Metabolomic profile of malignant ovarian tumors

Fedor E. Filippov; Denis S. Kutilin; Aleksey Yu. Maksimov; Natalya V. Porhanova

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South Russian

Journal of Cancer..

Vol. 5

No. 3, 2024

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South Russian

Journal of Cancer..

Vol. 5

No. 3, 2024

South Russian Journal of Cancer. 2024. Vol. 5, No. 3. P. 91-101

https://doi.org/10.37748/2686-9039-2024-5-3-8

https://elibrary.ru/avxoui

ORIGINAL ARTICLE

Metabolomic profile of malignant ovarian tumors

F. E. Filippov2, D. S. Kutilin1 , A. Yu. Maksimov1, N. V. Porkhanova2

1 National Medical Research Centre for Oncology, Rostov-on-Don, Russian Federation

2 Clinical Oncology Dispensary No. 1, Krasnodar, Russian Federation

k.denees@yandex.ru

ABSTRACT

Purpose of the study. Investigate the metabolomic profile in tissues of patients with serous ovarian adenocarcinoma.

Materials and methods. The study included 100 patients with serous ovarian adenocarcinoma. Chromatographic separation of

metabolites was performed on a Vanquish Flex UHPLC System

chromatograph,

which was coupled with an Orbitrap Exploris

480 mass spectrometer. Differences were assessed using the Mann-Whitney test with Bonferroni correction.

Results. In ovarian tumor tissue, 20 compounds had abnormal concentrations compared to normal tissue: increased levels of

kynurenine, phenylalanylvaline, lysophosphatidylcholine (18:3), lysophosphatidylcholine (18:2), alanylleucine, L-phenylalanine,

phosphatidylinositol (34:1), 5-methoxytryptophan, lysophosphatidylcholine (14:0), indoleacrylic acid and decreased levels of

myristic acid, decanoylcarnitine, aspartylglycine, malonylcarnitine, 3-methylxanthine, 3-oxododecanoic acid, 2-hydroxymyristic

acid, N-acetylproline, L-octanoylcarnitine and capryloylglycine.

Conclusion. A

significant metabolic

imbalance was found in ovarian tumor

tissue,

expressed in abnormal concentrations

of fatty acids and their derivatives, acylcarnitines, amino acids and their derivatives, phospholipids and nitrogenous base

derivatives. The concentrations of these 20 metabolites in tissues can serve as diagnostic markers of ovarian cancer. Thus,

metabolomic tissue profiling allowed both to identify potential markers

of the disease and to better understand the molecular

mechanisms of changes underlying the development of this disease.

Keywords: metabolites, ultra-high performance liquid chromatography and mass spectrometry, ovarian serous

adenocarcinoma, biomarkers

For citation: Filippov F. E., Kutilin D. S., Maksimov A. Yu., Porkhanova N. V. Metabolomic profile of malignant ovarian tumors. South Russian Journal of

Cancer. 2024; 5(3): 91-101. https://doi.org/10.37748/2686-9039-2024-5-3-8, https://elibrary.ru/avxoui

For correspondence: Denis S. Kutilin � PhD in Biology, Leading Researcher, Laboratory of Molecular Oncology, National Medical Research Centre for

Oncology, Rostov-on-Don, Russian Federation

Address: 63 14 line str., Rostov-on-Don 344037, Russian Federation

E-mail: k.denees@yandex.ru

ORCID: https://orcid.org/0000-0002-8942-3733

SPIN: 8382-4460, AuthorID: 794680

Scopus Author ID: 55328886800

Compliance with ethical standards: the research study is carried out in compliance with the ethical principles set forth by World Medical Association

Declaration of Helsinki, 1964, ed. 2013. The study was approved by the Committee on Biomedical Ethics at the National Medical Research Center for

Oncology (extract from the minutes of the meeting No. 17 dated 06/28/2022). Informed consent was received from all participants of the study

Funding: this work was not funded. The work was performed with scientific equipment provided by the Central Research Institute of the National Medical

Research Center for Oncology: https://ckp-rf.ru/catalog/ckp/3554742/

Conflict of interest: the authors declare that there are no obvious and potential conflicts of interest associated with the publication of this article

The article was submitted 21.07.2024; approved after reviewing 27.08.2024; accepted for publication 29.08.2024

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INTRODUCTION

In the last decade, among oncogynecological

diseases, ovarian cancer has occupied leading positions

in terms of morbidity and mortality in the

world and Russia [1, 2]. Malignant ovarian tumors

are divided into many histological subtypes, each of

which has distinctive biological and clinical characteristics.

There are serous carcinoma, endometrioid

carcinoma, mucinous carcinoma, light cell carcinoma,

malignant Brenner tumor, serous-mucinous

carcinoma, undifferentiated carcinoma and mixed

epithelial carcinoma. Serous adenocarcinoma is

the most common subtype [3, 4].

The overall five-year survival rate of ovarian cancer

patients does not exceed 40 %, which is due to

late diagnosis. To date, the sensitivity and specificity

of the main diagnostic methods of this disease

are insufficient to detect it at an early stage [5, 6].

New approaches are needed to improve diagnosis.

Metabolomics methods based on high-resolution

liquid chromatography and mass spectrometry

(MS) open up new prospects for the detection and

identification of biomarkers in the femtomolar and

attomolar ranges.

So in the work of Y. Ahmed-Salim and co-authors

analyzed the results of 32

publications in the field

of metabolic research in ovarian cancer. Most studies

have reported a violation of the regulation of

phospholipids and amino acids: histidine, citrulline,

alanine and methionine. At the same time, combinations

of more than one metabolite as a panel

in various studies achieved higher sensitivity and

specificity for diagnosis than a single metabolite;

for example, combinations of various phospholipids

[7].

In [8], the role of histidine and citrulline in the

development of ovarian cancer was confirmed,

and new lipid compounds (lysophosphatidylcholine

C16:1, phosphatidylcholine C32:2, C34:4

and

C36:6) potentially involved in cancer metabolism

were discovered.

