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Citation: Pinkeova, A.; Kosutova, N.;
Jane, E.; Lorencova, L.; Bertokova, A.;
Bertok, T.; Tkac, J. Medical Relevance,
State-of-the-Art and Perspectives of
“Sweet Metacode” in Liquid Biopsy
Approaches. Diagnostics 2024,14, 713.
https://doi.org/10.3390/
diagnostics14070713
Academic Editor: Edward J. Pavlik
Received: 7 February 2024
Revised: 23 March 2024
Accepted: 26 March 2024
Published: 28 March 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
diagnostics
Review
Medical Relevance, State-of-the-Art and Perspectives of “Sweet
Metacode” in Liquid Biopsy Approaches
Andrea Pinkeova
1,2
, Natalia Kosutova
1
, Eduard Jane
1
, Lenka Lorencova
1
, Aniko Bertokova
2
, Tomas Bertok
1,
*
and Jan Tkac 1, 2, *
1Institute of Chemistry, Slovak Academy of Sciences, Dubravska cesta 9, 845 38 Bratislava, Slovakia;
andrea.pinkeova@glycanostics.com (A.P.); natalia.kosutova@savba.sk (N.K.); eduard.jane@savba.sk (E.J.);
lenka.lorencova@savba.sk (L.L.)
2Glycanostics, Ltd., Kudlakova 7, 841 08 Bratislava, Slovakia; aniko.bertokova@glycanostics.com
*Correspondence: tomas.bertok@savba.sk (T.B.); jan.tkac@glycanostics.sk (J.T.)
Abstract: This review briefly introduces readers to an area where glycomics meets modern onco-
diagnostics with a focus on the analysis of sialic acid (Neu5Ac)-terminated structures. We present
the biochemical perspective of aberrant sialylation during tumourigenesis and its significance, as
well as an analytical perspective on the detection of these structures using different approaches for
diagnostic and therapeutic purposes. We also provide a comparison to other established liquid biopsy
approaches, and we mathematically define an early-stage cancer based on the overall prognosis and
effect of these approaches on the patient’s quality of life. Finally, some barriers including regulations
and quality of clinical validations data are discussed, and a perspective and major challenges in this
area are summarised.
Keywords: cancer; glycan; sialic acid; diagnostics; liquid biopsy
1. Current Challenges in Oncodiagnostics
Metastatic cancer remains, according to the WHO, one of the leading causes of global
deaths alongside ischaemic heart disease and stroke [
1
]. Moreover, according to the Centre
for Disease Control and Prevention, 70% of all medical decisions depend on laboratory
tests results [
2
]; hence, the gold standard in oncodiagnostics continues to be histological
evaluation of biopsied tissue. During the COVID-19 pandemic, we witnessed a decrease
in routine access to clinical diagnostics due to quarantine restrictions, and it is very likely
that another pandemic will occur in the future. Also, for many indications involving
imaging techniques such as (multiparametric) magnetic resonance imaging (mp)MRI, long
waiting periods and energy demands as well as CO
2
production are significant factors [
3
].
Alternative approaches involving non-invasive, affordable and reliable cancer-specific
tests are of high importance. Liquid biopsy tests offer promising solutions to the above-
mentioned challenges, being able to provide information about the presence of clinically
significant cancer due to the analysis of cancer-specific cell-free or circulating tumour DNA
(cf/ctDNA), miRNA, small metabolites, proteins, extracellular vesicles and/or circulating
tumour cells (CTC) [
4
]. Although often described as highly cancer-specific, the tissue
specificity of these biomarkers might be questionable; thus, the same holds true for their
use in a population-wide screening.
Post-translational modifications of proteins, mainly aberrant glycosylation [
5
], rep-
resent a promising biomarker, potentially integrating both of these specificities with a
typical tissue-specific marker—PSA (prostate-specific antigen) in the case of prostate can-
cer (PCa) [
6
,
7
]. Ethical questions related to the use of laboratory-developed tests not yet
involved in clinical guidelines should be considered. Any discomfort for the patient in
the case of overdiagnosis/overtreatment (physical and mental, e.g., PSAdynia) [
8
] or false
negativity/positivity also arising from biological variance in the presence/level of the
Diagnostics 2024,14, 713. https://doi.org/10.3390/diagnostics14070713 https://www.mdpi.com/journal/diagnostics
Diagnostics 2024,14, 713 2 of 22
biomarker should be investigated in detail. Primarily, the quality of life of the patient (QoL)
is of the highest importance. The QoL parameter is strongly affected not just by certain con-
ditions, but also by interventions with long-term consequences (as we propose in Figure 1).
This pertains especially for asymptomatic indolent patients with PCa treated by radical
prostatectomy, leading to erectile dysfunction and incontinence [
9
]. Overdiagnosis using
ultrasensitive methods and markers might lead to unnecessary biopsies, such as in the case
of PCa or breast cancers (BCa), creating a future barrier between patients and clinicians
due to an unpleasant experience. Hence, setting a proper threshold for the analysis of
biomarkers by every diagnostic method is a crucial step [
10
]. For innovative approaches,
reproducibility and transparency in basic research, accessibility of retrospective clinical
samples for some indications (such as early pancreatic cancer patient serum samples for
pre-clinical validations) [
11
], recruitment for prospective oncological trials or (governmen-
tal) funding opportunities due to various commercial risks are also significant concerns in
translational research [12–14].
Diagnostics 2024, 14, x FOR PEER REVIEW 2 of 22
negativity/positivity also arising from biological variance in the presence/level of the bi-
omarker should be investigated in detail. Primarily, the quality of life of the patient (QoL)
is of the highest importance. The QoL parameter is strongly affected not just by certain
conditions, but also by interventions with long-term consequences (as we propose in Fig-
ure 1). This pertains especially for asymptomatic indolent patients with PCa treated by
radical prostatectomy, leading to erectile dysfunction and incontinence [9]. Overdiagnosis
using ultrasensitive methods and markers might lead to unnecessary biopsies, such as in
the case of PCa or breast cancers (BCa), creating a future barrier between patients and
clinicians due to an unpleasant experience. Hence, seing a proper threshold for the anal-
ysis of biomarkers by every diagnostic method is a crucial step [10]. For innovative ap-
proaches, reproducibility and transparency in basic research, accessibility of retrospective
clinical samples for some indications (such as early pancreatic cancer patient serum sam-
ples for pre-clinical validations) [11], recruitment for prospective oncological trials or
(governmental) funding opportunities due to various commercial risks are also significant
concerns in translational research [12–14].
Figure 1. Graphic presentation of quality of life (QoL) parameter as a function of age. QoL naturally
decreases with greater age due to various accumulated conditions and poor health. Short-term de-
creases can also be seen (common injuries or illnesses) during risk development period (RD, ending
with dashed line). During the screening period, if an early stage or pre-cancerotic stage is diagnosed,
overdiagnosis and overtreatment (starting with a grey circle) might significantly and permanently
decrease patients’ QoL (dash-doed line), i.e., in cases of a radical prostatectomy leading to erectile
dysfunction and incontinence. In-time diagnostics and subsequent intervention (starting with a
black circle) yield a higher QoL for a longer time period, quantifiable by the area under the curve.
Future Decades and Human Health
In contrast with the above-mentioned PCa which occurs at a greater age and where,
in many cases, only active surveillance is recommended, since the tumour is mostly local-
ised and slow-growing [15], other oncological diseases require a prompt intervention.
This pertains, for example, to small-cell lung carcinoma, triple-negative BCa, pancreatic
ductal adenocarcinoma, glioblastoma and advanced ovarian cancer [16]. As in the case of
PCa (e.g., urinal exosomal non-coding RNA) [17], for a more aggressive and rapidly
spreading lung cancer, liquid biopsy tests are being developed and validated [18]. This
holds great promise for their future use in clinical diagnostics—for the benefit of patients,
Figure 1. Graphic presentation of quality of life (QoL) parameter as a function of age. QoL naturally
decreases with greater age due to various accumulated conditions and poor health. Short-term
decreases can also be seen (common injuries or illnesses) during risk development period (RD, ending
with dashed line). During the screening period, if an early stage or pre-cancerotic stage is diagnosed,
overdiagnosis and overtreatment (starting with a grey circle) might significantly and permanently
decrease patients’ QoL (dash-dotted line), i.e., in cases of a radical prostatectomy leading to erectile
dysfunction and incontinence. In-time diagnostics and subsequent intervention (starting with a black
circle) yield a higher QoL for a longer time period, quantifiable by the area under the curve.
