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located close to the head of the pancreas. Two different studies (Battini et al. 2017;
Phua et al. 2018) analyzed tissue sample from PDAC patients. Battini et al. enrolled
106 patients who underwent surgery for PDAC; tissue samples were analyzed to find
metabolic biomarkers associated with long-term survival. Analysis revealed higher
levels of glucose, ascorbate, and taurine to be correlated with long-term survivorship. Long-time survivors tended to have decreased levels of choline, ethanolamine,
glycerophosphocholine, p henylalanine, tyrosine, aspartate, threonine, succinate,
glycerol, lactate, glycine, glutamate, glutamine, and creatine. Among these
metabolites, the level of ethanolamine had the highest accuracy to distinguish
long-term from short-term survivors. The area under curve (AUC) was 0.86 ± 0.1,
with a sensitivity and specificity of 77.8% and 75%, respectively (Battini et al.
2017). Phua et al. analyzed the metabolomic profiles of tumor tissues from
25 patients, who underwent curative resection and adjuvant gemcitabine-based
therapy. Elevated lactic acid levels were found in the tumors of patients with poor
clinical outcomes after gemcitabine treatment. In contrast, patients with low levels of
lactic acid and higher protein expression of hENT1 were found to have a significantly longer survival time than all other groups (Phua et al. 2018). A very recent
study by Zhao et al. (2023 ) analyzed 105 tissue samples from patients with PDAC
and benign pancreatic (BP) cystic neoplasms as well as 240 serum samples from
PDAC, BP, and healthy controls (HC). A biomarker panel composed of proline,
creatine, and palmitic acid was constructed employing combined nontargeted and
targeted technology based on the consistency of the tissue and serum metabolomic
analyses. In both the training set and validation set, the biomarker panel showed
good performance in differentiating PDAC patients from HC or BP. The panel
outperformed CA19–9 in terms of diagnostic performance when comparing PDAC
and BP. Other NMR-based metabolomic studies focus on the characterization of the
tumor pro file in urine samples suggesting that urine is an excellent biofluid to track
the effect of treatment on patients or to recognize the need for invasive intervention.
Davis et al. (2013) showed that it is possible to differentiate between PDAC patients
(n = 32) from 25 healthy subjects (area under the receiver operating characteristic
curve (AUROC) of 0.988) and 32 with benign pancreatic disease (AUROC of 0.95)
with optimum precision using urine sample. The effect of full surgical resection on
the metabolomic profile is also measured, showing a recovery tendency toward a
normal profile. In a male cohort, Napoli et al. (2012) reported a characteristic urinary
PDAC metabolomic signature; however, the heterogeneity of the cohort (12 patients
with liver metastasis, 4 diabetic patients, and 3 pancreatitis) represents the study
limit. In a very recent study by Sahni et al. (2020), urinary metabolites from
92 PDAC patients (56 discovery cohort and 36 validation cohort) were compared
with 56 healthy volunteers. Fourteen biomarkers were established (six upregulated
and eight downregulated) and a panel of six biomarkers (i.e., trigonelline, glycolate,
hippurate, creatine, myoinositol, hydroxyacetone) have been shown to be very
capable of PDAC diagnosis. In particular, the selected panel demonstrated a very
high ability to diagnose early stage (I and II) PDAC patients, demonstrating its
effectiveness in early surgically resectable diagnosis of PDAC patients in at-risk
populations.
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3.2 Metabolomics and Colorectal Cancer
As previously underlined for pancreatic cancer, in CRC, glucose transporter
1 expression is upregulated in neoplastic cells in response to hypoxia, inducing
glycogen-metabolizing e nzymes, including glycogen synthase and glycogen phosphorylase. However, the altered glycogen metabolism and its potential impact on the
CRC biology remain poorly understood (Vyas et al. 2019). Increasing evidence
suggest that Gln is a fundamental metabolic substrate and energy source for cancer
cells that require Gln for their growth and survival, a dependence called “glutamine
addiction” (Vyas et al. 2019). Current studies have shown that tumor cells can carry
out cell proliferation–related metabolic processes and sustain tricarboxylic acid
(TCA) cycle, amino acid levels, exosamine, nucleotides, and other molecules
(Wise and Thompson 2010; Altman et al. 2016).
