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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. 76-90
https://doi.org/10.37748/2686-9039-2024-5-3-7
https://elibrary.ru/zovxqo
ORIGINAL ARTICLE
Urine transcriptomic profile in terms of malignant ovarian tumors
D. S. Kutilin1 , F. E. Filippov2, N. V. Porkhanova2, A. Yu. Maksimov1
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. Bioinformatic search for transcriptomic markers (based on metabolomic data) and their validation in
the urine of serous ovarian adenocarcinoma patients.
Materials and methods. The study included 70 patients with serous ovarian adenocarcinoma and 30 conditionally healthy
individuals. The search for metabolite regulator genes and gene regulator microRNAs was performed using the Random
forest machine learning method. Ribonucleic acid (RNA) was isolated using the RNeasy Plus Universal Kits. The level of
microRNA transcripts in urine was determined by real-time PCR. Differences were assessed using the Mann-Whitney test
with Bonferroni correction.
Results. Using the Random forest
method, metabolite-regulator gene (47
genes)
and metabolite-regulator microRNA (613
unique
microRNA) relationships were established. The identified microRNAs were validated by real-time PCR. Changes in the levels
of microRNA transcripts were detected: miR-382-5p, miR-593-3p, miR-29a-5p, miR-2110, miR-30c-5p, miR-181a-5p, let-7b-5p,
miR-27a-3p, miR-370-3p, miR-6529-5p, miR-653-5p, miR-4742-5p, miR-2467-3p, miR-1909-5p, miR-6743-5p, miR-875-3p,
miR-19a-3p, miR-208a-5p, miR-330-5p, miR-1207-5p, miR-4668-3p, miR-3193, miR-23a-3p, miR-12132, miR-765, miR-181b-5p,
miR-4529-3p, miR-33b-5p, miR-17-5p, miR-6866-3p, miR-4753-5p, miR-103a-3p, miR-423-5p, miR-491-5p, miR-196b-5p,
miR-6843-3p, miR-423-5p and miR-3184-5p in the urine of patients compared to conditionally healthy individuals.
Conclusion. Thus, urine transcriptome profiling allowed both to identify potential disease markers and to better understand
the molecular mechanisms of changes underlying ovarian cancer development.
Keywords: microRNAs, polymerase chain reaction, machine learning, bioinformatics, ovarian serous adenocarcinoma, urine,
biomarkers
For citation: Kutilin D. S., Filippov F. E., Porkhanova N. V., Maksimov A. Yu. Urine transcriptomic profile in terms of malignant ovarian tumors. South Russian
Journal of Cancer. 2024; 5(3): 76-90. https://doi.org/10.37748/2686-9039-2024-5-3-7, https://elibrary.ru/zovxqo
For correspondence: Denis S. Kutilin � Cand. Sci. (Biol.), 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. 15 dated 06/14/2022). 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 19.07.2024; approved after reviewing 22.08.2024; accepted for publication 27.08.2024
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South Russian Journal of Cancer 2024. Vol. 5, No. 3. P. 76-90
Kutilin D. S. , Filippov F. E., Porkhanova N. V., Maksimov A. Yu. Urine transcriptomic profile in terms of malignant ovarian tumors
INTRODUCTION
Ovarian cancer (OC) currently takes one of the
leading positions in terms of morbidity and mortality
in the world and the Russian Federation among
gynecological
malignancies
[1, 2]. OC
includes
many
subtypes of tumors, each of which has distinctive
biological and clinical characteristics. According to
the
WHO
classification, serous
carcinoma, endometrioid
carcinoma, mucinous carcinoma, light cell carcinoma,
malignant Brenner tumor, serous-mucinous
carcinoma, undifferentiated carcinoma and mixed
epithelial carcinoma are distinguished [3, 4].
In most patients, rheumatoid arthritis is sporadic,
usually detected late, and the overall 5-year survival
rate is only 30�40 %. Early detection of OC is the
most important factor in improving patient survival
[6]. New methodological
approaches, including
modern molecular biology approaches, are needed
for early detection and improved diagnosis of this
disease. The application of genomics and metabolomics
has opened a new
chapter
of
research,
which will allow the development of new tools for
early diagnosis and monitoring of the course of
oncological diseases. Advances in metabolomic
approaches using liquid or gas chromatography
combined with high-resolution mass spectrometry
(MS) have opened new prospects for simultaneous
detection and identification of biomarkers
in biological
samples [7].
Our earlier study [8] of the
urine
metabolomic
profile by ultrahigh-performance liquid chromatography
with mass spectrometric detection showed
that patients with serous ovarian carcinoma have
an imbalance in the content of certain fatty acids
and their derivatives, acylcarnitines, phospholipids,
amino acids and their derivatives, as well as some
derivatives of nitrogenous bases. At the same time,
26 metabolites with abnormal concentrations in
urine may have some potential as non-invasive biomarkers
of breast cancer in women belonging to
high-risk groups.
Thus, it was shown that 14 metabolites (kynurenine,
phenylalanyl valine, lysophosphatidylcholine
(18:3),
lysophosphatidylcholine (18:2),
alanyl
leucine, lysophosphatidylcholine (20:4), L-phenylalanine,
phosphatidylinositol
(34:1), 5-methoxytryptophan,
2-hydroxymyristic acid, 3-oxocholic acid,
lysophosphatidylcholine (14:0), indolacrylic
acid, lys
ophosphatidylserine
(20:4))
have significantly higher
concentrations compared to conditionally healthy individuals.
The content of 12 compounds, on the contrary,
was reduced (L-beta-aspartyl-L-phenylalanine,
myristic acid, decanoyl carnitine, aspartyl-glycine,
malonylcarnitine, 3-hydroxybutyrylcarnitine, 3-methylxanthine,
2,6-dimethylheptanoyl carnitine, 3-oxododecanoic
acid, N-acetylproline, L-octanoylcarnitine,
capriloylglycine) [8].
The determination of a number
of the above compounds
with high accuracy in urine samples is a procedure
that requires expensive equipment and is feasible
only in
a small
number of medical
institutions.