Future Decades and Human Health
In contrast with the above-mentioned PCa which occurs at a greater age and where, in
many cases, only active surveillance is recommended, since the tumour is mostly localised
and slow-growing [
15
], other oncological diseases require a prompt intervention. This
pertains, for example, to small-cell lung carcinoma, triple-negative BCa, pancreatic ductal
adenocarcinoma, glioblastoma and advanced ovarian cancer [
16
]. As in the case of PCa
(e.g., urinal exosomal non-coding RNA) [
17
], for a more aggressive and rapidly spreading
lung cancer, liquid biopsy tests are being developed and validated [
18
]. This holds great
promise for their future use in clinical diagnostics—for the benefit of patients, but also for
Diagnostics 2024,14, 713 3 of 22
healthcare systems, as the costs per cancer patient might be almost doubled if cancer is
diagnosed at a later stage [19,20].
In the current scientific literature, the glycoprofiling of proteins (i.e., partially or
completely identifying a glycan structure covalently attached to a protein backbone) is
not usually listed among “liquid biopsy” approaches, despite the fact that such assays
can indicate that attention needs to be paid to a specific organ or a group of organs and
identify the process of tumourigenesis at an early stage. Another important aspect of liquid
biopsy is to provide information about the stage of the disease and a prognosis for the
patient’s survival, possibly a design for a personalised therapy plan [
21
]. Herein we present
the current state-of-the-art for glycoprofiling of novel glycoproteomic biomarkers, mainly
focusing on sialic acids (N-acetylneuraminic acid—Neu5Ac—and N-glycolylneuraminic
acid—Neu5Gc—respectively) in early cancer diagnostics as a possible multi-omics liquid
biopsy approach for the analysis of prevalence and also for more aggressive cancer types,
such as non-small-cell lung cancer [
22
,
23
]. The importance of these methods is further
illustrated by the fact that the global incidence and mortality for different cancer diseases is
predicted to grow in the upcoming years. The predicted growth (in %) from 2020 to 2040
for the commonest solid tumours is shown in Figure 2[24].
Diagnostics 2024, 14, x FOR PEER REVIEW 3 of 22
but also for healthcare systems, as the costs per cancer patient might be almost doubled if
cancer is diagnosed at a later stage [19,20].
In the current scientic literature, the glycoproling of proteins (i.e., partially or com-
pletely identifying a glycan structure covalently aached to a protein backbone) is not
usually listed among “liquid biopsy” approaches, despite the fact that such assays can
indicate that aention needs to be paid to a specic organ or a group of organs and iden-
tify the process of tumourigenesis at an early stage. Another important aspect of liquid
biopsy is to provide information about the stage of the disease and a prognosis for the
patient’s survival, possibly a design for a personalised therapy plan [21]. Herein we pre-
sent the current state-of-the-art for glycoproling of novel glycoproteomic biomarkers,
mainly focusing on sialic acids (N-acetylneuraminic acid—Neu5Ac—and N-glycolylneu-
raminic acid—Neu5Gc—respectively) in early cancer diagnostics as a possible multi-om-
ics liquid biopsy approach for the analysis of prevalence and also for more aggressive
cancer types, such as non-small-cell lung cancer [22,23]. The importance of these methods
is further illustrated by the fact that the global incidence and mortality for dierent cancer
diseases is predicted to grow in the upcoming years. The predicted growth (in %) from
2020 to 2040 for the commonest solid tumours is shown in Figure 2 [24].
Figure 2. Commonest types of solid tumours (except for lymphomas and skin cancers) and their
expected increase in dierent parameters, i.e., predicted incidence (blue) and predicted mortality
(red) according to GLOBOCAN data between 2020 and 2040 (in %). The darker the colour, the more
the respective cancer is predicted to increase for the respective parameter. Data taken from ref. [24].
Liquid biopsy, oering “a universe in a vial of blood” as described in ref. [25], uses
only a minute amount of sample, and the greatest risk it possesses is a possibility of false
positive and false negative results, leading to stress for the patient, further testing and a
possibility for further development of cancer, respectively. These risks, however, are as-
sociated with every single laboratory method; hence, only by combining analysis of dif-
ferent biomarkers, methods and approaches, is suciently high diagnostic accuracy
achieved for resectable tumours [26,27]. Compared to imaging methods currently in use,
the result of a laboratory test is mostly quantitative, measured objectively and evaluated
as a part of a larger pool of analytes available. Medical images, being very complex, are
interpreted by humans, and thus are subjective [28]. In this review, we aim to critically
evaluate the possibilities of using sialic acid-terminated glycans (complex, often branched
Figure 2. Commonest types of solid tumours (except for lymphomas and skin cancers) and their
expected increase in different parameters, i.e., predicted incidence (blue) and predicted mortality
(red) according to GLOBOCAN data between 2020 and 2040 (in %). The darker the colour, the more
the respective cancer is predicted to increase for the respective parameter. Data taken from ref. [24].
Liquid biopsy, offering “a universe in a vial of blood” as described in ref. [
25
], uses
only a minute amount of sample, and the greatest risk it possesses is a possibility of false
positive and false negative results, leading to stress for the patient, further testing and
a possibility for further development of cancer, respectively. These risks, however, are
associated with every single laboratory method; hence, only by combining analysis of
different biomarkers, methods and approaches, is sufficiently high diagnostic accuracy
achieved for resectable tumours [
26
,
27
]. Compared to imaging methods currently in use,
the result of a laboratory test is mostly quantitative, measured objectively and evaluated
as a part of a larger pool of analytes available. Medical images, being very complex, are
interpreted by humans, and thus are subjective [
28
]. In this review, we aim to critically
evaluate the possibilities of using sialic acid-terminated glycans (complex, often branched
saccharides) in modern liquid biopsy approaches as well as the analytical platforms suitable
for routine screening in clinical practice.
Diagnostics 2024,14, 713 4 of 22
2. “Unpredictable” Metacode of Life—Biochemistry of Sialoglycoconjugates
Glycosylation is the commonest co- and post-translational modification of proteins in
eukaryotic cells [
29
–
32
]. At the same time, the majority of extracellular and serum proteins
are glycosylated, as well as the most commonly used oncomarkers, such as CEA (carci-
noembryonic antigen), AFP (
α
-fetoprotein) or PSA (prostate-specific antigen). Through en
bloc attachment, trimming/extension and terminal glycosylation in Golgi, glycosylation
is driven by glycan-modifying enzymes (GME), namely hydrolases and transferases [
33
].
The final glycan structure is, however, not possible to predict directly from a genetic code,
but rather is dependent on several factors, such as (i) expression of GME, (ii) availability of
precursors (activated monosaccharides) and (iii) Golgi
fragmentation [34,35]
. Availability
of the activated precursors (nucleotide sugars) is closely connected to glucose metabolism,
which in turn, is known to be significantly shifted in cancer due to the Warburg effect
(observed by Otto Warburg et al. in about 1920 and published later) [
36
]. The Warburg
effect, a shift toward anaerobic metabolism and increased lactate production in the presence
of oxygen and functional mitochondria, is inefficient in generating ATP as an energy-source-
providing molecule; however, it may store unrespirated carbon inside the cells for further
growth [
37
]. Furthermore, lactate promotes many essential processes, such as invasion,
immune escape (acidification of extracellular pH to 6.3–6.9, leading to apoptosis of natural
killer and natural killer T cells), metastasis and angiogenesis [
38
,
39
]. Glucose uptake (by
glucose transporters) is strongly increased in cancer cells, leading to possible starvation
in neighbouring cells [
40
], while changes in mTOR, p53 or KRAS signalling lead to an
accumulation of glycolysis intermediate products [
41
,
42
], shifting metabolism towards the
pentose phosphate pathway (generating precursors for nucleic acid synthesis and mitiga-
tion of ROS, reactive oxygen species, effects) [
43
] and hexosamine synthesis [
44
,
45
]. The
latter might affect the glycan composition and shape, in combination with the expression
and availability/activity of GMEs. In addition, pyruvate kinase PKM2 (muscle isoform)
catalysing a rate-limiting step in glycolysis (conversion of phosphoenol to pyruvate) has
been shown to be translocated into the nucleus, where it serves as a kinase for other protein
targets, promoting several pathological processes [
46
,
47
]. A schematic presentation of these
mechanisms and their effect on aberrant glycosylation is shown in Figure 3.
