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  • Kelechi Njoku
    Manchester University
  • Andrew Pierce
  • Bethany Geary
    Manchester University
  • Amy E. Campbell
    Manchester University
  • Janet Kelsall
    Manchester University
  • Rachel Reed
    Manchester University
  • Alexander Armit
    Manchester University
  • Rachel Da Sylva
    Manchester University
  • Liqun Zhang
    Manchester University
  • Heather Agnew
    Manchester University
  • Ivona Baricevic-Jones
    Manchester University
  • Davide Chiasserini
    Manchester University
  • Anthony D. Whetton
    Kingston University
  • Emma J. Crosbie
    Manchester University

BACKGROUND: A non-invasive endometrial cancer detection tool that can accurately triage symptomatic women for definitive testing would improve patient care. Urine is an attractive biofluid for cancer detection due to its simplicity and ease of collection. The aim of this study was to identify urine-based proteomic signatures that can discriminate endometrial cancer patients from symptomatic controls.

METHODS: This was a prospective case-control study of symptomatic post-menopausal women (50 cancers, 54 controls). Voided self-collected urine samples were processed for mass spectrometry and run using sequential window acquisition of all theoretical mass spectra (SWATH-MS). Machine learning techniques were used to identify important discriminatory proteins, which were subsequently combined in multi-marker panels using logistic regression.

RESULTS: The top discriminatory proteins individually showed moderate accuracy (AUC > 0.70) for endometrial cancer detection. However, algorithms combining the most discriminatory proteins performed well with AUCs > 0.90. The best performing diagnostic model was a 10-marker panel combining SPRR1B, CRNN, CALML3, TXN, FABP5, C1RL, MMP9, ECM1, S100A7 and CFI and predicted endometrial cancer with an AUC of 0.92 (0.96-0.97). Urine-based protein signatures showed good accuracy for the detection of early-stage cancers (AUC 0.92 (0.86-0.9)).

CONCLUSION: A patient-friendly, urine-based test could offer a non-invasive endometrial cancer detection tool in symptomatic women. Validation in a larger independent cohort is warranted.


  • Humans, Female, Biomarkers, Tumor, Case-Control Studies, Proteomics/methods, Biomarkers, Mass Spectrometry/methods, Endometrial Neoplasms/diagnosis, Fatty Acid-Binding Proteins, Extracellular Matrix Proteins
Original languageEnglish
Pages (from-to)1723-1732
Number of pages10
JournalBritish Journal of Cancer
Issue number9
Early online date17 Feb 2023
Publication statusPublished - 18 May 2023

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