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clinical features, but while the findings can seem relevant considering the population
level, they have a poor predictive value evaluating a single patient (Thrift and
Whiteman 2013).
Different studies, exploiting bacterial genome–based analysis or high-throughput
metabolomics, documented that more gastrointestinal metabolites can modulate
pathogen infection in various gut segments (Browne et al. 2017; Hirata and
Kunisawa 2017; Kunisawa et al. 2012). For example, they can influence biofilm
formation and cell adhesion, such as D-amino acids produced by Bacillus subtilis
(Mühlen and Dersch 2016). Moreover, increasing data showed a key metabolite’s
role in the modulation of the immune system regulating the development of some
adaptive immune cells, especially the T cells (Levy et al. 2017), which have a crucial
role in the development of gastrointestinal neoplasia. In fact, the Amedei group
showed that deregulated adaptive immune responses, characterized by a decreased
number of effective T cells, affect the development of gastric cancer and pancreatic
and colorectal cancers (Amedei et al. 2009; Niccolai et al. 2016; Niccolai et al.
2017).
One of the hallmarks of cancer is dysregulated metabolism and modifications in
the expres sion of many genes encoding metabolic enzymes, transporters, or regulatory effectors that have been related with tumor development (Robey et al. 2015).
First, Warburg suggested that fixed mitochondrial defects were mainly responsible
for both cancer development and its associated highly glycolytic phenotype. Instead,
the preser vation of oxidative metabolism in cancer and its maintenance in the
absence of exogenous substrates (‘The Metabolism of Tumours: Investigations
from the Kaiser Wilhelm Institute for Biology, Berlin-Dahlem.’, 1931, Warburg
1956) have been shown by subsequent data (Dickens and Greville 1933a, b)
indicating an increased metabolic range and an intrinsic capacity to use endogenous
substrates oxidatively when exogenous substrates are not accessible (Robey et al.
2015). Metabolism changes associated with cancer can indicate alterations in either
metabolic ability or control or both. Ability changes are well defined, while changing
control can eventually be of greater relative significance (Newsholme and Board
1991). Since control does not reside in any metabolic pathway at a single point and
controlling factors vary between intact cells and in vitro assays, changes observed in
individual pathway elements do not always translate into changes in metabolic flux
and vice versa.
Notably, different metabolic alterations associated with cancer can be interrelated
to cellular growth; in fact, tumor development requires the biosynthesis of lipids,
proteins, and nucleic acids. As previously reported, frequently, the expression of
oncogenes or the loss of cancer suppressors promote metabolic changes, by expression, activity, or flow of the main metabolic pathways. Various components of
glucose and glutamine metabolism have been documented as key regulators of
neoplasia metabolism. In view of the importance of metabolic changes in the two
fundamental cancer aspects, such as development and prognosis, the metabolomics
is an essential -omics study, as it can be used to estimate the modifications of the
principal metabolites (Burton and Ma 2019). It is well documented that with the
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neoplasia progression, the metabolic characteristics of cancerous cells change
(Aboagye and Bhujwalla 1999; Teahan et al. 2011), and typical metabolic changes
include deregulated amino acid and glucose absorption, augmented demand for
nitrogen, and increased use of anabolic metabolic pathways (Pavlova and Thompson
2016).
This metabolic reprogramming can be can be exploited for early cancer diagnosis
using biological fluids and decreasing the requirement for more invasive screening.
Using nuclear magnetic resonance (NMR) analysis (Wijeyesekera et al. 2012),
readily accessible urine and blood samples may be used to detect potential
biomarkers associated with cancer risk, presence, and prognosis. Blood flows into
every human organ, serving as a transport (in response to different stimuli) of
secreted/excreted molecules, while urine contains molecules removed by renal
filtration (Berger 1999a, b).