Three Levels of Regulation—Genes, Substrates and Golgi
Unlike proteins, the glycan structure cannot be directly predicted from any template,
such as the genetic code, but depends on many factors directly connected to genetic factors
such as GME expression, hence the denotation “metacode”. Often described as the third
alphabet of molecular biology, it remains unclear whether these carriers of biological codes
yield information which is universal in determining an organism’s fitness [
48
]. An abun-
dant component of these glycans in animals, decorating their terminal structures, is sialic
acid. Although sialic acids are actually a group of nine-carbon saccharides derived from
neuraminic acid, these days, the term is most commonly used as a synonym for Neu5Ac
(2-keto-5-acetamido-3,5-dideoxy-D-glycero-D-galactononulopyranos-1-onic acid) found in
humans. As posited by Darwin or Huxley, great apes are our closest evolutionary cousins,
but there are some minor differences at the genetic level [
49
]. One of these changes com-
prises an inactivating mutation in the CMP-N-acetylneuraminic acid hydroxylase (CMAH)
gene, resulting in accumulation of the precursor of Neu5Gc (NGNA) synthesis in humans.
Neu5Gc present in humans is thus closely linked to increased dietary uptake [
50
,
51
], for in-
stance, present in red meat [
52
] and possibly other food matrices. Food glycomics research,
especially the interaction of the human gut microbiome with glycome contained in food
matrices, such as milk, is currently on the rise [
53
]. Sialic acids in vertebrates are abundant
on cell surfaces, being an integral part of membrane-bound mucins or gangliosides (some
of which are even disialylated, such as GD2 (tumour-associated glycan epitope), recently
identified as a BCa stem cell marker that promotes tumorigenesis) [
54
], contributing to
cell–cell communication and/or adhesion processes. Sialic acids bound to proteins in
different ways also form a common part of soluble glycoproteins released into the envi-
Diagnostics 2024,14, 713 5 of 22
ronment. Neu5Gc, despite its negative effects on human health such as correlation with
chronic inflammation-mediated autoimmune diseases or cancer (e.g., BCa or ovarian can-
cers) [
55
–
57
], continues to be detected on various approved and marketed biotherapeutics
or xenografts [
58
]. The glycoengineering of therapeutic glycoproteins is, accordingly, a criti-
cal part of novel drug development and needs to be considered prior to any future approval
of cytokines, enzymes or immunoglobulins for therapeutic purposes [
59
]. Furthermore,
sialic acid-binding lectins and modifying enzymes, sialoside inhibitors and anti-Siglecs
(sialic acid-binding immunoglobulin-like lectins) antibodies are known to be druggable
targets for immunomodulatory purposes [
60
]. These facts highlight the relevance of sialic
acids in human health and of monitoring the changes in sialic acid content and structure in
different physiological (ageing) as well as pathological (inflammation, infection, cancer)
processes for a better understanding and potential use in future clinical diagnostics.
Diagnostics 2024, 14, x FOR PEER REVIEW 5 of 22
[53]. Sialic acids in vertebrates are abundant on cell surfaces, being an integral part of
membrane-bound mucins or gangliosides (some of which are even disialylated, such as
GD2 (tumour-associated glycan epitope), recently identified as a BCa stem cell marker
that promotes tumorigenesis) [54], contributing to cell–cell communication and/or adhe-
sion processes. Sialic acids bound to proteins in different ways also form a common part
of soluble glycoproteins released into the environment. Neu5Gc, despite its negative ef-
fects on human health such as correlation with chronic inflammation-mediated autoim-
mune diseases or cancer (e.g., BCa or ovarian cancers) [55–57], continues to be detected
on various approved and marketed biotherapeutics or xenografts [58]. The glycoengineer-
ing of therapeutic glycoproteins is, accordingly, a critical part of novel drug development
and needs to be considered prior to any future approval of cytokines, enzymes or immu-
noglobulins for therapeutic purposes [59]. Furthermore, sialic acid-binding lectins and
modifying enzymes, sialoside inhibitors and anti-Siglecs (sialic acid-binding immuno-
globulin-like lectins) antibodies are known to be druggable targets for immunomodula-
tory purposes [60]. These facts highlight the relevance of sialic acids in human health and
of monitoring the changes in sialic acid content and structure in different physiological
(ageing) as well as pathological (inflammation, infection, cancer) processes for a beer
understanding and potential use in future clinical diagnostics.
Figure 3. Schematic presentation of glycolytic pathway alteration during tumourigenesis as a part
of Warburg effect. This leads to increased CMP-Neu5Ac levels (precursor) and together with certain
sialyltransferases (STs) results in aberrant sialylation of glycoproteins. Fru = fructose; Glc = glucose;
GLUT = glucose transporter; Man = mannose; Neu5Ac = N-acetylneuraminic acid (NANA); PKM2
= pyruvate kinase muscle isoenzyme 2.
In eukaryotic cells, Neu5Ac is synthesised in the cytosol from hexosamine pathway
precursors, transferred to the nucleus for activation by enzyme CMP-Neu5Ac synthetase
and, in the form of CMP-Neu5Ac, subsequently transferred to the Golgi [61]. Here, sialyl-
transferases alongside other glycosyltransferases form differently glycosylated
Figure 3. Schematic presentation of glycolytic pathway alteration during tumourigenesis as a
part of Warburg effect. This leads to increased CMP-Neu5Ac levels (precursor) and together
with certain sialyltransferases (STs) results in aberrant sialylation of glycoproteins. Fru = fruc-
tose;
Glc = glucose
; GLUT = glucose transporter; Man = mannose; Neu5Ac = N-acetylneuraminic
acid (NANA); PKM2 = pyruvate kinase muscle isoenzyme 2.
In eukaryotic cells, Neu5Ac is synthesised in the cytosol from hexosamine pathway pre-
cursors, transferred to the nucleus for activation by enzyme CMP-Neu5Ac synthetase and,
in the form of CMP-Neu5Ac, subsequently transferred to the Golgi [
61
]. Here, sialyltrans-
ferases alongside other glycosyltransferases form differently glycosylated glycoconjugates.
The expression of these sialyltransferases might be dysregulated as a result of tumorigene-
sis [
62
]. Overexpressed sialoglycans, such as sialylated Lewis antigens (sLe
X
and sLe
A
),
Diagnostics 2024,14, 713 6 of 22
are known to promote tumour metastasis. Disialyl-T glycan, possibly regulated on the
surface of cancer cells by dysregulated oncogene MYC, leads to immune evasion [
63
,
64
].
These glyco-immune checkpoints suppressing anti-tumour reactivity by interacting with
immunoregulatory Siglec receptors on myeloid and lymphoid immune cells are of great
importance in cases of glycosylated CD24 or CD47, being a phagocytic inhibitor, e.g., for
cancer stem cells [
65
,
66
]. Figure 4shows schematic changes in N- and O-glycosylation
accompanying tumour formation and progression. Sialylation and hypersialylation of-
ten lead to immune system processes’ modulation, as in the case of factor H recognising
sialoconjugates and modulating complement activation [
67
], or the presence of
α
2,8-Sia
containing structures protecting a tumour from the cytotoxic effects of NK (natural killer)
cells [
68
]. Truncated sialylated O-glycans, on the other hand, were proposed as a target
for novel anti-cancer immunotherapy; however, the main obstacle in this area is a low
specificity and cross-reactivity [
69
]. There is a fourth, much less common and known sialic
acid linkage—
α
2,9-linked sialic acid—detailed by Miyata et al. in 2006, found in nature as
a sulphated polysialic chain isolated from sea urchin sperm flagella glycoprotein denoted
as flagellasialin [70].
Diagnostics 2024, 14, x FOR PEER REVIEW 6 of 22
glycoconjugates. The expression of these sialyltransferases might be dysregulated as a re-
sult of tumorigenesis [62]. Overexpressed sialoglycans, such as sialylated Lewis antigens
(sLe
X
and sLe
A
), are known to promote tumour metastasis. Disialyl-T glycan, possibly reg-
ulated on the surface of cancer cells by dysregulated oncogene MYC, leads to immune
evasion [63,64]. These glyco-immune checkpoints suppressing anti-tumour reactivity by
interacting with immunoregulatory Siglec receptors on myeloid and lymphoid immune
cells are of great importance in cases of glycosylated CD24 or CD47, being a phagocytic
inhibitor, e.g., for cancer stem cells [65,66]. Figure 4 shows schematic changes in N- and
O-glycosylation accompanying tumour formation and progression. Sialylation and hyper-
sialylation often lead to immune system processes’ modulation, as in the case of factor H
recognising sialoconjugates and modulating complement activation [67], or the presence
of α2,8-Sia containing structures protecting a tumour from the cytotoxic effects of NK
(natural killer) cells [68]. Truncated sialylated O-glycans, on the other hand, were pro-
posed as a target for novel anti-cancer immunotherapy; however, the main obstacle in this
area is a low specificity and cross-reactivity [69]. There is a fourth, much less common and
known sialic acid linkage—α2,9-linked sialic acid—detailed by Miyata et al. in 2006, found
in nature as a sulphated polysialic chain isolated from sea urchin sperm flagella glycopro-
tein denoted as flagellasialin [70].
