Metabolomic study of human tissue and urine in clear cell renal carcinoma by LC-HRMS and PLS-DA
Metabolomic study of human tissue and urine in clear cell renal carcinoma by LC-HRMS and PLS-DA
Joanna Nizioł 0 1 2 4 5
Vincent Bonifay 0 1 2 4 5
Krzysztof Ossoliński 0 1 2 4 5
Tadeusz Ossoliński 0 1 2 4 5
Anna Ossolińska 0 1 2 4 5
Jan Sunner 0 1 2 4 5
Iwona Beech 0 1 2 4 5
Adrian Arendowski 0 1 2 4 5
Tomasz Ruman 0 1 2 4 5
0 Department of General Surgery and Urology, John Paul II Hospital , Grunwaldzka 4 St., 36-100 Kolbuszowa , Poland
1 Department of Microbiology and Plant Biology, University of Oklahoma , Norman, OK 73019 , USA
2 Faculty of Chemistry, Rzeszow University of Technology , 35-959 Rzeszow , Poland
3 Joanna Nizioł
4 Center of Biofilm Engineering, Montana State University , 366 Barnard Hall, Bozeman, MT 59717 , USA
5 Department of Chemistry, Montana State University , 103 Chemistry and Biochemistry Building, Bozeman, MT 59717 , USA
Renal cell carcinoma (RCC) is the most prevalent and lethal malignancy of the kidney. Despite all the efforts made, no tissue biomarker is currently used in the clinical management of patients with kidney cancer. A search for possible biomarkers in urine for clear cell renal cell carcinoma (ccRCC) has been conducted. Non-targeted metabolomic analyses were performed on paired samples of surgically removed renal cancer and normal tissue, as well as on urine samples. Extracts were analyzed by liquid chromatography/high-resolution mass spectrometry (LC-HRMS). Hydroxybutyrylcarnitine, decanoylcarnitine, propanoylcarnitine, carnitine, dodecanoylcarnitine, and norepinephrine sulfate were found in much higher concentrations in both cancer tissues (compared with the paired normal tissue) and in urine of cancer patients (compared with control urine). In contrast, riboflavin and acetylaspartylglutamate (NAAG) were present at significantly higher concentrations both in normal kidney tissue as well as in urine samples of healthy persons. This preliminary study resulted in the identification of several compounds that may be considered potential clear cell renal carcinoma biomarkers.
Renal cell carcinoma; Mass spectrometry; Biomarker; Cancer biomarker; Kidney
Biomarkers provide a powerful approach to understanding
diseases with applications in epidemiology, clinical trials,
screening, diagnosis, and prognosis. Defined as alterations in
the constituents of tissues or body fluids, they often offer the
means for classification of a disease and can extend our
knowledge about the underlying pathogenesis of disease.
Theoretically, efficient biomarkers can also reflect the entire
spectrum of disease from the earliest manifestation to the
terminal stage. The development of cancer therapies is
increasingly dependent on the understanding of tumor biology, and
biomarkers are becoming essential tools in the field of
Renal cell carcinoma (RCC) is the most prevalent and
lethal malignancy of the kidney, accounting for nearly 90% of
all renal tumors and representing 2–3% of all adult malignant
]. RCC is among the ten most common cancers
worldwide and is the second most lethal urinary cancer after
bladder. According to statistics published by GLOBOCAN in
2012, there were approximately 337,800 new cases of renal
cancer and 143,400 kidney cancer-related deaths worldwide [
RCC is now thought to be a morphologically and
genetically heterogeneous disease that can be classified into several
different subtypes, such as clear cell RCC (ccRCC), papillary
RCC, and chromophobe RCC [
]. Clear cell RCC is the
most prevalent histologic subtype of kidney cancer,
accounting for more than 75% of all RCCs .
A favorable prognosis (95% survival after 5 years) can be
achieved by radical nephrectomy with nephron-sparing
surgery when kidney cancer is detected and treated at an early
]. Unfortunately, most patients do not experience early
warning signs, such as fever, fatigue, night sweats, or weight
loss. Thus, as many as one third of patients are at an advanced
stage of the disease and have metastatic tumors beyond the
kidney, at the time of diagnosis. The lack of adequate therapies
at this stage is usually associated with poor prognosis and
long-term survival rates (5 years) [
]. Furthermore, RCC
exhibits a high degree of intrinsic drug resistance and is,
furthermore, highly resistant to radiation treatments . This
limits the treatment options and their effectiveness, although
targeted therapies provide some survival benefit [12, 13].
Recent studies have renewed interest in the alterations in
cellular metabolism associated with a range of diseases,
including cancers, and it is now widely accepted that
metabolomics can be a powerful tool not only for disease detection,
diagnosis, as well as treatment guidance and assessment but
also for the elucidation of the molecular processes behind the
disease states [
RCC is currently recognized as a metabolic disease 
and was previously studied by the metabolomic analysis of
body fluids, such as plasma , serum [16–18], and urine
[19–23], as well as renal tissue . Analytical methods used
for RCC tissue metabolomic studies include 1H nuclear
magnetic resonance (NMR) , gas chromatography/mass
spectrometry (GC-MS) [21, 26, 27], liquid chromatography/mass
spectrometry (LC-MS) [21, 24, 27–31], mass spectrometry
using ambient ionization techniques, such as desorption
electrospray ionization (DESI) [32, 33], and probe
electrospray ionization (PESI) . Metabolomics of RCC
have been combined with other -omic approaches, such as
transcriptomics [29, 30] and proteomics .