In addition, increasing data suggesting that the metabolism of gut microbiota
secretes numerous compounds, including fatty acids, indole, and vitamin K, many of
which have toxic effects on the lumen, contributing to the carcinogenesis of gastrointestinal neoplasia, especially for colorectal and pancreatic cancer. Finally, different
microbiome researchers affirm that great and exhaustive information could be gained
by using a more integrative approach that also includes comprehensive fecal metabolite
analysis. The stool samples contain numerous molecules that mirror different phases of
nutrition such as ingestion, digestion, and, especially, absorption by gastrointestinal
tract and gut microbiota. Bacterial biomass (25–54%) exfoliated colonic epithelial
cells, undigested food residues (fiber, protein, DNA, mucopolysaccharides, etc.), and
small molecules or metabolites such as carbohydrates, organic acids, and amino acids
constitute the dry fecal matter. Fecal metabolomes are made up of these small
molecules.
There is a growing interest in using methods focused on metabolomics to
investigate cancer metabolism. NMR and mass spectrometry (MS) are the instrumental metabolomic techniques of the two main classes. The rew ards are intrinsically distinct from both of these two strategies. The MS platform provides sensitivity
and selectivity to metabolomic research, while NMR provides very high reproducibility, is quantitative, and requires minimal sample preparation steps to prevent
separation or derivatization (Emwas 2015). Due to the potential effect of
NMR-based metabolomics on the traditional clinical management of the different
cancer phases (diagnosis, prognosis, and risk assessment) using readily accessible
biofluids, the aim of this chapter is to provide an exhaustive and comprehensive
overview of the current literature available in this circumscribed but promising field.
In comparison, the use of MS-based or metabolomic analysis methods of cells,
tissues, and animal models has been documented elsewhere (Turano 2014; Xiao
and Zhou 2017). Interestingly, while breast cancer, for example, has been widely
studied using NMR-based systemic biofluid metabolomics (especially for the prediction of relapse risk) (McCartney et al. 2018), this field still appears in its embr yo
for GI cancers.
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2 Different Metabolic Approaches
Metabolomics belongs to the scientific domain of the -omic sciences. It deals with
the characterization of the metabolome, defined as the whole set of metabolites
(small molecules <1,500 Da) in a certain biological system (e.g., a cell, a tissue, an
organ, an entire organism) (Oliver et al. 1998). Metabolomics can be seen as the final
link of the logical chain connecting the main -omic sciences (genomics,
transcriptomics, proteomics); thus, the metabolome can be considered as the concrete realization (i.e., what is actually happening) of the genomic potential (i.e., what
could happen), defining the chemical entity nearer to the phenotype. While the
genome is (almost) invariant throughout the life span of an individual, the
metabolome, as the end product of the cellular machinery, may change as an effect
of lifestyle, stress, and, most importantly, onset of pathologies. Because the metabolite levels strongly depend on several internal stimuli and external perturbations,
metabolomics is an extremely valuable tool to identify disease profiles in the form of
endogenous metabolites (gene-derived metabolites) and exogenous metabolites
(environmentally derived metabolites), which provides vital information on the
underlying causes of diseases at a molecular level (Wishart 2016). Moreover, the
proteome and the transcriptome are different between different tissues and different
cells: there is not an overall (systemic) proteome or transcriptome of the whole
individual. The metabolic space represents an optimal level to analyze changes in
biological systems (Harrigan et al. 2005). In this view, metabolomics perfectly fits
within the spirit of systems biology and systems medicine, providing a holistic
overview of the complex biochemistry underlying life: metabolomics investigates
the final products (metabolites) of the biological reactions that take part both at the
systemic level (analyzing biofluids) and in specific organs or cell types (analyzing
samples from tissues or cells). In the context of biomedical applications,
metabolomics has a key role compared to the other -omics sciences, because, thanks
to its ability to detect in real time the response of the organisms to pathological
stressors (Ratnasekhar et al. 2015), it can catch the current biological state (e.g.,
health or disease) of an individual. Often, the earliest available signs of a disease are
alterations of the metabolome, resulting from compensatory mechanisms, which
start before the clinical manifestations of the disease. The detection of those early
signs potentially allows for an efficient prevention, when diet or low-dose treatments
are still enough.
Metabolomic data can be obtained through “targeted” and/or “untargeted”
approaches. The former is based on the absolute quantification of a priori determined
list of compounds, known (or hypothesized) to be relevant for the investigation at
hand. This approach requires that the analytical techniques used, including sample
preparation and unambiguous identification methods, should provide maximum
sensitivity and selectivity. The latter is based on the agnostic analysis of the whole
ensemble of data that could be gathered from the employed analytical technique,
without any prior hypothesis on significant features (Klupczyńska et al. 2015).