In this regard, it is extremely important to switch to
more accessible predictive markers, for example,
transcriptomic data. In this regard, the level of microRNA
transcripts
in urine is
of particular interest
[9].
microRNAs are short non-coding RNAs that regulate
gene expression by catalyzing the destruction of
mRNA, or by inhibiting the translation of mRNA into
protein. Mature microRNA is a single-stranded RNA
of the order of 22 nucleotides in size, obtained from
a primary transcript. microRNAs are transcriptional
regulators and modulate gene expression by interacting
with complementary nucleotide sequences of
target mRNAs [10]. microRNAs make a significant
contribution to the initiation and development of
various molecular events, including the initiation of
oncogenesis, progression, and metastasis of tumors,
which makes microRNAs potential biomarkers for
assessing the progression and prognosis of cancer
[11].
The study
of
the microRNA-mRNA
regulatory
network is of great importance both for elucidating
the molecular mechanisms underlying carcinogenesis
and for creating a panel of new biomarkers.
The purpose of the study was the bioinformatic
search for transcriptomic markers (based on metabolomic
data) and their validation in the urine of
patients with serous ovarian adenocarcinoma.
MATERIALS AND METHODS
The prospective study included 70 patients diagnosed
with ovarian cancer (serous adenocarcinoma
of low (n = 30) and high grade malignancy (n = 30),
T1a �4, T1b �3, T1c �5, T2a �3, T2b �5, T3a �14,
T3b �6, T3c � 30) and 30 conditionally healthy volunteers
(without any known pathologies) who make
up the control group.
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Urine samples were used as objects of research.
Before the study, the patients gave informed consent
to the
scientific use
of biological
samples. Urine
was
collected before the start of treatment.
Evaluation of microRNA expression
500 .l of urine sample was mixed with 900 .l of
QIAzol reagent (QIAGEN). Further isolation of total
RNA was carried out using the RNeasy Plus Universal
Kits kit according to the manufacturer's protocol. To
identify mature microRNAs and small U6 RNAs, the
method proposed by Balcells I. and co-authors was
used [12]. The isolated total RNA was used in a reverse
transcription reaction, which was performed
simultaneously with polyadenylation of RNA using
specific OC
primers. Next, the
obtained complementary
DNA was detected using real-time polymerase
chain reaction (PCR) (PCR-RV).
The
design
of specific oligonucleotide
primers
was
carried
out
using
the
Balcells
I. algorithm [12]. Several
sets of oligonucleotides were selected for each
micro-RNA, from which those characterized by the
highest efficiency of reverse transcription and PCR
were selected. The effectiveness of reverse transcription
was evaluated by the values of threshold cycles
(Ct) obtained by analyzing synthetic analogues of
microRNA and mRNA (Biosan CJSC, Russia) taken at
a known concentration. The efficiency of amplification
(E) for each system was evaluated by constructing
a calibration curve, using the dilution analysis of the
corresponding RNAs isolated from clinical samples
according to the protocol described above (the average
value of E was 2.0). The stability of expression
for the selection of reference genes was evaluated
using the geNorm algorithm [13]. The initial
list
of proposed
normalizers
for microRNAs
included: miR-191
(expression of this microRNA was the most stable in
13 compared tissues [14]);
miR-23a (as a normalizer
suitable for
the analysis of cervical samples [15]
and U6 (traditionally used as a separate standard for
normalization of microRNA expression data). Using
the geNorm algorithm, U6 was selected to normalize
microRNA expression data.
A reverse transcription reaction was performed
separately for each microRNA in one repeat. For re
verse transcription, a reaction mixture containing 1x
poly(A) buffer (BioLabs), 10 U/.l Reverse Transcriptase
MMLV (Synthol), 0.1 mM dNTPs (Synthol), 0.1
mM ATP (BioLabs), 1 .M OC primer, 0.5 U/.l Poly(A)
polymerase (BioLabs) and 1 mcg of total RNA. The
reaction was carried out for 15 minutes. at 16 oC,
15
min. at 42
�C, the reverse transcriptase was
then
inactivated for 2 min. at 95
�C.
The change in the relative expression of micro-
RNA was evaluated by PCR-RV. Amplification was
performed in 20
.l of a PCR mixture containing 1x
PCR buffer, 0.25 mM dNTPs, 2 mM MgCl2, 1 unit act.
Taq-DNA polymerase, 500 nM of direct and reverse
primers. OC-qPCR formulation of each sample was
performed in three repeats. The resulting mixtures
were incubated in a CFX 96
amplifier (Bio-Rad Laboratories,
USA) according to the following program:
2 minutes 94 �C, 50 cycles: denaturation at 95 �C for
10 seconds, annealing and elongation � 63
�C for 30
seconds. The results corresponding to
Ct >
40 were
found to be negative.
The relative expression (RE) was calculated using
the formula RE = 2-..Ct. The normalization of the
results was carried out according to the reference
locus and the expression level of the corresponding
target microRNAs in the samples of the control
group, sequentially according to the scheme given
below:
1. Normalization by reference locus:
.C(t) = C(t � C(t)reference, where C(t)reference is C(t)
target
of the reference locus.
2. Calculation of E-.C(t) for each microRNA for each
patient of the control group and the main group.
3. Calculation of the median E-.C(t) for each locus
for the control group and the main group.
4. Normalization for the control group and the
result as a multiplicity of changes: RE
= E-.C(t)median
of
the main group /E-.C(t)median of the control group (which
is identical to RE = E-..C(t) [16].
Statistical and bioinformatic 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 [17].
The search for metabolite regulatory genes and
microRNA regulators of genes was carried out using
the Random forest machine learning method,
which combines the Breiman bagging method and
the random subset method. The result of the "ran
South Russian Journal of Cancer 2024. Vol. 5, No. 3. P. 76-90
Kutilin D. S. , Filippov F. E., Porkhanova N. V., Maksimov A. Yu. Urine transcriptomic profile in terms of malignant ovarian tumors
dom forest" model is the predicted probability that programming language in the Rstudio shell, was
the target gene or microRNA is the true regulator of used to select minimal sets of microRNAs The ima
particular metabolite [18].
portance of variables was determined by counting
LASSO (Least Absolute Shrinkage and Selection the
number of bootstrap
models
with
a
non-zero
Operator), a
penalized logistic regression
in
the
R
coefficient of the variable [19].
