A urinary three-metabolite signature enables non-invasive identification of high-risk ovarian cancer patients.

Abstract

Reliable prognostic tools in ovarian cancer are urgently needed to guide risk-adapted treatment decisions, yet the clinical utility of urinary metabolites for non-invasive risk stratification remains largely undefined. Here, we define a clinically relevant urinary metabolite signature that enables non-invasive prognostic risk stratification in ovarian cancer. Using targeted ¹H NMR spectroscopy, we profiled 149 metabolites involved in energy metabolism, oxidative stress, mitochondrial function, nitrogen metabolism, amino acid degradation, gut microbiome activity and inflammation in pre-operative urine from 199 consecutive patients with newly diagnosed ovarian cancer treated in routine clinical practice between 2013 and 2022. Unsupervised clustering revealed biologically heterogeneous subgroups but lacked prognostic resolution and alignment with overt clinical phenotypes. However, single-metabolite analysis identified a condensed three-metabolite prognostic signature comprising glycine, alanine and citrate. A final parsimonious model integrating this metabolite-signature with clinical covariates outperformed established risk factors alone (FIGO-stage, surgical outcome), accurately predicted 60-month overall survival (AUC = 0.839) and stratified risk. Patients in the highest-risk quartile (Q4) had markedly shorter progression-free survival (Δmedian ≈ 56 months; HR = 2.63, 95% CI: 1.54-4.52, p < 0.001) and overall survival (Δmedian ≈ 86 months; HR = 2.49, 95% CI: 1.39-4.46, p = 0.009) compared to the lowest-risk group (Q1). We define a urinary three-metabolite signature that enables non-invasive identification of high-risk ovarian cancer patients beyond established clinical factors and may support molecular stratification and risk-adapted clinical decisions, thereby supporting the clinical scalability of urine as a matrix for metabolic risk profiling in ovarian cancer.