Figure 4. Schematic presentation of N-(left) and O-glycans (right) detected during tumourigenesis.
From a common biantennary complex type N-glycan, different structures are derived, such as (A)
α2,3-linked Sia, (B) Sia aached to LacdiNAc (sialyllactose diamine), (C) desialylated structure with
lactosamine repeats, (D) α2,8-linked polysialic acids and/or (E) antennary fucosylation. O-glycans,
on the other hand, are truncated during tumourigenesis, leading to the occurrence of Tn antigen
(bare GalNAc structure) or (A) sTn, (B) sT antigen and (C) sLewis antigens A or X.
Except for sialic acids, heparan sulphates make up the outermost part of cellular sur-
faces, carrying a negative charge involved in a membrane fixation of various ligands, in-
cluding viral particles [71]. For SARS-CoV-2 it was shown, however, that neuraminidase
treatment increased the ACE2–spike protein interaction; hence, sialic acids might play an
important role in the biorecognition of SARS-CoV-2 similar to the sialic acid-triggered
recognition of the influenza virus [72]. Sulphated glycans aached to a protein backbone,
such as syndecans or glypicans; proteoglycans, members of the so-called heparan sul-
phate proteoglycans family, are well-known examples of negative charge carriers with an
impact on viral adsorption and penetration, but also for cancer cell proliferation and
growth [73,74], even inducing a pro-migratory effect in BCa cell lines due to the presence
of a positive charge in the ligand structures [75]. Other examples of sialylated glycans
assisting the inactivation of CD4+ T cells and macrophages, inducing a tolerogenic effect
Figure 4. Schematic presentation of N-(left) and O-glycans (right) detected during tumourigenesis.
From a common biantennary complex type N-glycan, different structures are derived, such as (A)
α
2,3-
linked Sia, (B) Sia attached to LacdiNAc (sialyllactose diamine), (C) desialylated structure with
lactosamine repeats, (D)
α
2,8-linked polysialic acids and/or (E) antennary fucosylation.
O-glycans
,
on the other hand, are truncated during tumourigenesis, leading to the occurrence of Tn antigen (bare
GalNAc structure) or (A) sTn, (B) sT antigen and (C) sLewis antigens A or X.
Except for sialic acids, heparan sulphates make up the outermost part of cellular
surfaces, carrying a negative charge involved in a membrane fixation of various ligands,
including viral particles [
71
]. For SARS-CoV-2 it was shown, however, that neuraminidase
treatment increased the ACE2–spike protein interaction; hence, sialic acids might play
an important role in the biorecognition of SARS-CoV-2 similar to the sialic acid-triggered
recognition of the influenza virus [
72
]. Sulphated glycans attached to a protein backbone,
such as syndecans or glypicans; proteoglycans, members of the so-called heparan sulphate
proteoglycans family, are well-known examples of negative charge carriers with an impact
on viral adsorption and penetration, but also for cancer cell proliferation and growth [
73
,
74
],
even inducing a pro-migratory effect in BCa cell lines due to the presence of a positive
charge in the ligand structures [
75
]. Other examples of sialylated glycans assisting the
inactivation of CD4+ T cells and macrophages, inducing a tolerogenic effect in immune
Diagnostics 2024,14, 713 7 of 22
cells and potentiating cancer metastases, are tri- and tetra-antennary N-glycans, Tn and
sTn antigens present in O-glycans, and poly-N-acetyllactosamine chains present in N- and
O-glycans, respectively [76].
3. Techniques for Mapping the Human Oncosialome
Analysis of disialyl-T and glycans with similar functions, i.e., which play an impor-
tant role in immune evasion, can be used in “early diagnostics”—a precondition for any
liquid biopsy approach. Tumour growth is a biphasic process consisting of initial expo-
nential growth followed by linear growth. As shown in Figure 5, when a cell undergoes a
transformation and becomes a cancer cell (C), it starts to compete for resources with the
surrounding tissue. This so-called logistic model produces an S-shaped growth curve [
77
].
The growth rate (r) and death rate (
µ
) are different for both cases. A tumour (clinically
significant, cs—meaning a tumour which has surpassed the exponential growth phase in
the sigmoid growth curve and is now growing linearly) develops once r
2
>r
1
(the equi-
librium is disrupted). This is due to several factors, one of which being the expression of
cancer-related glycans, G(C), inhibiting the reaction of immune cells (IC). The diagnostics
aims to detect the state when the cancer cells persist, i.e.,
µ2≤
r
2
. Cancer cells and the
surrounding tissue (T) still co-exist in the same medium, hence the term (T + C)/K, where
K is a constant—the carrying capacity of the environment, determined by the available
resources. To overcome this drawback and secure nutrients and oxygen supply, the tumour
produces angiogenic factors, such as a vascular endothelial growth factor, which not only
induces vasculatures’ formation, but also creates a basis for future metastases’ dissemi-
nation [
78
]. Aberrant glycosylation has been shown to promote tumour angiogenesis by
extracellular matrix degradation. This activates the angiogenic signalling pathways [
79
].
Without vascularisation, the cells exposed to hypoxic stress activate hypoxia-inducible
factor 1
α
gene (HIF-1
α
), significantly affecting glycosylation—including sialylation through
the epimerisation of UDP-GlcNAc (GlcNAc = N-acetylglucosamine) by UDP-GlcNAc 2-
epimerase. This process contributes to oncogenic transcription factor stabilisation and
drives cell-surface biomolecule (hyper)sialylation [80].
Although more complex, if aberrant glycosylation forms a part of an early clinically
significant tumour development, detecting these aberrant signals might be a sign of early
cancer development and a prerequisite for in-time and, thus, less invasive interventions,
such as basic surgery. In this chapter, methods for early and non-invasive (i.e., liquid
biopsy-based approaches with no need for tissue biopsy) techniques developed and/or
proposed are summarised. Although single molecule glyco-analysis using low-temperature
scanning tunnelling microscopy has been performed only very recently, more user-friendly
and clinically compatible methods are of great importance for a routine evaluation and
detection of aberrant and cancer-related glycans [
81
]. The amount of information needed is
not necessarily too complex, but the presence/absence of some specific glycan epitopes
might be sufficient in the case of a multi-omics approach; thus, lectin-based technologies
might be of great importance in common immunochemistry/ELISA formats.
In general, the dynamics of cancer evolution could be described by different math-
ematical models—most commonly by ordinary differential equations (ODEs), but also
by partial differential equations, algebraic equations or empirical models. Many of these
models attempt to account for tumour heterogeneity, such as by the presence of prolifera-
tive and quiescent cells, sensitive and resistant cells or by heterogeneity on the gene level.
Genes, however, only represent the first step towards unveiling a greater picture of the
biomolecular diversity of tumours at both proteomic and glycomic levels [
81
]. The most
commonly used models include linear growth (constant zero-order growth), exponential
growth (first-order growth proportional to tumour burden), and the expansion of these
models by first-order shrinkage, taking into account natural tumour cells’ death rate and,
finally, logistic and Gompertz growth—both taking into account the realistic scenario of
decreasing the growth rate over time as the tumour burden increases [
77
]. The current
knowledge of tumour cell growth rate and proliferation kinetics depends on the measure-
Diagnostics 2024,14, 713 8 of 22
ment of the cell’s doubling time. These rates vary greatly depending on the specific tumour
and its cell type and are correlated with the histological type. In general, the doubling
time-affecting factors are (i) cell cycle duration, (ii) proportion of proliferating cells and their
birth rate and (iii) cell loss factors [
82
]. Tumour growth kinetics is also an important prog-
nostic and predictive marker, being evaluated in cases of a locally advanced non-small-cell
lung carcinoma as inversely proportional to progression-free survival, i.e., higher tumour
growth rate prior to any treatment refers to a poor prognosis [83]. Early-stage diagnostics
and in-time intervention thus increase the survival rate by removing the tumour while still
localised to one organ. Hugh J. Freeman defines early-stage (colon) cancer as “disease that
appears to have been completely resected with no subsequent evidence of involvement
of adjacent organs, lymph nodes or distant sites”, i.e., with no metastases yet present [
84
].