Table 1. Metabolites and genes regulating metabolic pathways
Metabolites* Regulator genes Enzymes
Kinurenin KYNU, KMO, KYAT3, IDO kynureninase, kynurenine-3-monooxygenase, kynurenine aminotransferase
3, indolamine-pyrrole-2,3-dioxygenase
Phenylalanine-Valine PAH Phenylalanine hydroxylase
Myristic acid
PPARA, PPARGC1A,
CYP4Z1, IYD, FASN,
PLA2G5, LGALS13
fatty acid synthase, beta-ketoacyl synthase domain, calcium-dependent
phospholipase A2, soluble lectin 13 binding galactoside
Lysophosphatidylcholine
(18:3), (18:2), (20:4), (14:0)
PLA2G2A, PLB1,
LPCAT1 phospholipase A2, lysophospholipase, LPC-acyltransferase
Decanoyl carnitine ACADM, ACADS, CROT Acyl-CoA dehydrogenase, Carnitine octanoyltransferase (Carnitine
O-Octanoyltransferase)
Malonyl carnitine CPT1, CPT1A, ACADM palmitoyl-CoA transferase, malonyl-CoA decarboxylase
Alanine-Leucine GAL, PGA3 galanin, pepsinogen A
3-hydroxybutyrylcarnitine ACADM, CRAT 3-hydroxyacyl CoA dehydrogenase, carnitine-O-acetyltransferase
3-methylxanthine PDE4D cAMP-specific 3�,5�-cyclic phosphodiesterase 4D
L-Phenylalanine PAH, DDC phenylalanine hydroxylase, DOPA decarboxylase
Phosphatidylinositol (34:1)
PIK3CA, PIK3CB, PIK3C2A,
PLCB1, PIGL
phosphatidylinositol-3-kinase, 1-phosphatidylinositol-4,5-bisphosphate
phosphodiesterase beta-1, N-acetylglucosaminylphosphati-
dylinositol-de-N-acetylase
2,6 dimethylheptanoyl
carnitine ACADM, CRAT 3-hydroxyacyl CoA dehydrogenase, carnitine-O-acetyltransferase
5-methoxytryptophan TPH1 tryptophanhydroxylase
3-oxododecanoic acid FASN, OXSM 3-oxoacyl synthase, fatty acid synthase
2- hydroxymyristic acid NMT1 n-myristoyl transferase 1
3-oxocholic acid FABP6 gastropine
Indolylacrylic acid KYAT1 kynurenine aminotransferase 1 (kynurenine aminotransferase 1)
N-acetylproline APEH N-acylpeptide hydrolase
L-octanoylcarnitine CROT, COT, CPT2, CPT1
Carnitine-O-octanoyltransferase, carnitine-O-palmitoyltransferase
2(Carnitine O-octanoyltransferase, Carnitine O-palmitoyltransferase
2)
Capriloyl glycine ACADM, ODC1,
GLYATL1
3-hydroxyacyl CoA dehydrogenase, ornithine decarboxylase 1,
Glycine N-acyltransferase
Lysophosphatidylserine GPR34, PLA1A lysophosphatidylserine new receptor 1, phospholipase a1
Note: * � the list of metabolites based on the data of the article [8]
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Table 2. Metabolites, regulatory genes of metabolic pathways and microRNAs interacting with them
Metabolites Regulator
genes microRNA
KYNU,
KMO,
Kinurenin
KYAT3,
IDO1
KMO:miR-30b-3p, miR-153-5p, miR-149-3p, miR-363-5p, miR-624-3p, miR
937-5p, miR-1233-3p, miR-1238-3p, miR-1972, miR-3200-5p, miR-4319,
miR-3689a-5p, miR-3689b-5p, miR-4478, miR-3689e, miR-4695-5p, miR4724-
5p, miR-664b-3p, miR-5684, miR-6758-5p, miR-6780a-5p, miR-67995p,
miR-6856-5p, miR-6867-5p, miR-6883-5p, miR-6894-5p, miR-6894-5p,
miR-7106-5p, miR-7106-5p, miR-1273h-5p, miR-12122. KYNU:miR-30a3p,
miR-200c-3p, miR-382-5p, miR-382-5p, miR-2117, miR-3654, miR-46523p,
miR-4743-3p, miR-6739-3p, miR-6879-3p, miR-6885-3p, miR-10397-5p,
miR-4638-5p, miR-30a-3p, miR-200c-3p, miR-382-5p, miR-382-5p, miR2117,
miR-3654, miR-4652-3p, miR-4743-3p, miR-6739-3p, miR-6879-3p,
miR-6885-3p, miR-10397-5p. KYAT3: miR-5692c, miR-5692b, miR-5692c,
miR-5692b, miR-5692c, miR-5692b. IDO1: miR-593-3p, miR-891a-3p, miR5683,
miR-6728-3p
2 Phenylalanine-Valine PAH miR-23a-3p, miR-4502, miR-12132
3 Myristic acid
PPARA,
PPARGC1A,
CYP4Z1,
IYD,
FASN,
PLA2G5,
LGALS13
IYD: miR-760, miR-29a-5p, miR-208a-5p, miR-30b-3p, miR-184, miR-195-3p,
miR-320a-3p, miR-373-5p, miR-483-3p, miR-551b-5p, miR-643, miR-646,
miR-1224-5p, miR-320b,miR-922, miR-1202, miR-1205, miR-1287-3p, miR-
513c-3p, miR-1321, miR-3144-3p, miR-3152-5p, miR-3185, miR-3191-5p,
miR-3199, miR-514b-5p, miR-4279, miR-3663-5p, miR-3681-5p, miR-3689a3p,
miR-3689b-3p, miR-4429, miR-4452, miR-3689c, miR-4531, miR-4533,
miR-3972, miR-3976, miR-451b, miR-4731-5p, miR-4796-3p, miR-47993p,
miR-5003-3p, miR-5195-3p, miR-5588-5p, miR-6509-3p, miR-6737-5p,
miR-6737-3p, miR-6752-3p, miR-6764-3p, miR-6779-5p, miR-6780a-5p,
miR-6824-3p, miR-6829-5p, miR-6830-5p, miR-6849-5p, miR-6849-3p, miR6882-
5p, miR-6894-3p, miR-7106-5p, miR-7844-5p, miR-8052, miR-8069,
miR-8078, miR-146a-5p, miR-607, miR-3614-5p, miR-4482-3p, miR-197-3p,
miR-744-3p, miR-3187-3p, miR-3652, miR-4420, miR-4430, miR-4633-5p,
miR-4642, miR-4781-3p, miR-5698, miR-6499-3p, miR-6787-3p, miR-68433p,
miR-6848-3p, miR-588, miR-4423-5p, miR-6501-5p.