Metabolomic studies of RCC have been used not only for
the identification of biomarkers [25, 28, 34], the
differentiation of different phenotypes of RCC [26, 32], detection of
metastases  but also to enhance understanding of the
pathogenesis, progression of disease, assessment of the response
to novel nonsurgical therapeutic strategies, and the early
detection of recurrences [16, 35].
There is, thus, an increasing awareness of the promise of
metabolomic characterization of kidney diseases, including
RCC [24, 31, 36, 37]. There is potential for early detection,
accurate diagnosis and staging, detection of metastasis,
individualized treatments, prediction of patients’ outcome, and
monitoring of response to treatment. However, comparative
profiling of low molecular weight compounds, such as sugars,
lipids and amino acids in cancer tissue, as contrasted with the
corresponding normal tissue, is still a poorly explored area.
Despite all the efforts made, there is still no agreement on
clinically relevant tissue and biofluid-based biomarkers that
could be used for the proper management of kidney cancer
patients or on the analytical procedures to be used. This
highlights the importance of continuous development and
refinement of metabolomic strategies.
In the present study, metabolic profiling has been
performed on both tissue and urine samples from patients with
renal cancer. Cancer tissue was compared with healthy kidney
tissue from the same patients, while urine from healthy
subjects served as control for the urine samples from the cancer
patients. Features that were significantly more or less
abundant in either tissue or urine samples were identified. A
number of potential biomarkers for renal cancer have been
Materials and methods
Human kidney tissue and urine samples were obtained
between June and September of 2015 from seven patients, who
had kidney cancer and were scheduled for radical
nephrectomy. Bioethics Committee at the University of Rzeszow
(Poland) approved the study protocol. Specimens and clinical
data from patients involved in the study were collected with
written consent. Patients had been diagnosed with ccRCC.
Four of the patients with ccRCC donated 1 cm3 of renal tissue
removed ex vivo after radical surgical resection of kidney.
These samples contained both cancerous and adjacent normal
tissue. All patients donated 100 ml of urine each. Urine control
samples were collected from 15 healthy volunteers, for which
the presence of renal tumors had been excluded by abdominal
ultrasound. For brevity, these samples are here referred to as
Bcancer urine^ and Bcontrol urine,^ respectively. Patient
characteristics are provided in Table 1.
Chemicals and reagents
Acetonitrile, tetrahydrofurane (THF), water, and formic acid
were of HPLC-MS grade and purchased from Aldrich.
For tissue-based metabolomics, data obtained for the cancer
tissue samples was compared with the data obtained for the
normal tissue. The cancer and normal tissue samples for each
patient were obtained from the same tissue specimen and were
located 8–9 mm apart. All cancerous tissue samples were
examined by uropathologists and graded according to both the
Fuhrman and the American Joint Committee on Cancer
clinical staging systems. Clinical characteristics of case and
controls groups are given in Table 1). Normal and cancer tissue
sections were cut out from central parts of RCC and normal
tissue (∼ 1 mg from a ∼ 1-×-1-mm area), respectively, from
each of the four specimens used in this study. Metabolites
from each section were obtained by application of fast three
times freezing/unfreezing temperature cycling with 100 μl of
water (Bwater extracts^) or THF (BTHF extracts^) and
vortexed. Samples were then rapidly frozen and solvents
removed by freeze-drying in speedvac-type equipment. Dried
extracts were dispensed into 130 μl of LC-MS-grade water
(for water-based analysis) or 1 ml of isopropyl alcohol (for
THF-extraction analysis). The mixtures were vortexed for 30 s
and centrifuged at 10,000×g for 1 min at ambient temperature,
and the supernatant was transferred to an autosampler vial
(2 ml) for LC-MS analysis.
For the urine-based study, data obtained for cancer patients
as a group was compared with data for the control group of 15
healthy volunteers. Urine samples were collected and handled
in a uniform manner to ensure consistency. Volumes of 100 μl
of urine were diluted with 130 μl of LC-MS-grade water and
subjected to vortexing, centrifugation, and supernatant
collection as described above. Ten microliters of each aqueous
solution were injected on Agilent UHPLC system. For each
sample data, acquisition was performed in triplicate.
Liquid chromatography/high-resolution mass spectrometry
(LC-HRMS) analyses were carried out using an Agilent
1290 ultra-high-performance liquid chromatograph
(UHPLC) coupled to an Agilent 6538 quadrupole
time-offlight (QqTOF) mass spectrometer fitted with an electrospray
ionization (ESI) source operated in positive ion mode
(Agilent, Santa Clara, CA, USA).
LC separation was carried out using a SeQuant®
ZIC®HILIC column (5 μm, 150 × 4.6 mm, The Nest Group, Inc.,
Mass., USA) with a flow rate of 0.3 ml/min. A linear gradient
was applied from 80 to 20% acetonitrile for the first 30 min,
followed by 5% acetonitrile for an additional 8 min. The
injection volume was 10 μl. Mass spectrometer parameters were
as follows: ion-source gas temperature, 325 °C; capillary
voltage, 4000 V; fragmentor voltage, 120 V; nebulizer pressure,
20 psi; sheath gas flow, 10 l/min; m/z range, 50–1100; data
acquisition rate, 4 GHz; and 1.3 spectrum recorded/s.
