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Multiple myeloma (MM) is the second most prevalent hematological malignancy characterized by rapid proliferation of plasma cells, which leads to overproduction of antibodies. MM affects around 15% of all hemato-oncology cases across the world. The present study involves identification of metabolomic alterations in the serum of an MM cohort compared to healthy controls using both LC-MRM/MS based targeted and GC-MS based untargeted approaches. Several MM specific serum metabolomic signatures were observed in this study. A total of 54 metabolites were identified as being significantly altered in MM cohort, out of which, 26 metabolites were identified from LC-MRM/MS based targeted analysis, whereas 28 metabolites were identified from the GC-MS based untargeted analysis. Receiver operating characteristic (ROC) curve analysis demonstrated that six metabolites each from both the datasets can be projected as marker metabolites to discriminate MM subjects with higher specificity and sensitivity. Moreover, pathway analysis deciphered that several metabolic pathways were altered in MM including pyrimidine metabolism, purine metabolism, amino acid metabolism, nitrogen metabolism, sulfur metabolism, and the citrate cycle. Comprehensively, this study contributes valuable information regarding MM induced serum metabolite alterations and their pathways, which could offer further insights into this cancer.
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Serum metabolomic alterations in multiple
myeloma revealed by targeted and untargeted
metabolomics approaches: a pilot study
Venkatesh Chanukuppa,
Tushar H. More,
Khushman Taunk,
Ravindra Taware,
Tathagata Chatterjee,
Sanjeevan Sharma
and Srikanth Rapole *
Multiple myeloma (MM) is the second most prevalent hematological malignancy characterized by rapid
proliferation of plasma cells, which leads to overproduction of antibodies. MM aects around 15% of all
hemato-oncology cases across the world. The present study involves identication of metabolomic
alterations in the serum of an MM cohort compared to healthy controls using both LC-MRM/MS based
targeted and GC-MS based untargeted approaches. Several MM specic serum metabolomic signatures
were observed in this study. A total of 54 metabolites were identied as being signicantly altered in MM
cohort, out of which, 26 metabolites were identied from LC-MRM/MS based targeted analysis, whereas
28 metabolites were identied from the GC-MS based untargeted analysis. Receiver operating
characteristic (ROC) curve analysis demonstrated that six metabolites each from both the datasets can
be projected as marker metabolites to discriminate MM subjects with higher specicity and sensitivity.
Moreover, pathway analysis deciphered that several metabolic pathways were altered in MM including
pyrimidine metabolism, purine metabolism, amino acid metabolism, nitrogen metabolism, sulfur
metabolism, and the citrate cycle. Comprehensively, this study contributes valuable information
regarding MM induced serum metabolite alterations and their pathways, which could oer further
insights into this cancer.
1. Introduction
Multiple myeloma (MM) is reported as the second most preva-
lent hematological malignancy (15%) across the world.
MM is
characterized by rapid proliferation of plasma cells and their
subsequent accumulation in bone marrow which results in
overproduction of antibodies and eventually leads to bone
It is one of the most dominant hematological
malignancies on the Indian subcontinent, where 4 out of every
100 000 people are detected with this cancer every year.
Development of MM is a complex process that is associated with
cytological abnormalities such as hyperdiploidy and chromo-
somal translocations.
Being a heterogeneous disease, MM can
be broadly categorized into the following types: monoclonal
gammopathy of undetermined clinical signicance (MGUS),
a benign form of MM; an intermediate stage called as smol-
dering myeloma and the symptomatic myeloma.
MGUS could
progress into smoldering myeloma, which nally turns into the
symptomatic myeloma. Despite recent developments in the
eld of hematological malignancy research, there is a signi-
cant gap regarding the exact nature of molecular changes that
occur during malignant evolution leading to MM.
It is well-known fact that malignant transformations are
associated with altered metabolic pathways mostly associated
with biosynthetic and bioenergetic processes.
Such metabolic
rearrangement or switching depicts an adjustment to aid cell
survival, tissue remodeling, tumor growth, as well as cancer
metastasis. Strikingly, as evident from available literature,
suggests that this metabolic adjustment is coordinated by
genomic programming and inveigled by the tumor microenvi-
ronment. Moreover, in some situations, the alteration in
metabolism plays a crucial role in oncogenesis.
Therefore, it is
of utmost importance to extract such metabolic deviations by
metabolomics approach and understand them in the context of
prognosis, therapy response as well as disease pathophysiology.
Metabolomics not only includes the utilization of various
analytical methodologies towards identication and quanti-
cation of the metabolites pertaining to a biological system, but
can monitor the changes at metabolites level in a variety of
clinical specimens such as serum, cells, tissue, urine and other
biological uids.
Earlier metabolomics studies in MM were
mostly carried out on in vitro models, such as cell lines, to
Proteomics Lab, National Centre for Cell Science, Ganeshkhind, Pune-411007, MH,
India. E-mail:; Fax: +91-20-2569-2259; Tel: +91-20-2570-8075
Savitribai Phule Pune University, Ganeshkhind, Pune-411007, MH, India
Army Hospital (R&R), Dhaula Kuan, New Delhi-110010, DL, India
Armed Forces Medical College, Pune-411001, MH, India
Electronic supplementary information (ESI) available. See DOI:
Cite this: RSC Adv.,2019,9,29522
Received 13th June 2019
Accepted 11th September 2019
DOI: 10.1039/c9ra04458b
29522 |RSC Adv.,2019,9, 2952229532 This journal is © The Royal Society of Chemistry 2019
RSC Advances
investigate metabolic alterations induced by the drug resis-
Apart from this, identication of potential targets
related to apoptosis
was also studied in context of MM.
Moreover, few clinical studies were also conducted to investi-
gate the serum metabolomics alterations upon disease remis-
However, to our knowledge, there is no report of
comprehensive serum metabolomic investigation using tar-
geted and untargeted metabolomic approaches for MM to
decipher the metabolic alterations crucial for understanding
the disease pathophysiology, which in future could be trans-
lated into disease diagnosis.
Therefore, in the present study, we have carried out an
elaborate MM serum metabolic proling of human patients
compared to healthy control subjects using both targeted and
untargeted approaches to identify metabolic ngerprint asso-
ciated with this disease.
2. Materials and methods
2.1 Subject selection, sample collection and processing
The study cohort for targeted metabolomic analysis comprised
of 48 volunteers (MM ¼24, control ¼24) recruited at the Armed
Forces Medical College (AFMC), Pune. For untargeted metab-
olomics analysis, 40 subjects (MM ¼21, control ¼19) from the
same cohort were utilized. As we collected the samples in a long
duration of sample collection period because of the reason that
the availability of the samples was scarce for this disease, the
number of samples for the LC-MS and GC-MS are dierent. As
we performed the GC-MS analysis earlier, the sample cohort for
this analysis has 21 healthy control and 19 MM serum samples.
