	Hepatocellular carcinoma	Plasma	Increase	Malignancy biomarker	MRM MS	Ahn et al. (2012)
	Hepatocellular carcinoma	Serum	Increase	Prognostic and recurrence biomarker	Lectin FLISA	Wang et al. (2009)
	Lung cancer	Serum and tumor biopsy	Increase	Malignancy biomarker	LA, ELISA	Liang et al. (2015)
Alpha-2-HS-glycoprotein (AHSG)	Breast cancer	Serum	Increase	Diagnostic biomarker	MALDI-TOF-TOF, IA	Yi et al. (2009)
Apolipoprotein A1 (APOA1)	Ovarian cancer	Ascitic fluid	Increase	Diagnostic biomarker	MS, WB, ELISA	Hariprasad et al. (2013)
Asporin (ASPN)	Stomach/gastric cancer	Tumor biopsy	Increase	Metastasis biomarker	IHC,qRT-PCR, IA	Ding et al. (2015)
Autotaxin (ENPP2)	Breast cancer	Serum	Increase	Diagnostic and prognostic biomarker	ELISA	Shao et al. (2019)
BAG family molecular chaperone regulator 4 (BAG4)	Colorectal/colon cancer	Plasma	Increase	Diagnostic biomarker	Microarray, WB	Rho et al. (2018)
Beta-dystroglycan (DAG1)	Renal clear cell carcinoma/kidney cancer	Tumor biopsy	Unstable pattern	Prognostic biomarker	MS, HPLC, WB	Aggelis et al. (2013)
Biantennary digalactosylated disialylated glycan (A2G2S2)	Renal clear cell carcinoma/kidney cancer	Plasma	Decrease	Malignancy biomarker	nano-LC, MS/MS, LA, SDS-PAGE	Gbormittah et al. (2015)
Biglycan (BGN)	Stomach/gastric cancer	Tumor biopsy	Increase	Prognostic biomarker	RT-PCR, WB	Wang et al. (2011a)
Brevican core protein (BCAN)	Glioblastoma	Tumor biopsy	Decrease	Invasiveness biomarker	RT-PCR, hybridization	Nakada et al. (2005)
Carbohydrate antigen 19-9 (CA19-9)	Bladder cancer	Serum	Increase	Prognostic biomarker	IA	Margel et al. (2007)
Carbohydrate antigen 19-9 (CA19-9)	Pancreatic cancer	Serum	Decrease	Prognostic biomarker	IA	Aoki et al. (2019)
CD40 ligand (GP39)	Colorectal/colon cancer	Plasma	Increase	Malignancy biomarker	UPLC, HILIC	Doherty et al. (2018)
CD44 antigen (CD44)	Bladder cancer	Tumor biopsy	Increase	Prognostic biomarker	qRT-PCR, FC, WB	Kobayashi et al. (2016)
	Colorectal/colon cancer	Plasma	Increase	Diagnostic biomarker	Microarray, WB	Rho et al. (2018)
	Glioma	Tumor biopsy	Increase	Invasiveness biomarker	IHC	Wiranowska et al. (2006)
	Leukemia	Bone marrow	Increase	Prognostic biomarker	qRT-PCR, FC	Khan et al. (2008)
	Leukemia	Serum	Increase	Prognostic biomarker	ELISA	Eisterer et al. (2004)
	Pancreatic cancer	Tumor biopsy	Increase	Prognostic biomarker	WB	Li et al. (2015)
Ceruloplasmin (CERU)	Hepatocellular carcinoma	Plasma	Increase	Malignancy biomarker	MRM MS	Ahn et al. (2012
Ceruloplasmin (CERU)	Lung cancer	Serum	Increase	Prognostic biomarker	MALDI-TOF MS, MS/MS, WB	Hongsachart et al. (2009)
Chitinase-3-like protein 1 (YKL-40)	Breast cancer	Serum	Increase	Prognostic biomarker	ELISA	Olfat et al. (2014)
Chondroitin sulfate proteoglycan 4 (NG2/CSPG4)	Glioblastoma	Tumor biopsy	Increase	Therapeutic biomarker	FISH, WB, qRT-PCR	Wang et al. (2011b)
	Glioblastoma	Tumor biopsy	Increase	Therapeutic biomarker	WB, IHC	Pellegatta et al. (2018)
	Glioma	Tumor biopsy	Increase	Invasiveness biomarker	IHC	Wiranowska et al. (2006)
	Leukemia	Bone marrow	Increase	Diagnostic biomarker	FC	Petrovici et al. (2010)
	Leukemia	Blood	Only detected	Prognostic biomarker	mAb 225.28, FC	Fenton et al. (2015)
	Soft tissue sarcoma	Tumor biopsy	Decrease	Therapeutic biomarker	WB, RNA-seq, qRT-PCR	Hsu et al., (2018)
Clusterin (Apo-J)	Hepatocellular carcinoma	Serum	Decrease	Diagnostic biomarker	Lectin FLISA, LC/MS	Comunale et al. (2011)
Clusterin (Apo-J)	Renal clear cell carcinoma/kidney cancer	Plasma	Decrease	Malignancy biomarker	nano-LC, MS/MS, LA, SDS-PAGE	Gbormittah et al. (2015)