CYP4Z1: miR-2110, FASN: miR-30c-5p, LGALS13: miR-4650-3p.
PLA2G5: miR-765, miR-3682-3p, miR-4533,miR-2467-3p,miR-4786-3p,miR1253,
miR-3191-5p,miR-6847-5p,miR-11181-3p,miR-3916.
PPARA:miR-181a-5p, miR-181b-5p, miR-20b-5p, miR-181d-5p, miR-22-3p,
miR-140-5p, miR-372-3p, miR-330-5p, miR-331-3p, miR-345-3p, miR-520d3p,
miR-551b-5p, miR-619-5p, miR-622, miR-2113, miR-665, miR-939-3p,
miR-1976, miR-3116, miR-3183, miR-4251, miR-3690, miR-550b-2-5p,
miR-4436a, miR-4443, miR-4515, miR-4717-5p, miR-4723-5p, miR-47455p,
miR-4749-5p, miR-4755-3p, miR-5591-5p, miR-6126, miR-6131, miR6134,
miR-6505-3p, miR-6734-3p, miR-6744-3p, miR-6753-3p, miR-67665p,
miR-6791-5p, miR-6805-3p, miR-6817-5p, miR-6852-5p, miR-6873-3p,
miR-6880-5p, miR-7151-3p, miR-8071, let-7b-5p, let-7e-5p, miR-224-3p,
miR-302a-3p, miR-326, miR-335-3p, miR-429, miR-511-5p, miR-8085, miR10394-
5p, miR-10524-5p, miR-9851-5p, miR-7107-3p, miR-7110-3p, miR7155-
3p, miR-7158-3p, miR-7976, miR-1233-5p, miR-4651, miR-6757-5p,
miR-6778-5p, miR-27a-5p, miR-34a-5p, miR-130b-5p, miR-196b-5p, miR607,
miR-1249-5p, miR-3689d, miR-5006-5p, miR-6756-5p, miR-6788-5p,
miR-6797-5p, miR-6851-5p.
PARGC1A: let-7a-5p, let-7b-5p, let-7c-5p, let-7e-5p, miR-23b-3p, miR-1385p,
miR-409-5p, miR-487a-3p, miR-193b-3p, miR-4458, miR-6884-5p, miR23a-
3p, miR-193a-3p, miR-485-3p, miR-3666, miR-3681-3p, miR-211-5p,
miR-485-5p, miR-342-5p, miR-452-5p, miR-511-5p, miR-508-5p, miR-573,
miR-659-3p, miR-764, miR-1825, miR-2116-3p, miR-2682-3p, miR-3929,
miR-4436a, miR-4649-5p, miR-4664-5p, miR-4713-5p, miR-4728-3p,
miR-122b-3p, miR-4768-5p, miR-4769-3p, miR-5003-5p, miR-5006-3p,
miR-5011-5p, miR-5591-3p, miR-5685, miR-6124, miR-6740-3p, miR-68183p,
miR-6833-3p, miR-6845-3p, miR-6892-3p, miR-7110-5p, miR-7703,
miR-7850-5p, miR-8075, miR-8485, miR-148a-5p, miR-214-3p, miR-222-3p,
miR-9898, miR-6083
4 Lysophosphatidylcholine PLA2G2A,
PLB1, LPCAT1
LPCAT1: miR-27a-3p, miR-370-3p, miR-4739, miR-4768-3p, miR-4783-3p.
PLB1: miR-3162-5p, miR-4529-3p, miR-4740-5p. PLA2G2A: miR-765, miR3652,
miR-6134, miR-6745, miR-6756-5p, miR-6769a-5p, miR-6785-5p,
miR-6769b-5p, miR-7847-3p
South Russian Journal of Cancer 2024. Vol. 5, No. 3. P. 76-90
Kutilin D. S. , Filippov F. E., Porkhanova N. V., Maksimov A. Yu. Urine transcriptomic profile in terms of malignant ovarian tumors
Table 2. (�ontinuation) Metabolites, regulatory genes of metabolic pathways and microRNAs interacting with them
Metabolites Regulator
genes microRNA
CROT: miR-33a-5p, miR-373-3p, miR-33b-5p, miR-17-5p, miR-500a-5p,
ACADM, miR-501-5p, miR-1250-3p, miR-4659b-3p, miR-219b-5p, miR-4795-3p,
5 Decanoyl carnitine ACADS, miR-6807-3p, miR-6867-5p, miR-522-3p, miR-4325, miR-5004-3p, miR-
CROT 6833-5p, miR-221-3p. ACADM: miR-4437, miR-5580-3p, miR-6529-5p,
miR-3184-5p, miR-4704-3p. ACADS: miR-484
6 Malonyl carnitine CPT1A,
ACADM
ACADM: miR-4437, miR-5580-3p, miR-6529-5p, miR-3184-5p, miR-47043p.