The untargeted approach can be implemented using “fingerprinting” or
“profiling” strategies. The word fingerprinting is intended for the high-throughput
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global analysis performed on raw analytical data, without the need of metabolite
quantification and signal identification. When couple d with multivariate statistical
techniques, it is especially useful to provide rapid classification of the “metabolic
fingerprints” of different samples according to the different biochemical status (e.g.,
healthy subjects vs patients).
With the word “profiling,” we mean the quantitative analysis involving the
precise identification of (possibly) all metabolites in the sample and the absolute
measurement of their concentrations. Classical univariate statistical tests are then
used to pinpoint significant changes in the concentrations of relevant metabolites that
could lead to the discovery of useful biomarkers. The advantage of this approach is
to give more biochemically interpretable results providing a direct connection with
the metabolic networks (Saccenti et al. 2016; Vignoli et al. 2018; Saccenti et al.
2015; Suarez-Diez et al. 2017) associated to the specific condition under investiga-
tion. While the whole fingerprint, by definition, contains more information than the
sum of all quantifiable metabolites, quantitative metabolic profiles can also be used
as input for multivariate statistical analysis and for sample classification (Vignoli
et al. 2019 ).
Current applications of metabolomics are performed primarily using, as analytical
platforms, either nuclear magnetic resonance (NMR) spectroscopy or mass spectrometry (MS); the latter is usually hyphenated to separation techniques such as gas
chromatography (GC) or high-pressure liquid chromatography (HPLC) and more
recently capillary electrophoresis (CE).
2.1 Mass Spectrometry
Mass spectrometry is a family of different analytical techniques based on the
common principle of determining extremely accurate masses of molecules in a
pure sample or in a mixture. As a basic description, in an MS apparatus, the sample
molecules are converted into ions, and the ions are accelerated, deflected, and then
detected according to the mass-to-charge ratio (m/z) of each ion. When the ions
reach the detector, the mass–to-ch arge ratio is registered to provide a spectrum where
a series of peaks are shown reporting the intensity of each ion generated by the
sample. The exact implementation of an MS instrument depends on the choice of i)
the method to produce ions (ion source), ii) the way to select different ions (mass
analyzer), and iii) the mean to detect the passage of different ions (detector).
Different configurations are possible, and each of them has advantages and
disadvantages depending on the particular use case.
The different ionization techniques can be roughly summarized as hard
techniques and soft techniques. The first group comprises physical processes that
transfer high amount of energy leading to multiple fragmentations of the molecules.
A typical example is electron ionization (EI) that consists of bombarding the
molecules with a beam of high-energy electrons. This yields a high degree of
fragmentation resulting in complex mass spectra highly informative for structural
characterizations. Further, because each molecule, in the same experimental
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condition, gives the same kind of fragments, the specific pattern of fragments allows
the identification of unknown compounds by comparison with mass spectral libraries
obtained under identical operating conditions. Conversely, soft techniques result in
little or no fragmentations at all. In this case, mass spectra are simpler (ideally with
only signals for the molecular ions), permitting a precise measure of the molecular
weights of the analytes. Common examples are chemical ionization (CI),
electrospray ionization (ESI), and matrix-assisted laser desorption ionization
(MALDI). The choice of the ion source determines the kind of samples that can be
analyzed, for example, usually EI is more suited for the analysis of samples in
gaseous phase, while ESI is the first choice for liquid samples.
The produced ions are then selected by the mass analyzer. Again, different
choices are possible, with systems based on magnetic or electric field either
static or dynamic. The main characteristics of mass analyzers are resolution
(i.e., the possibility to separate peaks with even small differences in m/z), accuracy
(i.e., the ratio between the measurement error and the true m/z value), sensitivity
(i.e., the signal intensity for a given concentration of the analyte), mass range (i.e.,
the range of m/z values analyzable), and speed (i.e., the time required to generate one
spectrum).