However, such studies in ovarian cancer are not

numerous compared to genomic and transcriptomic

ones, and most of them were performed on

equipment with lower resolution and a principle of

operation different from Orbitrap technology [9],

and biological fluids of patients, such as urine [10]

or blood [11].

The purpose of the study was to study the metabolic

profile of tissues of patients with serous ovarian

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adenocarcinoma in order to identify potential

diagnostic markers of the disease.

MATERIALS AND METHODS

The study included 100 patients diagnosed with

serous ovarian adenocarcinoma (T3a-c). Samples

of normal and tumor tissue obtained at the stage

of surgical treatment were used as objects of research.

The average age of the patients was 54.2

years.

Analysis of metabolites by the

HPLC-MS method

Surgical biopsies of tumor and normal ovarian

tissue were used for analysis, which were stored in

liquid nitrogen until the moment of metabolic and

molecular genetic studies. The samples were homogenized

at a temperature no higher than 4

�C.

The homogenate was mixed with 600 .l of acetonitrile

LC-MS (Merck, Germany)/methanol LC-MS

(Merck, Germany) in a ratio of 3/1, was stirred for 15

minutes using a vortex and incubated for 15 hours

at �20

�C. Proteins were precipitated by centrifugation

at 16000

g 0 �C for 30

minutes. The supernatant

was transferred to clean Eppendorf tubes. The

solvent was evaporated at 45

�C for 4

hours on a

SpeedVac vacuum evaporator (Eppendorf). The resulting

dry precipitate was dissolved in 300 .l of 95

% acetonitrile LC-MS solution (Merck, Germany) with

the addition of 0.1 % formic acid (Merck, Germany).

To better dissolve the sediment, the samples were

treated with ultrasound in an Elmsonic P 120

H ultrasonic

bath (ELMA, Germany). Further, the samples

were centrifuged for 30 min at 16000 g and the

resulting supernatant was used for chromatomass

spectrometric analysis.

Chromatographic separation of metabolites was

performed on a Vanquish Flex UHPLC System Thermo

Fisher Scientific chromatograph. The chromatograph

was paired with the Orbitrap Exploris 480

mass spectrometer, which has an electrospray ionization

source. A sample of metabolites in a volume

of 2 .l was divided on a Hypersil GOLD� C18 column

(1.9

.m, 150

. 2.1

mm), eluents: A � 0.1

% formic

acid LC-MS (Merck, Germany), B � acetonitrile

LC-MS (Merck, Germany) containing 0.1 % formic

South Russian Journal of Cancer 2024. Vol. 5, No. 3. P. 91-101

Filippov F. E., Kutilin D. S. , Maksimov A. Yu., Porkhanova N. V. Metabolomic profile of malignant ovarian tumors

acid (Merck, Germany). The following elution gradient

was used: 1

min � 5

% of eluent B, 15 min � linear

gradient of eluent B from 5 to 95 %, 2 min � 95 %

of eluent B, 0.5 min � change of eluent composition

to 5 % of eluent B, 3 min � 5

% of eluent B. The flow

of eluents is 200 .l/min.

Mass spectrometric analysis was performed on

an Orbitrap Exploris 480 (Thermo Fisher Scientific)

mass spectrometer with an electrospray ionization

source. The mass spectrometer was configured

for priority ion detection in the m/z range from 67

to 1000 Da with a resolution of 60,000. The spectra

were taken in the detection mode of positively

charged ions. The time to remove one spectrum is

20 minutes. Additional MS settings were as follows:

ion sputtering voltage = �3.5 kV; capillary temperature

= 320 �C; sample heater temperature = 300 �C;

protective gas = 35; auxiliary gas = 10

and radio frequency

S-lens � 50.

For mass spectrometric peaks to be identified,

compliance with specific metabolites from the

Human Metabolome Database was established

(http://www.hmdb.ca) and Metlin (Scripps Center

for Mass Spectrometry, USA; http://metlin.scripps.

edu). For this purpose, an accurately measured

mass of the chemical compound was used. Bioinformatic

analysis was performed using Compound

Discoverer Software (Thermo Fisher Scientific,

USA) and analysis of biochemical pathways using

the KEGG PATHWAY Database.

Statistical data processing

The differences were assessed using the

Mann-Whitney criterion for a threshold level of statistical

significance of p < 0.05, and the Bonferroni

correction was used to account for multiple comparisons.

The data analysis was carried out in the

Python programming language using the SciPy library

[12].

STUDY RESULTS

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During the conducted metabolomic

profiling,

100

samples of serous ovarian adenocarcinoma and 100

samples of conditionally normal ovarian tissues were

analyzed. 750 metabolites were identified. For

metabolites

whose intensities in the mass spectra differed

statistically significantly relative to

normal tissue,

P-value and FoldChange were determined (Table 1).

According to the data obtained, the metabolome

of the tumor tissue of patients with serous ovarian

carcinoma differed significantly from samples

of normal ovarian tissue of the same patients. In

the tumor tissue of the patients, 10 metabolites

(kynurenine, phenylalanyl valine, lysophosphatidylcholine

(18:3), lysophosphatidylcholine (18:2), alanyl

leucine, L-phenylalanine, phosphatidylinositol

(34:1), 5-methoxytryptophan, lysophosphatidylcholine

(14:0), indolacrylic acid) had significantly higher

concentrations In comparison with conditionally

normal tissue, the concentration of 10 compounds

(myristic acid, decanoyl carnitine, aspartyl-glycine,

malonylcarnitine, 3-methylxanthine, 3-oxododecanoic

acid, 2-hydroxymyristinic acid, N-acetylproline,

L-octanoylcarnitine, caprylylglycine), on the contrary,

was reduced.