Approximately 130 authentic standards (mixture of amino
acids, carbohydrates, energy metabolism metabolites, etc.)
were used to calibrate the retention time calculator with any
new column . Before starting LC-MS measurements, 30
authentic standards were injected to validate the state of the
Raw MS data was processed using the IDEOM version 19
 workflow. This utilizes XCMS Centwave  for peak
detection and mzMatch, R  for peak alignment between
triplicates and between samples, for filtering and for the
storage of the data in peak ML-formatted files. Feature alignment
was performed with a retention time window of 30 s and a
mass error window of 5 ppm. Scripts for XCMS  and
mzMatch are coded in the R environment.
In the alignment procedure, peaks obtained in three
different UHPLC-HRMS experiments (triplicate injections) are
determined to be formed from the same compound, based on
their appearance at nearly the same retention time and m/z
value. Signals of isotopomers were identified and assigned
to their respective quasi-molecular ion ([M + H]+ in positive
ion mode). The monoisotopic mass of the corresponding
neutral was obtained from that of the parent ion by subtracting the
proton mass. The alignment procedure results in a list of
Bfeatures,^ each associated with a monoisotopic mass (for
the neutral M), a retention time, and a total ion abundance.
The calculated mass values for the neutral compounds, M,
were used throughout the manuscript, instead of m/z for the
MH+ ions. Unless the identification of a parent ion in a group
of peaks as MH+ is erroneous, each feature will correspond to
an actual compound. Alignment of detected peaks was
performed separately for the set of samples extracted into THF
and into water, respectively.
A major objective of this metabolomic study is to identify
(putative) compounds that are over- or under-expressed in
renal cancer as opposed to normal renal tissue. For features,
the terms Bover-abundant^ and Bunder-abundant^ were used,
while Bover-expressed^ and Bunder-expressed^ were used for
(putatively) identified metabolites. Detailed LC-MS data
discussed in this work is available in the Electronic
supplementary material (ESM, Table S2).
Lists of detected features were matched against the
IDEOM’s version of the Kyoto Encyclopedia of Genes and
Genomes (KEGG) metabolite database  using a mass
error tolerance of 4 ppm. Retention times for authentic
standards, and a retention time prediction model, were included
for ZIC-HILIC chromatography data . For a putative
identification, the maximum difference allowed between
calculated and observed RT was 5% for authentic standards and 45%
for other metabolites. Putative identifications were also guided
by searches on the Madison-Qingdao Metabolomics
Consortium Database (MMCD)  and the Human
Metabolome Database (HMDB) .
Multivariate statistical analyses were performed using
Metaboanalyst 3.0 .
Results and discussion
Examples of total ion chromatograms for cancer and
normal tissue extracts are shown in the ESM (Fig. S1).
Similar comparison of chromatograms for cancer patient
urine and control urine samples is shown in the ESM (Fig.
S2). For water-extract-based analysis, a total of 4040
features were detected in the set of tissue samples and a total
of 3368 in the set of urine samples. Each feature is
associated with an exact mass, a retention time, and average
abundancies in cancer and normal tissue, as well as with
urine samples from patients with and without renal
cancers. Results for cancer tissue as compared with normal
tissue or in cancer urine as opposed to normal urine, are
listed in Table 2.
Analysis of water extracts of tissue
A total of 948 features were detected in the set. The relative
similarities and differences between the metabolomes of
the different samples were studied using statistical
methods. Principal component analysis (PCA) did not
yield a clear separation between cancer and normal tissue.
Therefore, supervised statistics, i.e., partial least square
discriminant analysis (PLS-DA), was applied as it
enhances the separation between groups by rotating the
Figure 1a shows the results for component 1 (C1) versus
component 2 (C2) and Fig. 1b for C2 versus C3. For each
patient, an arrow originates at the normal tissue and ends at
the cancer tissue, thus representing the metabolomic changes
caused by a transition to cancerous growth.
It is seen that it is mainly C2 that separates cancer from
normal tissues, while C1 is associated mainly with those
interpatient differences that are not strongly dependent on
the development of renal cancer. In the present sample set,
C1 mainly separates out patients 2 and 3 from patients 4 and
5. It is seen that C2 achieves a clear separation between
normal and cancerous renal tissue. (The separation for patient 5 is
less clear, due to unexplained outliers in each triplicate
It is noted that the respective cancer tissues have, for all
four patients, a somewhat more negative value for C1. Based
on this observation, it would seem that the metabolome
differentially associated with the development of renal cancer
has a pattern in common with interpatient differences for
normal tissue. Expressed differently, the normal tissue
metabolisms of some patients are, in some respects, more Bcancer
like^ than those of other patients.
As seen in Fig. 1, component 2 separates patients, based on
the metabolic changes that occur upon cancer development.
While such difference may also be related to a differential
response to influences external to the kidney, such as diet,
clues to possible metabolic patterns useful for the
classification of renal cancers are likely to be found within C3 and in the
magnitude of the change in C2.