The cancer cells show specific modifications in various points of lipid metabolism
(Santos and Schulze 2012). Free fatty acids, which may help to understand the
disease mechanism and physiological processes, are important substrates for lipid
synthesis. Unsaturated free fatty acids provide a significant amount of energy and are
closely connected to malignant tumors during cell proliferation (Huang et al. 2013).
Since then, with thousands of papers published, the area of CRC biomarker research
has expanded exponentially, but many initially promising findings have not been
transformed into clinically relev ant diagnostic approaches (Loktionov 2020). A very
recent systematic review by Tian et al. (2020 ) analyzed 69 articles of CRC and
revealed that 472 metabolites were altered in CRC patients. Twenty-six differential
metabolites (such as glycine, L-valine, L-lactic acid, L-alanine, L-phenylalanine,
among others) were reported more than ten times, 31 metabolites more than five
times, and 66 metabolites 3–5 times. Metabolite features have been shown to be
possible CRC diagnostic tools (Williams et al. 2013). Farshidfar et al. (2012)
employing metabolomic data obtained from both 1H-NMR and GC–MS platforms,
discriminated serum samples of patients with liver-limited metastasis from local
(stages II and III) CRC or extrahepatic metastasis patients.
A pilot study of Dalal et al. (2020) revealed that there is an increased level of lowmolecular-weight compounds in CRC patients’ urine in which ultrahigh performance liquid chromatography/time-of-flight–mass spectrometry (UPLC/TOF–MS)
and multivariate statistical analysis were used for metabolite profiling and data
analysis, respectively. Gu et al. (2019) analyzed serum samples to investigate
differential metabolomic profile between CRC patients (n = 40), colorectal polyp
patients (n = 32), and healthy controls (n = 38). The patients with colon polyp show
high risk to develop CRC, and it was well documented that pyruvate metabolism,
glycerolipid metabolism, Gln and glutamate metabolism, alanine, aspartate, and
glutamate metabolism were the most irregular metabolic pathways in them (compared with the metabolism of healthy contr ols). In addition, the metabolomic profile
of CRC patients has been distinguished from that of colorectal polyposis. The blood
samples obtained before diagnosis for CRC risk–related issues were analyzed by
Shu et al. (
including 12 glycerophospholipids and 9 other lipids, 7 aromatic compounds,
2018), and 35 metabolites were found to be associated with CRC,
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5 organic acids, and 4 other organic compounds, and a panel of 9 independent
metabolites could discriminate CRC from controls. Zhang et al. (2016a, b) analyzed
the serum levels of free fatty acids and demonstrated that C16:1, C18:3, C18:2, C18:
1, C20:4, and C22:6 were significantly decreased in CRC patients compared with
patients with benign colon diseases and healthy controls. C16:1, C18:2, C20:4, and
C22:6 panel showed excellent performance for the diagnosis of early-stage CRC.
The possibility to use urine profile for the diagnosis of early-stage cancer would be a
medical breakthrough. Wang et al. (2017) displayed a characteristic urinary
metabolomic fingerprint of stage I and stage II CRC patients (stage I/II vs stage
III/IV: R2Y = 0.41; Q2 = 0.45). In addition, the authors established both urinary
metabolomic variations with respect to esophageal cancer in early-stage CRC
samples, indicating that upper and lower GI cancers have different metabolomic
profiles and that both overlapping metabolites are correlated with tumor cell proliferation/growth associated with shared tumorigenesis pathways (disturbed gut microflora and urea metabolism) (Wang et al. 2017). Qiu et al. (2010) analyzed urine
samples from a group of patients (60 CRC-diseased individuals with different stages
of cancer) and 63 healthy volunt eers. In a predictive model, 187 volatile metabolites
were found in 90% of the samples, enabling CRC patients to be discriminated
against in the predictive portion by healthy controls. Moreover, six metabolites
with characteristic levels of expression could distinguish various CRC stages. To
find metabolite markers of colorectal cancer, Cheng et al. (Dunn and Ellis 2005)
investigated a cohort of 103 CRC patients and 101 healthy controls, and from the
total 163 volatiles detecte d, 19 metabolites were selected as potential biomarkers.