Thus, it was found that the concentrations of

myristic acid, 2-hydroxymyristic acid and 3-oxododecanoic

acid in tumor tissue were statistically significantly

(p < 0.01) reduced by 2.6

times, 4.8

times

and 1.4 times, respectively, compared with normal

tissue. The levels of decanoyl carnitine, malonylcarnitine

and L-octanoyl carnitine in tumor tissue

were statistically significantly (p < 0.0001) lower

by 5.3 times, 1.5 times and 6.7 times, respectively,

than in normal tissue. Statistically significantly

(p < 0.00000005), the concentration of a number of

phospholipids in tumor tissue in patients with ovarian

cancer was increased relative to normal ovarian

tissue: lysophosphatidylcholine (18:3) by 2.1 times,

lysophosphatidylcholine (18:2) by 3.4

times, phosphatidylinositol

(34:1) by 4.1

times and lysophosphatidylcholine

(14:0) by 1.9

times. Statistically

significant (p < 0.01) changes in the concentration

of some amino acids and their derivatives were

also found: an increase in the concentration of

kynurenine by 6.1 times, phenylalanyl valine by

2.2 times, alanyl leucine by 1.6

times, L-phenylalanine

by 1.8 times, 5-methoxytryptophan by 1.6 times

and indolacrylic acid by 1.5 times relative to normal

tissue, as well as a decrease in the concentration

of N-acetylproline by 1.7 times, caprylylglycine by

1.5 times and aspartyl glycine by 5.0 times, respectively,

relative to normal ovarian tissue. A change in

the content of nitrogenous base derivatives in ovarian

tumor tissue was also detected, i.e. a 2.3-fold

decrease in the concentration of 3-methylxanthine

(p < 0.0001).

��-�� 2024. �. 5, � 3. �. 91-101

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DISCUSSION

The HPLC-MS method identified 750

metabolites

of various classes, while the concentration of

10 metabolites in the tumor tissue was significantly

increased compared to conditionally normal tissue,

and the concentration of 10 compounds was lowered

on the contrary.

Fatty acids and their derivatives

In the tumor tissue, the concentrations of most

fatty acid derivatives � myristic acid, 2-hydroxy

myristic acid and 3-oxododecanoic acid were reduced

compared to conditionally normal tissue.

Tumor cells are characterized by a profound restructuring

of the metabolism of lipids and fatty acids. In

some types of tumors, the utilization of fatty acids

increases, while in others it is suppressed [13, 14].

Myristic acid (CH3(CH2 COOH, FoldChange 0.38,

)12

p = 0.0000241)

is

a saturated fatty acid with

an

aliphatic long chain, present in almost all living or

ganisms

[15]. Abnormal

levels

of myristic acid can

increase the risk of tumors

[16]. It

is

involved in

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the

implementation of several antitumor mechanisms,

Table 1. The difference between the metabolomic profile of tumor tissue and normal in patients with serous ovarian

adenocarcinoma

Metabolites m/z FoldChange,

tumor/normal tissue p-value

1. Fatty acids and their derivatives

Myristic acid 231.2 0.38 0.00002410

2-hydroxymyristic acid 267.2 0.21 0.00001000

3-oxododecanoic acid 237.1 0.74 0.01000060

2. Acylcarnitines

Decanoyl carnitine 316.2 0.19 0.000000003

Malonyl carnitine 230.1 0.65 0.000100002

L-octanoylcarnitine 288.2 0.15 0.000004011

3. Phospholipids

Lysophosphatidylcholine (18:3)

518.3 2.05 0.000000002

Lysophosphatidylcholine (18:2)

521.3 3.40 0.000000051

Lysophosphatidylcholine (14:0)

468.3 1.89 0.000000003

�� (34:1)

430.8 4.11 0.000000001

4. Aminoacids and their derivatives

Alanine-Leucine 185.1 1.55 0.000900000

Phenylalanine-Valine 265.2 2.15 0.000100000

L-Phenylalanine 166.1 1.84 0.000000002

Kinurenin 209.1 6.07 0.000001000

Aspartyl-glycine 208.1 0.20 0.000012400

5-methoxytryptophan 217.1 1.61 0.000004200

Indolylacrylic acid 171.1 1.49 0.010189400

N-acetylproline 140.1 0.59 0.000085630

Capriloyl glycine 202.1 0.65 0.010212890

5. Derivatives of nitrogenous bases

3-methylxanthine 167.1 0.44 0.000100000

South Russian Journal of Cancer 2024. Vol. 5, No. 3. P. 91-101

Filippov F. E., Kutilin D. S. , Maksimov A. Yu., Porkhanova N. V. Metabolomic profile of malignant ovarian tumors

such as the production of myristoleic acid, which

causes apoptosis, and in the synthesis of ceramides

de novo. According to a number of authors, the con

tent

of myristic acid in

biological

fluids

and tissues

is inversely associated with the risk of colorectal

cancer. However, the mechanisms underlying this

relationship have

not

been

fully studied [17-20].

2-hydroxymyristic acid O3, FoldChange

(C14H28

0.21, p = 0.00001) is a fatty acid containing an aliphatic

chain carrying a hydroxyl substituent at position

2, is a derivative of myristic acid. The physiological

function of hydroxy fatty acids remains

largely unknown. They have been shown to play a

specific role in signaling to cells [21]. 2-Hydroxymyristinic

acid is metabolically activated in cells to

form 2-hydroxymyristoyl-CoA, a potent inhibitor of

myristoyl-CoA [22]. Currently, the main mechanisms

by which 2-hydroxylation of fatty acids is associated

with metabolic adaptation and tumor growth re

main unclear [23].

3-oxododecanoic acid (C12 O3, FC = 0.74,

H22

p = 0.01000060) is a fatty acid that is a 3-oxo derivative

of decanoic acid. In the human body, 3-oxododecanoic

acid participates in a number of en

zymatic reactions [24]. Keto-fatty acids are often

reported as artifacts of fatty acid oxidation, but

relatively rarely as natural fatty acids. 3-Keto-fatty

acids, found as secondary components of animal

tissues, are usually intermediates of .-oxidation.

