Novel Biomarker Discovery in Urological Cancers Diagnosis via Metabolomic Approaches

Abstract

Introduction
The project explores potential for urinary metabolomics for more precise method for identifying urological diseases. Urinary metabolomics could offer an innovative alternative approach that could allow the early and precise prostate cancer (PCa) diagnosis by identifying unique metabolic fingerprints reflective of the disease state. Beyond this primary comparison, project explores the metabolic differences among other genitourinary cancers (GU), including bladder and kidney cancers, to see if a similar approach may hold broader diagnostic promise.

Methods
Urine samples were collected from 445 men diagnosed with PCa, benign prostatic hyperplasia (BPH), symptom controls (SC) and analysed by using flow infusion electrospray mass spectrometry (FIE-MS). Data interpretation employed principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) via MetaboAnalyst bioinformatic tool. Pairwise comparisons (PCa vs SC, BPH vs SC, PCa vs BPH) identified metabolites that best distinguished each group. For exploratory purposes, an additional 215 samples from patients with various GU cancers (including bladder and kidney cancers) underwent similar metabolomic evaluation.

Results
Initial multivariate comparisons showed BPH samples as metabolically distinct, yet still overlapping to some degree with PCa and SC profiles. The PCa vs SC comparison revealed overall similarity, but arachidonate-derived metabolites (prostaglandin A1, leukotriene B4 [LTB4], and 11,14,15-trihydroxyeicosatrienoic acid) and the steroid androsterol were significantly different (p<0.001). Although these markers were distinctive, receiver operating characteristic curve (ROC) analyses indicated modest classification accuracies (0.57–0.62). In contrast, BPH vs SC comparisons indicated clearer metabolic discrimination. Here, a panel of metabolites including polyhydroxylproline, sulfogalactosylcereamide, delta-tocopherol, LTB4, and 10,11-dihydro-20-dihydoxyl-LTB4 improved classification metrics. While individual metabolites achieved accuracies of 0.6–0.71, combining them raised overall accuracy to 0.826 (95% CI: 0.721–0.918), suggesting that metabolite panels may outperform single-metabolite markers. The PCa vs BPH analysis uncovered additional key metabolites (e.g.,polyhydroxylproline, prostaglandin A1, LTB4, delta-tocopherol) that contributed to moderate classification accuracies (0.64–0.69). These patterns highlighted underlying alterations in pathways related to inflammation and cellular proliferation. Examination of other GU cancers suggested metabolic shifts involving glycolysis, lipid metabolism, and oxidative balance, but smaller sample sizes limited definitive conclusions in these cohorts.

Conclusion
These findings suggest that examining urinary metabolites can uncover subtle, meaningful differences among the assessed groups. While this approach shows promise for developing more accurate diagnostic tools, identifying and validating a definitive set of biomarkers is still essential.

Date of Award	2024
Original language	English
Awarding Institution	Aberystwyth University
Supervisor	Luis Mur (Supervisor)