CMP-N-acetylneuraminate-beta-galactosamide-alpha-2,3-sialyltransferase (ST3Gal)	Skin cutaneous carcinoma	Tumor biopsy	Increase	Invasiveness and metastasis biomarker	IHC	Ferreira et al. (2013)
Complement C3 (C3)	Hepatocellular carcinoma	Serum	Increase	Diagnostic biomarker	LA, LC-MS/MS, WB	Liu et al. (2010)
Complement component C9 (C9)	Lung cancer	Tumor biopsy	Increase	Diagnostic biomarker	LC- ESI-MS-MS, HLE, WB	Narayanasamy et al. (2011)
C-type lectin domain family 12 member A (CLL-1)	Leukemia	Bone marrow	Increase	Diagnostic biomarker	FC	Larsen et al. (2012)
Cyclin H (CCNH)	Lung cancer	Serum	Decrease	Prognostic biomarker	FITC-labeled lectin staining, MALDI-TOF MS, MS/MS, WB	Hongsachart et al. (2009)
Decorin (DCN)	Colorectal/colon cancer	Tumor biopsy	Decrease	Diagnostic biomarker	iTRAQ, 2D-LC-MS/MS, IHC	Li et al. (2017)
	Soft tissue sarcoma	Tumor biopsy	Decrease	Aggressiveness biomarker	RT-PCR, IHC	Matsumine et al. (2007)
	Stomach/gastric cancer	Tumor biopsy	Increase	Malignancy biomarker	SDS-PAGE, HPLC	Theocharis et al. (2003)
Deleted in malignant brain tumors 1 protein (DMBT1)	Cervical cancer	Tumor biopsy	Only detected	Recurrence biomarker	IHC	Valero et al. (2018)
E-cadherin (CDH1)	Meningioma	Tumor biopsy	Increase	Malignancy biomarker	Microarray, IHC	Lusis et al. (2005)
Endocan (ESM1)	Leukemia	Serum	Decrease	Prognostic biomarker	RT-PCR, WB	Xu et al. (2014)
	Oral cancer	Plasma and tumor biopsy	Increase	Prognostic biomarker	ELISA	Yang et al. (2017a)
	Pituitary neuroendocrine tumor	Tumor biopsy	Increase	Invasiveness biomarker	IHC	Miao et al. (2016)
Epidermal growth factor receptor (EGFR)	Colorectal/colon cancer	Plasma	Increase	Diagnostic biomarker	Microarray, WB	Rho et al. (2018)
Erythropoietin (EPO)	Renal clear cell carcinoma/kidney cancer	Tumor biopsy	Increase	Prognostic and malignancy biomarker	RT-PCR	Westenfelder and Baranowski (2000)
Fetuin-A (AHSG)	Hepatocellular carcinoma	Serum	Increase	Diagnostic biomarker	Lectin ELISA, LC-MS/MS	Comunale et al. (2009)
Fibrinogen beta chain (FGB)	Bladder cancer	Urine	Increase	Diagnostic biomarker	WB, Microarray	Lindén et al. (2013)
Folate receptor 1 (FOLR1)	Ovarian cancer	Serum	Increase	Serological biomarker	ELISA	Leung et al. (2013)
Galectin-3 (LGALS3)	Breast cancer	Tumor biopsy	Increase	Prognostic and	Breast cancer	Zhang et al. (2014)
Galectin-3 (LGALS3)	Meningioma	Tumor biopsy	Only detected	Diagnostic biomarker	IHC	Bresalier et al. (1997)
Glucosylceramide synthase (UGCG)	Oral cancer	Tumor biopsy	Increase	Prognostic biomarker	IHC	Kim et al. (2016)
Glycosylphosphatidyl-inositol-80 (GPI-80)	Lung cancer	Serum	Increase	Prognostic biomarker	MALDI-TOF MS, MS/MS, WB	Hongsachart et al. (2009)
Glypican-1 (GPC1)	Glioblastoma	Tumor biopsy	Increase	Prognostic biomarker	IHC	Kazanskaya et al. (2018)
Glypican-1 (GPC1)	Pancreatic cancer	Plasma	Increase	Prognostic biomarker	FC	Qian et al. (2018)
Glypican-3 (GPC3)	Stomach/gastric cancer	Serum	Only detected	Diagnostic biomarker	IHC	Hishinuma et al. (2006)
Haptoglobin (Hp)	Bladder cancer	Serum	Increase	Diagnostic biomarker	ELISA	Pirincci et al. (2012)
	Hepatocellular carcinoma	Serum	Increase	Prognostic biomarker	Lectin ELISA	Asazawa et al. (2015)
	Lung cancer	Serum	Increase	Diagnostic biomarker	IA, LB, ELISA	Hoagland et al. (2007)
Hemopexin (HPX)	Hepatocellular carcinoma	Serum	Increase	Diagnostic biomarker	Lectin FLISA, LC-MS/MS	Comunale et al. (2009)
Hepatocyte growth factor (HGF)	Hepatocellular carcinoma	Serum	Increase	Diagnostic biomarker	LA, LC-MS/MS, WB	Liu et al. (2010)
Histidine-rich glycoprotein (HRG)	Hepatocellular carcinoma	Serum	Increase	Diagnostic biomarker	LA, LC-MS/MS, WB	Liu et al. (2010)
