Literature DB >> 30853626

Wide spectrum targeted metabolomics identifies potential ovarian cancer biomarkers.

Szymon Plewa1, Agnieszka Horała2, Paweł Dereziński1, Ewa Nowak-Markwitz2, Jan Matysiak1, Zenon J Kokot3.   

Abstract

AIMS: 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. MAIN
METHODS: 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. KEY
FINDINGS: 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. SIGNIFICANCES: 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.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Lipids; Metabolic pathways; Metabolites; Ovarian neoplasm; Serum

Mesh:

Substances:

Year:  2019        PMID: 30853626     DOI: 10.1016/j.lfs.2019.03.004

Source DB:  PubMed          Journal:  Life Sci        ISSN: 0024-3205            Impact factor:   5.037


  7 in total

1.  Pan-cancer analysis of pre-diagnostic blood metabolite concentrations in the European Prospective Investigation into Cancer and Nutrition.

Authors:  Marie Breeur; Pietro Ferrari; Laure Dossus; Mazda Jenab; Mattias Johansson; Sabina Rinaldi; Ruth C Travis; Mathilde His; Tim J Key; Julie A Schmidt; Kim Overvad; Anne Tjønneland; Cecilie Kyrø; Joseph A Rothwell; Nasser Laouali; Gianluca Severi; Rudolf Kaaks; Verena Katzke; Matthias B Schulze; Fabian Eichelmann; Domenico Palli; Sara Grioni; Salvatore Panico; Rosario Tumino; Carlotta Sacerdote; Bas Bueno-de-Mesquita; Karina Standahl Olsen; Torkjel Manning Sandanger; Therese Haugdahl Nøst; J Ramón Quirós; Catalina Bonet; Miguel Rodríguez Barranco; María-Dolores Chirlaque; Eva Ardanaz; Malte Sandsveden; Jonas Manjer; Linda Vidman; Matilda Rentoft; David Muller; Kostas Tsilidis; Alicia K Heath; Hector Keun; Jerzy Adamski; Pekka Keski-Rahkonen; Augustin Scalbert; Marc J Gunter; Vivian Viallon
Journal:  BMC Med       Date:  2022-10-19       Impact factor: 11.150

2.  Lipid Alterations in Early-Stage High-Grade Serous Ovarian Cancer.

Authors:  M V Iurova; V V Chagovets; S V Pavlovich; N L Starodubtseva; G N Khabas; K S Chingin; A O Tokareva; G T Sukhikh; V E Frankevich
Journal:  Front Mol Biosci       Date:  2022-04-14

3.  Serum lipidome screening in patients with stage I non-small cell lung cancer.

Authors:  Agnieszka Klupczynska; Szymon Plewa; Mariusz Kasprzyk; Wojciech Dyszkiewicz; Zenon J Kokot; Jan Matysiak
Journal:  Clin Exp Med       Date:  2019-07-01       Impact factor: 3.984

4.  Untargeted Metabolomic Characterization of Ovarian Tumors.

Authors:  Xiaona Liu; Gang Liu; Lihua Chen; Fei Liu; Xiaozhe Zhang; Dan Liu; Xinxin Liu; Xi Cheng; Lei Liu
Journal:  Cancers (Basel)       Date:  2020-12-04       Impact factor: 6.639

5.  Identification and quantification of honeybee venom constituents by multiplatform metabolomics.

Authors:  Agnieszka Klupczynska; Szymon Plewa; Paweł Dereziński; Timothy J Garrett; Vanessa Y Rubio; Zenon J Kokot; Jan Matysiak
Journal:  Sci Rep       Date:  2020-12-10       Impact factor: 4.379

6.  Cancer Alters the Metabolic Fingerprint of Extracellular Vesicles.

Authors:  Mari Palviainen; Kirsi Laukkanen; Zeynep Tavukcuoglu; Vidya Velagapudi; Olli Kärkkäinen; Kati Hanhineva; Seppo Auriola; Annamari Ranki; Pia Siljander
Journal:  Cancers (Basel)       Date:  2020-11-06       Impact factor: 6.639

7.  Wide-Targeted Metabolome Analysis Identifies Potential Biomarkers for Prognosis Prediction of Epithelial Ovarian Cancer.

Authors:  Eiji Hishinuma; Muneaki Shimada; Naomi Matsukawa; Daisuke Saigusa; Bin Li; Kei Kudo; Keita Tsuji; Shogo Shigeta; Hideki Tokunaga; Kazuki Kumada; Keigo Komine; Hidekazu Shirota; Yuichi Aoki; Ikuko N Motoike; Jun Yasuda; Kengo Kinoshita; Masayuki Yamamoto; Seizo Koshiba; Nobuo Yaegashi
Journal:  Toxins (Basel)       Date:  2021-06-30       Impact factor: 4.546

  7 in total

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