Whereas, later we collected some additional samples, which
made the total of 24 serum samples each for healthy controls
and MM for the LC-MS analysis. The inclusion criteria for MM
cohort consisted of only freshly diagnosed patients with
minimum 18 years of age and without any past or present
anticancer drug interventions. For the control cohort, age and
gender matched individuals devoid of hypertension or diabetes
was selected. ESI Table S1summarizes the demographic
information of the study cohort used in this study. The study
cohort was advised overnight fasting and blood samples were
collected the following morning. 5 mL of peripheral blood was
collected in serum separator tubes (BD VacutainerSSTII
Advance Tubes). The peripheral blood samples were kept for
clotting at room temperature and serum separation from the
blood was achieved by centrifuging samples at 1500gfor 5 min
at 15 C. Serum samples were then transferred to cryo-vials,
labeled and subsequently liquid nitrogen were used to snap
freeze the samples. Post snap freezing, the serum samples were
stored at 80 C ultra-low temperature freezer until further use.
All experiments were performed in compliance with guidelines
mentioned in Declaration of Helsinki (DoH, 2013). Ethical
committees of National Centre for Cell Science (NCCS), Pune
and Armed Forces Medical College (AFMC), Pune approved this
study (IRB# NCCS/IEC/2016-I/8). The experimental aspects and
investigations to be undertaken were clearly explained to each
participants and written informed consent was obtained from
them. Each subject participated voluntarily for this study.
2.2 Targeted LC-MRM/MS based metabolomic proling
In-house built MRM library of 120 metabolites was used to
investigate the metabolic alterations in the serum of MM
study cohort. These metabolites were selected based on the
previous literature survey of various cancer metabolomic
MRM library are discussed in our previous report
and the
same have been utilized for this study as well. Metabolites
extraction was executed by the addition of 200 mL methanol
to 25 mL of every serum samples, vortexed vigorously and
incubated at 20 C overnight. Moreover, d2L-phenylalanine
(40 ng) was added to the samples prior to extraction, which
served as an internal standard. Post incubation, the samples
were vortexed vigorously and subjected to 14 000gcentri-
fugation at 4 C for 10 min. The supernatant was then passed
through centrifugal lters (Corning® Costar® Spin-X®) with
0.22 mm nylon membrane. Filtrate obtained was then vacuum
dried and reconstituted in 25 mL of sample reconstitution
buer (a mixture of 6.5 mL acetonitrile, 2.5 mL methanol,
1 mL water and 0.2 mL formic acid; all the solvents were LC-
MS grade) for acquiring the samples in positive ionization
mode mass spectrometry. In case of negative ionization
mode, the vacuum dried metabolites were reconstituted in 25
mL of ultrapure LC-MS grade water. Subsequently, 10 mLof
each sample was analyzed using a Shimadzu Prominence
HPLC system (Shimadzu Corporation, Japan) connected to
a triple quadrupole mass spectrometer, 4000 QTRAP (AB
SCIEX, USA). For positive mode ionization, metabolites were
separated through the XBridgeHILIC column (5 mm, 4.6
150 mm, Waters Corp, USA) and AtlantisT3 column (5 mm,
4.6 150 mm, Waters Corp, USA) was used for negative
ionization mode. QC samples, prepared from the mixture of
pure metabolite standards, were run aer every 5 serum
metabolite sample injections to assess the uniform instru-
ment performance.
The LC buers and chromatography parameters along with
MS sample acquisition parameters were adopted from our
earlier studies as mentioned elsewhere.
Positive ionization
chromatographic separation was carried out using 32 min
gradient program of mobile phase A (10 mM ammonium
formate with 0.1% formic acid in water) and mobile phase B
(acetonitrile with 0.1% formic acid) with 700 mLmin
rate. The gradient started with 95% B and further decreased to
40% B over next 15 min and held constant for 6 min, and then
the column was gradually returned to 95% B and equilibrated
for another 9 min. The negative mode ionization chromatog-
raphy was carried out with elution rate of 500 mLmin
40 min gradient program consisting of 0% B at initial stage
tained for 5 min in same state and eventually returned to 0% B.
Finally, the column was equilibrated with 100% solvent A for
9 min. Mass spectra were acquired using following set of
parameters; MS source temperature 400 Cwithinterface
heater on, curtain gas: 30 and entrance and exit potentials: 10,
two ion source gasses at 45 arbitrary units. LC-MRM/MS data
analysis was performed using Analyst 1.5 soware (Sciex, USA).
This journal is © The Royal Society of Chemistry 2019 RSC Adv.,2019,9, 2952229532 | 29523
Paper RSC Advances
Analyst quantitation wizard was used for the peak area inte-
gration. For elimination of biasness and to maintain quality
control measures, samples were randomized during sample
analysis and peak integration performed in a blinded manner.
Further, the peak area values obtained aer performing the
integration analysis were exported to a spreadsheet le for
further analysis.
2.3 Untargeted GC-MS-based metabolic proling
Metabolite extraction was performed as described in LC-MS
methodology. Additionally, two-step derivatization method was
applied to the dried metabolite extract to produce methyl and silyl
derivatives. First step was methylation, which was achieved by
addition of 30 mL of methoxyamine hydrochloride solution dis-
solved in pyridine (20 mg mL
) and incubating at 37 Cfor90min.
In the second step, silylation reaction was performed by adding 30
mL of BSTFA with 1% TMCS solution followed by 1 h incubation at
70 C. 1 mL of the derivatized sample was injected into the injection
port (250 C) of the Agilent 5975C GC system (Agilent Technologies,
USA) operating under splitless mode and connected to Agilent
5977A mass selective detector. Metabolites were separated on HP-5
MS ultra-inert fused silica capillary column (30 m 0.25 mm0.25
with a ow rate of 1 mL min
. Following parameters were used to
obtain the mass spectra; column inlet temperature 280 C, MS
quadrupole temperature 150 C, ion source temperature 230 C. GC
oven temperature settings are as follows; the gas chromatography
program started with initial oven temperature of 50 Cheldfor
2 min, then temperature was gradually increased to 100 Cat
200 Cat3Cmin
and maintained for 2 min at this temperature.
The column temperature was lowered to 50 Cinnext5minand
equilibrated for 2 min. Full scan mode range from m/z50 to 550
was used for the metabolite data acquisition. Integration and
deconvolution of chromatograms were carried out by using the
AMDIS algorithm of Agilent Chemstation soware. Metabolites
identity was conrmed using NIST 11 standard mass spectral
database (NIST, Gaithersburg, MD). Peak areas of the metabolites
were exported in a matrix format to spreadsheet le aer chro-
matographic integration. Metabolic standards initiative (MSI)
guidelines were followed for performing the all metabolomic
Fig. 1 Multivariate statistical analysis of targeted LC-MRM/MS MM serum metabolomics: (a) distribution of MM subjects (n¼24, blue) and healthy
controls (n¼24, green) in OPLS-DA score plot, (b) permutation validation of OPLS-DA score plot. Total 200 permutations were carried out on
OPLS-DA model and obtained R
¼0.978 and Q
¼0.952, where R
and Q
indicate the explained variance and predictive ability respectively, (c)
hierarchical clustering analysis depicting the segregation of MM (blue) and healthy controls (green).
29524 |RSC Adv.,2019,9, 2952229532 This journal is © The Royal Society of Chemistry 2019
RSC Advances Paper
2.4 Data pre-processing and statistical analysis
The peak areas of identied metabolites in both targeted and
untargeted approaches were subjected to statistical analysis
Sweden). Metabolites having missing value >50% were dis-
carded from the datasets and those metabolites having <50%
missing value were subjected to missing value imputation by
half of the minimum positive value. Both datasets were
normalized to remove the major dierences and make meta-
bolic features more comparable to each other.