Histidine-rich glycoprotein (HRG)	Leukemia	Serum	Increase	Diagnostic and prognostic biomarker	qRT-PCR, ELISA,	Ahmed et al. (2017)
Interleukin-6 receptor subunit beta (IL6ST)	Colorectal/colon cancer	Plasma	Increase	Diagnostic biomarker	Microarray, WB	Rho et al. (2018)
Kininogen (KNG1)	Hepatocellular carcinoma	Serum	Increase	Prognostic and recurrence biomarker	Lectin FLISA	Wang et al. (2009)
Leptin receptor (LEPR)	Meningioma	Tumor biopsy	Decrease	Prognostic biomarker	Microarray, RT-PCR, IHC	Schmidt et al. (2016)
Leucine-rich alpha-2-glycoprotein (LRG1)	Colorectal/colon cancer	Plasma and tumor biopsy	Increase	Prognostic biomarker	ELISA, WB	Zhang et al. (2018)
Liver carboxylesterase (CE)	Hepatocellular carcinoma	Serum	Increase	Diagnostic biomarker	LA, LC-MS/MS, WB	Liu et al. (2010)
Matrix metalloproteinase (MMP-9)	Pituitary neuroendocrine tumor	Tumor biopsy	Increase	Invasiveness biomarker	IHC, RT-PCR, WB	Hussaini et al. (2007)
Mesothelin (MSLN)	Ovarian cancer	Serum	Increase	Diagnostic and prognostic biomarker	ELISA	Huang et al. (2006)
Metalloproteinase inhibitor (TIMP-1)	Pituitary neuroendocrine tumor	Tumor biopsy	Decrease	Aggressiveness biomarker	ELISA, NB	Pereda et al. (2000)
Monocyte differentiation antigen CD14	Hepatocellular carcinoma	Serum	Increase	Diagnostic biomarker	LA, LC-MS/MS, WB	Liu et al. (2010)
Mucin-1 (MUC1)	Breast cancer	Serum	Increase	Diagnostic biomarker	ACS:180 BR array	Gion et al. (2001)
	Breast cancer	Tumor biopsy	Increase	Diagnostic biomarker	IHC, qRT-PCR	Ghosh et al. (2014)
	Lymphoma	Serum	Increase	Prognostic biomarker	IA	Batlle et al. (2005)
	Meningioma	Tumor biopsy	Increase	Malignancy biomarker	Microarray, IHC	Lusis et al. (2005)
	Ovarian cancer	Tumor biopsy	Increase	Therapeutic biomarker	IHC	Wang et al. (2007)
	Prostate adenocarcinoma	Tumor biopsy	Increase	Therapeutic biomarker	IHC	Drake et al. (2015)
	Renal clear cell carcinoma/kidney cancer	Serum	Increase	Malignancy biomarker	MALDI-TOF-MS	Gao et al. (2013)
Mucin-16 (MUC16)	Bladder cancer	Tumor biopsy	Increase	Prognostic biomarker	nanoLC-ESI-MS-MS, RT-PCR	Cotton et al. (2017)
Mucin-16 (MUC16)	Bladder cancer	Serum	Increase	Prognostic biomarker	IA	Margel et al. (2007)
Mucin-16 (MUC16)	Breast cancer	Serum	Increase	Prognostic biomarker	IHC	Fang et al. (2017)
	Lymphoma	Serum	Increase	Prognostic biomarker	IA	Dilek et al. (2005)
	Lymphoma	Serum	Increase	Prognostic biomarker	IA	Batlle et al. (2005)
Mucin-4 (MUC4)	Oral cancer	Tumor biopsy	Decrease	Prognostic biomarker	IHC	Kohli et al. (2019)
Mucin-4 (MUC4)	Ovarian cancer	Tumor biopsy	Increase	Diagnostic biomarker	IHC	Chauhan et al. (2006)
Mucin-5AC (MUC5AC)	Pancreatic cancer	Serum	Increase	Serological biomarker	LA	Yue et al. (2009)
Neprilysin (CD10)	Renal clear cell carcinoma/kidney cancer	Serum	Increase	Invasiveness biomarker	LTQ-FT-ICR-MS, qRT-PCR	Boysen et al. (2012)
Osteopontin	Cervical cancer	Tumor biopsy	Increase	Diagnostic biomarker	IHC	Song et al. (2009)
Oviduct-specific glycoprotein (OVGP1)	Ovarian cancer	Serum	Increase	Diagnostic biomarker	WB, ELISA	Maines-Bandiera et al. (2010)
OX-2 membrane glycoprotein (CD200)	Lymphoma	Tumor biopsy	Increase	Malignancy biomarker	IHC	Dorfman and Shahsafaei (2011)
Perlecan (HSPG2)	Glioblastoma	Tumor biopsy	Increase	Prognostic biomarker	IHC	Kazanskaya et al. (2018)
Peroxiredoxin-2 (PRDX2)	Oral cancer	Saliva	Increase	Diagnostic biomarker	MS, WB	Heawchaiyaphum et al. (2018)
P-glycoprotein (ABCB1)	Oral cancer	Tumor biopsy	Increase	Prognostic biomarker	IHC	Kim et al. (2016)
Plasma kallikrein (KLKB1)	Lung cancer	Plasma	Increase	Diagnostic biomarker	LC-ESI-MS, MS, WB.	Heo et al. (2007)