For the beta-oxidation of fatty acids by mitochondria,

the presence of carnitine, an important cofactor

of metabolic processes, is an indispensable

condition. There are more than 1,000 types of acylcarnitines

in the human body, the general function

of which is to transport acyl groups of organic acids

and fatty acids from the cytoplasm to the mitochondria

so that they can be broken down during beta

oxidation to produce energy [25]. This is one of the

most efficient ways of energy production in cells,

therefore, tissues with high energy consumption

mainly depend on the utilization of fatty acids [26].

Cancer is a pathological condition characterized

by high energy consumption. Glucose and glutamine

as energy substrates are considered a distinctive

feature of tumor cells, and the metabolic

switch that allows their use in almost anaerobic

conditions is known as the Warburg effect [27]. The

canonical interpretation of the Warburg effect implies

that cells bypass the mitochondrial respiratory

chain to synthesize ATP even with sufficient oxygen

supply [28]. However, it is obvious that the Warburg

effect needs to be considered in a more general

metabolic context, which also includes the utilization

of fatty acids in accordance with the effectiveness

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of these substrates in terms of ATP output.

Metabolic flexibility is a phenomenon observed in

different types of cancer and within the same type

of cancer at different stages of progression. Carnitine-

induced fatty acid oxidation plays a critical

role in the production of NADH, FADN2, NADPH,

and ATP, which can contribute to the development

of tumors [29].

Acylcarnitines

Metabolic reprogramming of tumor cells regulates

the content of acylcarnitines with different

chain lengths in order to create a balance between

production, energy consumption and synthesis of

metabolic intermediates to meet the requirements

of rapid proliferation [30]. Acylcarnitines have cytotoxicity

and immunomodulatory properties that

can be used by the tumor for growth and survival

in situ [31]. Thus, a change in the level of malonylcarnitine

is associated with the risk of developing

breast cancer.

Malonylcarnitine is a metabolite that accumu

lates with a specific violation of fatty acid oxidation

caused by a violation of the intake of long-chain

acylcarnitine esters into the mitochondria and in

sufficiency of the mitochondrial respiratory chain

with a deficiency of complex 11

and malonyl-CoA

decarboxylase [32].

L-octanoylcarnitine is a physiologically active

form of octanoylcarnitine [33], which is found in

deficiency of medium chain acyl-CoA dehydrogenase

(MCAD). L-octanoylcarnitine is involved in lipid

peroxidation (HMDB: HMDB0000791), fatty acid

metabolism (HMDB: HMDB0000791), mitochondrial

beta oxidation of short-chain saturated fatty

acids (HMDB: HMDB0000791) and lipid transport

(HMDB: HMDB0000791). Changes in its concentrations

have been recorded in blood and faeces in

colorectal cancer, Crohn's disease and ulcerative

colitis [34].

Decanoyl carnitine is classified as an acylcarnitine

with a medium chain length. A change in the

concentration of decanoyl carnitine was found in

renal cell carcinoma and breast cancer [35].

��-�� 2024. �. 5, � 3. �. 91-101

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The study of fluctuations in the content of acylcarnitines

can contribute to a better understanding

of the mechanisms of oncological diseases and the

development of methods for their diagnosis and

treatment.

Phospholipids

In this study, an increase in the concentration of

lysophosphatidylcholines and phosphatidylinositol

was observed in ovarian tumor tissue. Lysophospholipids

are secreted by various types of cells, including

tumor cells. These chemical compounds play

an important role in the development, activation,

and regulation of the immune system [36]. Changes

in the composition and content of phospholipids

and lysophospholipids have previously been shown

in prostate cancer and are considered as potential

biomarkers [37]. Lysophospholipids function as signaling

molecules through their specific membrane

receptors. In addition, some of the lysophospholipids

have tumor-promoting activity and are therefore

called "oncolipids" [38]. Recent studies have shown

that phospholipids are candidates for PH biomarkers.

Several comprehensive prospective studies of

lipids have been conducted, such as lysophosphatidylcholines,

phosphatidylcholines, ceramides and

sphingomyelins, the concentrations of which differ

in patients with rheumatoid arthritis compared with

healthy ones [39, 10].

Lysophosphatidylcholines, also called lysolecithins,

are a class of chemical compounds formed

from phosphatidylcholines by the enzyme phospholipase

A2. Lysophosphatidylcholines are the most

common phospholipids in the blood and key lipids

in various pathophysiological conditions such as

inflammation, endothelial activation and atherogenesis

[40]. Among other properties, they act as

a signaling molecule released by apoptotic cells to

attract phagocytes, which then phagocytize apoptotic

cells [41].

Phosphatidylinositols are minor phospholipids

of the inner membrane layer of eukaryotic cells,

important components of intracellular signaling

pathways. Phosphatidylinositol is a substrate for

a variety of signaling kinase molecules that can

attach a phosphate group to inositol. The main

biological functions of phosphatidylinositols are

a membrane stabilizer (HMDB: HMDB0009799)

and a molecular messenger (signaling molecule

(HMDB: HMDB0009799)). Phosphatidylinositols

are involved in such important signaling pathways

and processes as fatty acid metabolism

(HMDB: HMDB0009799), lipid peroxidation (HMDB:

HMDB0009799), apoptosis, cell adhesion [42], cell

migration and proliferation [43]. Their content increases

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in the blood (HMDB: HMDB0009799) in a

number of oncological diseases, including breast

cancer, colorectal cancer and stomach cancer [44].

Amino acids and their derivatives

An abundant supply of nutrients, such as amino

acids, is necessary for the increased metabolic

needs of tumor cells that maintain high proliferative

activity [45].

The alteration of tryptophan metabolism in cancer

via the kynurenine pathway has attracted widespread

attention as a mechanism by which tumors

can elude immune control [45].

Kynurenine (.-(o-aminobenzene)-.-aminopropio

nic acid) is an intermediate product of the enzymatic

breakdown of tryptophan and the biosynthesis of

nicotinic acid in the human body. During enzymatic

oxidation, kynurenine is converted to 3-hydroxykynurenine.

