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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)

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Open Access Gold as soon as Publ. Version/Full Text is submitted to ZB.

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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.

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Publication type Article: Journal article

Document type Review

Corresponding Author

Keywords 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

ISSN (print) / ISBN 2234-943X

e-ISSN 2234-943X

Journal Frontiers in Oncology

Quellenangaben Volume: 13, Article Number: 1120178

Publisher Frontiers

Publishing Place Avenue Du Tribunal Federal 34, Lausanne, Ch-1015, Switzerland

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Diabetes and Cancer (IDC)
Institute of Experimental Genetics (IEG)

Grants 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