In conclusion, it is demonstrated that with PLS-DA of
metabolomic data, it is possible to discriminate between
cancerous and normal renal tissue . BWhile the authors
acknowledge that this conclusion is based on analysis of data
obtained from a small sample pool, a much larger
investigation that involves over 100 patients is currently in progress to
confirm differences, reported in the pilot study herein,
between metabolomic profiles of cancer and healthy renal
Of the 948 features detected in the tissue samples, the
abundancies for a large majority were not significantly
different in cancer versus normal tissue. Using a minimum fold
change of 2, seven features were found to have a higher
average abundance in the cancer tissue and nine compounds a
higher average abundance in the normal tissue. These are
listed in Table 2 as compounds 1, 2, 12–16, 6, 10, 13, 16,
18, 23, 24, and 26–28, respectively. A listing of compounds
with standard errors is in Tables S1 and S2 in the ESM. The
cancer-to-control abundance ratios for these features are
shown in the form of bar charts in Figs. 2 and 3.
Two features that were over-abundant in cancer tissue were
putatively identified as carnitines (14, 15). In particular,
acetylcarnitine (14) had a very high abundance in cancer
tissue, while the abundance in normal renal tissue was two
orders of magnitude lower. Decanoylcarnitine (15), was also
over-expressed in the cancer tissue, though at much lower
abundance. Similar observations have been made previously
[23, 24] and attributed to fatty acid oxidation disorders
(FAOD) and inhibition of the β-oxidation pathway. Several
additional carnitines were putatively identified in this work
(14–28). Five of these (16–20) were over-expressed in cancer
tissue, though they did not fulfill minimum fold change
For each pair (tissue, normal or cancer and urine, control or cancer), the high abundance values are set in italics. Putative identifications are given if
mentioned in the text, if they are known human metabolites or otherwise may seem relevant to include
B–^ peak not detected
a Experimental monoisotopic neutral mass
requirements. Acetylcarnitine, is an acetic acid ester of
carnitine, which for example is used by the body to transport fatty
acids into the matrices of mammalian mitochondria where
fatty acid metabolism occurs . Acetylcarnitine is naturally
found in healthy human body, but it is also taken as a dietary
supplement. In human plasma and tissues, acetylcarnitine is the
most abundant naturally occurring derivative. Acetylcarnitine
was recently pointed out as a promising biomarker for
hepatocellular carcinoma .
The abundances of carnitine (19) were high both in tissue
and urine samples. Cancer-to-control ratios were found to be
moderately high being 1.6 and 1.5 for tissue and urine cancer/
control pairs, respectively; it should be noted that tissue result
is similar to the one shown by Ganti and co-workers .
Much more interesting results were found for other carnitines.
For example, hydroxyacylcarnitines found such as
hydroxybutyrylcarnitine (18) and hydroxypropionylcarnitine
(17) were found in tissue with 2.3 and 3.1 cancer-to-control
ratios, respectively. What is interesting, first compound—
hydroxybutyrylcarnitine was also found to be even in higher
abundance in cancer urine with a ratio of 5.7; result similar to
the one shown by Ganti et al. who showed also higher cancer
patients’ concentration of this compound (ca. four times) in
urine comparison. Two other carnitines were found to show
Fig. 1 PLS-DA plot based on LC-MS data processed in Metaboanalyst
3.0, showing a component 2 versus component 1 and b component 2
versus component 3. For each patient, an arrow starts at the location of the
normal tissue and ends at the location of the respective cancer tissue.
Sample Bcancerz4^ is based on an inferior vena cava tumor thrombus
similar pattern of ratios—4,8-dimethylnonanoylcarnitine (21)
and 2-dodecenoylcarnitine (20) for which cancer-to-control
ratios grow from 1.0 to 7.5 in case of 21 and from 1.4 to 4.1 for 20
when switching from tissue to urine results. Two other
carnitines—hexanoylcarnitine (22) and 3-methylglutarylcarnitine
(23) present higher abundances in normal tissue samples, and
only 23 was detected in urine with 3.4 cancer-to-control ratio.
Other carnitines found exclusively in urine samples such
as butenylcarnitine (24), heptanoylcarnitine (25),
2,6dimethylheptanoylcarnitine (26), pimelylcarnitine (27),
and dodecanedioylcarnitine (28) were detected exclusively
in urine, all of them were in higher abundances in cancer
patient urine with ratios of 3.1, 1.8, 4.1, 6.7, and 7.2 for
compounds 24–28, respectively. It should be noted that 26 was
also found by Ganti et al. with a cancer-to-control ratio of 2.0.
The abundance of feature 1 (Table 2) appeared to be about
300 times higher in the tumor tissue extracts than in the
normal tissue control samples. Using available databases, this
compound was putatively assigned to difructose anhydride, a
non-digestible disaccharide which stimulates calcium
absorption in rat and human intestine . This compound is not
included in the HMDB and does not seem to have been
implicated in cancer metabolism. It is, however, known that
cancer tissue often contains oxidated sugars and lipids [51, 52].
Feature 2 is putatively identified with norepinephrine
sulfate, which is known to be present in both kidneys and in
urine. Norepinephrine sulfate is related to epinephrine, which
is an important hormone and neurotransmitter that is involved
in a multitude of metabolic pathways. In particular,
epinephrine is known to protect cancer cells from apoptosis .