Once the cell activates its immune evasion mechanisms (such as sialoconjugates expressed
on its surface), affecting its own survival rate and due to angiogenesis and its growth/death
rate at the expense of the surrounding tissue, the tumour starts to show signs of clinical sig-
nificance and increases its metastatic potential. Early detection significantly influences the
expenses related to cancer cure in oncology. In cases where a population consists of already
diagnosed patients with early-stage cancer, diagnostic/imaging methods and surgery are
supported. In cases of metastatic disease being present, investments in palliative care are
necessary [85].
Diagnostics 2024, 14, x FOR PEER REVIEW 8 of 22
Figure 5. Schematic presentation of logistic mathematical model for cancer cell (C) growth in the
same medium as healthy tissue (T) and negatively interacting immune cells (ICs, with more or less
constant concentration) unless aberrant and cancer-specic glycosylation takes place, in which case,
novel G(C) glycan expressed on the cell surface causes a phenomenon known as immune evasion.
As a consequence, death rate μ becomes lower than the growth rate r and cancer cells persist, pro-
ducing angiogenic factors and securing nutrients and oxygen in competition with its surroundings.
Hence, these processes are an early sign of tumour development.
In general, the dynamics of cancer evolution could be described by dierent mathe-
matical models—most commonly by ordinary dierential equations (ODEs), but also by
partial dierential equations, algebraic equations or empirical models. Many of these
models aempt to account for tumour heterogeneity, such as by the presence of prolifer-
ative and quiescent cells, sensitive and resistant cells or by heterogeneity on the gene level.
Genes, however, only represent the rst step towards unveiling a greater picture of the
biomolecular diversity of tumours at both proteomic and glycomic levels [81]. The most
commonly used models include linear growth (constant zero-order growth), exponential
growth (rst-order growth proportional to tumour burden), and the expansion of these
models by rst-order shrinkage, taking into account natural tumour cells’ death rate and,
nally, logistic and Gomper growth—both taking into account the realistic scenario of
decreasing the growth rate over time as the tumour burden increases [77]. The current
knowledge of tumour cell growth rate and proliferation kinetics depends on the measure-
ment of the cell’s doubling time. These rates vary greatly depending on the specic tu-
mour and its cell type and are correlated with the histological type. In general, the dou-
bling time-aecting factors are (i) cell cycle duration, (ii) proportion of proliferating cells
and their birth rate and (iii) cell loss factors [82]. Tumour growth kinetics is also an im-
portant prognostic and predictive marker, being evaluated in cases of a locally advanced
non-small-cell lung carcinoma as inversely proportional to progression-free survival, i.e.,
higher tumour growth rate prior to any treatment refers to a poor prognosis [83]. Early-
stage diagnostics and in-time intervention thus increase the survival rate by removing the
tumour while still localised to one organ. Hugh J. Freeman denes early-stage (colon) can-
cer as “disease that appears to have been completely resected with no subsequent evi-
dence of involvement of adjacent organs, lymph nodes or distant sites”, i.e., with no me-
tastases yet present [84]. Once the cell activates its immune evasion mechanisms (such as
sialoconjugates expressed on its surface), aecting its own survival rate and due to angi-
ogenesis and its growth/death rate at the expense of the surrounding tissue, the tumour
starts to show signs of clinical signicance and increases its metastatic potential. Early
detection signicantly inuences the expenses related to cancer cure in oncology. In cases
where a population consists of already diagnosed patients with early-stage cancer,
Figure 5. Schematic presentation of logistic mathematical model for cancer cell (C) growth in the
same medium as healthy tissue (T) and negatively interacting immune cells (ICs, with more or less
constant concentration) unless aberrant and cancer-specific glycosylation takes place, in which case,
novel G(C) glycan expressed on the cell surface causes a phenomenon known as immune evasion. As
a consequence, death rate
µ
becomes lower than the growth rate rand cancer cells persist, producing
angiogenic factors and securing nutrients and oxygen in competition with its surroundings. Hence,
these processes are an early sign of tumour development.
3.1. Complexity of Glycans as a Hope and Barrier in Early Diagnostics
Sialo-oncomarkers have been known for many years but, due to huge complexity in the
structures present in biological samples and their biological variability, their role in cancer
development and/or progression is not fully understood. Figure 6depicts the commonest
sialic acid structures, namely G
D2
, G
D3
and G
M2
, along with sTn antigen and sialyl-Lewis
(sLe) antigens. sLe
A
(CA19-9) is an FDA-approved oncomarker, a tetrasaccharide usually
linked to O-glycans containing
α
2,3-linked sialic acid, commonly recommended for the
management of patients with pancreatic cancer (pancreatic ductal adenocarcinomas), but
Diagnostics 2024,14, 713 9 of 22
not suitable for screening purposes [
86
]. The same holds true for CA125, a mucin-16
molecule used for the management of metastatic ovary carcinomas with rather low AUC
value (from the ROC—receiver operating characteristic curve) of 0.632 [
87
]. This complex-
ity of glycans enhanced by different types of glycosidic bonds and possible branching
makes them exceptionally difficult to analyse in a routine manner. Highly skilled operators
and precision are needed for the entire process, including data interpretation. Two main
approaches may be used for the analysis, i.e., (i) “heavy machinery” such as mass spectrom-
etry, liquid chromatography, capillary electrophoresis and/or their combination and (ii) the
use of glycan-recognising molecules, such as antibodies, lectins—glycan-binding proteins
of non-immune origin, aptamers, synthetic receptors, engineered neolectins and boronic
acid derivatives [
88
–
91
]. This latter approach enables the development of more clinically
compatible detection platforms, such as ELLBA (Enzyme-Linked Lectin Binding Assay) [
92
]
or microarray format [
93
,
94
]. The method can be further divided as (i) label/label-free, (ii)
with/without glycan release (in situ glycoprofiling) and (iii) with/without any chemical or
biochemical treatment (i.e., derivatisation and enzyme treatment).
Commonly used methods in the analysis of glycans involve mass spectrometry (MS),
often in combination with various other analytical tools, such as chromatography. As MS is
unable to determine the type of glycosidic bond based on the mass-to-charge ratio analysis
of the ionised fragments alone, chemical or biochemical methods in the pre-analytical stage
(i.e., derivatisation or enzymatic modification) are necessary, as well as permethylation
of the isolated glycans increasing ionisation efficiency and stability of the analysed gly-
cans, which possess a high level of microheterogeneity [
95
]. Sialidases (enzymes able to
selectively distinguish and hydrolyse a specific type of glycosidic bond between sialic
acid and galactose residues) are used to distinguish between
α
2,3- and
α
2,6-bonds when
used in combination. Sialylated glycans were recently shown to be enriched in a Ti
3
C
2
T
x
MXene-based (2D inorganic nanomaterial, generally carbides, nitrides or carbonitrides
of various transition metals) cartridge prior to MS analysis [
96
]. Apart from these and
other exoglycosidases, PNGase F (cleaving the bond between the innermost GlcNAc and
asparagine residues) and endoglycosidase H (endo H, cleaving the bond between the
GlcNAc residues of the chitobiose core in high mannose and hybrid glycans) can be used
to release the glycans from proteins for further enrichment strategies, such as hydrophilic
interaction liquid chromatography (HILIC) and electrostatic repulsion liquid chromatog-
raphy (ERLIC) [
97
,
98
]. Also, different enrichment strategies prior to analysis, such as the
use of lectins with different specificities or TiO
2
for sialic acid-containing glycopeptides
or glycoproteins, are often used [
99
]. Linkage-specific chemical derivatisation involves
methods distinguishing the type of glycosidic bonds based on the products with different
masses, such as lactonisation/dimethylamidation, of the analysed
fragments [100,101]
.
Other approaches involve methylester, ethylester, amide or isopropylamide formation in
the case of
α
2,6-bound sialic acid [
102
]. The derivatisation of glycans can result in the loss
of sialic acid residues during sample pretreatment, permethylation, and labelling of the
reducing end of glycans. Accordingly, glycan processing needs to be performed carefully
to yield as much information as possible about the native structure of the respective glycan.
Mass spectra interpretation might be further complicated by the fact that sialic acids form
salts (-COOH group forms -COONa, -COOK, etc.) with different masses, leading to the
presence of multiple peaks for one epitope [102].