The pathway of L-tryptophan biotransformation

with the formation of "kynurenine" metabolites

plays an important role in the mechanisms

of immunoregulation and "negative" control of im

mune inflammation [46].

In addition to the main pathways of tryptophan

catabolism, there are secondary ones, one of them

leads to indolylacrylic acid (C11H9NO2, indolacrylate),

the biological role of which in animals is

still unclear [47]. Stimulating the production of indolacrylic

acid can promote anti-inflammatory reactions

and have therapeutic value [47]. In our study,

the level of indolacrylic acid is elevated in ovarian

tumor tissue. The production of indolacrylic acid

may contribute to the development of anti-inflammatory

reactions [47]. It has been shown to selectively

affect breast cancer cells, but does not affect

untransformed primary fibroblasts. In our study, an

increase in indolacrylic acid was accompanied by

an increase in the content of kynurenine.

5-methoxytryptophan (C12 N2O3), which is an en

H14

dothelial factor with anti-inflammatory properties, is

synthesized from L-tryptophan by 2 enzymes: tryptophan

hydroxylase-1 and hydroxyindole-O-methyltransferase

[48]. It controls the migration and acti

South Russian Journal of Cancer 2024. Vol. 5, No. 3. P. 91-101

Filippov F. E., Kutilin D. S. , Maksimov A. Yu., Porkhanova N. V. Metabolomic profile of malignant ovarian tumors

vation of macrophages by inhibiting NF-kB

[49], and

also regulates epithelial-mesenchymal transition

and metastasis [50].

Changes in the metabolism of another aromatic

amino acid, phenylalanine and its derivatives, are

also associated with inflammation and immune activation.

Neurauter G. et al showed that the concentration

of phenylalanine in serum in patients with

ovarian carcinoma correlates with the concentration

of markers of immune activation and the development

of oxidative stress [51].

We also found a decrease in the content of aspartyl

glycine dipeptide in tumor tissue. This compound

is probably a product of incomplete breakdown

of proteins and peptides. It is known that

some dipeptides have physiological or cellular

signaling effects, although most of them are simply

short-lived intermediates on the way to specific

amino acid degradation pathways. Some dipeptides

are also considered as biomarkers of diseases [52].

Concentrations of N-acetyl-L-proline (C7 NO3)

H11

and caprylylglycine also decrease. N-acetylproline

is a biologically available N-terminal form of the proteinogenic

alpha amino acid L-proline. N-terminal

acetylation of proteins is a widespread and highly

conserved process in eukaryotes, which is involved

in the protection and stability of proteins [53]. A

number of studies have shown the association of

N-acetyl-L-proline with colorectal cancer [54] and

metastatic melanoma [55]. Caprylylglycine is a lipid

amino acid consisting of caprylic acid and glycine.

Acylglycines are usually minor metabolites of fatty

acids [55].

Nitrogenous base derivatives and steroids

In our study, a decrease in the concentration

of 3-methylxanthine was found in ovarian tumor

tissue. 3-methylxanthine (C6H6N4O2) is a methyl

derivative of purine with a ketone group (3,7-dihydropurine-

2,6-dione). Some evidence suggests that

methylxanthines have antitumor effect [56]: they inhibit

PI3K/Akt/mTOR and stimulate PTEN, promoting

apoptosis and autophagy [57].

CONCLUSION

A significant change in metabolism was found

in the ovarian tumor tissue, presented in abnormal

concentrations of fatty acids and their derivatives,

acylcarnitines, amino acids and their derivatives,

phospholipids and derivatives of nitrogenous bases.

Concentrations of these metabolites in tissues

can serve as diagnostic markers of ovarian cancer.

Thus, the metabolic profiling of tissues allowed

both to identify potential markers of the disease

and to better understand the molecular mechanisms

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of changes underlying the development of

this disease.

References

1.

Reid BM, Permuth JB, Sellers TA. Epidemiology of ovarian cancer: a review. Cancer Biol Med. 2017 Feb;14(1):9�32.

https://doi.org/10.20892/j.issn.2095-3941.2016.0084

2. Malignant neoplasms in Russia in 2018 (morbidity and mortality). Edited by A. D. Kaprin, V. V. Starinsky, G. V. Petrova. Moscow:

P. A. Herzen MNIOI � Branch of the National Medical Research Radiological Center, 2019, 250 p.

3.

Tsandekova MR, Porkhanova NV, Kutilin DS. Molecular characteristic of serous ovarian adenocarcinoma: implications for

diagnosis

and

treatment.

Modern

Problems

of

Science

and

Education.

2020;(1):55.https://doi.org/10.17513/spno.29428,

EDN: LTMXTL

4.

Meinhold-Heerlein I, Fotopoulou C, Harter P, Kurzeder C, Mustea A, Wimberger P, et al. The new WHOclassification of ovarian,

fallopian tube, and primary peritoneal cancer and its clinical implications. Arch Gynecol Obstet. 2016

Apr;293(4):695�700.

https://doi.org/10.1007/s00404-016-4035-8

5.

Rooth C. Ovarian cancer: risk factors, treatment and management. Br J Nurs. 2013 Sep 12;22(17):S23�30.

https://doi.org/10.12968/bjon.2013.22.Sup17.S23

6.

Ahmed-Salim Y, Galazis N, Bracewell-Milnes T, Phelps DL, Jones BP, Chan M, et al. The application of metabolomics in

ovarian cancer management: a systematic review. Int J Gynecol Cancer. 2021

May;31(5):754�774.

https://doi.org/10.1136/ijgc-2020-001862

��-�� 2024. �. 5, � 3. �. 91-101

�� . �., �� . �. , �� .�., �� . �. ��

7.

Plewa S, Horala A, Derezinski P, Nowak-Markwitz E, Matysiak J, Kokot ZJ. Wide spectrum targeted metabolomics identifies

potential ovarian cancer biomarkers. Life Sci. 2019 Apr 1;222:235�244. https://doi.org/10.1016/j.lfs.2019.03.004

8.