There is no systematic method for processing, preparing, and analyzing fecal
samples to date, despite the growing acceptance of fecal metabolomics. The fact
that this form of matrix is a semisolid mixture of endogenous and exogenous
components intensifies this lack of standardization, so a very complicated sample
preparation for metabolomic analysis is required. In addition, fecal metabolite
analysis has never been examined through a systematic review or a systematic
study, differently from urine, serum, plasma, cerebrospinal fluid, and saliva
biofluids, until Tian et al. (2020) study. Monleón et al. (2009) conducted their
study using a 1H-NMR on a small cohort of 11 controls and 21 CRC patients and
showed that fecal water extracts have an abundance of small metabolites such as
lactate, glucose, and amino acids. As predictors of earlier diagnosis in various stages
of CRC, Lin et al. investigated NMR-based fecal metabolomic fingerprinting. In
particular, their first study’s results showed that the fecal metabolic profiles of
healthy subjects can be well discriminated against those of CRC patients even in
the early stage (stage I/II). In addition, at stage I/II, the levels of glucose, lactate,
short chain fatty acid (SCFA’s), glutamate, and succinate differed significantly from
those at stages III and IV, providing valuable molecular details on CRC staging. In a
second study, the same group enrolled a total of 70 CRC patients and 70 healthy
controls to rough out parallel CRC biopsy metabolites and virtually non-neoplastic
tissue preoperative and postoperative fecal samples from the same patients. A recent
study by Niccolai et al. (2019) inves tigated fecal SCFA profiles (quality and
quantity) of patients with various bowel disorders, including colorectal cancer,
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adenoma, and celiac disease, using gas chromatography and mass spectrometry. A
recent study by Nannini et al. compared the fecal NMR metabolomic profiles of
patients with CRC or adenomatous polyposis (AP) with those of healthy controls.
The metabolomic analyses revealed that fecal sample profiles differed among CRC,
AP, and HC patients, and some discriminatory metabolites including acetate, butyrate, propionate, 3-hydroxyphenylacetic acid, valine, tyrosine, and leucine were
identified (Nannini et al. 2021).
3.3 Metabolomics and Liver Cancer
The liver is the human metabolic center and controls the levels of expression of
various metabolites, so examination of tissue metabolomics is especi ally important
when evaluating the incidence of HCC (Kimhofer et al. 2015). Metabolomic study
showing elevated glycolysis, gluconeogenesis, and β-oxidation, with decreased
tricarboxylic acid (TCA) cycle activity, has profiled the key metabolic changes in
liver cancer. As previously reported, aerobic glycolysis is frequently observed in
various tumors, and it has a prominent role in the metabolism of glucose for liver
cancer (Huang et al. 2013). Glycolysis is associated with activated oncogenes and
mutated tumor suppressors and, as in other tumors, in liver cancer tissue, glucose
transporters (GLUTs), glycolysis-related enzyme hexokinase2 (HK2), pyruvate
kinase M2 (PKM2), and lactate dehydrogenase (LDH) A are overexp ressed,
indicating an increased activity of glycolysis (Jones and Thompson 2009). In
addition, hypoxic tumor microenvironment (TME), and overexpression of
β-catenin stabilizes the hypoxia-inducible factor (HIF)-1af in liver cancer to stimulate glycolytic enzymes (Shang et al. 2016). In this type of tumor, fatty acid
β-oxidation is increased to address energy shortages and decrease tumor dependency
on glucose. Metabolic modification enhances fatty acid (FA) biosynthesis and
metabolism of glycerolipids, leading to the accumulation of fatty acids and lipids
(Jiang et al. 2006; Budhu et al. 2013). Moreover, almost all amino acids in liver
cancer are increased due to the reduction in catabolism of amino acids (Huang et al.