Platelet-derived growth factor receptor beta (PDGFR-b)	Meningioma	Tumor biopsy	Increase	Malignancy biomarker	Microarray, IHC	Lusis et al. (2005)
Podocalyxin (PODXL)	Meningioma	Tumor biopsy	Increase	Aggressiveness biomarker	microarray	Schulten and Hussein (2019)
Podoplanin (PDPN)	Oral cancer	Tumor biopsy	Increase	Metastasis and prognostic biomarker	IHC	Yuan et al. (2006)
Poliovirus receptor (CD155)	Soft tissue sarcoma	Tumor biopsy	Increase	Recurrence biomarker	RT-PCR	Atsumi et al. (2013)
Prominin-1 (CD133)	Skin cutaneous carcinoma	Tumor biopsy	Increase	Prognostic biomarker	IHC, Microarray	Xu et al. (2016)
Proteoglycan 4 (PRG4)	Ovarian cancer	Serum	Increase	Diagnostic and prognostic biomarker	2D-DIGE, ELISA and IHC	Bi et al. (2017)
S100 calcium-binding protein A10 (GP11)	Colorectal/colon cancer	Plasma	Decrease	Malignancy biomarker	UPLC, HILIC	Doherty et al. (2018)
Securin (PTTG1)	Meningioma	Tumor biopsy	Increase	Prognostic biomarker	Microarray, RT-PCR, IHC	Schmidt et al. (2016)
Serglycin (SRGN)	Leukemia	Plasma and bone marrow	Increase	Diagnostic biomarker	ELISA, IHC	Niemann et al. (2007)
Serum paraoxonase/arylesterase 1 (PON1)	Hepatocellular carcinoma	Serum	Increase	Diagnostic and malignancy biomarker	TLC, 2D LC-ESI-MS/MS	Sun et al. (2012)
Serum paraoxonase/arylesterase 1 (PON1)	Lung cancer	Serum	Decrease	Diagnostic and prognostic biomarker	LC-MS/MS, iTRAQ, WB	Ahn et al. (2014)
Syndecan-1 (SDC1)	Breast cancer	Tumor biopsy	Increase	Aggressiveness and prognostic biomarker	IHC	Barbareschi et al. (2003)
	Cervical cancer	Tumor biopsy	Increase	Prognostic biomarker	IHC	Kim et al. (2011)
	Glioblastoma	Tumor biopsy	Increase	Prognostic biomarker	IHC	Kazanskaya et al. (2018)
	Glioma	Tumor biopsy	Increase	Malignancy biomarker	RT-PCR, IA	Watanabe et al. (2006)
	Ovarian cancer	Tumor biopsy	Increase	Prognostic biomarker	IHC	Kusumoto et al. (2010)
	Stomach/gastric cancer	Tumor biopsy	Decrease	Prognostic biomarker	IHC	Wiksten et al. (2001)
Tenascin-C (TNC)	Cervical cancer	Tumor biopsy	Increase	Diagnostic biomarker	IHC	Yang et al. (2017b)
Tyrosine-protein kinase (FYN)	Lung cancer	Serum	Decrease	Prognostic biomarker	FITC-labeled lectin staining, MALDI-TOF MS, MS/MS, WB	Hongsachart et al. (2009)
Tyrosine-protein kinase receptor UFO (AXL)	Renal clear cell carcinoma/kidney cancer	Serum	Increase	Invasiveness biomarker	LTQ-FT-ICR-MS, qRT-PCR	Boysen et al. (2012)
Vascular endothelial growth factor (VEGF)	Pituitary neuroendocrine tumor	Tumor biopsy	Increase	Serological biomarker	RT-PCR, WB	Xiao et al. (2011)
Versican (VCAN)	Cervical cancer	Tumor biopsy	Increase	Prognostic biomarker	IHC	Kodama et al. (2007)
Versican (VCAN)	Ovarian cancer	Tumor biopsy	Increase	Prognostic biomarker	IHC	Kusumoto et al. (2010)
Vimentin (VIM)	Ovarian cancer	Serum	Increase	Diagnostic and prognostic biomarker	2D-DIGE, ELISA and IHC	Bi et al. (2017)
von Willebrand factor (VWF)	Colorectal/colon cancer	Plasma	Increase	Diagnostic biomarker	Microarray, WB	Rho et al. (2018)
WAP four-disulfide core domain protein 2 (HE4)	Breast cancer	Serum	Increase	Diagnostic biomarker	ELISA	Gündüz et al. (2016)
WAP four-disulfide core domain protein 2 (HE4)	Ovarian cancer	Serum	Increase	Diagnostic biomarker	ELISA	Leung et al. (2016)
Zinc-alpha-2-glycoprotein (ZAG)	Oral cancer	Saliva	Increase	Diagnostic biomarker	MS, WB	Heawchaiyaphum et al. (2018)
	Prostate adenocarcinoma	Urine	Increase	Diagnostic biomarker	WB, IHC	Katafigiotis et al. (2012)
	Prostate adenocarcinoma	Serum	Increase	Aggressiveness biomarker	LC-MS/MS	Bondar et al. (2007)
	Soft tissue sarcoma	Tumor biopsy	Decrease	Metastasis and invasiveness biomarker	IHC, WB, RT-PCR	Liu et al. (2018b)

