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

Andrea Romano*, Tea Lanisnik Rizner*, Henrica Maria Johanna Werner, Andrzej Semczuk, Camille Lowy, Christoph Schroeder, Anne Griesbeck, Jerzy Adamski, Dmytro Fishman, Janina Tokarz, BIOENDOCAR

*Corresponding author for this work

Research output: Contribution to journal(Systematic) Review article peer-review

Abstract

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.
Original languageEnglish
Article number1120178
Number of pages21
JournalFrontiers in Oncology
Volume13
Issue number1
DOIs
Publication statusPublished - 6 Apr 2023

Keywords

  • endometrial cancer
  • proteomics
  • metabolomics
  • biomarker
  • machine learning
  • MULTIDIMENSIONAL LIQUID-CHROMATOGRAPHY
  • MASS-SPECTROMETRY
  • DOXORUBICIN RESISTANCE
  • BIOMARKER DISCOVERY
  • RISK CLASSIFICATION
  • PROTEIN EXPRESSION
  • QUALITY ASSESSMENT
  • CARCINOMA
  • IDENTIFICATION
  • VERIFICATION

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