MS data-set was normalized to median, cube root transformed
and range scaling method was used for data scaling. Similarly,
GC-MS data was normalized to sum, cube root transformed
and auto-scaled.
Univariate and multivariate statistical
analyses were applied to these normalized datasets in order to
identify the dierentially expressed metabolites that can
unambiguously discriminate MM subjects from their respec-
tive controls.
Univariate statistical methods such as Wil-
coxon rank t-test (p<0.05)andlog2foldchange($0.58/
#0.58) were used to select statistically signicant metabo-
lites in both the datasets. Furthermore, multivariate statistical
methods like principal component analysis (PCA), partial least
squares discriminant analysis (PLS-DA) and orthogonal partial
least squares discriminant analysis (OPLS-DA) were employed
to visualize the overall distribution of serum metabolites
among the study cohort as well as their ecacy in discrimi-
nation of MM subjects from the respective controls. Unlike
PCA, which is an unsupervised multivariate statistical
approach, PLS-DA and OPLS-DA are supervised multivariate
statistical methods and a cross validation is needed to avoid
over-tting of the data. The OPLS-DA model was veried by
building 200 random permutation models and comparing
their performance (R
-goodness of tandQ
-predictive ability
of the model) with the original model.
Similarly, PLSDA
model was also veried.Furthermore,variableimportancein
projection (VIP) score of OPLS-DA model was used to rank the
metabolites as per their contribution in segregation of MM
subjects from the controls in the score plot. To select the
marker metabolites, receiver operating characteristic (ROC)
curve analysis was carried out to identify the features with
highest specicity, sensitivity and accuracy to segregate the
MM subjects from controls.
2.5 Pathway analysis
Both the datasets, targeted and untargeted were explored
towardsmetabolicpathwayanalysisforidentication of the
MM inducible metabolic pathway alterations. The analyses
were carried out by MetPA tool of the MetaboAnalyst 3.0 web
application ( Pathway impact
score was used as a measure to identify the altered metabolic
pathways. The pathway impact score was calculated based on
number of matched metabolites from datasets to a particular
metabolic pathway. The hypergeometric test was used for over-
representation analysis that tested for its enrichment in
Table 1 Statistically signicant dierentially regulated serum metabolites identied through the LC-MS based targeted approach
Sr. no. Metabolite VIP score p-Value FDR adjusted p-value Fold change AUC
1 NAD 1.69 1.28 10
7.97 10
17.56 0.97 16.49 13.84
2 Adenosine 1.69 1.99 10
7.97 10
13.78 0.97 23.65 18.12
3 CYTIDINE 1.63 1.78 10
3.56 10
5.56 0.97 23.68 23.49
4 Adenine 1.60 9.36 10
2.50 10
3.62 0.98 27.44 17.32
5 Oxaloacetic acid 1.55 9.02 10
1.44 10
0.01 0.99 18.23 17.46
6 Guanosine 1.51 5.17 10
6.89 10
3.03 0.97 21.45 25.68
7L-Methionine 1.38 2.41 10
2.75 10
0.60 0.94 24.58 27.84
8 Mono-ethylmalonate 1.38 3.06 10
3.06 10
0.63 0.92 27.96 21.52
9L-Leucine 1.34 4.65 10
4.13 10
0.67 0.91 14.94 16.75
10 L-Homoserine 1.31 2.56 10
2.05 10
0.64 0.90 22.34 25.73
11 L-Threonine 1.30 4.42 10
2.95 10
0.64 0.89 25.62 21.98
12 Thymine 1.28 3.68 10
1.92 10
0.62 0.87 18.82 16.72
13 Methyl malonate 1.18 3.48 10
2.53 10
1.63 0.90 12.57 15.64
14 L-Citrulline 1.17 3.02 10
1.86 10
0.52 0.86 18.93 21.88
15 Uracil 1.17 2.49 10
9.04 10
0.65 0.81 28.72 24.42
16 L-Cysteine 1.17 1.79 10
6.83 10
0.71 0.84 22.26 26.84
17 L-Arginine 1.15 1.79 10
6.83 10
0.65 0.82 15.82 19.68
18 Xanthosine dihydrate 1.09 0.000619 0.001415 0.62 0.76 25.68 18.94
19 Riboavin 1.09 4.14 10
1.95 10
2.90 0.87 19.84 23.46
20 UTP 1.08 4.98 10
0.000159 0.49 0.92 22.56 26.78
21 Succinic acid 1.07 4.09 10
0.000136 1.50 0.84 14.98 21.36
22 L-Glutamine 1.06 6.66 10
0.000197 0.67 0.81 18.96 19.14
23 Xylitol 1.04 0.000213 0.000567 1.73 0.86 22.46 21.58
24 Adonitol 1.04 0.000124 0.000355 1.81 0.87 18.92 27.65
25 Cytosine 1.01 6.42 10
0.000197 2.96 0.85 19.74 23.74
26 Thyroxine 1.00 0.000147 0.000406 0.60 0.79 29.76 19.52
This journal is © The Royal Society of Chemistry 2019 RSC Adv.,2019,9, 2952229532 | 29525
Paper RSC Advances
aspecic pathway and was compared according to random
hits. Further the genes associated with the dierentially
expressed metabolites were identied using Kyoto Encyclo-
pedia of Genes and Genomes (KEGG) database and Human
Metabolome Database (HMDB).
3. Results
3.1 LC-MRM/MS based targeted metabolomic proling
In targeted approach, 81 metabolites were consistently detec-
ted in all samples out of 120 metabolites targeted. We have
built the LC-MRM/MS analysis method for this study by
nding the MRM transitions for each metabolite using pure
standards purchased from Sigma Aldrich. Representative
HILIC and T3 column chromatograms of LC-MRM/MS analysis
are presented in ESI Fig. S1.Univariate statistical methods
were applied to identify metabolites that are signicantly
diering in their concentrations among MM subjects and
controls.Further,toextractthe MM inducible metabolomic
signature, multivariate statistical methods such as PCA, PLS-
DA and OPLS-DA were carried out. Modest segregation was
of concentration dierences among the metabolites of MM
study cohort (ESI Fig. S2). Moreover, a supervised multivar-
iate statistical approach such as OPLS-DA was used to sharpen
the dierences between the MM subjects and their respective
controls. Robust group segregation was evident from the
OPLS-DA score plot which indicates the metabolic alterations
exist in the MM study cohort (Fig. 1a). The model based on
OPLS-DA statistics was further authenticated by the permuta-
tion test utilizing 200 permutations in between the data
groups to avoid biasness due to data over tting (Fig. 1b). The
permutation test yielded R
value of 0.978 and Q
value of
0.952 depicting that the original model has performed better
than the permutated models and assures of good prediction
ability. VIP score was used as criteria to select the metabolites
that had highest impact to group separation in score plot of
OPLS-DA. Combination of univariate and multivariate statis-
tical approaches (VIP > 1, FDR adjusted p<0.05,log2FC>
0.58/<0.58) were used to propose the panel of metabolites
that can discriminate MM subjects from the controls (Table 1).