CPT1A: miR-653-5p, miR-328-3p, miR-6866-3p, miR-1296-3p, miR1322,
miR-6883-5p, miR-7-2-3p, miR-335-3p, miR-520a-3p, miR-4310, miR4287,
miR-6718-5p, miR-6785-5p, miR-6869-3p, miR-7856-5p, miR-93-5p,
miR-4293, miR-4322, miR-4707-3p, miR-24-3p, miR-6849-3p
7 Alanine-Leucine GAL,
PGA3
GAL: miR-922, miR-4742-5p, miR-4753-5p, miR-4436b-3p, miR-5004-5p,
miR-5089-3p, miR-15b-5p, miR-138-1-3p, miR-302d-5p, miR-6810-5p,
miR-3976. PGA3: miR-2467-3p, miR-1909-5p, miR-6743-5p, miR-1913,
miR-2115-5p, miR-4646-5p, miR-5006-5p, miR-6857-5p, miR-11399,
miR-5008-5p, miR-4649-3p,miR-423-5p, miR-3679-5p, miR-423-3p, miR1296-
3p, miR-3126-5p, miR-6759-3p, miR-3180, miR-6763-5p, miR-769-3p,
miR-3139, miR-5571-5p, miR-6768-5p, miR-761, miR-3151-5p, miR-18a-5p,
miR-4672, miR-6873-3p, miR-6875-3p, miR-3156-5p, miR-6771-5p, miR6879-
5p, miR-3945
8
9
10
3-hydroxybutyrylcarnitine
3-methylxanthine
L-Phenylalanine
ACADM,
CRAT
PDE4D
PAH, DDC
ACADM: miR-4437, miR-5580-3p, miR-6529-5p, miR-3184-5p, miR-47043p.
CRAT: miR-936, miR-1207-5p, miR-6764-5p, miR-7150, miR-10392-3p
PDE4D: miR-18a-5p, miR-31-5p, miR-148a-3p, miR-301a-3p, miR-148b-3p,
miR-875-5p, miR-6766-3p, miR-26a-5p, miR-103a-3p, miR-107, miR-1395p,
miR-362-3p, miR-339-5p, miR-18b-5p, miR-448, miR-487a-3p, miR4429,
miR-203a-3p, miR-211-3p, miR-124-3p, miR-149-5p, miR-99b-3p,
miR-372-3p, miR-373-5p, miR-520a-3p, miR-520d-3p, miR-625-5p, miR641,
miR-1301-3p, miR-449c-5p, miR-1266-5p, miR-1321, miR-1912-3p,
miR-2114-5p, miR-3125, miR-3187-5p, miR-4261, miR-4280, miR-3646,
miR-3689a-5p, miR-3689b-5p, miR-3922-5p, miR-4446-3p, miR-3689d,
miR-3689e, miR-4492, miR-4502, miR-4511, miR-3977, miR-4646-5p,
miR-4675, miR-4698, miR-4741, miR-4756-5p, miR-4768-3p, miR-5193,
miR-1295b-5p, miR-5589-3p, miR-6500-3p, miR-548az-5p, miR-6504-5p,
miR-6511a-5p, miR-6512-5p, miR-6809-5p, miR-6809-3p, miR-6829-5p,
miR-6839-5p, miR-6859-5p, miR-5787, miR-6077, miR-6796-3p, miR-6860,
miR-7114-5p, miR-7151-3p, miR-8080, miR-8081, miR-8086, miR-195-5p,
miR-3136-5p, miR-6080, miR-6888-5p, miR-340-5p, miR-4439, miR-3148,
miR-6857-3p, miR-497-5p
PAH: miR-23a-3p, miR-4502, miR-12132. DDC: miR-875-3p, miR-3166,
miR-4502, miR-3158-3p
STUDY RESULTS
Bioinformatic analysis of the relationship of the
urine metabolomic profile with gene and microRNA expression.
Using the Random forest machine learning
method implemented in the R programming language,
the analysis of the metabolomic data from the article
[8] was carried out,
as well as the Human Metabolome
Database (HMDB, https://hmdb.ca/metabolites).
At the first stage, the metabolite-enzyme and enzyme-
gene regulator relationships were established.
The results are presented in table 1.
The results of the metabolite-gene-microRNA relationship
are presented in Table 2 and Fig. 1�2. It
can be seen that the content of metabolites detected
in
urine
is
regulated by a complex
network
of rna
and microRNA interactions. For two metabolites,
myristic acid and phosphatidylinositol, 237 and 143
micro-RNAs were detected, respectively, regulating
the
content
of these
substances
in
biological
fluids.
Thus, bioinformatic analysis
has
determined
a
list
of 613 unique microRNAs involved in the regulation
of the concentration of 21 metabolites. Of the 613
microRNAs, only the microRNAs with the maximum
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Table 2. (�ontinuation) Metabolites, regulatory genes of metabolic pathways and microRNAs interacting with them
Metabolites Regulator
genes microRNA
PIK3CA,
11 Phosphatidylinositol
(34:1)
PIK3CB,
PIK3C2A,
PLCB1,
PIGL
PIGL: miR-4651, miR-5087, miR-6499-3p, miR-6739-3p, miR-6764-5p,
miR-212-5p, miR-659-3p, miR-3189-3p, miR-3934-3p, miR-378g, miR-4519,
miR-6819-5p.
PIK3C2A: miR-503-5p, miR-301b-3p, miR-6838-5p, miR-23a-5p, miR-29a
5p, miR-23b-5p, miR-510-5p, miR-1264, miR-2113, miR-1286, miR-36193p,
miR-4423-3p, miR-4436a, miR-4484, miR-1343-3p, miR-6074, miR6760-
3p, miR-6867-5p, miR-212-5p, miR-150-5p, miR-378a-5p, miR-518a5p,
miR-1224-3p, miR-764, miR-6821-3p.
PIK3CA: let-7i-5p, let-7e-5p, miR-19a-3p, miR-19b-3p, let-7g-5p, miR-152
3p, miR-202-5p, miR-4429, miR-198, miR-548e-5p, miR-548o-3p, miR2114-
5p, miR-4430, miR-4493, miR-4659b-3p, miR-122b-5p, miR-4803,
miR-5006-3p, miR-6797-3p, miR-1972, miR-2116-5p, miR-3157-5p, miR3191-
5p, miR-514b-5p, miR-4303, miR-4277, miR-3606-5p, miR-3614-3p,
miR-3679-3p, miR-676-5p, miR-378g, miR-4446-5p, miR-4477b, miR-4486,
miR-4652-3p, miR-6819-5p, miR-6857-5p, miR-6868-3p, miR-6893-3p,
miR-7162-3p, miR-10526-3p, miR-12126, miR-139-5p, miR-422a.