Magnetic sector analyzers provide high resolution (HR) and high sensitivity,
being the most suited for quantitative analysis; however they are bulky, costly,
slow, and cannot be coupled with MA LDI ionizers. Conversely, basic quadrupole
analyzers are compact, affordable, fast, but limited in resolution and mass range, and
are less suited for quantitative analysis. Time-of-flight (TOF) analyzers are very fast
(thus, as a drawback, they need fast detectors), allow the analysis of an extensive
range of m/z, and are the most suited for MA LDI ionizers (constituting the common
MALDI–TOF instruments, especially appreciated in proteomics). The family of ion
trap analyzers comprises different configurations (linear, cylindrical, Orbitrap) that,
broadly speaking, provide good resolution and sensitivity in compact benchtop
instruments, with the cons of limited throughput and limited quantitative results.
Fourier-transform ion cyclotron resonance (FT-ICR) mass spectrometers provide
exceptional resolution, accuracy, and sensitivity, at the point of providing exact
masses; however, they are bulky and expensive machines.
The final element of the mass spectrometer is the detector that produces the
electric signals used to record the spectra, which are represented as graphs showing
the signal intensity as a function of the m/z ratio. Usually, detectors are electron
multipliers, though other devices including Faraday cups, ion-to-photon detectors,
and other kind of inductive detectors could be employed. A specific combination of
ion source, analyzer, and detector constitute a specific MS instrument. Some
combinations are more common and advantageous than o thers, depending on the
problem at hand. Further, a combination of two or more analyzers is possible in
tandem mass spectrometry (MS/MS or MSn) to maximize the strengths and complementary of the different techniques. A few common examples, combinations of three
quadrupoles (triple quadrupole analyzer, QqQ), quadrupole and TOF (QTOF–
QqTOF), and linear ion trap and Orbitrap (LQT–Orbitrap) are found in different
machines from different vendors. When the sample to be analyzed is a complex
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mixture of different analytes, the MS analysis is usually preceded by a chromatographic separation step. In this way, the chromatographic fractions arrive one
by one to the MS apparatus that record a spectrum for each chromatographic peak.
Typical combinations are MS with gas chromatography (GC–MS), liquid chromatography (LC–MS or HPLC–MS), and capillary electrophoresis (CE–MS). GC is
largely used in metabolomics and in the analytical sciences in general because it is a
very efficient separation technique with high sensitivity and high reproducibility. Its
weakness is that it is limited to compounds that are volatile or can be made volatile
via chemical derivatization. Derivatization brings its own problems; examples
include glutamine and glutamic acid converted to pyroglutamic acid (Nagana
Gowda et al. 2015) and arginine converted to ornithine (Psychogios et al. 2011),
thus producing incorrect readings. Instead, LC works with liquid samples (does not
require the samples to be volatile or derivatized) and can separate a large range of
metabolites. Nevertheless, liquid chromatography in metabolomics still has issues:
polar metabolites, such as sugars and many amino acids, are often not retained
by conventional reverse phase LC columns (Rojo et al. 2012). Often, LC
chromatograms are very crowded with overlapping peaks due to a suboptimal
separation power. In these conditions, two-dimensional liquid chromatography
(2DLC) offers a way to increase peak capacity and separation power. However,
there are still problems such as mismatch between the mobile phase used on each
dimension and column re-equilibration times (Jones et al. 2012). CE separations are
very efficient, require only small amounts of sample, reagents, and solvent, and are
relatively inexpensive (cheap fused-silica capillaries vs costly LC columns). Reproducibility tends to be one of the major challenges of this technique. Usually, EI is the
main ion source in GC – MS instruments, while ESI is the preferred source for
LC–MS instruments.
MS-based approaches are largely employed in metabolomics because they
require only small amounts of sample (0.01–0.2 mL), are highly sensitive (can detect
metabolites in the picomolar range), and can take advantage of an arsenal of mature
software for compound identification (Vignoli et al. 2019). Reproducibility has been
one of the major issues of MS-based techniques, although this situation is rapidly
improving, and coefficients of variations less than 20% could be easily reached
(Ramakrishnan et al. 2016). The exact quantification of molecules is not trivial, due
the need of extensive calibrations, and the identification of metabolites with identical
molecular masses is still challenging. For these reasons, MS techniques are more
suited (but not limited) for “targeted” metabolomic investigations.
