Advances in urinary biomarkers for endometrial cancer detection.

Authors

Molly Dore, Jiexin Cao, Holly Baker-Rand, Ananya Choudhury, Anthony D Whetton, Richard D Unwin, Emma J Davidson, Kelechi Njoku

Published in

British journal of cancer. Aug 11, 2026. Epub Aug 11, 2026.

Abstract

The incidence of endometrial cancer is rising globally, placing significant burden on diagnostic pathways for women with abnormal uterine bleeding. Current evaluation relies primarily on transvaginal ultrasound followed by invasive endometrial visualisation and sampling, although most symptomatic women have no sinister underlying pathology. Urine offers a promising non-invasive and easily repeatable biofluid capable of capturing tumour-associated signals through both systemic renal filtration and locally shed uterine material. This review summarises emerging evidence on urinary biomarkers for endometrial cancer detection, including somatic mutations, DNA methylation markers, proteins and peptides, vibrational spectroscopy signatures, microRNAs, metabolites and urine cytology. These biomarkers collectively reflect key biological processes driving endometrial carcinogenesis, including hormonal and metabolic dysregulation, immune activation, epithelial-stromal disruption, cellular proliferation and genomic instability. Among current candidates, DNA methylation panels and mutation-based assays have demonstrated encouraging diagnostic performance, with studies reporting sensitivities and specificities exceeding 80-90% in symptomatic populations. Urine cytology also shows strong diagnostic potential in selected cohorts, while metabolomic, proteomic and spectroscopy-based approaches remain promising but require further standardisation and prospective validation. Although not yet suitable for population screening, urinary biomarkers show considerable potential for risk stratification and triage of symptomatic women, potentially reducing unnecessary invasive investigations. Clinical translation will require standardised protocols, multi-centre validation and integration into existing diagnostic pathways.

PMID:
42581092
Bibliographic data and abstract were imported from PubMed on 12 Aug 2026.

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