In terms of the reported number of glycoprotein biomarkers, hepatocellular carcinoma, lung cancer, and ovarian cancer were the leading types of cancer (Fig. 2A). Tumor biopsy, serum, and plasma (total 92%) were preferred primarily as sample origins in preclinic and clinical validation studies. A limited number of studies were carried out by the use of bone marrow, urine, blood, saliva, and ascitic fluid (Fig. 2B).

FIG. 2.

The conceptual summary of the conducted studies about glycoprotein biomarkers for the 20 different types of cancers. (A) Bar graph exhibiting the distribution of numbers of proposed glycoprotein biomarkers for all cancer types. (B) Pie chart indicating the portion of glycoprotein biomarkers detected in the different sample origins. (C) Pie chart indicating changes in the expression patterns of reported glycoprotein biomarkers. (D) Pie chart showing the distribution of different clinical applications where glycoprotein biomarkers are targeted to be used. (E) Pie chart showing the portion of major methods that were used to detect glycoprotein biomarkers. (F) Pie chart indicating the proportion of the cellular compartments where the glycoprotein biomarkers exist.

When the expression patterns of glycoproteins were examined, it was observed that ∼82% of glycoproteins have increased expressions in cancer. If the expression pattern stated as only detected, the direction of expression was not examined in that study. Also, if the expression pattern is stated as an unstable pattern, the direction of expression was considered both increased and decreased (Fig. 2C).

The reported glycoprotein biomarkers have been proposed for numerous clinical applications such as prognosis (35%), diagnosis (31%), and malignancy (12%). Moreover, only 4% of the reported glycoprotein biomarkers were suggested as therapeutic targets for the treatment of cancers using various experimental methodologies (Fig. 2D, E).

Based on the cellular compartmentalization information provided by GeneCards database (Stelzer et al., 2016) and the Metascape toolbox (Zhou et al., 2019), the majority of the reported glycoprotein biomarkers (>90%) were either localized on the plasma membrane or secreted to the extracellular region (Fig. 2F). A limited number of listed glycoprotein biomarkers were involved in the Golgi apparatus, nucleus, cytosol, and cytoskeleton. This issue may be originated from the limitations of the detection techniques and analytical sensitivity. Since the detection of tiny amounts of glycoproteins is an uneasy procedure, the observations provided above might represent the localization of the glycoproteins that are available in high amounts in the plasma membrane and/or extracellular space.

Among 88 different glycoproteins, mucin-1 and syndecan-1 were mutually associated with six different cancer types. CD44 antigen and chondroitin sulfate proteoglycan 4 were common for five and four different cancer types, respectively. Six glycoprotein biomarkers (alpha-1-acid glycoprotein, decorin, endocan, haptoglobin, mucin-16, and zinc-alpha-2-glycoprotein) were common for three different cancer types. Also, 13% of the glycoprotein biomarkers were common for two different cancer types, whereas 75% of glycoprotein biomarkers were specific to a certain cancer type.

Considering that proteins do not function alone, and major biological processes are mediated through protein interactions, we aimed to map the physical interactions of glycoprotein biomarkers and reconstruct a protein-protein interaction (PPI) network around them. For this purpose, we employed gene symbols associated with the glycoprotein markers from UniProtKB (The UniProt Consortium, 2019) and GeneCards: The Human Gene Database (Stelzer et al., 2016), and the high confidence human protein interactome (with confidence score ≥0.8) (Karagoz et al., 2016).