It revealed that 26 metabolites were signicantly diered
among MM study cohort, out of which 11 metabolites were up-
regulated while 15 metabolites were down-regulated in MM
subjects as compared to control (Table 1). Hierarchical
Fig. 2 Heat map of LC-MRM/MS based dierential metabolites between healthy controls and MM serum samples. The colors from green to red
indicate the increased amount of metabolites (C1C24: healthy controls, M1M24: multiple myeloma).
29526 |RSC Adv.,2019,9,2952229532 This journal is © The Royal Society of Chemistry 2019
RSC Advances Paper
clustering analysis depicted distinct clusters of MM and
control subjects, which demonstrate metabolic adaptation of
MM subjects is dierent when compare to healthy controls
(Fig.1c).Furtherheatmapforthedierentially expressed
metabolites found through LC-MS analysis were showed in
Fig. 2 and S4.
3.2 GC-MS based untargeted metabolomic proling
In untargeted approach, 153 metabolites were detected in MM
study cohort. Retention time and NIST library metabolite
matching score information of the altered metabolites identi-
ed using untargeted GC-MS approach is mentioned in ESI
Table S2as per the MSI guidelines.
For untargeted data
analysis, the metabolites that had matching score >80% with
the NIST library search were only considered. ESI Fig. S3
depicts a representative GC-MS spectrum for MM serum
metabolites. The same set of statistical treatments (univariate
and multivariate) mentioned above were performed for the
untargeted dataset in order to identify the metabolites that are
altered signicantly upon MM induction. Multivariate statis-
tical models like PCA and OPLS-DA were built from the untar-
geted dataset to detect the metabolic ngerprint unique to MM.
PCA 2D score plot depicted the fairly segregated MM and
control population, which indicates the intrinsic concentration
dierences exist among the study cohort (ESI Fig. S4).
Furthermore, distinct group separation was observed in the
OPLS-DA score plot and metabolic features contributing most to
the segregation were identied by the VIP score > 1 (Fig. 3a). A
permutation test with 200 permutations was performed to
validate the performance of the OPLS-DA model that was built
for the MM untargeted metabolite dataset. The permutation test
indicates the original model (R
value ¼0.967 and Q
value ¼
0.81) is better than permutated models and has not over tted
the data (Fig. 3b). Hierarchical cluster analysis was also in
accordance with our earlier ndings, revealing clear segregation
of study cohort into MM subjects and controls (Fig. 3c). Total 28
most signicant metabolites were discovered by using
Fig. 3 Multivariate statistical analysis of untargeted GC-MS MM serum metabolomics. (a) OPLS-DA score plot depicting the robust separation of
MM subjects (n¼19, blue) from healthy controls (n¼21, green), (b) permutation validation of OPLS-DA plot obtained after performing 200
random permutations which yielded R
¼0.967 and Q
¼0.81, (c) hierarchical clustering analysis representing good clustering of study subjects.
This journal is © The Royal Society of Chemistry 2019 RSC Adv.,2019,9,2952229532 | 29527
Paper RSC Advances
combination of univariate and multivariate approaches (VIP > 1,
FDR adjusted p< 0.05, log 2 FC > 0.58/<0.58) of which 16
metabolites were up-regulated and 12 metabolites were down-
regulated in MM as compared to control (Table 2). Further,
heat map for the dierentially expressed metabolites found
through GC-MS analysis were showed in Fig. 4 and S5.
3.3 Marker metabolite selection from targeted and
untargeted datasets
Both targeted and untargeted datasets were subjected to the
ROC curve analysis to select the metabolites with the highest
specicity, sensitivity and accuracy capable of discriminating
the MM subjects from respective controls. The area under the
curve (AUC) is used as a measure of the predictive ability of
a metabolite in ROC curve analysis. Based on AUC, six metab-
olites each from both the datasets were projected as marker
metabolites to discriminate the MM subjects from their
respective controls. In targeted LC-MRM/MS dataset, metabo-
lites such as NAD, adenosine, cytidine, adenine, oxaloacetic
acid and guanosine were identied as marker metabolites and
their expression pattern along with ROC curve plot is repre-
sented in Fig. 5a and b. Likewise, in GC-MS analysis, D-ribo-
hexitol, beta-D-glucopyranosiduronic acid, hexadecanoic acid
1,5-anhydro-D-sorbitol, uridine and D-talofuranose are consid-
ered as predictive metabolites and their ROC curve plot along
with expression pattern is depicted in Fig. 5c and d.
3.4 Identication of altered MM metabolome associated
pathways and genes
To evaluate the impact of MM on metabolic pathways, metab-
olites from both the MRM based LC-MRM/MS and GC-MS
analysis were subjected for pathway analysis using the MetPa
tool of MetaboAnalyst 3.0. Pathway analysis results are
demonstrated in Fig. 6. Top pathways emerged as pyrimidine
metabolism; alanine, aspartate and glutamate metabolism;
glycine, serine and threonine metabolism; cysteine and methi-
onine metabolism; purine metabolism; nitrogen metabolism;
sulfur metabolism; and citrate cycle (ESI Table S3). In order to
identify the MM dierentially regulated metabolites associated
genes, 54 serum metabolites were mapped using Kyoto Ency-
clopedia of Genes and Genomes (KEGG) and human metabolic
data base. This analysis resulted in identication of 676 genes
which could be related to the MM dierentially expressed
metabolites (ESI Table S4).