Diagnostics 2024,14, 713 10 of 22
Diagnostics 2024, 14, x FOR PEER REVIEW 10 of 22
spectra interpretation might be further complicated by the fact that sialic acids form salts
(-COOH group forms -COONa, -COOK, etc.) with dierent masses, leading to the pres-
ence of multiple peaks for one epitope [102].
Figure 6. Schematic presentation of dierent sialic acid-containing epitopes/antigens in the com-
monest human carcinomas (excluding haematological malignancies, i.e., from top to boom: brain,
skin, lung, breast, pancreas, ovaries, colon and prostate), showing also dierent glycosidic bonds
between sialic acid and underlying monosaccharide—namely α3, α6 and α8; s = sialyl, Le = Lewis
antigen, C = ceramide. The structures were drawn based on data available in several publications
[69,103–106].
3.2. Alternatives to Mass Spectrometry in Glycan Analysis
Lectins, on the other hand, can often be used in a miniaturised format of analysis
with a minute sample consumption and high throughput, such as microarray [94,107],
lectin-based ELISA (ELLBA) [108] or as a part of biosensors [109,110] with dierent trans-
ducers, i.e., electrochemical, optical, etc. Two main drawbacks of lectins as analytical tools,
namely (i) a lack of analytical sensitivity due to low binding anity (high KD values) and
(ii) limitations in availability of lectins recognising less common, disease-specic struc-
tures or rare epitopes, such as Neu5Gc, were formulated by Haab in 2012 [111]. Two ad-
ditional problems involve the so-called “promiscuity” of these molecules and lectin spec-
ications published by dierent suppliers, and nally their use in a sandwich format of
analysis. Lectins often recognise a variety of dierent glycans which are structurally sim-
ilar, their anity being aected not only by the terminal monosaccharide unit but also by
the second or even third monosaccharide in the chain, such as in the case of Maackia
amurensis agglutinin and its two isoforms [112]. In some cases, lectins and the information
they yield can easily be misused or misinterpreted if they bind to structurally distinct
epitopes, such as in the case of WGA (wheat germ agglutinin), which binds to GlcNAc but
also to Neu5Ac-galactose epitopes [113,114].
When using lectins (and especially sialic acid-binding lectins) in the analytical tech-
nologies listed above, for an easy and user-friendly manipulation of the suggested diag-
nostic kits, direct use of a biological sample (with minimal to no sample treatments)
should be a priority [115]. For the purpose of liquid biopsies, the tissue specicity of the
proposed biomarker should always be ensured by choosing an appropriate antibody en-
riching only a single protein from a sample for further glycoproling. Enrichment of the
glycoprotein of interest on the surface of the plate well or magnetic particle is followed by
Figure 6. Schematic presentation of different sialic acid-containing epitopes/antigens in the
commonest human carcinomas (excluding haematological malignancies, i.e., from top to bottom:
brain, skin, lung, breast, pancreas, ovaries, colon and prostate), showing also different glycosidic
bonds between sialic acid and underlying monosaccharide—namely
α
3,
α
6 and
α
8; s = sialyl,
Le = Lewis
antigen,
C = ceramide
. The structures were drawn based on data available in several
publications [69,103–106].
3.2. Alternatives to Mass Spectrometry in Glycan Analysis
Lectins, on the other hand, can often be used in a miniaturised format of analysis
with a minute sample consumption and high throughput, such as microarray [
94
,
107
],
lectin-based ELISA (ELLBA) [
108
] or as a part of biosensors [
109
,
110
] with different trans-
ducers, i.e., electrochemical, optical, etc. Two main drawbacks of lectins as analytical tools,
namely (i) a lack of analytical sensitivity due to low binding affinity (high K
D
values) and
(ii) limitations in availability of lectins recognising less common, disease-specific structures
or rare epitopes, such as Neu5Gc, were formulated by Haab in 2012 [
111
]. Two additional
problems involve the so-called “promiscuity” of these molecules and lectin specifications
published by different suppliers, and finally their use in a sandwich format of analysis.
Lectins often recognise a variety of different glycans which are structurally similar, their
affinity being affected not only by the terminal monosaccharide unit but also by the sec-
ond or even third monosaccharide in the chain, such as in the case of Maackia amurensis
agglutinin and its two isoforms [
112
]. In some cases, lectins and the information they
yield can easily be misused or misinterpreted if they bind to structurally distinct epitopes,
such as in the case of WGA (wheat germ agglutinin), which binds to GlcNAc but also to
Neu5Ac-galactose epitopes [113,114].
When using lectins (and especially sialic acid-binding lectins) in the analytical technolo-
gies listed above, for an easy and user-friendly manipulation of the suggested diagnostic
kits, direct use of a biological sample (with minimal to no sample treatments) should be
a priority [
115
]. For the purpose of liquid biopsies, the tissue specificity of the proposed
biomarker should always be ensured by choosing an appropriate antibody enriching only
a single protein from a sample for further glycoprofiling. Enrichment of the glycoprotein of
interest on the surface of the plate well or magnetic particle is followed by glycoprofiling
using a lectin conjugated to a label (fluorophore or HRP enzyme—horseradish peroxidase).
There are, however, methods using no labels at all, such as optical and electrochemical
biosensors [
99
,
116
]. The two main issues with this sandwich configuration involve (i) a
possible cross-reaction of the lectin with the underlying antibody (immunoglobulin gamma,
IgG) containing in its Fc fragment two biantennary N-glycans at Asn297 (see Figure 7),
Diagnostics 2024,14, 713 11 of 22
commonly terminated with sialic acid and (ii) blocking the surface against non-specific
interactions, as the blocking agent might contain impurities promoting these interactions.
As there are many commonly used blocking agents varying substantially in their composi-
tion, additives content or purity, the cross-reaction of lectins (and even some other proteins)
with some of these might be an issue [
117
]. This cross-reactivity issue can be mitigated
or eliminated by a mild NaIO
4
oxidation protocol introduced in 2007 for IgG glycans,
followed by the attachment of hydrazide derivatives to the aldehyde groups formed [
118
].
This, however, leads to an increase in K
D
values for the Ab-Ag binding pairs due also to
the affection of amino acid side chains [
27
]. Gentler conditions can be used in cases where
only sialic acid needs to be oxidised compared to other structures buried deeper inside the
glycan chain, i.e., the use of 25–150 mM NaIO
4
for glycoprofiling using SNA and Con A
lectins, respectively.
3.3. Glycosylation of Immunoglobulins in Diagnostics and Therapy
Despite the fact that the glycosylation of IgG is inherently a limiting factor when
used together with some lectins, IgG glycans play an important role in the antibody’s
nature—whether
the antibody acts as a pro-inflammatory or anti-inflammatory agent. The
trimming of sialic acid and galactose units from the N-glycan’s non-reducing end leads to
greater binding of the mannose-binding protein to these structures, activating a complement
via a so-called lectin pathway [
119
,
120
]. Mannose, being more accessible for biorecognition
due to the loss of one or two galactose residues in the biantennary structure (occurrence of
so-called G0 and G1 IgGs), which also lowers IgG’s half-life in the bloodstream, is positively
correlated with the onset of rheumatoid arthritis and osteoarthritis, as previously shown in
a pioneering paper by Parekh et al. in 1985 [
121
]. Furthermore, monoclonal IgG produced
in non-human systems might contain glycan structures different from those normally
found in the human body [
122
], affecting the therapeutic effect of a drug administered
to a patient, causing immunogenic issues. Novel “glycoengineered” antibodies (amongst
other proteins, such as cytokines and enzymes) include non-fucosylated obinutuzumab
(Gazyva
®
) binding more efficiently to the Fc
γ
receptor IIIa present on immune effector cells
than to non-fucosylated mogamulizumab (Poteligeo®) [59,123,124].
Although there are some cases where the functional significance of a glycan structure
for human health has been revealed, there remains a need to decipher the “glycocode”
(or sugar code, as has been repeatedly proposed by H. Gabius since 1998) [
125
–
127
] for
detecting several pathological processes. Figure 8shows various analytical configurations
for in situ detection of glycans (i.e., without the glycan being released from a protein
backbone prior to the analysis) using lectins or glycan-binding proteins, such as antibodies
which, unfortunately, are still in limited supply [
128
]. Without using an antibody monolayer
during the glycoprofiling process, no tissue specificity can be guaranteed. Although some
studies suggest that a whole-serum level glycoprofiling might be used for diagnostics
of some cancers, interference with other comorbidities and thus higher false positive
results might be an issue. Complementary methods based on this approach have been
published for primary and metastatic brain tumours [
129
], hepatocellular [
130
], breast [
131
],
colorectal [
132
] or lung cancers [
133
]. In addition, comparing healthy serum glycome
from individuals of different ethnicity and/or age yields important information about
the baseline of physiological structures present in these individuals, helping to search
for pathological processes occurring subsequently, making this approach a useful tool in
the area of biomarker discovery [
134
,
135
]. Some of the configurations take advantage of
the use of magnetic particles offering high-throughput, low costs, energy consumption
efficiency, increased disease specificity of the assay and sensitivity [
136
]. An important
aspect to be taken into account in designing the diagnostic assay is the peroxidase-like
activity of magnetic nanoparticles possibly affecting the background signal in colorimetric
assays [137].