The reconstructed PPI network was visualized by Cytoscape (v3.5.0) (Shannon et al., 2003) (Fig. 3). The reconstructed PPI subnetwork consisted of 961 nodes (i.e., 88 glycoprotein biomarkers and their physically interacting first neighbors) and 1743 edges (i.e., physical PPIs between these proteins). The proteins that play central roles in the modular organization and information flow within the network was identified by the topological analysis (using degree metric) of the reconstructed subnetworks. The top three central proteins were the epidermal growth factor receptor (EGFR, degree = 674), the proto-oncogene c-Fyn (FYN, degree = 470), and the cyclin H (CCNH, degree = 73).

FIG. 3.

Network consisting of protein-protein interactions reconstructed around glycoprotein biomarkers. Proposed glycoprotein biomarkers are represented in larger nodes and dark gray color. As represented in the legend, nodes are sized and darkened in gradient based on the degree which glycoprotein biomarkers have.

Excess of EGFR is frequently related to high tumor grade, also causes poor treatment results, including chemotherapy and hormonal therapy resistance. EGFR overexpression has been determined as a powerful marker in head and neck, bladder, esophageal, ovarian, and cervical cancers, while it is a modest marker in gastric, endometrial, and colorectal cancers (Correia et al., 2014). Fyn has various functions associated with immune and neurological processes. Furthermore, Fyn attended in biological processes such as growth and proliferation, cell-cell adhesion, cell cycle entry, mitogenic signaling, and integrin-mediated interactions (Saito et al., 2010).

The upregulation expression pattern of Fyn was reported in several cancers, including melanoma, glioblastoma, breast cancer, prostate cancer, and squamous cell carcinoma (Elias and Ditzel, 2015). A recent study on CCNH suggested that its upregulation is associated with poor prognosis and promotes the growth of ovarian cancer (Peng et al., 2020)

To further elucidate the intertwined roles of these glycoproteins in signaling, metabolic, and disease pathways, we also performed pathway enrichment analysis through the Metascape (Zhou et al., 2019) using data from Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa et al., 2007), Gene Ontology (The Gene Ontology Consortium, 2019), and Reactome (Fabregat et al., 2018) databases.

The statistical significance of the enrichment results was represented by adjusted p-values obtained through Fisher's Exact Test and Benjamini–Hochberg correction procedure. Pathway enrichment analyses of the listed glycoproteins resulted in the following pathways (Supplementary Table S1): diseases of glycosylation, extracellular matrix (ECM) organization, matrisome-associated pathway, proteoglycans in cancer, and integrin 3 pathway, whereas gene ontology biological process enrichment analysis of glycoproteins resulted in the following terms: regulated exocytosis, platelet degranulation, regulation of cell adhesion, tissue remodeling, blood vessel development, and so on (Fig. 4A).

FIG. 4.

The enrichment results, including pathway and GO terms, of the genes encoding glycoprotein biomarkers. (A) Bar graph of top 20 enriched pathway and GO biological process terms across genes encoding glycoprotein biomarkers, which is darkened considering p-values. (B) Bar graph of top 20 enriched GO biological process terms across genes encoding glycoprotein biomarkers, which is darkened considering p-values. (C) The network representing the top 20 enriched pathways and GO biological process terms, in which nodes are darkened considering cluster ID. Nodes having the same cluster ID are close to each other. GO, gene ontology.

The two following pathways, diseases of glycosylation and proteoglycans in cancer, clearly indicated that listed glycoproteins are literally addressing the human diseases, including cancer. The response to the wounding process was also associated with cancer recently and suggested that hallmarks of cancer are also the hallmarks of wound healing (MacCarthy-Morrogh and Martin, 2020). The study reports how tissue repair and cancer have common cellular and molecular patterns that are regulated in a wound, but misregulated in cancer. Listed glycoproteins were enriched with the response to wounding process. It wounding process thought to be like as MacCarthy-Morrogh and Martin scenario, tumors can be accepted as wounds that failed to heal (Dvorak, 2015).

The ECM organization and matrisome associated pathways were also enriched (Fig. 4A). When we consider the invasion and the metastasis of the tumors, ECM is the fundamental factor that influences tumor microenvironment and migration. The ECM provides not only signaling pathways through integrins and other membrane receptors but also supporting surrounding tissue and organs (Lu et al., 2012). Cancer cells can disrupt ECM fibers by matrix metalloproteinases that cause the migration of cells through collagens and basement membranes of the blood and lymph vessels that led to tumor invasion. The invasion triggers the cytokine stimulation in nearby tissues, which is an urgent call for the recruitment of tumor-associated macrophages to stop invasion through paracrine growth factor signaling loop (Conklin et al., 2011).

The enriched pathways and processes, including regulation of exocytosis, regulation of cell adhesion, ECM organization, tissue remodeling, response to growth factor, blood vessel development, and regulation of endocytosis, were all associated with the tumor microenvironment, migration, and invasion processes. Therefore, understanding the associations between the ECM and cancer cells may unveil possible therapeutic approaches in controlling cancer (Pal et al., 2020). The interactions of each pathway and process elements are represented in Figure 4B. The intertwined elements display how these pathways and processes are closely related to each other.