4. Discussion
At present, detection of MM is majorly dependent on morpho-
logical criteria and its multi-symptomatic nature makes diagnosis
and prognosis challenging. This demands the identication of
novel clinically relevant molecular markers and potential targets
for developing new therapies for MM. Cancer and metabolism
share close connectivity as malignant cells undergoes profound
Table 2 Statistically signicant dierentially regulated serum metabolites identied through the GC-MS based untargeted approach
Sr. no. Metabolite VIP score p-Value FDR adjusted p-value Fold change AUC
1D-Ribo-hexitol 2.30 4.20 10
6.43 10
7.08 1.00 19.36 23.58
2 Beta-D-glucopyranosiduronic acid 2.25 1.81 10
1.39 10
5.81 1.00 28.92 23.74
3 Hexadecanoic acid 1.93 8.82 10
4.50 10
1.80 0.95 18.62 14.85
4 1,5-Anhydro-D-sorbitol 1.84 3.49 10
1.07 10
0.25 0.89 26.35 28.61
5 Pseudo uridine 1.83 2.35 10
8.97 10
6.11 0.95 12.32 16.78
6D-Talofuranose 1.77 1.51 10
3.84 10
0.37 0.91 29.84 26.74
7 Glucofuranoside 1.66 1.54 10
3.36 10
2.73 0.91 23.48 25.68
8N-Acetyl glucosamine 1.52 2.39 10
3.03 10
0.56 0.82 19.46 23.42
9 Purine 1.51 1.15 10
1.88 10
0.45 0.84 26.59 28.32
10 Beta-D-galactopyranoside 1.51 1.23 10
1.88 10
0.42 0.85 21.65 25.38
11 Pyrimidine 1.50 6.16 10
1.18 10
0.40 0.83 20.28 22.07
12 2-Piperidinecarboxylic acid 1.50 1.60 10
2.22 10
0.44 0.81 14.63 16.09
13 D-Lactose 1.42 3.18 10
2.01 10
1.64 0.75 12.59 16.83
14 D-Xylofuranose 1.42 4.41 10
4.49 10
2.80 0.77 15.97 16.23
15 L-Threonine 1.42 2.06 10
1.50 10
0.20 0.69 15.86 14.93
16 Nonadecanoic acid 1.41 8.80 10
8.42 10
2.36 0.83 24.46 26.66
17 L-Threitol 1.40 3.58 10
3.92 10
2.24 0.81 26.32 27.39
18 D-Glucopyranose 1.39 3.00 10
2.00 10
2.39 0.81 22.71 25.80
19 2-Alpha-mannobiose 1.38 5.87 10
3.10 10
1.50 0.73 27.28 29.84
20 Cystathionine 1.33 3.51 10
2.07 10
0.63 0.80 23.40 14.67
21 Stearate 1.27 2.82 10
1.96 10
2.11 0.87 15.82 13.26
22 Inositol 1.25 1.42 10
1.15 10
1.54 0.80 18.54 19.23
23 L-Asparagine 1.20 1.94 10
1.48 10
0.49 0.85 25.63 27.52
24 Arachidonic acid 1.20 1.13 10
4.93 10
1.60 0.71 16.92 20.84
25 Beta-D-galactofuranose 1.19 7.39 10
3.54 10
2.04 0.84 24.36 19.56
26 D-Xylopyranose 1.18 1.31 10
1.15 10
0.23 0.79 25.87 27.32
27 Stigmasterol 1.18 1.38 10
1.15 10
0.54 0.80 28.39 27.65
28 Maltitol 1.10 7.65 10
3.55 10
3.51 0.74 26.57 28.34
29528 |RSC Adv.,2019,9, 2952229532 This journal is © The Royal Society of Chemistry 2019
RSC Advances Paper
metabolic rearrangements to support its proliferative nature and
bioenergetics. Hence, in this study, we attempted to explore the
serum metabolomic alterations in MM using LC-MRM/MS based
targeted as well as GC-MS based untargeted metabolomics. The
study showed profound changes in serum metabolites of MM
patients as compared to controls. The major metabolic changes
identied are discussed further herewith.
Targeted LC-MRM/MS analysis unveiled signicant alter-
ations of 26 metabolites in MM serum as compared to control.
These dierentially expressed metabolites consist of 11 up-
regulated and 15 down-regulated metabolites. NAD was
observed to be up-regulated in MM subjects as compared to the
controls, which is considered as a key molecule in various
biological processes including energy metabolism and enzy-
matic regulation.
It is also the substrate for non-redox reac-
tions that aect gene expression such as Ca
transport and
NAD is involved in the important post-translational
modications of proteins such as acylation and deacylation by
SIRTs enzymes.
Moreover, the aberrant NAD metabolism is
reported in various cancers.
Adenosine, which is found to be
increased in MM serum, is an important immunosuppressive
Extracellular adenosine bind to adenosine receptors
of immune cells suppressing the pro-inammatory activity of
the cells.
Furthermore, the activation of adenosine receptors
increases inammatory molecules and immunoregulatory cells
governing a long-lasting immunosuppressive environment.
This leads to the discouragement of any antitumor immune
responses induced by the body. Oxaloacetic acid (OAA), a key
intermediate in the TCA cycle involved in energy production is
downregulated in MM subjects when compared with controls.
The reason for depleted levels of oxaloacetic acid in serum
could be explained by the fact that oxaloacetate along with
citrate is used to generate acetyl COA and aspartate that is used
in the synthesis of nucleotides and lipids quite extensively in
proliferating cells. It is also in agreement with the earlier report
that OAA can induce apoptosis in hepatocellular carcinoma
cells via inhibition of glycolysis and restoring oxidative phos-
Thus, decreased levels of OAA in MM serum
samples also suggest a prevalence of glycolysis over oxidative
phosphorylation in MM subjects. Guanosine is formed as an
intermediate of the nucleic acid breakdown.
During purine
salvage, guanosine can be cleaved further to release guanine,
which is salvaged back to GMP by the enzyme hypoxanthine-
guanine phosphoribosyl transferase (HGPRT).
concentrations of guanosine in MM serum imply that the
purine salvage is highly active in MM. In addition, elevated
levels of cytidine, and adenine in MM represents an overall
increase in nucleotide metabolism to support proliferation.
Fig. 4 Heat map of GC-MS based dierential metabolites between healthy controls and MM serum samples. The colors from green to red
indicate the increased amount of metabolites (C1C21: healthy controls, M1M19: multiple myeloma).
This journal is © The Royal Society of Chemistry 2019 RSC Adv.,2019,9, 2952229532 | 29529
Paper RSC Advances
In untargeted GC-MS analysis, signicant changes in 28
metabolites were observed that included 16 up-regulated and 12
down-regulated metabolites. Concentration of hexadecanoic acid
(palmitic acid) was found to be increased in MM serum as
compared to controls. It is reported that increased palmitic acid
level is associated with TL4 mediated invasiveness in pancreatic
Moreover, palmitic acid supplementation has shown to
rescue adiponectin-induced apoptosis in MM cells.
our observation of increased palmitic acid in MM might be
linked withits role in invasiveness and proliferationthat needs to
be further investigated. Further, decrement in 1,5-anhydro-D-
sorbitol was observed in MM serum, which is an indicator of
glycaemic control. Its levels manifest the activity of polyol path-
ways that are mostly functional in hyper glycaemic conditions.
Increase in uridine levels reects active involvement of pyrimi-
dine metabolism which is common metabolic alteration
observed in cancer.
It is well-known fact that, N or O linked N-
acetylglucosamine (GlcNAc) is one of the prominent post-
translational modications observed on proteins displayed on
the cell surface of malignant cells. Moreover, it is now established
that GlcNAc modied proteins aid in cell migration and inva-
GlcNAc is reported to be mediating cell signaling directly
and indirectly.
The modication of epithelial cadherin (E-
cadherin) with O-linked b-GlcNAc is reported to impair cell
Fig. 5 Marker metabolites selected by ROC curve analysis (a) concentration dierences between signicant metabolites from targeted analysis of MM
(red) and healthy control (green) samples demonstrated by box-and-whiskerplots,(b)theROCcurveanalysisofmarkermetabolitesviz. NAD (AUC ¼
0.97, sensitivity ¼0.95, specicity ¼0.96, CI ¼95%), adenosine (AUC ¼0.97, sensitivity ¼0.95, specicity ¼0.92, CI ¼95%), cytidine (AUC ¼0.97,
sensitivity ¼0.91, specicity ¼0.96, CI ¼95%), adenine (AUC ¼0.98, sensitivity ¼0.95, specicity ¼0.96, CI ¼95%), oxaloacetic acid (AUC ¼0.99,
sensitivity ¼0.92, specicity ¼0.97, CI ¼95%) and guanosine (AUC ¼0.97, sensitivity ¼0.87, specicity ¼0.88, CI ¼95%) from targeted analysis, (c)
concentration dierences between signicant metabolites from untargeted analysis of MM (red) and healthy control (green) samples demonstrated by
box-and-whisker plots, (d) the ROC curve analysis of marker metabolites viz. D-ribohexitol (AUC ¼1, sensitivity ¼0.94, specicity ¼0.95, CI ¼95%),
beta-D-glucopyranosiduronic acid (AUC ¼1, sensitivity ¼0.94, specicity ¼0.95, CI ¼95%), hexadecanoic acid (AUC ¼0.95, sensitivity ¼0.84,
specicity ¼0.85, CI ¼95%), anhydro-D-sorbitol (AUC ¼0.89, sensitivity ¼0.80, specicity ¼0.85, CI ¼95%), uridine (AUC ¼0.95, sensitivity ¼0.84,
specicity ¼0.90, CI ¼95%) and D-talofuranose (AUC ¼0.91, sensitivity ¼0.75, specicity ¼0.79, CI ¼95%) from GC-MS analysis.
