PuSH - Publikationsserver des Helmholtz Zentrums München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
Romano, A.* ; Rizner, T.L.* ; Werner, H.M.J.* ; Semczuk, A.* ; Lowy, C.* ; Schröder, C.* ; Griesbeck, A.* ; Adamski, J. ; Fishman, D.* ; Tokarz, J.

Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: A systematic review.

Front. Oncol. 13:1120178 (2023)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Endometrial cancer is the most common gynaecological malignancy in developed countries. Over 382,000 new cases were diagnosed worldwide in 2018, and its incidence and mortality are constantly rising due to longer life expectancy and life style factors including obesity. Two major improvements are needed in the management of patients with endometrial cancer, i.e., the development of non/minimally invasive tools for diagnostics and prognostics, which are currently missing. Diagnostic tools are needed to manage the increasing number of women at risk of developing the disease. Prognostic tools are necessary to stratify patients according to their risk of recurrence pre-preoperatively, to advise and plan the most appropriate treatment and avoid over/under-treatment. Biomarkers derived from proteomics and metabolomics, especially when derived from non/minimally-invasively collected body fluids, can serve to develop such prognostic and diagnostic tools, and the purpose of the present review is to explore the current research in this topic. We first provide a brief description of the technologies, the computational pipelines for data analyses and then we provide a systematic review of all published studies using proteomics and/or metabolomics for diagnostic and prognostic biomarker discovery in endometrial cancer. Finally, conclusions and recommendations for future studies are also given.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Scopus
Cited By
Altmetric
4.700
0.000
2
2
Tags
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern

Zusatzinfos bearbeiten
Eigene Tags bearbeiten
Privat
Eigene Anmerkung bearbeiten
Privat
Auf Publikationslisten für
Homepage nicht anzeigen
Als besondere Publikation
markieren
Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Biomarker ; Endometrial Cancer ; Machine Learning ; Metabolomics ; Proteomics; Multidimensional Liquid-chromatography; Mass-spectrometry; Doxorubicin Resistance; Biomarker Discovery; Risk Classification; Protein Expression; Quality Assessment; Carcinoma; Identification; Verification
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 2234-943X
e-ISSN 2234-943X
Zeitschrift Frontiers in Oncology
Quellenangaben Band: 13, Heft: , Seiten: , Artikelnummer: 1120178 Supplement: ,
Verlag Frontiers
Verlagsort Avenue Du Tribunal Federal 34, Lausanne, Ch-1015, Switzerland
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Diabetes and Cancer (IDC)
Institute of Experimental Genetics (IEG)
POF Topic(s) 30201 - Metabolic Health
Forschungsfeld(er) Helmholtz Diabetes Center
Genetics and Epidemiology
PSP-Element(e) G-502594-001
G-500600-001
Förderungen National Centre for Research and Development Poland NCBiR grant ERA-NET
Estonian Research Council
German Federal Ministry for Education and Research (BMBF)
Dutch Cancer Society
MIZS, Ministry of Education, Science and Sports Slovenia
EU
DOI 10.3389/fonc.2023.1120178
WOS ID 000973323400001
Scopus ID 85153704793
PubMed ID 37091170
Erfassungsdatum 2023-10-06
[➜Korrektur melden]