Urinary metabolomic signatures associated with early adverse events during volumetric modulated arc therapy for prostate cancer: A secondary analysis of a public dataset.

Abstract

Volumetric modulated arc therapy (VMAT) is an advanced radiotherapy technique for prostate cancer that improves dose conformity but is often accompanied by early urinary toxicities. Non-invasive biomarkers for predicting such adverse events remain limited. In the present study, a secondary analysis of a public urinary metabolomics dataset (MxP^® Quant 500; 630 metabolites) from 11 patients treated with VMAT (76 Gy/38 fractions) was performed. Patients were grouped as patients with urinary toxicities (n=7; Grade 1) and those without (n=4). Patient-level Spearman's rank correlation coefficients (ρ) between the metabolite concentration and fraction number were calculated and summarized within each group. Overlaps in the top 60 positively correlated metabolites and bottom 60 negatively correlated metabolites across groups were identified and assessed using OmicsNet 2.0. Four metabolites [cholesteryl ester 20:4, taurodeoxycholic acid, triglyceride (TG) 18:0_32:2 and TG 18:3_34:1] were positively correlated in the toxicity group but negatively correlated in controls. Conversely, five metabolites, including phosphatidylcholines (PCs; PC aa C42:2 and PC ae C42:1), TG 16:0_38:1, fatty acid (FA) 20:2 and diglyceride 18:1_18:1, showed the opposite trend. Network analysis indicated impaired lipolysis, specifically the hydrolysis of TGs into FAs, involving carboxyl ester lipase and adipose TG lipase/hormone-sensitive lipase pathways. One patient with toxicity was on ursodeoxycholic acid, possibly influencing bile acid-related metabolites. Overall, early urinary adverse events in VMAT were associated with lipid metabolism dysregulation. No metabolites remained significant after false discovery rate correction, and this exploratory, hypothesis-generating analysis suggested that urinary metabolomics may serve as a non-invasive biomarker platform for toxicity prediction, warranting validation in larger cohorts.