2013). In comparison to changes in the entire metabolic system, tissue metabolomics
is the most successful medium for studying targeted responses to pathogenesis and
can provide clear information on metabolic modifications and upstream regulations
(Lu et al. 2016; Johnson et al. 2016); however, the collection of HCC tissue is
invasive, limiting its clinical application. Serum metabolomics still has great potential for biomarker discovery in order to establish a reliable and noninvasive approach
for early HCC diagnosis, although its targeting capability and specificity are limited
(Zeng et al. 2014; Chen et al. 2009). Han J et al. identified six metabolites in both
tissue and serum samples. The greatest difference was in purine metabolism, bile
acid synthesis, and glycerophospholipid metabolism, which were closely correlated
with HCC growth (Han et al. 2019). Due to their need for nuclei acid synthesis and
their function as cofactors promoting cell proliferation and survival, purines are the
most abundant metabolic products (Yin et al. 2018). By coordinating salvage and de
novo biosynthetic pathways, cellular purine levels are preserved, the latter being
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enhanced in tumor cells as purine nucleotides are necessary for tumor cell proliferation (Pedley and Benkovic 2017). Jee SH et al. performed serum metabolomic
analysis of 75 HCC patients and 134 age-matched and gender-matched cancer-free
subjects. They showed the clinical relevance of deregulation of tyrosine metabolism;
phenylalanine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis;
valine, leucine, and isoleucine degradation; valine, leucine, and isoleucine biosynthesis; tryptophan metabolism; glycerophospholipid metabolism; linoleic acid
metabolism; primary bile acid biosynthesis; and fatty acid metabolism. Specifically,
the analysis indicated that dysregulation of these metabolic processes may be a key
mechanism underlying progression and HCC growth. The role of some pathologic
pathways in HCC was investigated by previous related metabolomic studies. Aromatic amino acids (AAAs), branched-chain amino acids (BCAAs),
glycerophospholipids, and bile acids have been suggested as possible metabolic
markers for HCC in this context (Fages et al. 2015 ; Xiao et al. 2012; Wang et al.
2013). Specifically, in the serum of HCC and liver cirrhosis patients, an amino acid
imbalance has been reported, showing a decrease in BCAAs and an increase in
AAAs (especially tyrosine) (Fages et al. 2015). An in vitro 1H NMR spectroscopy
research was conducted by Soper et al. (2002) to classify liver biopsy samples for
normal, cirrhotic, or hepatocellular carcinoma based on a computer-based statistical
classification strategy, identifying changes in lipids, choline, and creatine. Ninetyeight percent of hepatocellular carcinomas in this series were differentiated on the
basis of reduced lipid and increased choline content from nonmalignant tissues.
3.4 Metabolomics and Gastric Cancer
As previously reported, metabolic dysregulations are common for several cancer
types, such as deregulated glucose and amino acid uptake, increased nitrogen
demand, and the use of glycolysis intermediates for development and NADPH
biosynthesis. Based on different biological samples, including tissue, plasma, and
urine, more studies have been conducted to investigate the broad network of
metabolites in GC cancers.
Several evidence indicate that lactic acid concentrations display a consistent rise
in urine (Hu et al. 2011; Jung et al. 2014; Chen et al. 2016) and tissue (Hirayama
et al. 2009; Kaji et al. 2020) of GC profiles, while glucose levels are considerably
lower compared to nonmalignant profiles. The high lactate level may be due to
dysregulated metabolism of most cancer cells, known as the Warburg effect. The
overexpression of glucose transporters and type II hexokinase can result in glucose
decrease as seen in gastric cancer tissues. Increased activity of fructose-6-phosphokinase, acting as control of glucose output to the glycolysis pathway, can also
contribute to lowering amount in GC tissue (Gatenby and Gillies 2004).