Diagnostics 2024,14, 713 12 of 22
Diagnostics 2024, 14, x FOR PEER REVIEW 12 of 22
consumption eciency, increased disease specicity of the assay and sensitivity [136]. An
important aspect to be taken into account in designing the diagnostic assay is the peroxi-
dase-like activity of magnetic nanoparticles possibly aecting the background signal in
colorimetric assays [137].
Figure 7. A ribbon model (with amino acid side chains) of human immunoglobulin (IgG) (source:
protein data bank, pdb code: 1MCO) with two conserved N-glycans at Fc fragment’s Asn297. Brown
and grass-green = two heavy chains, dark green and magenta = two light chains. (A) IgG structure—
frontal view, with depicted sialic acids on both sides, (B) semi-prole of the IgG with a Gaussian
surface molecular model of the glycan (blue) accessible for biorecognition and (C) detail of sialic
acid bound to galactose residue via α6-glycosidic bond recognised for instance by SNA-I isolectin.
Figure 8. Dierent sensing congurations commonly used in ELLBA and/or microarray formats.
From a complex sample, the analyte of interest can be extracted from the sample by Ab-modied
magnetic particles, and then released (a–g) or used in a direct glycoproling format (h) on a well-
boom or entirely in solution—without any molecules being immobilised on the ELISA plate sur-
face—except for the blocking agent inhibiting non-specic surface binding (i). The signal generation
Figure 7. A ribbon model (with amino acid side chains) of human immunoglobulin (IgG) (source:
protein data bank, pdb code: 1MCO) with two conserved N-glycans at Fc fragment’s Asn297.
Brown and grass-green = two heavy chains, dark green and magenta = two light chains. (A) IgG
structure—frontal
view, with depicted sialic acids on both sides, (B) semi-profile of the IgG with
a Gaussian surface molecular model of the glycan (blue) accessible for biorecognition and (C) de-
tail of sialic acid bound to galactose residue via
α
6-glycosidic bond recognised for instance by
SNA-I isolectin.
Diagnostics 2024, 14, x FOR PEER REVIEW 12 of 22
consumption eciency, increased disease specicity of the assay and sensitivity [136]. An
important aspect to be taken into account in designing the diagnostic assay is the peroxi-
dase-like activity of magnetic nanoparticles possibly aecting the background signal in
colorimetric assays [137].
Figure 7. A ribbon model (with amino acid side chains) of human immunoglobulin (IgG) (source:
protein data bank, pdb code: 1MCO) with two conserved N-glycans at Fc fragment’s Asn297. Brown
and grass-green = two heavy chains, dark green and magenta = two light chains. (A) IgG structure—
frontal view, with depicted sialic acids on both sides, (B) semi-prole of the IgG with a Gaussian
surface molecular model of the glycan (blue) accessible for biorecognition and (C) detail of sialic
acid bound to galactose residue via α6-glycosidic bond recognised for instance by SNA-I isolectin.
Figure 8. Dierent sensing congurations commonly used in ELLBA and/or microarray formats.
From a complex sample, the analyte of interest can be extracted from the sample by Ab-modied
magnetic particles, and then released (a–g) or used in a direct glycoproling format (h) on a well-
boom or entirely in solution—without any molecules being immobilised on the ELISA plate sur-
face—except for the blocking agent inhibiting non-specic surface binding (i). The signal generation
Figure 8. Different sensing configurations commonly used in ELLBA and/or microarray formats.
From a complex sample, the analyte of interest can be extracted from the sample by Ab-modified mag-
netic particles, and then released (a–g) or used in a direct glycoprofiling format (h) on a well-bottom or
entirely in solution—without any molecules being immobilised on the ELISA plate
surface—except
for the blocking agent inhibiting non-specific surface binding (i). The signal generation in the case of
ELLBA takes advantage of the amplification by enzymes linked to a biomolecule or to streptavidin
used in combination with biotinylated recognition elements.
4. Perspectives of Glycan Liquid Biopsies and Clinical Validations
Glycans in the form of different glycoconjugates as a novel type of diagnostic biomarker
are more readily obtainable than other biomarkers, such as miRNAs, ct/cfDNA/RNA, ex-
tracellular vesicles including exosomes and circulating tumour cells, as they might be more
Diagnostics 2024,14, 713 13 of 22
abundant in body fluids and do not require any amplification, unlike nucleic acids (which
are all comparably simple to obtain) [
138
]. While miRNAs usually need Northern blotting,
in situ hybridisation methods using locked probes, microarray approaches, next-gen. se-
quencing or real-time quantitative PCR [
139
], the detection of glycans can be performed
within a short time period using only whole serum with no special pre-treatment and an
ELLBA approach. As cancer diagnostics medical devices are regulated by the FDA as class
III devices due to the possible harms caused by false positive or false negative results,
these methods need to be highly reliable, reproducible and robust, with sufficient clinical
evidence with regard to their performance [
140
]. This is most probably why miniaturised,
easy-to-fabricate, portable and cheap biosensors are not routinely used for cancer screening.
Other obstacles to liquid biopsy cancer screening aiming to detect any pre-malignant lesions
for an early intervention include geography, services coordination, accessibility, medical
guidelines and recommendations and health insurance [
141
]. Challenges in liquid biopsy
mainly involve technical complications in the isolation process of CTCs, lack of knowledge
about tumour-specific variants of cfDNA, low ctDNA concentrations in the early stages of
a disease or accurate quantification of scarce and small-size miRNA in body fluids [
142
].
Age, gender, ethnicity and disease history might also affect the quantity of circulating
miRNA [
143
]. The situation is even more complicated in the case of exosomal miRNA,
where exosomes have to be isolated from a pool of other extracellular vesicles, such as
microvesicles with a size similar to that of exosomes in their lower size range [
144
,
145
].
This issue can, however, be readily overcome by a proper isolation technique, such as
affinity-based methods involving highly specific antibodies against exosome-specific sur-
face receptors (such as tetraspanins CD63, CD81, etc.), leading to a population of exosomes
more uniform than that achieved by ultracentrifugation [
146
]. A comparison of miRNA
and selected sialoglycans to detect a specific solid tumour based on an AUC parameter
(obtained from ROC analysis, see section below) is summarised in Table 1.
Performance of Glycans in Clinical Practice
In verification and validation studies detailing new clinical biomarkers, a receiver
operating characteristics curve (ROC) is commonly used, with the area under the curve
(AUC) parameter as the main indicator of the biomarker/assay performance. Sensitivity
(true positive rate) and specificity (true negative rate) are extracted from the ROC based on
a desired or optimal threshold and overall diagnostic accuracy, and negative and positive
predictive values are calculated. The threshold for oncodiagnostics should always be set
at a sufficiently high sensitivity level (e.g., 90–95% sensitivity), even at the cost of low
specificity possibly leading to unnecessary biopsies (as in the case of PCa diagnostics),
as missing the tumour during the examination might lead to disease progression and
ultimately the patient’s death. According to Verbakel et al., a threshold based on a desired
combination of specificity and sensitivity also has a direct relationship with the relative
costs of false positives and false negatives; if an individual should be treated when the risk
of an event is 10% (1:9), correctly treating one patient with an event (disease) justifies an
unnecessary treatment for nine healthy individuals [
147
]. Furthermore, when using ROC
for data evaluation, all of this method’s limitations must be considered, such as: (i) ROC
does not account for prevalence (proportion of people with a disease at one particular
point in time; not the same as incidence); (ii) ROC does not account for misclassification
of patients due to, for instance, false negative biopsies or falsely elevated serum levels
of some biomarkers; (iii) ROC requires the data to be properly balanced (by way of their
count/quantity and quality as well) to mitigate some significant effects such as age, gender,
ethnicity, comorbidities, etc. (which may lead to over-optimistic performance); (iv) ROC
requires a normal distribution of input data and cohort sizes comparable with the popula-
tion studied. When combining several marker types which have already been shown to
increase the overall clinical significance [
26
], the lowest possible number of these markers
should be maintained to preclude overfitting and cost-effectivity of diagnostics. Although
the ROC curve is most suitable in the early stages of clinical research and verification of a
Diagnostics 2024,14, 713 14 of 22
proposed biomarker, a change in the AUC parameter has little to no meaning for clinical
practice. Other methods highlighting the net benefits for patients should be introduced at
later stages, as the ROC curve does not allow for clinical interpretation of the data [
148
,
149
].
