Literature DB >> 33106272

The application of metabolomics in ovarian cancer management: a systematic review.

Yousra Ahmed-Salim1, Nicolas Galazis2, Timothy Bracewell-Milnes3, David L Phelps4, Benjamin P Jones5, Maxine Chan6, Maria D Munoz-Gonzales7, Tomoko Matsuzono8, James Richard Smith9, Joseph Yazbek9, Jonathan Krell9, Sadaf Ghaem-Maghami10, Srdjan Saso11.   

Abstract

Metabolomics, the global analysis of metabolites in a biological specimen, could potentially provide a fast method of biomarker identification for ovarian cancer. This systematic review aims to examine findings from studies that apply metabolomics to the diagnosis, prognosis, treatment, and recurrence of ovarian cancer. A systematic search of English language publications was conducted on PubMed, Science Direct, and SciFinder. It was augmented by a snowball strategy, whereby further relevant studies are identified from reference lists of included studies. Studies in humans with ovarian cancer which focus on metabolomics of biofluids and tumor tissue were included. No restriction was placed on the time of publication. A separate review of targeted metabolomic studies was conducted for completion. Qualitative data were summarized in a comprehensive table. The studies were assessed for quality and risk of bias using the ROBINS-I tool. 32 global studies were included in the main systematic review. Most studies applied metabolomics to diagnosing ovarian cancer, within which the most frequently reported metabolite changes were a down-regulation of phospholipids and amino acids: histidine, citrulline, alanine, and methionine. Dysregulated phospholipid metabolism was also reported in the separately reviewed 18 targeted studies. Generally, combinations of more than one significant metabolite as a panel, in different studies, achieved a higher sensitivity and specificity for diagnosis than a single metabolite; for example, combinations of different phospholipids. Widespread metabolite differences were observed in studies examining prognosis, treatment, and recurrence, and limited conclusions could be drawn. Cellular processes of proliferation and invasion may be reflected in metabolic changes present in poor prognosis and recurrence. For example, lower levels of lysine, with increased cell invasion as an underlying mechanism, or glutamine dependency of rapidly proliferating cancer cells. In conclusion, this review highlights potential metabolites and biochemical pathways which may aid the clinical care of ovarian cancer if further validated. © IGCS and ESGO 2021. No commercial re-use. See rights and permissions. Published by BMJ.

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Keywords:  ovarian cancer

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Year:  2020        PMID: 33106272     DOI: 10.1136/ijgc-2020-001862

Source DB:  PubMed          Journal:  Int J Gynecol Cancer        ISSN: 1048-891X            Impact factor:   3.437


  4 in total

Review 1.  Serum Free Amino Acid Profiling in Differential Diagnosis of Ovarian Tumors-A Comparative Study with Review of the Literature.

Authors:  Agnieszka Horala; Szymon Plewa; Pawel Derezinski; Agnieszka Klupczynska; Jan Matysiak; Ewa Nowak-Markwitz; Zenon J Kokot
Journal:  Int J Environ Res Public Health       Date:  2021-02-23       Impact factor: 3.390

2.  Extracellular vesicle-derived miR-320a targets ZC3H12B to inhibit tumorigenesis, invasion, and angiogenesis in ovarian cancer.

Authors:  Yan Huang; Midie Xu; Chuyu Jing; Xiaohua Wu; Xiaojun Chen; Wei Zhang
Journal:  Discov Oncol       Date:  2021-11-17

3.  Targeted Microchip Capillary Electrophoresis-Orbitrap Mass Spectrometry Metabolomics to Monitor Ovarian Cancer Progression.

Authors:  Samyukta Sah; Sylvia R Yun; David A Gaul; Andro Botros; Eun Young Park; Olga Kim; Jaeyeon Kim; Facundo M Fernández
Journal:  Metabolites       Date:  2022-06-09

4.  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

  4 in total

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