Metabolic Fingerprinting of Urine Reveals Metabolite Changes in Women With Breast Cancer.

BACKGROUND

Breast cancer (BC) is one of the most common and serious cancers affecting women worldwide. Mammography facilitates early detection, but improving diagnostic accuracy remains essential to further reduce mortality. This study explores metabolite patterns in urine and blood to see how they differ between women with BC, those with other breast conditions, and healthy individuals.

METHODS

Metabolite profiling used direct infusion high-resolution mass spectrometry (DI-MS) on urine samples from breast cancer (BC; n = 118), benign breast disease (BBD; n = 148), symptomatic control (SC; n = 95), and healthy control (HC; n = 39) groups. Multivariate statistical analyses were performed using the R-based MetaboAnalyst v6.0 platform and the MixOmics package to compare urine with plasma and serum metabolomes.

RESULTS

Partial least squares-discriminant analysis (PLS-DA) suggested poor discrimination between (1) groups (BC vs. HC, BC vs. BBD and BC vs. SC). However, ANOVA (corrected for false discovery rate) targeted urinary metabolites that significantly (p < 0.05) differed in BC compared to other groups which included m/z suggested to be lactic acid/lactate, lactose 6-phosphate, D-ribose sugar, glutamic acid, histidine, L-valine and urea. PLS-DA suggested some discrimination with BC histological types (invasive vs. pre-invasive) and BC molecular subtypes (Luminal A, Luminal B, HER2-enriched and triple-negative). Comparisons between urine, serum and plasma metabolomes using Data Integration Analysis for Biomarker Discovery using Latent Components (DIABLO) suggested only moderate correlation. Based on our observations, a network model is proposed linking down-regulated glutamate/glutamine, histidine, and urea production and increased pentose-phosphate pathway and lactic acid levels in the BC urine metabolome.

CONCLUSION

This study suggests that urine metabolomics may improve our understanding of BC development and have potential diagnostic value. These findings warrant validation in larger patient cohorts.