Both features 1 and 2 are over-abundant not only in cancer
tissue but also in urine samples from patients with kidney
cancer. They are discussed further below under
BCross-comparison of tissue and urine results.^
As mentioned above, 13 features were found to have a
higher abundance in normal tissue, as compared wwith cancer
tissue, features 5–13 in Table 2. One of the thirteen features
was not detected at all in cancer tissue, namely feature 13.
Feature 13 is putatively identified with
The biggest fold changes, > 50, for normal versus cancer
tissue were observed for features 11 and 12. Feature 11, but
not 12, was present also in urine but without significant
difference between RCC and control patient groups. Feature 11 is
putatively identified as N-(3-oxooctanoyl)homoserine, and
f e a t u r e 1 2 a s a l a n y l - α - t h i o p h e n y l g l y c i n e o r
S-(phenylacetothiohydroximoyl)-cysteine. The latter
compound is involved in the biosynthesis of glucosinolates
Two features that were over-abundant in normal tissue,
were also significantly more abundant in normal versus cancer
urine. Apart from 13 (NAAG), this was the case also for 9,
which has the putative identification of riboflavin. Both will
be discussed in more detail below.
For several of the features that were over-abundant in
normal tissue, it was observed that the abundances in the urine
samples were reversed, i.e., higher in cancer than in normal
urine, albeit with a smaller fold change. This was the case for
features 6, 10, and 23. This pattern can be expected for
compounds that are not retained or consumed, by cancer tissue, but
excreted. Three of the four features have putative
identifications based on mass alone but without additional evidence
they remain highly uncertain.
Five of the features that were over-abundant in normal
tissue were not detected in urine, namely 7, 5, 8, 12, 15, and
75. Feature 8 was 15-fold over-abundant in normal as
compared with cancer tissue. Assignment to a compound was
difficult as metabolite data base searches yielded 18 isomers
(Table S1 in the ESM). Feature 7, also over-abundant by
15fold in normal tissue, was putatively identified with
aminononanoic acid, an amino fatty acid.
Analysis of THF extracts of tissue
Additional analysis based on THF-extracts of metabolites was
performed. Selected results of this analysis are presented in
Analysis of urine samples
Among the 3368 features detected in the set of urine samples,
16 were determined to be over-abundant (features 2, 4, 6, 10,
15, 16, 18–21, 23–28) and 44 to be under-abundant (features
3, 9, 11, 13, 17) in cancer urine, when compared with control
urine. The abundance data is presented as bar charts in Figs. 2
The abundance pattern for the 11 features (6, 10, 15, 18, 20,
21, 23, 24, 26–28) over-abundant in cancer urine have a
dominant common pattern. All except features 24–28 were
detected also in tissue. Seven of the features were either not detected
at all, or detected with an insignificant abundance, in urine
from non-cancer patients. Third, the hits in the MMDB and
HMDB were few, and none were obvious candidates for
known human metabolites. While other scenarios are possible,
this pattern is consistent with the features being due to drugs
given to the cancer patients or to their metabolites. Such
compounds are indeed expected to be present in both types of
tissue and in cancer urine, but not in control urine. Because
a wide range of synthetic compounds are possible, any
putative identifications would be very unreliable and not included
in Table 2.
Cross-comparison of tissue and urine results
It is preferable for a cancer biomarker to be detected in an
easily available body fluid, preferably urine. The present study
involves four different types of samples. The paired cancer
and normal tissues are both obtained from patients with
cancer, while the cancer urine samples were obtained from the
patients that donated the tissue while the normal urine samples
were obtained from a different group of patients that had no
signs of renal cancer. As illustrated in the previous sections,
comparing the abundances of features between these four
types of samples, give important clues as to the origin of
compounds and, therefore, to the identification of possible
renal cancer biomarkers.
The most obviously promising biomarkers would be
compounds that are present in cancer cells and leaked into the
urinary space . Unless such compounds also diffuse into
neighboring normal renal tissue, they should be found among
features that are over-abundant in both renal cancer tissue and
in the urine of cancer patients, but essentially absent in normal
tissue and control urine. In the present study, seven features
stand out in this respect: 2, 4, 15, 16, and 18–20. The charts in
Figs. 2 and 3 graphically illustrates the abundance ratios of
Compounds that are under-expressed, or absent, in cancer
tissue, as opposed to healthy tissue are also potential
biomarkers. However, such compounds may, or may not, be
List of features that are over-abundant in either cancer tissue or normal tissue extracted with THF
Cancer/normal ratios higher than six are set in italics
a Experimental monoisotopic neutral mass
under-expressed in cancer urine as they are likely to still enter
the urine from healthy kidney tissue. Notable features that are
under-abundant in both cancer tissue and in cancer urine are 9,
11, and 13.
Feature 13 at m/z 304.0905 yielded one hit in MMCD,
namely N-acetylaspartylglutamate (NAAG). This feature
was only detected in normal tissue and not in cancer tissue.