The biological processes of listed glycoproteins were identified as metabolic process, cellular component organization, response to stimulus, immune system process, developmental process, signaling, locomotion, cell proliferation, and cell killing (Fig. 4C). These processes are also associated with cancer hallmarks, primarily the tumor microenvironment, invasion, and migration.

Glycoproteins were also investigated for their tumorigenic potentials as tumor suppressors or oncogenes considering the information provided by Lawrence et al. (2014), which presents a comprehensive catalog, including a total of 1217 tumor suppressor and 803 oncogenes. Among the glycoprotein biomarkers, 6 of them were reported as tumor suppressors (CDH1, DCN, DMBT1, GPC3, HRG, and VIM) and 8 glycoproteins as oncogenes (AXL, CDH1, EGFR, FYN, MUC1, MUC4, PTTG1, and VIM).

Moreover, screening of the drug candidates considering these glycoproteins as potential drug targets through gene-drug interactions provided through Drugbank (Wishart et al., 2018), DGIdb (Edwards et al., 2011), GeneXpharma (Turanlı et al., 2017), and Comparative Toxicogenomics Database (CTD) (Davis et al., 2009) resulted in antineoplastic agents (docetaxel, fluorouracil, sorafenib, bevacizumab, capecitabine, carboplatin, cisplatin, dasatinib, and doxorubicin), corticosteroids (dexamethasone and methylprednisolone), immune system suppressors (methotrexate, tacrolimus, and prednisone), an alkaloid (colchicine), a cardioprotective agent (dexrazoxane), a monoclonal antibody drug (rituximab), and antitumor antibiotics (streptozocin and mitomycin) as potential repositioned drug candidates (Supplementary Table S2). These drugs mostly interacted with the plasma membrane and extracellular space glycoproteins.

Future Perspectives

Many studies discovered the glycan alterations and glycoprotein biomarkers in different cancer types as we reviewed. Except for cancer, glycans are also highly dynamic and alterations are observed in other common complex diseases as well. For instance, the aberrant immunoglobulin G (IgG) glycome composition or changes in IgG glycosylation were associated with the systemic lupus erythematosus (Vučković et al., 2015) and the remission status of rheumatoid arthritis (Sebastian et al., 2016).

Moreover, N-glycosylation of IgG was proposed as a potential biomarker of Parkinson's disease (Russell et al. 2017) and was associated with both chronological and biological aging (Yu et al., 2016), as well as the pathogenesis of hypertension (Wang et al., 2016b). Liu et al. (2018c) reported that loss of galactose and sialic acid impresses dyslipidemia development. The potential of variations in the composition of the N-glycome as biomarkers for several diseases such as metabolic syndrome (Lu et al., 2011), type 2 Diabetes Mellitus (Ge et al., 2018), and hematological cancers (Lauc et al., 2013) was already reported providing evidence for considering glycan alterations and glycoprotein biomarkers as potential candidates for the prognoses and treatment of virtually every disease.

The main bottleneck in glycan-based studies is using tumor biopsies as samples (Tables 1, 2). Since invasive operations are not patient friendly, there is an urgent need for noninvasive alternatives. Human serum, plasma, feces, hair, saliva, and urine hold the promise of being noninvasive samples for the identification of novel biomarkers, which can be detected in routine clinical diagnosis, prognosis, and targeted cancer therapies. Using urine, saliva, or blood-based test for early detection of cancer is a longstanding dream of cancer scientists. Such a discovery would make moving forward the war against cancer and would be more patient friendly.

The most common bottleneck in cancer research that scientists have been faced with is the elucidation of stable biomarkers, which can be routinely detectable in easily collectible samples mentioned above (Ren et al., 2020). There are just a few studies that consider noninvasive glycan biomarkers for distinguishing early stages of the diseases (Gressner et al., 2009; Qin et al., 2017; Yamasaki et al., 2015). Although a limited number of studies were conducted about noninvasive glycan biomarkers, it is not quixotic to dream about a future glycodiagnostic biomarker discovered from a single hair, urine sample, or saliva, since it is now possible to integrate the data from genomics, proteomics, metabolomics, and glycomics with pathway analysis.

Since all cells have a dense glycocalyx, the most secreted proteins are glycosylated (Svarovsky and Joshi, 2014). This common nature of glycan abundances highlights the crucial roles performed by them not only for normal morphological and physiological duties but also for abnormal disease-related pathological processes like cancer (Ohtsubo and Marth, 2006). Glycosylation is constituted on surfaces of the cell, which is the primary point of contact in cellular interactions through extracellular matrices (Adamczyk et al., 2012) when any disturbances occur. Hence, the causes of disease states on glycan biosynthesis may be more conspicuous than disease-associated aberrations in proteins. Thus, gaining knowledge about glycans and glycosylation patterns has become a promising window that allows researchers to identify potential biomarker candidates for different cancer types.