Swiatly A, Plewa S, Matysiak J, Kokot ZJ. Mass spectrometry-based proteomics techniques and their application in ovarian

cancer research. J Ovarian Res. 2018 Oct 1;11(1):88. https://doi.org/10.1186/s13048-018-0460-6

9.

Veenstra TD. Metabolomics: the final frontier? Genome Med. 2012

Apr 30;4(4):40. https://doi.org/10.1186/gm339

10.

Guskova ON, Alliluev IA, Verenikina EV, Polovodova VV, Rogozin MA, Myagkova TYu, et al. Changes in urine metabolite

concentration as a minimally invasive marker of ovarian serous adenocarcinoma. Russian Journal of Biotherapy.

2023;22(3):43-50. (In Russ.). https://doi.org/10.17650/1726-9784-2023-22-3-43-50, EDN: KRLBXC

11.

Guskova ON, Alliluyev IA, Verenikina EV, Menshenina AP, Cherkasova AA, Ardzha AYu, et al. Features of the blood plasma

metabolome of patients with serous ovarian carcinoma. Modern Problems of Science and Education. 2023;(3):89.

(In Russ.). https://doi.org/10.17513/spno.32678, EDN: HJTHUD

12.

Jones E., Oliphant E., Peterson P. SciPy: Open source scientific tools for python, 2001.

13.

Koundouros N, Poulogiannis G. Reprogramming of fatty acid metabolism in cancer. Br J Cancer. 2020

Jan;122(1):4�22.

https://doi.org/10.1038/s41416-019-0650-z

14.

Zhao S, Cheng L, Shi

Y, Li

J, Yun

Q, Yang H. MIEF2

reprograms

lipid metabolism to drive

progression

of ovarian

cancer through

ROS/AKT/mTOR signaling pathway. Cell Death Dis. 2021 Jan 5;12(1):18. https://doi.org/10.1038/s41419-020-03336-6

15.

Zazula R, Moravec M, Pehal F, Nejtek T, Protus M, Muller M. Myristic Acid Serum Levels and Their Significance for Diagnosis

of Systemic Inflammatory Response, Sepsis, and Bacteraemia. J Pers Med. 2021 Apr 16;11(4):306.

https://doi.org/10.3390/jpm11040306

16. Matta M, Deubler E, Chajes V, Vozar B, Gunter MJ, Murphy N, et al. Circulating plasma phospholipid fatty acid levels

and breast cancer risk in the Cancer Prevention Study-II Nutrition Cohort. Int J Cancer. 2022 Dec 15;151(12):2082�2094.

https://doi.org/10.1002/ijc.34216

17. Aglago EK, Murphy N, Huybrechts I, Nicolas G, Casagrande C, Fedirko V, et al. Dietary intake and plasma phospholipid concentrations

of saturated, monounsaturated and trans fatty acids and colorectal cancer risk in the European Prospective

Investigation into Cancer and Nutrition cohort. Int J Cancer. 2021

Apr 28;149(4):865�882.

https://doi.org/10.1002/ijc.33615

18. Brown DG, Rao S, Weir TL, O�Malia J, Bazan M, Brown RJ, et al. Metabolomics and metabolic pathway networks from human

colorectal cancers, adjacent mucosa, and stool. Cancer Metab. 2016;4:11. https://doi.org/10.1186/s40170-016-0151-y

19.

Sinha R, Ahn J, Sampson JN, Shi J, Yu G, Xiong X, et al. Fecal Microbiota, Fecal Metabolome, and Colorectal Cancer Interrelations.

PLoS

One.

2016;11(3):e0152126.

https://doi.org/10.1371/journal.pone.0152126

20.

Wang X, Wang J, Rao B, Deng L. Gut flora profiling and fecal metabolite composition of colorectal cancer patients and

healthy individuals. Exp Ther Med. 2022 Apr;23(4):250. https://doi.org/10.3892/etm.2022.11175

21. Jenske R, Vetter W. Enantioselective analysis of 2- and 3-hydroxy fatty acids in food samples. J Agric Food Chem. 2008

Dec 24;56(24):11578�11583. https://doi.org/10.1021/jf802772a

22.

Lemay AM, Courtemanche O, Couttas TA, Jamsari G, Gagne A, Bosse Y, et al. High FA2H and UGT8 transcript levels predict

hydroxylated hexosylceramide accumulation in lung adenocarcinoma. J Lipid Res. 2019

Oct;60(10):1776�1786.

https://doi.org/10.1194/jlr.M093955

iНе можете найти то, что вам нужно? Попробуйте сервис подбора литературы.

23.

Sun

L,

Yang

X,

Huang

X,

Yao

Y,

Wei

X,

Yang

S,

et

al.

2-Hydroxylation

of

Fatty

Acids

Represses

Colorectal

Tumorigenesis

and

Metastasis via the YAP Transcriptional Axis. Cancer Res. 2021 Jan 15;81(2):289�302.

https://doi.org/10.1158/0008-5472.CAN-20-1517

24.

Batsika CS, Mantzourani C, Gkikas D, Kokotou MG, Mountanea OG, Kokotos CG, et al. Saturated Oxo Fatty Acids (SOFAs):

A Previously Unrecognized Class of Endogenous Bioactive Lipids Exhibiting a Cell Growth Inhibitory Activity. J Med Chem.

2021

May 13;64(9):5654�5666. https://doi.org/10.1021/acs.jmedchem.0c02058

25.

McCann MR, George De la Rosa MV, Rosania GR, Stringer KA. L-Carnitine and Acylcarnitines: Mitochondrial Biomarkers for

Precision Medicine. Metabolites. 2021 Jan 14;11(1):51. https://doi.org/10.3390/metabo11010051

26.

Console L, Scalise M, Mazza T, Pochini L, Galluccio M, Giangregorio N, et al. Carnitine Traffic in Cells. Link With Cancer.