PIK3CB: miR-23b-3p, miR-362-5p, miR-3666, miR-3064-5p, miR-4465,
miR-199a-3p, miR-199b-3p, miR-212-5p, miR-150-5p, miR-6504-5p, miR204-
3p, miR-671-5p, miR-1263, miR-3646, miR-4430, miR-4682, miR-5093,
miR-6165, miR-6715a-3p, miR-7850-5p, miR-9500, miR-130b-5p, miR3619-
5p, miR-32-3p, miR-623, miR-542-5p, miR-548j-5p, miR-544b, miR3614-
5p, miR-3652, miR-548aw, miR-5703, miR-8077, miR-2117.
PLCB1: miR-103a-3p, miR-107, miR-423-5p, miR-3129-5p, miR-139-5p,
miR-124-3p, miR-138-1-3p, miR-302c-5p, miR-876-5p, miR-1244, miR1322,
miR-548s, miR-4267, miR-3692-3p, miR-4433a-3p, miR-4436a, miR3978,
miR-4647, miR-4659a-3p, miR-4670-3p, miR-5194, miR-548az-3p,
miR-6783-3p, miR-6860, miR-7151-5p, miR-8056, miR-8063, miR-502-3p
12 2,6 dimethylheptanoyl
carnitine
ACADM,
CRAT
ACADM: miR-4437, miR-5580-3p, miR-6529-5p, miR-3184-5p, miR-47043p.
CRAT: miR-936, miR-1207-5p, miR-6764-5p, miR-7150, miR-10392-3p
13 5-methoxytryptophan TPH1 TPH1: miR-320a-3p, miR-450a-2-3p, miR-320b, miR-2110, miR-4435, miR5693,
miR-5702, miR-6830-5p, miR-12118
14 3-oxododecane acid FASN miR-30c-5p
15 2-hydroxymyristic acid NMT1
NMT1: miR-181a-5p, miR-214-3p, miR-491-5p, miR-432-5p, miR-922,
miR-1202, miR-1205, miR-1972, miR-2110, miR-2682-3p, miR-3160-5p,
miR-3176, miR-4303, miR-4291, miR-4447, miR-3972, miR-4667-5p, miR4690-
3p, miR-4700-5p, miR-23b-3p, miR-615-3p
16 3-oxocholic acid FABP6
FABP6: miR-208a-5p, miR-330-5p, miR-196b-5p, miR-3180-3p, miR-3181,
miR-4278, miR-3689f, miR-4754, miR-4786-3p, miR-5190, miR-5195-3p,
miR-6745, miR-6751-5p, miR-6769a-5p, miR-6771-5p, miR-6792-5p, miR6821-
5p, miR-7156-3p, miR-10226, miR-10392-5p
17 Indolylacrylic acid KYAT1 KYAT1: miR-423-5p, miR-6842-5p, miR-597-3p, miR-4710, miR-6741-5p,
miR-6796-5p, miR-4447, miR-193b-3p
18 N-acetylproline APEH miR-1289
19 L-octanoylcarnitine CROT,
CPT2
CROT: miR-33a-5p, miR-373-3p, miR-33b-5p, miR-17-5p, miR-500a-5p,
miR-501-5p, miR-1250-3p, miR-4659b-3p, miR-219b-5p, miR-4795-3p,
miR-6807-3p, miR-6867-5p, miR-522-3p, miR-4325, miR-5004-3p, miR6833-
5p, miR-221-3p. CPT2: miR-433-3p, miR-6843-3p, miR-6848-3p, miR208a-
5p, miR-6742-3p, miR-34a-5p
20 Capriloyl glycine
ACADM,
ODC1,
GLYATL1
ACADM: miR-4437, miR-5580-3p, miR-6529-5p, miR-3184-5p, miR-47043p.
ODC1: miR-423-5p, miR-3184-5p, miR-7973, miR-193b-3p. GLYATL1:
miR-1207-5p, miR-4668-3p, miR-4742-3p, miR-4999-5p, miR-664b-3p, miR6846-
3p, miR-6893-3p
21 Lysophosphatidylserine
(20:4)
GPR34,
PLA1A
PLA1A: miR-3153, miR-7110-3p, miR-6754-5p, miR-6887-3p. GPR34: miR3193,
miR-2909, miR-4738-5p, miR-486-3p, miR-6808-5p
South Russian Journal of Cancer 2024. Vol. 5, No. 3. P. 76-90
Kutilin D. S. , Filippov F. E., Porkhanova N. V., Maksimov A. Yu. Urine transcriptomic profile in terms of malignant ovarian tumors
interaction strength with the mRNA of the genes regulating
the content of metabolites were selected. The final
list contained 91 microRNAs, presented in Table 3.
Features of the content of microRNA
transcripts in the urine of patients with serous
ovarian adenocarcinoma
The generated list of 91 microRNAs regulating
the activity of 37 genes was used for validation by
real-time PCR on urine samples of patients and conditionally
healthy volunteers.
A statistically significant (p < 0.005) change in the
transcript level of 47 microRNAs relative to conditionally
healthy volunteers was found in the urine of
patients
with
serous
ovarian
adenocarcinoma (Fig. 3).
A
significant increase (p
<
0.05) in the level of
miR-382-5p by 1.9 times, miR-593-3p by 3.4 times,
miR-29a-5p by 2.6 times, miR-2110 by 2.5 times,
Table 3. The final list of microRNAs involved in the regulation of the concentration of metabolites
Metabolites microRNA
1 Kinurenin
KMO: miR-30b-3p, miR-153-5p, miR-149-3p, miR-363-5p. KYNU: miR-30a-3p,
miR-200c-3p, miR-382-5p, miR-382-5p. KYAT3: miR-5692c, miR-5692b, miR5692c.
IDO1: miR-593-3p, miR-891a-3p.