29530 |RSC Adv.,2019,9, 2952229532 This journal is © The Royal Society of Chemistry 2019
RSC Advances Paper
adhesion and promote tumour cell invasion.
We have observed
decreased levels of GalNAc in MM serum which strengthens the
hypothesis of its preferred utilization for post-translational
protein glycosylation in malignant condition. For rapid and
continuous synthesis of cell constituting basic machinery,
proliferating cancerous cells have high demands of nucleotides.
This gets achieved by either nucleotide salvage pathways of
purines and pyrimidines or de novo synthesis or a combination of
Nucleotide salvage pathway utilizes downstream compo-
nents of degraded nuclear material like DNA or RNA in the form
of purines and pyrimidines to resynthesize the dNTPs for DNA/
RNA formation.
Decrement of both purines and pyrimidines
in GC-MS analysis reinforce hyperactivity of the nucleotide
salvage pathway to full the proliferative demand.
The signicant metabolites obtained from both approaches
were further evaluated for their role in various physiological
pathways. The metabolic pathway analysis revealed perturba-
tions in alanine, aspartate and glutamate metabolism, pyrimi-
dine metabolism, cysteine and methionine metabolism,
glycine, serine and threonine metabolism, purine metabolism,
nitrogen metabolism, sulfur metabolism, and citrate cycle (TCA
cycle). Several metabolites from purine and pyrimidine metab-
olism have been deregulated which is in good agreement with
previous reports from various cancer metabolomics studies.
Sulfur metabolism is important in the generation of sulfur-
containing amino acids that perform many vital functions.
Alterations in the TCA cycle, as observed in this study, has been
demonstrated to play a vital role in carcinogenesis.
Our results
demonstrate that certain key metabolic pathways including
NAD metabolism, TCA cycle, nucleotide metabolism and
sulphur metabolism etc. gets altered in MM and targeting these
pathways and its intermediary metabolites may help in devel-
oping novel targets for treating MM.
5. Conclusions
Overall, the present study demonstrates the potential of serum
metabolomics approach towards the segregation of MM cohort
from healthy controls. Increased metabolite coverage was ob-
tained with the use of dierent metabolomics platforms such as
LC-MRM/MS and GC-MS. The results obtained are noteworthy
and helpful in discrimination of MM individuals from healthy
controls. We believe that these results may be useful in future
diagnostic, prognostic and therapeutic development. In addi-
tion, these results will help to understand the disease patho-
physiology of MM in the context of altered cancer metabolism.
However, thorough validation of the inferences drawn from this
study in a large cohort of samples is required before its imple-
mentation in the clinical setting. Validation in large cohort of
patients for the metabolite based biosignature, identied in this
study, can help diagnose MM in early stages. Early diagnosis
will eventually lead to proper on-time therapeutic interventions
from the clinicians, which could help in better management of
this disease, thereby improving the patient life and survival
Author contributions
Conceived the study: VC, TC, SS, SR; designed the study: VC, TC,
SS, SR; performed the experiments: VC, THM; compiled and
analyzed data: VC, THM, KT, RT, SR; statistical analysis: VC,
THM, SR; provided clinical samples: TC, SS; provided chemicals
and reagents: SR. All authors reviewed the manuscript and
contributed in writing.
Conicts of interest
Authors declare that they do not have any conict of interests.
General: We thank all the volunteers for participating in this
study. Funding: authors would like to acknowledge Department
of Biotechnology (DBT), Govt. of India, grant no. BT/PR10855/
BRB/10/1330/2014 and NCCS intramural funding. VC and RT
acknowledge Council of Scientic and Industrial Research
(CSIR), New Delhi, India for fellowship.
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29532 |RSC Adv.,2019,9, 2952229532 This journal is © The Royal Society of Chemistry 2019
RSC Advances Paper
... For example, some species of serum amino acids, lipids such as ceramides and lysophosphatidylcholines (LPCs), secondary metabolites such as acylcarnitines (AcyCNs) may be induced by increasing the high-protein or overall lipid load from the diet (10)(11)(12). At the same time, metabolomics analysis of MM has confirmed that the content of these dietderived endogenous metabolites was significantly altered: higher levels of branched-chain amino acids (BCAAs), aromatic amino acids (AAAs), Glutamic acid (Glu) and decreased levels of Glutamine (Gln), several species of AcyCNs have been exhibited in MM patients at diagnosis compared with the stage of achieving complete remission (13)(14)(15)(16); higher levels of LPCs [16:0 (palmitoyl) and LPC 18:0 (stearoyl)], as well as lower values of ceramides, suggested the potential of MM event compared with the control set (17)(18)(19). These consistent results documented that alterations of serum metabolite may presage the onset of MM. ...
Full-text available
Background: Recent studies from targeted and untargeted metabolomics have consistently revealed that diet-related metabolites, including carnitine (C0), several species of acylcarnitines (AcyCNs), amino acids, ceramides, and lysophosphatidylcholines (LPCs) may serve as potential multiple myeloma (MM) biomarkers. However, most of these approaches had some intrinsic limitations, namely low reproducibility and compromising the accuracy of the results. Objective: This study developed and validated a precise, efficient, and reliable liquid chromatography tandem mass spectrometric (LC-MS/MS) method for measuring these 28 metabolic risk factors in human serum. Design: This method employed isopropanol to extract the metabolites from serum, gradient elution on a hydrophilic interaction liquid chromatographic column (HILIC) for chromatographic separation, and multiple reaction monitor (MRM) mode with positive electrospray ionization (ESI) for mass spectrometric detection. Results: The correlation coefficients of linear response for this method were more than 0.9984. Analytical recoveries ranged from 91.3 to 106.3%, averaging 99.5%. The intra-run and total coefficients of variation were 1.1-5.9% and 2.0-9.6%, respectively. We have simultaneously determined the serological levels of C0, several subclasses of AcyCNs, amino acids, ceramides, and LPCs within 15 min for the first time. Conclusion: The established LC-MS/MS method was accurate, sensitive, efficient, and could be valuable in providing insights into the association between diet patterns and MM disease and added value in further clinical research.
... Aided by highperformance liquid chromatography (HPLC) and mass spectrometry detection, metabonomics analysis of MM patients and healthy individuals was performed and demonstrated that the differential metabolites were mainly enriched in amino acid metabolism [44]. Additional cohorts have also been studied by untargeted/targeted metabolomics, showing the abnormal amino acid metabolisms in MM, rather than the traditional views of abnormal glucose metabolism in a majority of cancers, which may be related to the biological function of MM [25,[44][45][46][47][48]. Therefore, differential amino acid metabolic profiles in MM patients have been brought to this topic and are summarized based on the insights gained from the results. ...