An increase of five metabolites and intermediates of tricarboxylic acid (TCA)
cycle (alpha-ketoglutaric acid, malic acid, fumarate, succinate, citric acid) are
observed in blood (Song et al. 2012;Aaetal.2012), urine (Yang et al. 2011;Hu
et al. 2011), and tissue (Hirayama et al. 2009; Aa et al. 2012; Chen et al. 2010; Song
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et al. 2011) samples from gastric cancer patients. Different reasons could explain
these alterations; one is that cancer cells use a small glucose amount in oxidative
phosphorylation, and these cells could also use fumarate to generate energy and
succinate (the latter is one of the byproducts of this reaction) in particular
environments such as hypoxia or glucose deprivation (Farah et al. 2012 ). Another
explanation could be that several amino acids such as tyrosine, proline, threonine,
phenylalanine, or glutamine can be converted in cancer cells in the intermediates of
TCA cycle.
Indeed, amino acids are an important alternative energy source for cancer cells.
Metabolomic studies identified higher levels of several amino acids such as proline,
valine, phenylalanine, glycine, serine, tryptophan in gastric cancer tissues (Jung
et al. 2014; Chen et al. 2010; Wu et al. 2010), but lower levels in blood of patients
with GC (Lario et al. 2017) or CRC (Miyagi et al. 2011). The reason for this
difference could be that cancer tissues highly assimilate in the bloodstream freeamino acids because of the overexpression of L-type amino acid transporter
1 (LAT1) especially in advanced GC tissues (Ichinoe et al. 2015).
Tryptophan and kynurenic acid lower levels have been found more in the blood of
GC patients compared to CRC patients (Tian et al. 2020) and to the sequence of the
steps preceding the GC status (Lario et al. 2017; Kuligowski et al. 2016) known as
Correa’s cascade (non-active gastritis, chronic active gastritis, and precursor lesions
of gastric cancer) (Correa and Piazuelo 2012). Tryptophan metabolism has been
demonstrated in eliminating the host immune system in cancer cells (Prendergast
et al. 2011). Tryptophan can be metabolized to kynurenic acid, picolinic acid, and
NAD+ through the kynurenine pathway, a major pathway in tryptophan metabolism.
Lower tryptophan levels could be caused by the overexpression of tryptophanmetabolizing enzymes such as indoleamine 2,3-dioxygenase (IDO) biomarker of
GC and predictor of recurrence and prognosis (Nishi et al. 2018).
Glutamine is the only amino acid that is an exception. Indeed, in the colon and
gastric tumor tissues, glutamine is in the same amount as that of healthy ones. It has
been noted that glutamine is a favored amino acid for cancer cell s to produce energy
(Medina et al. 1992; Moreadith and Lehninger 1984). In many cancer cells types,
high glutaminase activity and low glutamine synthase activity has been report ed.
Since glutamate in tumor tissues is the most abundant amino acid, the conversion of
glutamine to glutamate in tumor tissues may be enhanced.
An increased rate of lipogenesis and upregulation of mitochondrial fatty acid
β-oxidation are common features of lipid metabolism in cancer cells, and gastric
cancer shows a similar tendency and presents typical changes with respect to
different metabolites involved in lipid metabolism. In gastric cancer tissue, fatty
acids such as hexadecenoic acid, docosahexaenoic acid, eptanoic acid, and
β-hydroxybutyrate are significantly abundant than in chronic superficial gastritis
(Aa et al. 2012). Of these, β-hydroxybutyrate is the typical product of β-oxidation
fatty acid degradation, indicating more severe microenvironmental decomposition of
fatty acids. Fat is absorbed by the rapid metabolism of lipids to fatty acids and
eventually ketone bodies, which may explain the weight loss of patients in later GC
stages. These signatures suggest that cancer cells use massive fatty acids, especially
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for lipid raft and lipid-modified signaling molecules, to meet the demand for cell
membrane synthesis (Poursaitidis and Lamb 2018).