2 Phenylalanine-Valine PAH: miR-23a-3p, miR-4502, miR-12132
3 Myristic acid
IYD: miR-760, miR-29a-5p. CYP4Z1: miR-2110, FASN: miR-30c-5p, LGALS13:
miR-4650-3p. PLA2G5: miR-765, miR-3682-3p. PPARA: miR-181a-5p, miR-181b5p,
miR-20b-5p. PPARGC1A: let-7a-5p, let-7b-5p, let-7c-5p
4 Lysophosphatidylcholine LPCAT1: miR-27a-3p, miR-370-3p, miR-4768-3p. PLB1: miR-3162-5p, miR-45293p.
PLA2G2A: miR-765, miR-3652
5 Decanoyl carnitine CROT: miR-33a-5p, miR-373-3p, miR-33b-5p, miR-17-5p. ACADM: miR-4437,
miR-5580-3p, miR-6529-5p. ACADS: miR-484
6 Malonyl carnitine ACADM: miR-4437, miR-5580-3p, miR-6529-5p. CPT1A: miR-653-5p, miR-3283p,
miR-6866-3p
7 Alanine-Leucine GAL: miR-922, miR-4742-5p, miR-4753-5p. PGA3: miR-2467-3p, miR-1909-5p,
miR-6743-5p
8 3-hydroxybutyrylcarnitine ACADM: miR-4437, miR-5580-3p, miR-6529-5p
9 3-methylxanthine PDE4D: miR-18a-5p, miR-31-5p, miR-148a-3p
10 L-Phenylalanine PAH: miR-23a-3p. DDC: miR-875-3p, miR-3166
11 Phosphatidylinositol (34:1)
PIGL: miR-4651, miR-5087, miR-6499-3p. PIK3C2A: miR-503-5p, miR-301b-3p.
PIK3CA: let-7i-5p, let-7e-5p, miR-19a-3p. PLCB1: miR-103a-3p, miR-107,
miR-423-5p
12 2,6 dimethylheptanoyl carnitine ACADM: miR-4437, miR-5580-3p, miR-6529-5p� CRAT: miR-936, miR-1207-5p
13 5-methoxytryptophan TPH1: miR-320a-3p, miR-450a-2-3p, miR-320b
14 3-oxododecanoic acid FASN: miR-30c-5p
15 2-hydroxymyrystine acid NMT1: miR-181a-5p, miR-214-3p, miR-491-5p
16 3-oxocholic acid FABP6: miR-208a-5p, miR-330-5p, miR-196b-5p
17 Indolylacrylic acid KYAT1: miR-423-5p, miR-6842-5p, miR-597-3p
18 N-acetylproline APEH: miR-1289
19 L-octanoylcarnitine CROT: miR-33a-5p, miR-373-3p, miR-33b-5p, miR-17-5p. CPT2: miR-433-3p, miR6843-
3p
20 Capriloyl glycine ACADM: miR-4437, miR-5580-3p, miR-6529-5p. ODC1: miR-423-5p, miR-31845p.
GLYATL1: miR-1207-5p, miR-4668-3p
21 Lysophosphatidylserine (20:4)
PLA1A: miR-3153, miR-7110-3p. GPR34: miR-3193, miR-2909
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Fig. 1. Metabolites, regulatory genes of metabolic pathways and microRNAs interacting with them
South Russian Journal of Cancer 2024. Vol. 5, No. 3. P. 76-90
Kutilin D. S. , Filippov F. E., Porkhanova N. V., Maksimov A. Yu. Urine transcriptomic profile in terms of malignant ovarian tumors
Fig. 2. Metabolites, regulatory genes of metabolic pathways and microRNAs interacting with them
����-���������� �������������� ������ 2024. �. 5, � 3. �. 76-90
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miR-30c-5p by 2.9 times, miR-181a-5p by 2.6
times was found, let-7b-5p 2.6 times, miR-27A-3p
1.9 times, miR-370-3p 2.6 times, miR-6529-5p 2.5
times, miR-653-5p 2.2 times, miR-4742-5p 2.4
times, miR-2467-3p 2.6 times, miR-1909-5p 3.5
times, miR-6743-5p 4.9 times, miR-875-3p 2.3
times, miR-19a-3p 4.9 times, miR-208a-5p 2.6 times,
miR-330-5p 3.2 times, miR-1207-5p by 3.5 times,
miR-4668-3p by 4.2 times, miR-3193 is 2.6 times
higher than their level in the urine of conditionally
healthy individuals.
There was also a significant decrease (p < 0.05)
in the level of miR-23a-3p by 20.0 times, miR-12132
by 4.0 times, miR-765 by 1.8 times, miR-181b-5p by
4.0 times, miR-4529-3p by 1.8 times, miR-33b-5p by
3.1 times, miR-17-5p by 4.6 times, miR-6866-3p by
1.7 times, miR-4753-5p by 14.3 times, miR-103a-3p
by 19.6 times, miR-423-5p by 3.0 times, miR-491-5p
by 1.7 times, miR-196b-5p by 5.0 times, miR-6843-3p
2.3 times, miR-423-5p 4.6 times and miR-3184-5p
2.6 times relative to their urine levels in conditionally
healthy individuals.
Thus, the microRNA profile miR-382-5p,
miR-593-3p, miR-29a-5p, miR-2110, miR-30c-5p,
miR-181a-5p, let-7b-5p, miR-27a-3p, miR-2110,
miR-30c-5p, miR-181a-5p, let-7b-5p, miR-27a-3p,
miR-370-3p, miR-6529-5p, miR-653-5p, miR-4742-5p,
miR-2467-3p, miR-1909-5p, miR-6743-5p, miR-875-3p,
miR-19a-3p, miR-208a-5p, miR-330-5p, miR-1207-5p,
miR-4668-3p, miR-3193, miR-23a-3p, miR-12132,
miR-765, miR-181b-5p, miR-4529-3p, miR-33b-5p,
miR-17-5p, miR-6866-3p, miR-4753-5p, miR-103a-3p,
miR-423-5p, miR-491-5p, miR-196b-5p, miR-6843-3p,
miR-423-5p and miR-3184-5p are differential for patients
and conditionally healthy individuals.