2.2 Nuclear Magnetic Resonance Spectroscopy
Nuclear magnetic resonance (NMR) spectroscopy is a powerful analytical technique
that is based on the interaction between electromagnetic radiation and atomic nuclei.
Nuclei with either an odd mass number (A) or an odd atomic number (Z) possess a
nonzero nuclear spin (an intrinsic angular moment), and thus, being charged, they
also have an associated magnetic moment. When placed in an external magnetic
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field, these magnetic moments are distributed in quantized energy levels. Using a
radiofrequency of the appropriate wavelength (that, for a given nucleus, depends on
the strength of the applied magnetic field), it is possible to induce a transition of part
of these magnetic moments from a low-energy state to a high-energy state. This
energy transfer causes an absorption at a characteristic frequency (resonance) that
can be regis tered and proces sed to appear as a peak in the NMR spectrum. 1H, 13C,
31P, and 15 N are the most commonly employed nuclei. In detail, 1H-NMR spectra
are especially used in metabolomics due to the ubiquities of 1H nuclei in organic
molecules and its relative higher sensitivity with respect to other nuclei. Although in
principle, a given nucleus resona tes at a frequency that depends only on the applied
magnetic field (for example 1H nuclei resonate at 600 MHz when placed in a field of
14.1 T), in practice, for nuclei attached to a molecule, their resonance frequencies
depend on the chemical environment where they are located (i.e., the molecular
structure). In fact, the electron cloud surrounding the nucleus act as shield, so the
nucleus experiences a slightly different field from the external one, thus resonating at
a slightly different frequency. This fact is key to understand the power and usefulness of NMR spectroscopy. For example, considering a 1H-NMR spectrum of a
particular molecule, the molecule exhibits multiple peaks in the NMR spectrum
depending on the number of 1H nuclei that resonate at (slightly) different
frequencies. Analyzing the different features of these peaks, important information
can be inferred, for instance (i) the positions of the peaks (resonance frequencies) are
related to the chemical environment in which each resonating proton or proton’s
group is located, thus revealing the functional groups present in the molecule, (ii) the
shape of the peaks (that can appear as small groups of adjacent peaks with characteristic splitting) give information about the number of protons attached to the carbon
directly connected to the carbon bearing the resonating protons, thus helping in
deciphering the skeleton of the molecule, and (iii) the area of the peaks is directly
related to the concentration of the molecule, thus providing quantitative information
(Calabrò et al. 2014). The NMR spectrum resulting from the analysis of a complex
mixture (e.g., a biological fluid) is equal to the superimposition of all the NMR
spectra of each single molecule contained in the mixture, as long as the molecules are
present in concentrations above the detection limit. With regard to this aspect, NMR
is less sensitive than MS, with a detection limit only in the range of micromolar
concentrations. Further, the spectra of complex biofluids can be very crowded
and difficult to analyze. A common solution is to increase the magnetic field (with
an increment in resolution and sensitivity) and to acquire spectra with higher
dimensions, i.e., with more nuclei observed at the same time, as in spectra where
carbon and proton resonances are combined and shown in a bidimensional plot.
Unfortunately, often, p eak overlap reduces the amount of obtainable information in
terms of metabolite identification and quantification. Usually, the assignment is
mostly based on literature data and public databases, such as the Human
Metabolome Database (HMDB) (Wishart et al. 2007, 2009, 2012). The major
NMR strengths for metabolomic research are its intrinsically quantitative nature,
which allows to obtain concentrations without dedicated calibrations, and its high
reproducibility (Vignoli et al. 2019; Takis et al. 2018). Sample preparation is also
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extremely easy and fast, with very few sample handlings steps, because there is no
need of physical separation of the analytes or extensive sample pretreating. Further,
the development of high resolution-(HR) 1H magic angle spinning (MAS) spectra
(Tomlins et al. 1998; Cheng et al. 1998; Garrod et al. 1999) made the acquisition of
data viable on small slices of tissue without any pretreatment (Cacciatore et al.
