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Published on: August 2, 2024
A Urinary Three-Metabolite Signature Enables Noninvasive Identification of Patients with High-Risk Ovarian Cancer
Alexander Max Funk1,2, Mareike Brieske2,3,4, Franziska Maria Schwarz2,3,4
1Institute for Clinical Chemistry and Laboratory Medicine, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
Purpose:
Reliable prognostic tools in ovarian cancer are urgently needed to guide risk-adapted treatment decisions, yet the clinical utility of urinary metabolites for noninvasive risk stratification remains largely undefined. Here, we define a clinically relevant urinary metabolite signature that enables noninvasive prognostic risk stratification in ovarian cancer.
Experimental Design:
We used targeted 1H nuclear magnetic resonance spectroscopy to profile 149 metabolites related to energy metabolism, oxidative stress, mitochondrial function, nitrogen metabolism, amino acid degradation, gut microbiome activity, and inflammation. Metabolites were measured in preoperative urine samples from 199 consecutive patients with newly diagnosed ovarian cancer treated in routine clinical practice between 2013 and 2022.
Results:
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 (Fédération Internationale de Gynécologie et d'Obstétrique stage and 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% confidence interval (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 with the lowest-risk group (Q1).
Conclusions:
We define a urinary three-metabolite signature that enables noninvasive identification of patients with high-risk ovarian cancer beyond established clinical factors. This signature may support molecular stratification and risk-adapted clinical decisions, thereby underscoring the clinical scalability of urine as a matrix for metabolic risk profiling in ovarian cancer.