DISCUSSION
In our study, using machine learning methods,
links were established between metabolites that
changed the concentrations of relatively healthy
donors and genes encoding proteins involved in the
synthesis and degradation of these metabolites, as
well as links between metabolite regulatory genes
and microRNA regulators of these genes.
Bioinformatics
analysis
has
identified a list
of 613
unique micro-RNAs involved in the regulation of the
concentration of 21 metabolites. Of the 613 microR-
NAs, only the microRNAs with the maximum interaction
strength with the mRNA of the genes regulating
the content of
metabolites were selected.
The final list
contained 91 micro-RNAs, of which 47 changed the
level of their transcripts in urine (validated by PCR).
Fig. 3. Changes in the level of 91 micro-RNA transcripts in the urine of patients with serous ovarian adenocarcinoma
Note: * � statistically significant (p < 0.05) change in the transcript level relative to the control group
South Russian Journal of Cancer 2024. Vol. 5, No. 3. P. 76-90
Kutilin D. S. , Filippov F. E., Porkhanova N. V., Maksimov A. Yu. Urine transcriptomic profile in terms of malignant ovarian tumors
The level of transcripts miR-382-5p, miR-593-3p,
miR-29a-5p, miR-2110, miR-30c-5p, miR-181a-5p,
let-7b-5p, miR-27a-3p, miR-370-3p changed most
significantly in patients with OC miR-6529-5p,
miR-653-5p, miR-4742-5p, miR-2467-3p, miR-1909-5p,
miR-6743-5p, miR-875-3p, miR-19a-3p, miR-208a-5p,
miR-330-5p, miR-1207-5p, miR-4668-3p, miR-3193,
miR-23a-3p, miR-12132, miR-765, miR-181b-5p,
miR-4529-3p, miR-33b-5p, miR-17-5p, miR-6866-3p,
miR-4753-5p, miR-103a-3p, miR-423-5p, miR-491-5p,
miR-196b-5p, miR-6843-3p, miR-423-5p and
miR-3184-5p relative to their urine levels in conditionally
healthy individuals.
According to a number of authors, changes in
the expression level of some of these microRNAs
are associated with serous
ovarian
cancer: hsa
miR-382-5p, hsa-miR-27a-3p, hsa-miR-1207-5p,
hsa-miR-423-5p [20], hsa-miR-593-3p [21], hsamiR-
29a-5p [22, 23] and hsa-miR-30c-5p [24, 25] and
hsa-miR-30a-5p [26].
Nevertheless, the micro-RNA panel we identified
(miR-382-5p, miR-593-3p, miR-29a-5p, miR-2110,
miR-30c-5p, miR-181a-5p, let-7b-5p, miR-27a-3p,
miR-370-3p, miR-6529-5p, miR-653-5p, miR-4742-5p,
miR-2467-3p, miR-1909-5p, miR-6743-5p, miR-875-3p,
miR-19A-3p, miR-208a-5p, miR-330-5p, miR-1207-5p,
miR-4668-3p, miR-3193, miR-23A-3p, miR-12132,
miR-765, miR-181b-5p, miR-4529-3p, miR-33b-5p,
miR-17-5p, miR-6866-3p, miR-4753-5p, miR-103a-3p,
miR-423-5p, miR-491-5p, miR-196b-5p, miR-6843-3p,
miR-423-5p and miR-3184-5p) is unique and in this
combination in literary sources not represented.
Obviously, transcriptomic imbalance begins in
tissues and leads to a metabolic imbalance, which
eventually affects the composition of body fluids,
including urine.
Modern
clinical
oncogynecology has
a serious
need for effective biomarkers, changes in the levels
of which can serve as evidence of the occurrence of
a malignant process. Non-invasive and inexpensive
PCR analysis of micro-RNA in urine makes it a particularly
attractive screening tool. The application
of this approach may allow for frequent testing of
women belonging to high-risk groups and ensure
long-term patient monitoring.
CONCLUSION
Bioinformatics
analysis
revealed a list of 613
unique microRNAs involved in the regulation of 21
metabolites. At the same time, the level of transcripts
of 38 microRNAs (miR-382-5p, miR-593-3p,
miR-29a-5p, miR-2110, miR-30c-5p, miR-181a-5p,
let-7b-5p, miR-27a-3p, miR-370-3p, miR-6529-5p,
miR-653-5p, miR-4742-5p, miR-2467-3p,
miR-1909-5p, miR-6743-5p, miR-875-3p,
miR-19a-3p, miR-208a-5p, miR-330-5p, miR-1207-5p,
miR-4668-3p, miR-3193, miR-23a-3p, miR-12132,
miR-765, miR-181b-5p, miR-4529-3p, miR-33b-5p,
miR-17-5p, miR-6866-3p, miR-4753-5p, miR-103a-3p,
miR-423-5p, miR-491-5p, miR-196b-5p, miR-6843-3p,
miR-423-5p and miR-3184-5p) urine has diagnostic
potential in ovarian cancer and is the basis for
further research.. Thus, transcriptomic profiling
of
urine made it possible both to identify potential
markers of the disease and to better understand
the molecular mechanisms of changes underlying
the development of OC.
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South Russian Journal of Cancer 2024. Vol. 5, No. 3. P. 76-90
Kutilin D. S. , Filippov F. E., Porkhanova N. V., Maksimov A. Yu. Urine transcriptomic profile in terms of malignant ovarian tumors
Information about authors:
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
Fedor E. Filippov � MD, oncologist of the Department of Oncogynecology, Clinical Oncology Dispensary No. 1, Krasnodar, Russian Federation
Natalya V. Porhanova � Dr. Sci. (Med.), Associate Professor of the Department of Oncology, oncologist, Clinical Oncology
Dispensary
No. 1,
Krasnodar, Russian Federation
SPIN: 2611-4840, AuthorID: 589928
Aleksey
Yu. Maksimov � Dr. Sci. (Med.), Professor, Deputy
General Director, 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
Contribution of the authors:
Kutilin D. S. � concept and design of the study, conducting the experiment, bioinformatics analysis, writing the manuscript;
Filippov F. E. � conducting the experiment, writing the manuscript;
Porkhanova N. V. � statistical data processing;
Maksimov A. Yu. � editing the manuscript.