Although glycomics has already reached clinical practice in the case of glycoengineered
therapeutic proteins or even a diagnostic test (Glyco Liver Profile, Helena Biosciences) [
150
],
some obstacles have to be overcome if wide use of these methods is to be expected in the
near future. These barriers can be summarised as follows: (i) a lack of glycoconjugate
analytical standards or calibrators in the case of diagnostic tests; (ii) limited access to so-
called high-throughput glycomics in clinical practice, while at the same time these methods
are largely focused solely on N-glycans and rely on a skilled operator and databases
based on long-term data mining with well-established processes, with limited automation
possibilities at the time [
151
]; and (iii) the human glycome is a highly complex and dynamic
cellular component which is being constantly changed over time, possessing a high level of
inter-individual variability, so instead of generating a huge amount of information during
a single analysis, only the most crucial information (such as aberrant sialylation) should be
extracted in an easy and reproducible manner, yielding the relevant clinical information
needed for in-time intervention and proper patient management. These analyses would,
however, need to be made over an extended period of time to properly determine a
“baseline signal” for every individual [
152
]. These methods, however, could (and definitely
should) still become a part of common clinical practice in assessing the risk of a disease
being present. For this purpose, high-quality clinical data need to be obtained in large,
multinational/multicentric epidemiological clinical studies in the future.
Table 1. Examples of recently published serum-derived oncomarkers—comparison of miRNAs (miR) as
promising and established liquid biopsy biomarkers and different sialic acid-containing (Sia) glycans.
Tumour Location miRNAs (Combinations) AUC Ref. Sia-Containing Glycans AUC Ref.
Bladder
miR-106a-5p, miR-145-5p,
miR-132-3p,
miR-7-5p,
miR-148b-3p
0.922 [153] Protein-bound Sia 0.825 [154]
Breast miR15a,
miR16 0.884 [155] Different Sia-glycan isoforms Up to 0.980 [156]
Colorectum
miR-1246,
miR1268b,
miR4648
0.821 [157]
H5N4F1,
H4N4F1,
H5N4F1S2,61
0.830 [158]
Lung
miR-210,
miR-1290,
miR-150,
miR-21-5p
0.930 [159] Different glycan isoforms + CRP 0.942 * [160]
Skin (melanoma)
miR-149-3p,
miR-150-5p,
miR-193a-3p
0.970 [161] Total serum Neu5Gc 0.925 [162]
Ovaries
miR-92a,
miR-200c,
miR-320b,
miR-320c,
miR-335,
miR-375,
miR-486
0.870 [163]Total sialylation ratio (α-2,3-Sia) +
CA125 0.985 * [164]
Pancreas
miR-215-5p,
miR-122-5p,
miR-192-5p,
miR-30b-5p,
miR-320b
0.811 [165]Combination of CA4, A3F0L and
CFa glycan-isoforms 0.807 [166]
Prostate
miR-4286,
miR-27a-3p,
miR-29b-3p
0.892 *
(+PSA and PV) [167]α-2,3-Sia/PSA + PHI 0.985 * [168]
Stomach 12-miRs panel 0.920 [169]
H5N5F1E2 glycan + other markers
0.892 * [170]
Testis miR-371a-3p 0.966 [171] 5 N-glycan score 0.870 [172]
H = hexose, N = NAc hexose, F = fucose, AUC = area under the curve parameter from ROC curve, CRP = C-
reactive protein, PSA = prostate-specific antigen, PHI = prostate health index, PV = prostate volume; * designates
AUC values obtained by combining these markers with those in current use.
Diagnostics 2024,14, 713 15 of 22
Glycomic applications and perspectives in clinical practice have been presented so far
in detail; however, the reasons behind the fact that their use in clinical routine is absent
could be summarized in a few points:
1.
Mass spectrometry (a robust gold standard in structural glycobiology) workflows
need to be further transformed to advance the translation to clinics for suitable
quantification of glycoconjugates. Mass spectrometry imaging for the observation of
spatial distribution of glycans in histological samples is also of eminent importance.
2.
From a regulatory point of view, since there is currently a lack of any glycan detection
diagnostic kits on the market based on common immunosorbent assays, any new
products will be reviewed and judged according to common practices with ELISA.
There are some aspects, however, which are unique for Enzyme-Linked Lecin Bind-
ing Assay (ELLBA) platforms, such as suppressing non-specificity based on lectin
substrate promiscuity and low affinity between the lectins and glycans [173].
3.
In the therapeutic area, the first anti-core 1 O-glycans monoclonal antibody NEO-201
has been involved and registered in a phase I clinical trial in 2018, showing promising
results in the treatment of solid tumours in 2023 [
174
]. Investments and support
from large pharma companies in next rounds of clinical trials could help to accelerate
advancement in the area and thus help to bring new molecules into clinical routine
much sooner.
5. Conclusions
This review focused on the molecular interactions of sialylated glycans
in vivo
in
physiological and pathological processes, their analysis and a comparison with current
state-of-the-art methods, especially cutting-edge liquid biopsy approaches, such as miRNAs
analysis. We showed that glycans have a number of advantages, including a short pre-
analytical phase, short and easy-to-perform analytical protocols in the case of ELLBA
formats of analysis in combination with glycan-binding receptors, and high levels of
diagnostic accuracy. As yet, glycans are not commonly used in clinical practice due to
the lack of analytical standards and due to the need to implement these practices into the
high-throughput solutions preferred by clinics. Since any success in cancer therapy and
the subsequent quality of a patient’s life is strongly related to the stage of the disease at
the time of diagnosis, early-stage diagnostics in combination with the non-invasive nature
(to reduce any barriers between patient and clinician) of the glycans analysis and possible
organ specificity currently represent an urgent medical need to be met, where glycans can
provide a promising tool for decades to come.
Author Contributions: Conceptualization, T.B. and J.T.; resources, T.B. and J.T.; writing—original
draft preparation, A.P.; N.K.; E.J.; L.L.; A.B.; T.B. and J.T.; writing—review and editing, A.P.; N.K.; E.J.;
L.L.; A.B.; T.B. and J.T.; visualization, T.B.; supervision, T.B. and J.T.; project administration, T.B. and
J.T.; funding acquisition, T.B. and J.T. All authors have read and agreed to the published version of
the manuscript.
Funding: The publication was supported by EIC Accelerator grant 190185443 (HORIZON). The
authors wish to acknowledge the financial support received from projects of the Slovak Research and
Development Agency (APVV-22-0496, APVV-21-0329 and APVV-22-0345) and from VEGA 2/0120/22
and 2/0164/24. This publication was jointly supported by Qatar University CG-CAM-22/23-504.
The findings achieved herein are solely the responsibility of the authors. This work was supported by
funding received from the V4-Korea 2023 Joint Call.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: Authors Andrea Pinkeova, Aniko Bertokova and Jan Tkac were employed by
the company Glycanostics. The remaining authors declare that the research was conducted in the
Diagnostics 2024,14, 713 16 of 22
absence of any commercial or financial relationships that could be construed as a potential conflict
of interest.
Abbreviations
AFP α-fetoprotein
AUC area under the curve
BCa breast cancer
CA carbohydrate antigen (mucin)
CD cluster of differentiation
CEA carcinoembryonic antigen
ELLBA Enzyme-Linked Lectin Binding Assay
FDA Food and Drug Administration
GD, GM tumour associated glycan antigens
HRP horseradish peroxidase
IgG Immunoglobulin G
KRAS Kirsten rat sarcoma virus
(s)Le (sialyl)-Lewis antigen
mTOR mammalian target of rapamycin
NANA N-acetylneuraminic acid (human form of sialic acid)
Neu5Ac N-acetylneuraminic acid (human form of sialic acid)
NK natural killer cells
PCa prostate cancer
PHI prostate health index by Beckman Coulter
PKM pyruvate kinase muscle isoenzyme
PSA prostate-specific antigen
PV prostate volume
QoL quality of life
ROC receiver operating characteristic curve
ROS reactive oxygen species
RT-qPCR real-time quantitative polymerase chain reaction
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