Such rapid proliferation of tumor cells results in substantial upregulation of
nucleotide synthesis. Uric acid, an end product of nucleotide catabolism, has been
detected in higher amount in plasma samples of gastric cancer patients (Yu et al.
2011).
Moreover, the levels of adenosine diphosphate (ADP), adenosine triphosphate
(ATP), guanosine diphosphate (GDP), and guanosine-5′-triphosphate (GTP) are
higher in GC tissue both in colon cancer and healthy gastric tissues. These different
levels could suggest a relatively faster cell growth compared to colon cells. Another
fascinating alternative is that the levels of nucleotide pools can indicate the availability and dependence of oxygen in each tissue because purine and pyrimidine
pools have been found to reduce hypoxic stress (Hisanaga et al. 1986).
Increased level of creatinine, a waste product of muscle metabolism, was detected
in urine samples of tumor patients (Chan et al. 2016), which might be induced by
lower total body skeletal mass among cachectic subjects (Eisner et al. 2011;
Swaminathan et al. 2000). Changes in the amount of inositol in patients with gastric
malignancy are examined in either tumor tissue (Aa et al. 2012; Chen et al. 2010;Wu
et al. 2010) or urine samples (Chan et al. 2016), but their function and importance are
not well known.
Blood and urine are the recommended biofluids for the investigation of gastric
cancer biomarkers because they are less invasive, one is systemic, and the other is
closed to the disease region. However, most of the metabolic alteration detectable in
blood or urine samples are common to several cancer or pathological types. Indeed,
Yu et al. (2011) identified a common metabolomic profile between GC and intestinal
metaplasia in plasma samples. However, Ikeda et al. (2012), despite the small sample
size, identified characteristic metabolites levels when comparing the blood samples
of GC, esophageal, and colorectal cancer patients with those of healthy controls.
Higher amounts of 3-hydroxypropionic acid were found in the blood of GC patients,
providing sensitivity and specificity for the diagnosis of 84.6% and 71.4%, while
pyruvate, present in lower amounts in GC provided a sensitivity of 70% and a
specificity of 90.9%.
Several published evidence suggested an interesting potential of urine samples for the
diagnosis of gastric cancer. Chen et al. (2016) after combining 14 urinary metabolites
(alanine, glycine, valine, isoleucine, serine, threonine, proline, methionine, tyrosine,
tryptophan, 2-methylacetoacetate, levulinic acid, p-cresol, benzylmalonic acid), present
in higher amount in urine samples of GC patients versus healthy controls (159 GC
patients and 134 HC), obtained a diagnostic model with a specificity of 85% and a
sensitivity of 77.4%, superior to those obtained with serum biomarkers. Importantly,
in early gastric cancer patients (n° = 37) analyzed by Chen Y. and colleagues, the
14 candidate metabolites increased drastically. Three metabolites predicted the prognosis
of the patient. A worse prognosis was associated with higher levels of proline, p-cresol,
and 4-hydroxybenzoic acid.
Kwon H.N et al. analyzed urine samples of GC patients (n° = 103) and HS
(n° = 100) including a higher number of stage I GC (n° = 46), in a larger
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independently validated study (Kwon et al. 2020). Based on the metabolites
identified and quantified by NMR in urine spectra, Kwon H.N and coworkers
obtained an optimized swinging door algorithm (OPS-DA) model discriminating
GC (all stages) with HS with a sensitivity of 94.1% and a specificity of 93.9%. This
metabolomic approach showed a robust performance even with the earliest, stage IA
samples, exhibiting a specificity of 97% and a sensitivity of 94.7%.