2013). In conclusion, for all these reasons, NMR is more suited (but not limited) for
“untargeted” metabolomic investigations, especially when the “fingerprinting”
approach is applied. This approach means the global, high-throughput, rapid analysis performed on the raw spectral data, without signal assignment or metabolite
quantification. It is mainly used to provi de sample classification using multivariate
statistical techniques, and it can be considered as a black-box tool to discriminate
between samples from different biological statuses or origins.
2.3 Other Approaches
Although only seldom used in metabolomic research, vibrational spectroscopy
techniques can offer rapid, high-throughput, and non-destructive analysis of a
wide range of sample types, producing characteristic spectra with potentially useful
diagnostic and prognostic applications (Hackshaw et al. 2020). Vibrational spectroscopy consists of infrared (near-IR and mid-IR) and Raman spectroscopy. The basis
of vibrational spectroscopy is the transition between quantized vibrational energy
states of molecules due to the interaction between the material and the radiation from
an electromagnetic source. Compared with that required for NMR or MS, the
vibrational spectroscopic instrumentation is relatively simple; it consists in small
benchtop instruments containing all basic optical components such as mirrors, light
sources, lenses, and detectors. Further, some dispersive optical spectrometers incorporate no moving parts at all, which result in robust, very fast, and highly reproducible instruments. Additionally, these instruments are stable over time and can be
easily maintained and operated by non-specialist users (Botros et al. 2008). The
mid-IR spectroscopy (4000–400 cm
sample and the IR beam that absorbs the functional groups in the sample and vibrates
as stretching, bending, deformation, or combination of all and provides the fingerprint characteristics of the chemical or biochemical substances in the sample. Mid-IR
peaks are sharp and well resolved, allowing for the identification of some analytes
within a sample by matching the peak patterns against libraries of previously
recorded reference spectra. It is also a quantitative method. One drawback is that
water absorbs IR radiation very strongly, making the analysis of aqueous samples
challenging. However, the use of short path–length transmission cells and attenuated
total reflectance sampling accessories can mitigate this issue (Botros et al. 2008).
The near-IR spectroscopy (12,500–4,000 cm
combination vibrations. These molecular overtone and combination bands are typically very broad, leading to complex and unspecific spectra: it can be difficult to
assign specific features to specific chemical components. Multivariate statistical
techniques are often employed to extract the desired chemical information. As
-1
) is based on the interaction between the
-1
) is based on molecular overtone and
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such, near-IR analysis is not typically used for target metabolite identification but is
used for untargeted spectral profile comparisons. An advantage is that these
instruments are very simple, cheap, and easy to use (Botros et al. 2008). Raman
spectroscopy is an inelastic light-scattering phenomenon, the incident photon is
irradiated on the sample, and the molecules scatter the light. Although most of the
scattered light has the same frequency as the incident light, some of them have
different frequencies due to the interaction between the oscillation of light and
molecular vibration. This phenomenon is called Raman scattering, and, unlike IR
spectroscopy, Raman spectroscopy has a very weak water signal and minimal water
interference, which is a great advantage for the analysis of biological samples
(Hackshaw et al. 2020).
Overall, these techniques allow for rapid differentiation of samples according to
different properties, and some successful application in metabolomics have been
reported in the literature (Hackshaw et al. 2020; Botros et al. 2008).