While NAAG is normally present in urine of healthy subjects,
it was here found to be less abundant in urine from the cancer
patients. NAAG is one of the three most prevalent dipeptide
neurotransmitters in the mammalian nervous system. The
simplest explanation for the absence of NAAG in renal cancer
may be denervation of the tumor, something that is commonly
observed and has been reported to enhance cancer metastasis
. However, NAAG appears to play important roles in the
neural system related to regulation of energy supply [55, 56],
and it cannot be excluded that its absence is essential for tumor
NAAG in urine does not seem to previously have been
considered for cancer detection. However, the observation that
the decrease in NAAG in the urine of cancer patients is
substantial, suggests that the concentration of NAAG is urine may
be used as an indicator of RCC size or activity.
Feature 9 is also observed to be over-abundant in both
cancer tissue and urine. This feature was putatively assigned
to riboflavin. This compound is one of eight B-complex
vitamins, and it plays a key role in maintaining human health. The
significance of riboflavin was discussed in many publications
Features that are over-abundant in both cancer tissue and
cancer urine (2, 4, 15, 16, 18–20) are, as mentioned above,
strong candidates for potential kidney cancer biomarkers.
Feature 15 is putatively identified with decanoylcarnitine,
which is a carnitine ester with decanoic acid. This compound
is present in blood plasma in cases of fatty acid oxidation
defects (FAOD), such as long-chain 3-hydroxylacyl-CoA
deh y d r o g e n a s e ( L C H A D ) d e f i c i e n c y , c a r n i t i n e
palmitoyltransferase I (CPT I) deficiency, carnitine
palmitoyltransferase II (CPT II) deficiency, and
mediumchain acyl-coenzyme A dehydrogenase deficiency .
It is of high importance to state that that this compound was
also found in urine samples from cancer patients at three times
higher abundance compared with control (vide infra). Thus, it
is a very good candidate for a new kidney cancer biomarker.
Acylcarnitines, which are intermediates in the key energy
metabolic pathways of fatty acid β-oxidation and amino acid
catabolism, were found previously at higher concentrations
in tumor tissues [23, 24] and urine as compared with a set of
matched control patients without RCC. Additionally, some
studies have reported that several carnitine-type metabolites
c o u l d a l s o b e c o n s i d e r e d e a r l y R C C b i o m a r k e r s .
Acylcarnitines could be emanating either from the tumor
itself, or their appearance is the result of a systemic response to
the presence of the tumor cells. A possible explanation for
these changes is that highly undifferentiated cancer cells
require more energy; they rely on fatty acid β-oxidation to
maintain its viability. On the other hand, enzymes of
βoxidation seem to be downregulated as RCC progresses,
suggesting reduced oxidation of acyl-CoAs and, consequently,
accumulation of carnitine species in cancer cells .
Norepinephrine sulfate (NE sulfate) showed significant
increases both in cancer tissue and urine samples from patients
with kidney cancer (2, Fig. 2). This compound was identified
at concentrations approximately 5-fold higher in RCC tissue
relative to normal but also at about two times higher
concentrations in urine samples from the patients with kidney cancer
compa re d with hea lthy c ontrol group ( 2, F ig . 2).
Norepinephrine sulfate is formed from free norepinephrine
by the enzyme phenol sulfotransferase. In the human body,
norepinephrine sulfate is present in plasma in concentration
about two to four times higher than free norepinephrine .
NE sulfate concentration in plasma increases after
sympathetic nervous system activation by an exhausting incremental
exercise test and remain elevated up to 2 h after exercise.
Liquid chromatography/high-resolution mass spectrometry
analysis of extracts from cancer and healthy tissue regions
allowed the identification of up- and downregulated
compounds that could potentially serve as renal cancer
biomarkers, ccRCC. Similar analyses of urine from cancer
patients and from a healthy control group yielded additional
putative biomarkers. The putative identifications of
compounds were based on exact mass and on data base hits on
important human metabolites, known to be relevant for
cancer. Cross-comparison of two sets of results allowed the
identification of four kidney cancer biomarkers that are either
over- or under-expressed in both cancer tissue and urine from
cancer patients. Hydroxybutyrylcarnitine, decanoylcarnitine,
propanoylcarnitine, carnitine, dodecanoylcarnitine, and
norepinephrine sulfate were found in distinctly higher
concentrations in both cancer tissues and in urine of cancer patients
compared with controls. In contrast, feature assigned to
riboflavin and NAAG were present at significantly higher
concentrations both in normal kidney tissue as compared with renal
cancer tissue and in urine samples of healthy persons than in
urine from the cancer patients. All eight mentioned
compounds may be considered potential clear cell renal carcinoma
biomarkers. Preliminary research presented in this work will
be followed in the future with larger-scale study based on
higher amount of patients.
Funding information The work was supported by grant 2016/23/B/ST4/
00062 from the National Science Centre, Poland.
Compliance with ethical standards
The authors declare that they have no conflict of
Open Access This article is distributed under the terms of the Creative
C o m m o n s A t t r i b u t i o n 4 . 0 I n t e r n a t i o n a l L i c e n s e ( h t t p : / /
creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a link
to the Creative Commons license, and indicate if changes were made.
1. Johnson CH , Ivanisevic J , Siuzdak G . Metabolomics: beyond biomarkers and towards mechanisms . Nat Rev Mol Cell Biol . 2016 ; 17 : 451 - 9 .