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Simple Summary Multiple myeloma (MM) is the second most common hematological malignancy and remains incurable. Recent evidence substantiates the interaction of gut microbiota and MM, together with abnormal amino acid metabolism and MM. Moreover, the association between gut microbiota and host amino acid metabolism on MM has been highlighted. This article presents a review of the literature on the relationship between gut microbiota, metabolism, and MM, together with strategies to modulate the microbiota. Abstract Although novel therapies have dramatically improved outcomes for multiple myeloma (MM) patients, relapse is inevitable and overall outcomes are heterogeneous. The gut microbiota is becoming increasingly recognized for its influence on host metabolism. To date, evidence has suggested that the gut microbiota contributes to MM, not only via the progressive activities of specific bacteria but also through the influence of the microbiota on host metabolism. Importantly, the abnormal amino acid metabolism, as well as the altered microbiome in MM, is becoming increasingly apparent, as is the influence on MM progression and the therapeutic response. Moreover, the gut-microbiota–host-amino-acid metabolism interaction in the progression of MM has been highlighted. Modulation of the gut microbiota (such as fecal microbiota transplantation, FMT) can be modified, representing a new angle in MM treatment that can improve outcomes. In this review, the relationship between gut microbiota, metabolism, and MM, together with strategies to modulate the microbiota, will be discussed, and some unanswered questions for ongoing and future research will be presented.
Liquid biopsies—a source of circulating cell-free nucleic acids, proteins and extracellular vesicles—are currently being explored for the quantitative and qualitative characterisation of the tumour genome and as a mode of non-invasive therapeutic monitoring in cancer. Emerging data suggest that liquid biopsies might offer a potentially simple, non-invasive, repeatable strategy for diagnosis, prognostication and therapeutic decision making in a genetically heterogeneous disease like multiple myeloma (MM), with particular applicability in subsets of patients where conventional markers of disease burden may be less informative. In this review, we describe the emerging utility of the evaluation of circulating tumour DNA, extracellular RNA, cell-free proteins and metabolites and extracellular vesicles in MM.
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Introduction Invasive ductal carcinoma (IDC) is a type of breast cancer, usually detected in advanced stages due to its asymptomatic nature which ultimately leads to low survival rate. Identification of urinary metabolic adaptations induced by IDC to understand the disease pathophysiology and monitor therapy response would be a helpful approach in clinical settings. Moreover, its non-invasive and cost effective strategy better suited to minimize apprehension among high risk population. Objective This study aims toward investigating the urinary metabolic alterations of IDC by targeted (LC-MRM/MS) and untargeted (GC–MS) approaches for the better understanding of the disease pathophysiology and monitoring therapy response. Methods Urinary metabolic alterations of IDC subjects (63) and control subjects (63) were explored by targeted (LC-MRM/MS) and untargeted (GC–MS) approaches. IDC specific urinary metabolomics signature was extracted by applying both univariate and multivariate statistical tools. Results Statistical analysis identified 39 urinary metabolites with the highest contribution to metabolomic alterations specific to IDC. Out of which, 19 metabolites were identified from targeted LC-MRM/MS analysis, while 20 were identified from the untargeted GC–MS analysis. Receiver operator characteristic (ROC) curve analysis evidenced 6 most discriminatory metabolites from each type of approach that could differentiate between IDC subjects and controls with higher sensitivity and specificity. Furthermore, metabolic pathway analysis depicted several dysregulated pathways in IDC including sugar, amino acid, nucleotide metabolism, TCA cycle etc. Conclusions Overall, this study provides valuable inputs regarding altered urinary metabolites which improved our knowledge on urinary metabolomic alterations induced by IDC. Moreover, this study identified several dysregulated metabolic pathways which offer further insight into the disease pathophysiology.
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Most cancer cells perform glycolysis despite having suffcient oxygen. The specifc metabolic pathways of cancer cells have become the focus of cancer treatment. Recently, accumulating evidence indicates oxidative phosphorylation (OXPHOS) and glycolysis can be regulated with each other. Thus, we suggest that the glycolysis of cancer cells is inhibited by restoring or improving OXPHOS in cancer cells. In our study, we found that oxaloacetate (OA) induced apoptosis in HepG2 cells in vivo and in vitro. Meanwhile, we found that OA induced a decrease in the energy metabolism of HepG2 cells. Further results showed that the expression and activity of glycolytic enzymes were decreased with OA treatment. Conversely, the expression and activity of enzymes involved in the TCA cycle and OXPHOS were increased with OA treatment. The results indicate that OA can inhibit glycolysis through enhancement of OXPHOS. In addition, OAmediated suppression of HIF1α, p-Akt, and c-myc led to a decrease in glycolysis level. Therefore, OA has the potential to be a novel anticancer drug.
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Invasive ductal carcinoma (IDC) is the most common type of breast cancer and the leading cause of breast cancer related mortality. In the present study, metabolomic profiles of 72 tissue samples and 146 serum samples were analysed using targeted liquid chromatography multiple reaction monitoring mass spectrometry (LC-MRM/MS) and untargeted gas chromatography mass spectrometry (GC-MS) approaches. Combination of univariate and multivariate statistical treatment identified significant alterations of 42 and 32 metabolites in tissue and serum samples of IDC, respectively when compared to control. Some of the metabolite changes from tissue were also reflected in serum, indicating a bi-directional interaction of metabolites in IDC. Additionally, 8 tissue metabolites and 9 serum metabolites showed progressive change from control to benign to IDC suggesting their possible role in malignant transformation. We have identified a panel of three metabolites viz. tryptophan, tyrosine, and creatine in tissue and serum, which could be useful in screening of IDC subjects from both control and benign. The metabolomic alterations in IDC showed perturbations in purine and pyrimidine metabolism, amino sugar metabolism, amino acid metabolism, fatty acid biosynthesis etc. Comprehensively, this study provides valuable insights into metabolic adaptations of IDC, which can help to identify diagnostic markers as well as potential therapeutic targets.
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Multiple myeloma (MM) is among the most compelling examples of cancer in which research has markedly improved the length and quality of lives of those afflicted. Research efforts have led to 18 newly approved treatments over the last 12 years, including seven in 2015. However, despite significant improvement in overall survival, MM remains incurable as most patients inevitably, yet unpredictably, develop refractory disease. Recent advances in high-throughput ‘omics’ techniques afford us an unprecedented opportunity to (1) understand drug resistance at the genomic, transcriptomic, and proteomic level; (2) discover novel diagnostic, prognostic, and therapeutic biomarkers; (3) develop novel therapeutic targets and rational drug combinations; and (4) optimize risk-adapted strategies to circumvent drug resistance, thus bringing us closer to a cure for MM. In this review, we provide an overview of ‘omics’ technologies in MM biomarker and drug discovery, highlighting recent insights into MM drug resistance gleaned from the use of ‘omics’ techniques. Moving from the bench to bedside, we also highlight future trends in MM, with a focus on the potential use of ‘omics’ technologies as diagnostic, prognostic, or response/relapse monitoring tools to guide therapeutic decisions anchored upon highly individualized, targeted, durable, and rationally informed combination therapies with curative potential.