3 Metabolic Alterations in GI Cancers
3.1 Metabolomics and Pancreatic Cancer
Glycolysis or glycolytic flux, as defined in the introduction, provides energy and
biomass for cell development, division, and proliferation, but one of the cancer
hallmarks is significantly elevated glycolytic flux, even in the presence of normal
mitochondrial function. This process is controlled by glucose transporters, ratelimiting enzymes, and the intermediates of glycolysis, which regulate redox homeostasis, glycosylation, and biosynthesis. Further, pentose phosphate pathway (PPP),
hexosamine biosynthesis pathway (HBP), serine biosynthesis, and tricarboxylic acid
(TCA) cycle branching from glycolysis, can promote tumorigenesis either alone or
in combination (Poursaitidis and Lamb 2018). The metabolic intermediates of the
TCA cycle play a key role in the metabolic aberrations in pancreatic cancer. Some
studies have suggested that the enzymes regulating the metabolic pathways, glycolysis, and TCA cycle play a fundamental role in metabolic alteration. In addition,
lipid metabolism is involved in tumorigenesis, and elevated fatty acid synthesis is
one of the most important metabolic alterations in cancer cell metabolism. Several
studies have highlighted an associati on of altered lipid metabolism and increased
expression of enzymes involved in lipid metabolism with pancreatic ductal adenocarcinoma (PDAC). In PDAC, abnormal metabolism depends on the anomalous
behavior of some oncogenes altering the consumption of physiological nutrients by
cellular factors (Ying et al. 2016). Moreover, the activation of alterations in genes
and oncogenic signaling pathways is carried out by metabolic reorganization. In
detail, some studies show that Kirsten rat sarcoma virus (K-RAS) and other oncogene mutations (and tumor suppressors) are key to enhancing PDAC growth by
directly reprogramming cellular metabolism (DeBerardinis et al. 2008; Cetinbas
et al. 2016). It is now assured that the K-RAS gene plays a critical role in the
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metabolism of PDAC glucose. Excessive glucose absorption and overexpression of
glycolytic enzymes, including the transporter of type 1 glucose, hexokinase 1/2,
phosphofructokinase, and lactate dehydrogenase A characterize the PDAC. For
these considerations, the metabolome study is a new approach to the identification
of cancer signatures, although there are currently no widely used metabolic markers
in clinical practice for the diagnosis, prognosis, or prediction of therapy response
(especially chemotherapy). Serum samples are widely used in clinics to assess the
existence of tumor markers such as carcinoembryonic antigen and carbohydrate
antigen 19–9; however, due to the presence of false positive results provided by
other non-neo plastic conditions, these tests have strong sensitivity but low specificity. To date, the ability to diagnose PDAC through metabolomics in the blood has
been demonstrated, and there are an increasing number of studies that show the
differences between metabolic profiles of PDAC patients (Yang et al. 2011; Mayerle
et al. 2018; Zhang et al. 2012b). Based on a small cohort of PC patients (17 correlated
with 23 healthy subjects), OuYang (Yang et al. 2011) et al. showed that full
1H-NMR serum spectra could be used to differentiate between the two groups
using main component analysis to distinguish altered 3-hydroxybu tyrate and lactate
levels in PDAC patients. These alterations have also been observed by Zha ng and his
colleagues (Zhang et al. 2012b) in PDAC plasma samples (n = 19) with lower
citrate, low-density lipoprotein, high-density lipoprotein, valine, lysine, leucine,
isoleucine, histidine, glutamine, glutamate, alanine, higher N-acetyl glycoprotein
(NAG) concentrations, very low-density lipoprotein, lipid glyceryl, dimethylamine,
and acetone concentrations compared with controls. Moreover, the authors identified
differences in the plasma metabolomic profile of PDAC patients compared to
chronic pancreatitis patients (n = 20). Mayerle et al. investigated the blood metabolic profile of 914 patients with PDAC, chronic pancreatitis (CP), liver cirrhosis,
and healthy controls. The authors identified nine metabolites and additionally CA
19–9 able to distinguish PDAC from CP with a negative predictive value for cancer
of 99.9% (Mayerle et al. 2018). In a recent pilot study by Michálková et al. (2018),
they took into consideration ten pancreatic cancer patients and ten healthy subjects
and performed the metabolomic profiling of the plasma of patients. They found that
pancreatic cancer patients and healthy controls can be distinguished with a high level
of accuracy, and the most significant differences between both groups were found in
the levels of 3-hydroxybutyrate and lactate. Other statistically significant metabolites
were alanine, glutamine, and valine, which may be regarded as a new panel of PC
biomarkers. In a well-designed analysis, Bathe et al. (2011) demonstrated the ability
to differentiate PDAC (n = 56) from benign pancreatic conditions (including benign
masses and chronic pancreatitis) and patients with gallstone disease (n = 43) by
NMR metabolomic analysis of serum. The same group also documented the difference in the metabolomic profile of malignant and benign pancreatic and
periampullary lesions using 1H-NMR and GC–MS on a larger monocentric cohort
(n = 157) (McConnell et al. 2017). Indeed, this is a significant finding because it is
not always possible to differentiate PC from other non-pancreatic adenocarcinomas
in the clinic, such as periampullary adenocarcinomas, especially when they are
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