2. Cairns RA , Harris IS , Mak TW . Regulation of cancer cell metabolism . Nat Rev Cancer . 2011 ; 11 : 85 − 95 .
3. Ferlay J , Soerjomataram I , Ervik M , Dikshit R , Eser S , Mathers C , Rebelo M , Parkin DM , Forman D , Bray F. GLOBOCAN 2012 v1. 0: Cancer incidence and mortality worldwide: IARC Cancer Base No . 11 , Lyon , France: International Agency for Research on Cancer. Available from: http://globocan.iarc.fr. 2013 .
4. Moch H , Cubilla AL , Humphrey PA , Reuter VE , Ulbright TM . The 2016 WHO classification of tumours of the urinary system and male genital organs-part A: renal, penile, and testicular tumours .
Eur Urol . 2016 ; 70 : 93 - 105 .
5. Shuch B , Amin A , Armstrong AJ , Eble JN , Ficarra V , LopezBeltran A , et al. Understanding pathologic variants of renal cell carcinoma: distilling therapeutic opportunities from biologic complexity . Eur Urol . 2015 ; 67 : 85 − 97 .
6. Cheville JC , Lohse CM , Zincke H , Weaver AL , Blute ML .
Comparisons of outcome and prognostic features among histologic subtypes of renal cell carcinoma . Am J Surg Pathol . 2003 ; 27 : 612 - 24 .
7. Linehan WM , Walther MM , Zbar B. The genetic basis of cancer of the kidney . J Urol . 2003 ; 170 : 2163 - 72 .
8 . B l e u m e r I , O o s t e r w i j k E , D e M u l d e r P, M u l d e r s P F.
Immunotherapy for renal cell carcinoma . Eur Urol . 2003 ; 44 : 65 - 75 .
9. Mancini V , Battaglia M , Ditonno P , Palazzo S , Lastilla G , Montironi R , et al. Current insights in renal cell cancer pathology .
Urol Oncol . 2008 ; 26 : 225 - 38 .
Gil AM , Pinho PG , Monteiro MS , Duarte IF . NMR metabolomics of renal cancer: an overview . Bioanalysis . 2015 ; 7 : 2361 - 74 .
Eur Urol . 1990 ; 17 : 72 - 5 .
Linehan WM , Srinivasan R , Schmidt LS . The genetic basis of kidney cancer: a metabolic disease . Nat Rev Urol . 2010 ; 7 : 277 - 85 .
Zira AN , Theocharis SE , Mitropoulos D , Migdalis V , Mikros E. 1H NMR metabonomic analysis in renal cell carcinoma: a possible diagnostic tool . J Proteome Res . 2010 ; 9 : 4038 - 44 .
Gao H , Dong B , Liu X , Xuan H , Huang Y , Lin D. Metabonomic profiling of renal cell carcinoma: high-resolution proton nuclear magnetic resonance spectroscopy of human serum with multivariate data analysis . Anal Chim Acta . 2008 ; 624 : 269 - 77 .
Lin L , Yu Q , Yan X , Hang W , Zheng J , Xing J , et al. Direct infusion mass spectrometry or liquid chromatography mass spectrometry for human metabonomics? A serum metabonomic study of kidney cancer . Analyst . 2010 ; 135 : 2970 - 8 .
Lin L , Huang Z , Gao Y , Yan X , Xing J , Hang W. LC-MS based serum metabonomic analysis for renal cell carcinoma diagnosis, staging, and biomarker discovery . J Proteome Res . 2011 ; 10 : 1396 - 405 .
Kim K , Taylor SL , Ganti S , Guo L , Osier MV , Weiss RH . Urine metabolomic analysis identifies potential biomarkers and pathogenic pathways in kidney cancer . OMICS . 2011 ; 15 : 293 − 303 .
Kind T , Tolstikov V , Fiehn O , Weiss RH . A comprehensive urinary metabolomic approach for identifying kidney cancer . Anal Biochem . 2007 ; 363 : 185 - 95 .
Pathway analysis of kidney cancer using proteomics and metabolic profiling . Mol Cancer . 2006 ; 5 : 1 - 17 .
Urinary acylcarnitines are altered in human kidney cancer . Int J Cancer . 2012 ; 130 : 2791 - 800 .
Wettersten HI , Hakimi AA , Morin D , Bianchi C , Johnstone ME , Donohoe DR , et al. Grade-dependent metabolic reprogramming in kidney cancer revealed by combined proteomics and metabolomics analysis . Cancer Res . 2015 ; 75 : 2541 - 52 .
Gao H , Dong B , Jia J , Zhu H , Diao C , Yan Z , et al. Application of ex vivo (1)H NMR metabonomics to the characterization and possible detection of renal cell carcinoma metastases . J Cancer Res Clin Oncol . 2012 ; 138 : 753 - 61 .
Catchpole G , Platzer A , Weikert C , Kempkensteffen C , Johannsen M , Krause H , et al. Metabolic profiling reveals key metabolic features of renal cell carcinoma . J Cell Mol Med . 2011 ; 15 : 109 - 18 .
Fructose-1 ,6 -bisphosphatase opposes renal carcinoma progression .
Nature . 2014 ; 513 : 251 - 5 .
L- 2 -Hydroxyglutarate: an epigenetic modifier and putative oncometabolite in renal cancer . Cancer Discov . 2014 ; 4 : 1290 - 8 .