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Oxidized form of cellular nicotinamide adenine dinucleotide (NAD+) is currently intensively investigated topic in longevity science. However, if ageing is considered a defense mechanism against cancer, caution should be implemented regarding the use of NAD+ and its precursors. In the hypothesis presented NAD+ is shown as an important factor related to cancer formation and prevention. NAD+ depletion with age may play a major role in the process of cancer formation by limiting (1) energy production, (2) DNA repair, (3) genomic stability and signaling. Disruption of any of these processes could increase the cancer risk due to impaired genomic stability. NAD+ content is a critical protective factor in early carcinogenesis and can become detrimental factor later in cancer progression and promotion phase. Namely, NAD+ restoration could prevent or reverse the phenotype of malignant cells at early stages by inducing cellular repair and stress adaptive response as well as regulate cell cycle arrest and apoptotic removal of damaged cells. Contrary, during cancer promotion, progression and treatment increased NAD+ levels could have deleterious effects on the malignancy process due to growth advantage, increased resistance and greater cell survival. NAD+ levels can be increased with exercise, caloric restriction and ingestion of NAD+ precursors and intermediates or could be increased by using PARP and CD 38 inhibitors. The evidence indicating that modulation of NAD+ levels could be important in cancer prevention, initiation and progression phase is presented.
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There is an intimate interplay between cellular metabolism and the pathophysiology of disease. Mitochondria are essential to maintaining and regulating metabolic function of cells and organs. Mitochondrial dysfunction is implicated in diverse diseases, such as cardiovascular disease, diabetes and metabolic syndrome, neurodegeneration, cancer, and aging. Multiple reversible post-translational protein modifications are located in the mitochondria that are responsive to nutrient availability and redox conditions, and which can act in protein–protein interactions to modify diverse mitochondrial functions. Included in this are physiologic redox signaling via reactive oxygen and nitrogen species, phosphorylation, O-GlcNAcylation, acetylation, and succinylation, among others. With the advent of mass proteomic screening techniques, there has been a vast increase in the array of known mitochondrial post-translational modifications and their protein targets. The functional significance of these processes in disease etiology, and the pathologic response to their disruption, are still under investigation. However, many of these reversible modifications act as regulatory mechanisms in mitochondria and show promise for mitochondrial-targeted therapeutic strategies. This review addresses the current knowledge of post-translational processing and signaling mechanisms in mitochondria, and their implications in health and disease.
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Cancer is a disease characterized by unrestrained cellular proliferation. In order to sustain growth, cancer cells undergo a complex metabolic rearrangement characterized by changes in metabolic pathways involved in energy production and biosynthetic processes. The relevance of the metabolic transformation of cancer cells has been recently included in the updated version of the review "Hallmarks of Cancer", where the dysregulation of cellular metabolism was included as an emerging hallmark. While several lines of evidence suggest that metabolic rewiring is orchestrated by the concerted action of oncogenes and tumor suppressor genes, in some circumstances altered metabolism can play a primary role in oncogenesis. Recently, mutations of cytosolic and mitochondrial enzymes involved in key metabolic pathways have been associated with hereditary and sporadic forms of cancer. Together, these results suggest that aberrant metabolism, once seen just as an epiphenomenon of oncogenic reprogramming, plays a key role in oncogenesis with the power to control both genetic and epigenetic events in cells. In this review, we discuss the relationship between metabolism and cancer, as part of a larger effort to identify a broad-spectrum of therapeutic approaches. We focus on major alterations in nutrient metabolism and the emerging link between metabolism and epigenetics. Finally, we discuss potential strategies to manipulate metabolism in cancer and tradeoffs that should be considered. More research on the suite of metabolic alterations in cancer holds the potential to discover novel approaches to treat it.
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O-GlcNAcylation is a reversible post-translational modification. O-GlcNAc addition and removal is catalyzed by O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA), respectively. More recent evidence indicates that regulation of O-GlcNAcylation is important for inflammatory diseases and tumorigenesis. In this study, we revealed that O-GlcNAcylation was increased in the colonic tissues of dextran sodium sulfate (DSS)-induced colitis and azoxymethane (AOM)/DSS-induced colitis-associated cancer (CAC) animal models. Moreover, the O-GlcNAcylation level was elevated in human CAC tissues compared with matched normal counterparts. To investigate the functional role of O-GlcNAcylation in colitis, we used OGA heterozygote mice, which have an increased level of O-GlcNAcylation. OGA+/- mice have higher susceptibility to DSS-induced colitis than OGA+/+ mice. OGA +/- mice exhibited a higher incidence of colon tumors than OGA+/+ mice. In molecular studies, elevated O-GlcNAc levels were shown to enhance the activation of NF-κB signaling through increasing the binding of RelA/p65 to its target promoters. We also found that Thr-322 and Thr352 in the p65-O-GlcNAcylation sites are critical for p65 promoter binding. These results suggest that the elevated O-GlcNAcylation level in colonic tissues contributes to the development of colitis and CAC by disrupting regulation of NF-κB-dependent transcriptional activity.
Pancreatic cancer (PC) is an aggressive malady with proclivity for early metastasis. Overexpression of toll-like receptor 4 (TLR4) in pancreatic ductal adenocarcinoma, the most common type of pancreatic malignancy, correlates to tumor size, lymph node involvement, venous invasion and pathological stage. Lipopolysaccharides (LPS) are natural TLR4 ligands that have been shown to increase the invasive ability of PC cells. However, rapid inactivation of circulating LPS and low systemic absorption of inhaled LPS from the bronchoalveolar compartment make other agonists such as saturated fatty acids more suitable to be considered for TLR4-related cell invasiveness. Interestingly, PC risk was strongly associated to intake of saturated fat from animal food sources, in particular to consumption of saturated palmitic acid (PA). In the present study, we investigated the influence of PA on the invasive capacity of human PC cells AsPC-1. Using specific inhibitors, we found that PA stimulation of these tumor cells induced a TLR4-mediated cell invasion. Our results also indicate that the signaling events downstream of TLR4 involved generation of reactive oxygen species, activation of nuclear factor-kappa beta, and secretion and activation of matrix metalloproteinase 9 (MMP-9). Furthermore, PA stimulation decreased the levels of the micro RNA 29c (miR-29c). Of note, while inhibition of miR-29c increased MMP-9 mRNA levels, MMP-9 secretion and activation, and invasiveness, miR-29c mimic abrogated all these PA-stimulated effects. These results strongly suggest that miR-29c could be an attractive potential pharmacological agent for antitumoral therapy in PC.
Cancer was recognized as a genetic disease at least four decades ago, with the realization that the spontaneous mutation rate must increase early in tumorigenesis to account for the many mutations in tumour cells compared with their progenitor pre-malignant cells. Abnormalities in the deoxyribonucleotide pool have long been recognized as determinants of DNA replication fidelity, and hence may contribute to mutagenic processes that are involved in carcinogenesis. In addition, many anticancer agents antagonize deoxyribonucleotide metabolism. Here, we consider the extent to which aspects of deoxyribonucleotide metabolism contribute to our understanding of both carcinogenesis and to the effective use of anticancer agents.