Front Cell Dev Biol. 2020;8:583850. https://doi.org/10.3389/fcell.2020.583850

27.

Warburg O, Wind F, Negelein E. The metabolism of tumors in the body. J Gen Physiol. 1927 Mar 7;8(6):519�530.

https://doi.org/10.1085/jgp.8.6.519

South Russian Journal of Cancer 2024. Vol. 5, No. 3. P. 91-101

Filippov F. E., Kutilin D. S. , Maksimov A. Yu., Porkhanova N. V. Metabolomic profile of malignant ovarian tumors

28.

Ganapathy V, Thangaraju M, Prasad PD. Nutrient transporters in cancer: relevance to Warburg hypothesis and beyond.

Pharmacol Ther. 2009 Jan;121(1):29�40. https://doi.org/10.1016/j.pharmthera.2008.09.005

29.

Zhang J, Wu G, Zhu H, Yang F, Yang S, Vuong AM, et al. Circulating Carnitine Levels and Breast Cancer: A Matched Retrospective

Case-Control Study. Front Oncol. 2022;12:891619. https://doi.org/10.3389/fonc.2022.891619

30.

Wang Y, Chen Y, Guan L, Zhang H, Huang Y, Johnson CH, et al. Carnitine palmitoyltransferase 1C regulates cancer cell senescence

through mitochondria-associated metabolic reprograming. Cell Death Differ. 2018 Mar;25(4):735�748.

https://doi.org/10.1038/s41418-017-0013-3

31.

Ganti S, Taylor SL, Kim K, Hoppel CL, Guo L, Yang J, et al. Urinary acylcarnitines are altered in human kidney cancer.

Int J Cancer. 2012 Jun 15;130(12):2791�2800. https://doi.org/10.1002/ijc.26274

32. Santer R, Fingerhut R, Lassker U, Wightman PJ, Fitzpatrick DR, Olgemoller B, et al. Tandem mass spectrometric determination

of malonylcarnitine: diagnosis and neonatal screening of malonyl-CoA decarboxylase deficiency. Clin Chem. 2003

Apr;49(4):660�662. https://doi.org/10.1373/49.4.660

33.

Huang Z, Lin L, Gao Y, Chen Y, Yan X, Xing J, et al. Bladder cancer determination via two urinary metabolites: a biomarker

pattern approach. Mol Cell Proteomics. 2011 Oct;10(10):M111.007922. https://doi.org/10.1074/mcp.M111.007922

34. Chace DH, DiPerna JC, Adam BW, Hannon WH. Errors caused by the use of D,L-octanoylcarnitine for blood-spot calibrators.

Clin Chem. 2001 Apr;47(4):758�3869.

35.

Chang W, Fa H, Xiao D, Wang J. Targeting phosphatidylserine for Cancer therapy: prospects and challenges. Theranostics.

2020;10(20):9214�9229. https://doi.org/10.7150/thno.45125

36.

Rolin J, Maghazachi AA. Effects of lysophospholipids on tumor microenvironment. Cancer Microenviron. 2011Dec;4(3):393�

403.

https://doi.org/10.1007/s12307-011-0088-1

37.

Li X, Nakayama K, Goto T, Kimura H, Akamatsu S, Hayashi Y, et al. High level of phosphatidylcholines/lysophosphatidylcholine

ratio in urine is associated with prostate cancer. Cancer Sci. 2021 Oct;112(10):4292�4302.

https://doi.org/10.1111/cas.15093

38. Min HK, Lim S, Chung BC, Moon MH. Shotgun lipidomics for candidate biomarkers of urinary phospholipids in prostate

cancer. Anal Bioanal Chem. 2011 Jan;399(2):823�830. https://doi.org/10.1007/s00216-010-4290-7

39.

Zeleznik OA, Clish CB, Kraft P, Avila-Pacheco J, Eliassen AH, Tworoger SS. Circulating Lysophosphatidylcholines, Phosphatidylcholines,

Ceramides, and Sphingomyelins and Ovarian Cancer Risk: A 23-Year Prospective Study. J Natl Cancer Inst.

2020

Jun 1;112(6):628�636. https://doi.org/10.1093/jnci/djz195

40.

Li X, Wang L, Fang P, Sun Y, Jiang X, Wang H, et al. Lysophospholipids induce innate immune transdifferentiation of endothelial

cells, resulting in prolonged endothelial activation. J Biol Chem. 2018 Jul 13;293(28):11033�11045.

https://doi.org/10.1074/jbc.RA118.002752

41.

Kaynak A, Davis HW, Kogan AB, Lee JH, Narmoneva DA, Qi X. Phosphatidylserine: The Unique Dual-Role Biomarker for Cancer

Imaging and Therapy. Cancers (Basel). 2022 May 21;14(10):2536. https://doi.org/10.3390/cancers14102536

42.

Wood MN, Ishiyama N, Singaram I, Chung CM, Flozak AS, Yemelyanov A, et al. .-Catenin homodimers are recruited to phosphoinositide-

activated membranes to promote adhesion. J Cell Biol. 2017 Nov 6;216(11):3767�3783.

https://doi.org/10.1083/jcb.201612006

43.

Ramos AR, Elong Edimo W, Erneux C. Phosphoinositide 5-phosphatase activities control cell motility in glioblastoma: Two

phosphoinositides PI(4,5)P2

and PI(3,4)P2 are involved. Adv Biol Regul. 2018 Jan;67:40�48.

https://doi.org/10.1016/j.jbior.2017.09.001

44.

Sikalidis AK. Amino acids and immune response: a role for cysteine, glutamine, phenylalanine, tryptophan and arginine in

T-cell function and cancer? Pathol Oncol Res. 2015 Jan;21(1):9�17. https://doi.org/10.1007/s12253-014-9860-0

45.

iНе можете найти то, что вам нужно? Попробуйте сервис подбора литературы.

Badawy AAB. Kynurenine Pathway of Tryptophan Metabolism: Regulatory and Functional Aspects. Int J Tryptophan Res.