Saito K , Arai E , Maekawa K , Ishikawa M , Fujimoto H , Taguchi R , et al. Associated transcriptomic profiles of clear cell renal cell carcinoma . Sci Rep . 2016 ; 6 : 1 - 12 .
Hakimi AA , Reznik E , Lee CH , Creighton CJ , Brannon AR , Luna A , et al. An integrated metabolic atlas of clear cell renal cell carcinoma . Cancer Cell . 2016 ; 29 : 104 - 16 .
Multivariate statistical differentiation of renal cell carcinomas based on lipidomic analysis by ambient ionization imaging mass spectrometry . Anal Bioanal Chem . 2010 ; 398 ( 78 ): 2969 .
Alfaro CM , Jarmusch AK , Pirro V , Kerian KS , Masterson TA , Cheng L , et al. Ambient ionization mass spectrometric analysis of human surgical specimens to distinguish renal cell carcinoma from healthy renal tissue . Anal Bioanal Chem . 2016 ; 408 : 5407 - 14 .
Yoshimura K , Chen LC , Mandal MK , Nakazawa T , Yu Z , Uchiyama T , et al. Analysis of renal cell carcinoma as a first step for developing mass spectrometry-based diagnostics . J Am Soc Mass Spectrom . 2012 ; 23 : 1741 - 9 .
Metabolomics . 2014 ; 10 : 1210 - 22 .
DNA methylation of the SLC16A3 promoter regulates expression of the human lactate transporter MCT4 in renal cancer with consequences for clinical outcome . Clin Cancer Res . 2013 ; 19 : 5170 - 81 .
The nephrologist's tumor: basic biology and management of renal cell carcinoma . J Am Soc Nephrol . 2016 ; 27 : 2227 - 37 .
IDEOM: an excel interface for analysis of LC-MS-based metabolomics data . Bioinformatics . 2012 ; 28 : 1048 - 9 .
PeakML/mzMatch: a file format, java library, R library, and toolchain for mass spectrometry data analysis . Anal Chem . 2011 ; 83 : 2786 - 93 .
Smith CA , Want EJ , O'Maille G , Abagyan R , Siuzdak G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification . Anal Chem . 2006 ; 78 : 779 - 87 .
Kanehisa M , Goto S , Sato Y , Kawashima M , Furumichi M , Tanabe M . Data, information, 1598 knowledge and principle: back to metabolism in KEGG . Nucleic Acids Res . 2014 ; 42 : D199 - 205 .
Cui Q , Lewis A , Hegeman AD , Anderson ME , Li J , Schulte CF , et al. Metabolite identification via the Madison metabolomics consortium database . Nat Biotechnol . 2008 ; 26 : 162 - 4 .
HMDB 3.0-the human metabolome database in 2013 . Nucleic Acids Res . 2013 ; 41 : D801 - 7 .
Xia J , Wishart DS . Using MetaboAnalyst 3.0 for comprehensive metabolomics data analysis . Curr Protoc Bioinformatics . 2016 ; 55 : 1 - 14 .
Expert Rev Mol Diagn . 2008 ; 8 : 617 - 33 .
Lu Y , Li N , Gao L , Xu YJ , Huang C , Yu K , et al. Acetylcarnitine is a candidate diagnostic and prognostic biomarker of hepatocellular carcinoma . Cancer Res . 2016 ; 76 : 2912 - 20 .
Minamida K , Sujaya IN , Tamura A , Shigematsu N , Sone T , Yokota A , et al. The effects of di-D fructofuranose-1,2′:2,3′-dianhydride (DFA III) administration on human intestinal microbiota . J Biosci Bioeng . 2004 ; 98 : 244 - 50 .
Beloribi-Djefaflia S , Vasseur S , Guillaumond F. Lipid metabolic reprogramming in cancer cells . Oncogene . 2016 ; 5 : 1 - 10 .
F a d a k a F, A j i b o y e B , O j o O , A d e w a l e O , O l a y i d e I , Emuowhochere R. Biology of glucose metabolization in cancer cells . J Oncol Sci . 2017 ; 3 : 45 - 51 .
Sastry KSR , Karpova Y , Prokopovich S , Smith AJ , Essau B , Gersappe A , et al. Epinephrine protects cancer cells from apoptosis via activation of cAMP-dependent protein kinase and BAD phosphorylation . J Biol Chem . 2007 ; 282 : 14094 - 100 .
Li S , Sun Y , Gao D . Role of nervous system in cancer metastasis (review) . Oncol Lett . 2013 ; 5 : 1101 - 11 .
Coyle JT . The nagging question of the function of NAcetylaspartylglutamate . Neurobiol Dis . 1997 ; 4 : 231 - 8 .
Powers HJ. Riboflavin (vitamin B-2) and health . Am J Clin Nutr.
Vogel S , Dindore V , Engeland M , Goldbohm RA , Brandt PA , Weijenberg MP . Dietary folate, methionine, riboflavin, and vitamin B-6 and risk of sporadic colorectal cancer . J Nutr . 2008 ; 138 : 2372 - 8 .
J Pediatr . 1993 ; 122 : 708 - 14 .
Goldstein DS , Eisenhofer G , Kopin IJ . Sources and significance of plasma levels of catechols and their metabolites in humans . J Pharmacol Exp Ther . 2003 ; 305 : 800 - 11 .