3.5 Metabolomics and Esophageal Cancer
Esophageal cancers (ECs) together with GCs are malignant tumor types of upper
gastrointestinal tract with high morbidity and mortality rates.
Abnormal carbohydrate metabolism is common to several cancer types. Indeed,
several metabolites (including lactic acid, glucose, citrate, fumaric acid) involved in
cellular respiration have been frequently reported. With respect to GC, the majority
of metabolomic studies on blood of EC patients (Zhu et al. 2017; Zhang et al. 2011,
2012a; Hasim et al. 2012b) revealed higher citrate levels, while its downregulation
was detected in tissue and urine of EC samples. Lower levels of isocitrate were
detected in EC (Tokunaga et al. 2018) (while it remains upregulated in GC tissues
(Cai et al. 2010)).
Most of the other TCA intermediates (alpha-ketoglutarate, malic acid, cis-aconitic
acid, succinic acid, fumaric acid) were upregulated in blood, tissue, and urine of EC
patients (Zhu et al. 2017; Zhang et al. 2012a; Davis et al. 2012). In addition, a limited
oxidative phosphorylation due to the accumulat ion of TCA intermediates is
documented in EC, supporting the alterations of the metabolites of glycolysis,
accelerating the accumulation of lactic acid in blood and tissue. Altered pathways
include changes in amino acid metabolism. Most of the essential and nonessential
amino acids were found to be upregulated in both GC and EC tissues, while lower
levels were detectable in blood and urine biospecimens (Tian et al. 2020). Additionally, fatty acid metabolism dysregulation has been extensively demonstrated in
esophageal cancer. However, the findings regarding fatty acid levels in blood
samples of EC are controversial. Reduced fatty acid levels are detected in blood
EC samples (Zhu et al. 2020; Zhang et al., 2012a) compared to other cancer types
such as colorectal cancer (Qiu et al. 2009) and cervical carcinoma (Hasim et al.
2012a). Hasim et al. (2012a , b) detected in blood samples from patients with lymph
node metastasis and in patients with late stage (>Ib2) disease, higher levels of
unsaturated lipid compared with patients with non-lymph node metastasis and with
early-stage (≤Ib2) disease. In addition, Xu et al. (2013) and Wang et al. (2016) found
a higher fatty acid biosynthesis in EC blood samples. Despite the difference in the
trend of fatty acids, most of the studies mentioned detected lower levels of
acylcarnitines and higher levels of carnitine in EC blood compared to those of
healthy controls. Indeed, carnitine and acylcarnitines are essential for the transport
of long-chain fatty acids across the mitochondrial membrane for degradation and
energy production. Many cancer-related abnormalities in energy metabolism and
intermediate metabolic disorders are also closely related due to abnormal levels of
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acylcarnitine (Qiu et al. 2009; Chen et al. 2009; Peluso et al. 2000; Adlouni et al.
1988; Sewell and Böhles 1995). A dysregulated energy metabolism is demonstrated
in EC blood samples by abnormal levels of lysophosphatidylcholines (lysoPCs). In a
reaction catalyzed by lysophospholipase A1, lysoPCs can hydrolyze into fatty acids
and subsequently decay in the mitochondria to generate energy through β-oxidation
(Wang et al. 2012). LysoPCs in a reaction catalyzed by lysophospholipase D
(lysoPLD) can also be transformed into lysophosphatidic acid (LPA). Interestingly,
the serum levels of LysoPC (14:0) demonstrated to be downregulated in esophageal
cancer plasma and together with LPA (18:1) showed a decreased trend with EC
progression (Wang et al. 2016; Xu et al. 2013; Zhu et al. 2020) in serum samples.
