Wide spectrum targeted metabolomics identifies potential ovarian cancer biomarkers.
Lipids
Metabolic pathways
Metabolites
Ovarian neoplasm
Serum
Journal
Life sciences
ISSN: 1879-0631
Titre abrégé: Life Sci
Pays: Netherlands
ID NLM: 0375521
Informations de publication
Date de publication:
01 Apr 2019
01 Apr 2019
Historique:
received:
24
12
2018
revised:
21
02
2019
accepted:
04
03
2019
pubmed:
12
3
2019
medline:
29
3
2019
entrez:
12
3
2019
Statut:
ppublish
Résumé
Despite of almost a hundred years of research on cancer metabolism, the biological background of cancerogenesis and cancer-related reprogramming of metabolism remains not fully understood. In order to comprehensively and effectively diagnose and treat the deadliest diseases, the mechanisms underlying these diseases have to be discovered urgently. Among the gynecological malignancies, ovarian cancer is the most common cause of death. The aim of the study was to search for potential cancer-related differences in concentrations of metabolites and interactions between them in serum of women with ovarian cancer and benign ovarian tumor in comparison with healthy controls using targeted metabolomics. These metabolites might serve as biomarkers in the future. We used wide spectrum targeted metabolomics to evaluate serum concentrations of metabolites related to ovarian cancer and compared them against benign ovarian tumors and healthy controls. The measurements were performed using high performance liquid chromatography coupled with triple quadrupole tandem mass spectrometry technique in highly-selective multiple reaction monitoring mode. In this study we confirmed our previous findings about the role of histidine and citrulline in ovarian cancer as well as we indicated new lipid compounds (lysoPC a C16:1, PC aa C32:2, PC aa C34:4 and PC aa C 36:6) potentially involved in cancer metabolism. We indicated interesting interactions between metabolites for further in-depth research which could potentially serve as clinically useful biomarkers in future. Moreover, the presented work attempts to visualize a possible 3D-network of relationships between the molecules found to be related to ovarian malignancy.
Identifiants
pubmed: 30853626
pii: S0024-3205(19)30154-7
doi: 10.1016/j.lfs.2019.03.004
pii:
doi:
Substances chimiques
Biomarkers, Tumor
0
Citrulline
29VT07BGDA
Histidine
4QD397987E
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
235-244Informations de copyright
Copyright © 2019 Elsevier Inc. All rights reserved.