2017;10:1178646917691938.

https://doi.org/10.1177/1178646917691938

46. Wlodarska M, Luo C, Kolde R, d�Hennezel E, Annand JW, Heim CE, et al. Indoleacrylic Acid Produced by Commensal Peptostreptococcus

Species Suppresses Inflammation. Cell Host Microbe. 2017 Jul 12;22(1):25�37.

https://doi.org/10.1016/j.chom.2017.06.007

47.

Tanaka M, Toth F, Polyak H, Szabo A, Mandi Y, Vecsei L. Immune Influencers in Action: Metabolites and Enzymes of the

Tryptophan-Kynurenine Metabolic Pathway. Biomedicines. 2021

Jun 25;9(7):734.

https://doi.org/10.3390/biomedicines9070734

��-�� 2024. �. 5, � 3. �. 91-101

�� . �., �� . �. , �� .�., �� . �. ��

48.

Kanova M, Kohout P. Tryptophan: A Unique Role in the Critically Ill. Int J Mol Sci. 2021

Oct 28;22(21):11714.

https://doi.org/10.3390/ijms222111714

49.

Cheng

HH,

Kuo

CC,

Yan

JL,

Chen

HL,

Lin

WC,

Wang

KH,

et

al.

Control

of

cyclooxygenase-2

expression

and

tumorigenesis

by endogenous 5-methoxytryptophan. Proc Natl Acad Sci U S A. 2012

Aug 14;109(33):13231�13236.

https://doi.org/10.1073/pnas.1209919109

50.

Neurauter G, Grahmann AV, Klieber M, Zeimet A, Ledochowski M, Sperner-Unterweger B, et al. Serum phenylalanine

concentrations in patients with ovarian carcinoma correlate with concentrations of immune activation markers and of iso

prostane-8. Cancer Lett. 2008 Dec 8;272(1):141�147. https://doi.org/10.1016/j.canlet.2008.07.002

51.

Ozawa H, Hirayama A, Shoji F, Maruyama M, Suzuki K, Yamanaka-Okumura H, et al. Comprehensive Dipeptide Analysis

Revealed Cancer-Specific Profile in the Liver of Patients with Hepatocellular Carcinoma and Hepatitis. Metabolites. 2020

Nov 1;10(11):442. https://doi.org/10.3390/metabo10110442

52.

Sass JO, Mohr V, Olbrich H, Engelke U, Horvath J, Fliegauf M, et al. Mutations in ACY1, the gene encoding aminoacylase 1,

cause a novel inborn error of metabolism. Am J Hum Genet. 2006 Mar;78(3):401�409. https://doi.org/10.1086/500563

53.

Lin Y, Ma C, Liu C, Wang Z, Yang J, Liu X, et al. NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis

in patients with colorectal cancer. Oncotarget. 2016 May 17;7(20):29454�29464.

https://doi.org/10.18632/oncotarget.8762

54.

Frankel AE, Coughlin LA, Kim J, Froehlich TW, Xie Y, Frenkel EP, et al. Metagenomic Shotgun Sequencing and Unbiased Metabolomic

Profiling Identify Specific Human Gut Microbiota and Metabolites Associated with Immune Checkpoint Therapy

Efficacy in Melanoma Patients. Neoplasia. 2017 Oct;19(10):848�855. https://doi.org/10.1016/j.neo.2017.08.004

55.

Shojaei-Zarghani S, Yari Khosroushahi A, Rafraf M, Asghari-Jafarabadi M, Azami-Aghdash S. Dietary natural methylxanthines

and colorectal cancer: a systematic review and meta-analysis. Food Funct. 2020 Dec 1;11(12):10290�10305.

https://doi.org/10.1039/d0fo02518f

56.

Liu H, Song J, Zhou Y, Cao L, Gong Y, Wei Y, et al. Methylxanthine derivatives promote autophagy in gastric cancer cells

targeting PTEN. Anticancer Drugs. 2019 Apr;30(4):347�355. https://doi.org/10.1097/CAD.0000000000000724

Information about authors:

Fedor E. Filippov � oncologist of the Department of Oncogynecology, Clinical Oncology Dispensary No. 1, Krasnodar, Russian Federation

Denis S. Kutilin � Cand. Sci. (Biol.), Leading Researcher, Laboratory

of Molecular Oncology, National Medical Research Centre for Oncology,

Rostov-on-Don, Russian Federation

ORCID: https://orcid.org/0000-0002-8942-3733, SPIN: 8382-4460, AuthorID: 794680, Scopus Author ID: 55328886800

Aleksey Yu. Maksimov � Dr. Sci. (Med.), Professor, Deputy Director General, National Medical Research Centre for Oncology, Rostov-on-Don,

Russian Federation

ORCID: https://orcid.org/0000-0002-9471-3903, SPIN: 7322-5589, AuthorID: 710705, Scopus Author ID: 56579049500

Natalya V. Porhanova � Dr. Sci. (Med.), Associate Professor of the Department of Oncology, oncologist, Clinical Oncology Dispensary No. 1,

Russian Federation, Krasnodar, Russian Federation

SPIN: 2611-4840, AuthorID: 589928

Contribution of the authors:

Filippov F. E. � conducting the experiment, writing the manuscript;

Kutilin D. S. � concept and design of the study, conducting the experiment, bioinformatics analysis, writing the manuscript;

Maksimov A. Yu. � editing the manuscript;

Porkhanova N. V. � statistical data processing.

Metabolomic profile of malignant ovarian tumors Текст научной статьи по специальности «Клиническая медицина»

Аннотация научной статьи по клинической медицине, автор научной работы — Fedor E. Filippov, Denis S. Kutilin, Aleksey Yu. Maksimov, Natalya V. Porhanova

Похожие темы научных работ по клинической медицине , автор научной работы — Fedor E. Filippov, Denis S. Kutilin, Aleksey Yu. Maksimov, Natalya V. Porhanova

Текст научной работы на тему «Metabolomic profile of malignant ovarian tumors»