Cancer cells also display alte ration in the nucleotide metabolism. A few nucleosides
also showed significant variations in EC samples. Levels of 1-methyladenosine, N
2
N
-dimethylguanosine, N2-methylguanosine, and cytidine were significantly
increased while the concentration of uridine was significantly lower in cancer
patients compared to control serum samples (Djukovic et al. 2010). In EC tissues,
the levels of nucleoside triphosphates [adenosine triphosphate (ATP), cytidine
triphosphate (CTP), guanosine-5′-triphosphate (GTP), and uridine-5′-triphosphate
(UTP)] were statistically significantly lower, whereas those of nucleoside
monophosphates, such as guanosine monophosphate (GMP), were much higher
compared to healthy tissues (Tokunaga et al. 2018). Upregulated metabolism of
hypoxanthine was observed to be higher in blood, tissues, and urine of EC patients
than that of the controls. Different studies have shown that in tumor cells, enzymes
associated with the purine biosynthetic pathway are enhanced bec ause purine
nucleotides are necessary for the proliferation of tumor cells. Based on detected
biomarkers, several authors suggested statistical models to accurately diagnose the
EC using less-invasive biological specimens such as blood and urine. Zhu et al.
(2017) obtained a better perfor mance using glucose as EC predictor with a sensitivity of 83.3%, a specificity of 100%, and an AUC of 0.952 among all the identified
serum biomarkers (low level in EC: pipecolic acid, glucos e, glutamic acid, oleic
acid; high level in EC: lactic acid, cholesterol, myo-inositol-1-phosphate). Zhang
et al. (2012b) compared the model obtained with the biomarkers identified using
LC–MC versus those identified with NMR obtaining a specificity of 86%, a sensitivity of 77%, and an AUC of 0.82 with the former and a speci ficity of 88%, a
sensitivity of 82%, and an AUC of 0.86 with the latter. Nevertheless, by combining
the best biomarkers obtained with both techniques, they get a sensitivity and a
specificity of 91% and an AUC of 0.95. These models were created to use the
serum profile to distinguish EC patients from those considered at high risk of
developing EC such as patients with Barrett’s esophagus (BE) and patients with
high-grade dysplasia (HGD). Urine samples (Davis et al. 2012; Vignoli et al. 2019)
have also been used to distinguish patients with EC and BE patients obtaining an
AUC of 0.943 using 1H-NMR profiling approach. Wang et al. (2016) provided a
serum metabolomic model based on UHPLC–QTOF/MS approach to distinguish the
esophageal cancer progression (from stage I to stage III). Another attempt to provide
patient stratification according to the severity of the disease was proposed by Jin
2
,
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et al. (2014) using a GC/MS serum metabolomic approach to distinguish EC patients
with or without lymph node metastasis obtaining a predictive accuracy of 90%.
Finally, Ouyang et al. (2023) identi fied significant NMR-based metabolic alterations
in serum, urine, and tumor tissues in EC patients compared to those of the controls.
In addition, significant alterations of many metabolites in serum and urine were
linked to the metabolic profiles of EC cancer tissues. Both in serum and urine
creatine, glycine was selected as the potential biofluid biomarker panel for EC
detection.
4 Conclusion
In this exhaustive chapter, we have reported the most recent and significate
metabolomic studies in human gastrointestinal cancers. Therefore, we can conclude
that the comparative 1H NMR or MS examination of urine, blood, biopsy, and/or
feces in GI cancer patients suggested a large variety of biomarker candidates. As
previously reported, the early stage of gastrointestinal cancers usually presents no
symptoms, so they are diagnosed at advanced stages resulting in a poor prognosis.
The discovery of predictive biomarkers could lead to early diagnosis improving the
length and especially the life quality of GI patients. To minimize unfavorable
prognosis and medical costs, it is therefore crucial to establish low-cost and noninvasive diagnostic techniques. We may conclude that biofluid NMR or MS analysis
could be a high-performance, quantitative, and reproducible test that completely
suits the notion of large-scale noninvasive population screening of gastrointestinal
cancers. Finally, we think that in future, the biofluid NMR or MS analysis could also
be used to monitor the treatment efficacy favoring a tailored and personalized
therapy.
Conflict of Interest All other authors have nothing to disclose.
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