It is well established that in tumor cells, glycans on cell surface glycoproteins are essentially altered, and as a result, those cells have a diverse ‘glycan coat’ when compared to healthy cells. Therefore, pioneers of the glycobiology field should envisage the construction of a broad atlas of cell-specific glycomes for the whole body of the human as providing a database or the amalgamation of protein glycosylation features in gene and protein databases. Access to databases for glycomics and glycoproteomics is essential for the glycan-based code of human disease research (Schjoldager et al., 2020). Genomic and proteomic databases can be searched for the expression of glycosylation-related genes to unveil glycosylation signatures related to cancer development, progression, invasion, and metastasis (Rodriguez et al., 2018).

Although aberrant glycosylation has arisen as a hallmark of cellular oncogenesis, the understanding of a tumor-associated glycome is fundamentally associated with the elucidation of the metastasis-associated processes (Häuselmann and Borsig, 2014). The translational and clinical applications of altered glycan structures in cancer have not yet been fully achieved (Cagnoni et al., 2016). However, in recent years, lectins were studied for deciphering the glycome and cracking the glyco-code for cancer therapy as a logical target.

There are effortful studies that shed light on the glycol-code of cancers with the focus of developing therapeutic strategies using lectin–glycan interactions (Dalziel et al., 2014). If the increasing amount of data on glycomics and glycoproteomics can be combined with the advances of other omics technologies, it will have a major impact on revealing therapeutic targets and patient stratification. Also, glycoengineering of the glycan structures is another important issue that should be mentioned. The re-designing of the known glycans through glycoengineering strategies can serve as the production of therapeutic proteins, including antibody-based therapeutics and immunotherapeutics (Buettner et al., 2018; Dicker and Strasser, 2015; Jefferis, 2009).

As cancer researchers make great strides for detection, prognosis, and therapeutic targeting, the amount of data will be increased exponentially, which allows us to see advanced analysis using artificial intelligence (AI) and machine learning (ML). Researchers have started to use AI algorithms to gather not only information on glycans but also glycan-based modifications (glycosylation, fucosylation, sialylation, etc.) to elucidate a comprehensive prediction of the detection of cancer.

Currently, glycan marker discovery is carried out mostly by analyzing high-throughput data producer platforms such as MS types and lectin microarrays (Lai et al., 2018; Manimala et al., 2006). However, these approaches are high dimensional and have chaotic patterns with a high level of noise sourced from measurements, disease heterogeneity, and biological variability (Tang et al., 2010). Attuned computational methods are needed to overcome a problem from such high-throughput data producer platforms in the way of discovery of biomarkers.

ML methods such as support vector machines (SVM) and deep neural networks have been employed for the marker selection in the above-mentioned methods (Hwang et al., 2020; Lai et al., 2018; Tang et al., 2010). This review might provide the learning data for such methods. The success of future glycodiagnostics will eventually base upon the utility of new and improved artificial intelligence methods. The improvement of these techniques might reveal better identification of crucial cancer biomarkers and accelerate the unveiling of glycodiagnostics, glycoprognostics, and glycotherapeutics.

Concluding Remarks

As a final remark, it seems that glycan alterations and glycobiomarkers will be outstanding for efficient biologics to personalized medicine treatments of cancer in the context of heterogeneity and specificity/sensitivity in the near future. We have summarized the major impacts of glycosylation, fucosylation, and sialylation alterations on understanding cancers. Glycoproteins have been valuable vehicles not only for the elucidation of pathomechanisms of the cancers but also for developing therapeutic strategies for cancers.

The system biomedicine approach believes that there is a holistic harmony of many crucial life events at different levels (i.e., nucleic acids, proteins, or metabolites) and argues with the traditional approaches that believe in single gene/single protein/single molecule. With this context, glycan biomarkers can be potential candidates, but a more robust cluster that is constituted from different omics level biomarkers is needed to eliminate patient variability, biomarker characteristics from many ethnicities, and differences coming from daily life routines.

From a translational medicine point of view, improvements in the new gene-editing methods are now providing more extensive analyses and genome-wide scans of functions of glycans to elucidate possible glycan interactions and functions and engineer design of glycoproteins that allow discovering and evaluating advanced drug and vaccine designs. If glycomics information can be combined with the other omics levels (i.e., transcriptomics, proteomics, and metabolomics), the personalized medicine strategies that will be applied to the patients will be splendid. The integration of the knowledge that comes from different omics levels will be amalgamated to yield clinically practicable glycan-based cancer diagnostics and therapeutics. Improvements in this intriguing field are eagerly awaited.

Footnotes

Acknowledgments

The scholarships under the TUBITAK 2211-C Doctoral Fellowship Program provided to Medi Kori and Busra Aydin; the scholarships under the TUBITAK 2211-A Doctoral Fellowship Program provided to Gizem Gülfidan Yıldız; and the scholarships under the YOK 100/2000 Doctoral Fellowship Program provided to Medi Kori, Busra Aydin, and Gizem Gülfidan Yıldız are greatly acknowledged.

Author Disclosure Statement

The authors declare they have no conflicting financial interests.

Funding Information

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Supplementary Material

Abbreviations Used

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