Evidence map›Paper›PMID 42271577›Full record

ArticleCancer medicine2026

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

Nur Aimi Aliah Zainurin, Anuradha U K H Bambarandhage, Michelle Moreno Escalona, Dimitra Ivanova, Tim Gate, Helen Tench, Manfred Beckman, Helen Phillips, Mandana Pennick, Luis A J Mur

Abstract read
In one paragraph

Article in Cancer medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Nur Aimi Aliah ZainurinDepartment of Life Sciences, Aberystwyth University, Aberystwyth, UK.
Anuradha U K H BambarandhageDepartment of Life Sciences, Aberystwyth University, Aberystwyth, UK.
Michelle Moreno EscalonaDepartment of Life Sciences, Aberystwyth University, Aberystwyth, UK.
Dimitra IvanovaDepartment of Life Sciences, Aberystwyth University, Aberystwyth, UK.
Tim GateWrexham Maelor Hospital, Betsi Cadwaladr University Health Board, Wrexham, UK.
Helen TenchBronglais General Hospital, Hywel Dda University Health Board, Aberystwyth, UK.
Manfred BeckmanDepartment of Life Sciences, Aberystwyth University, Aberystwyth, UK.
Helen PhillipsDepartment of Life Sciences, Aberystwyth University, Aberystwyth, UK.
Mandana PennickGlan Clwyd Hospital, Betsi Cadwaladr University Health Board, Bodelwyddan, UK.
Luis A J MurDepartment of Life Sciences, Aberystwyth University, Aberystwyth, UK.ORCID https://orcid.org/0000-0002-0961-9817

Funding

Institute of Biomedical ScienceMajlis Amanah Rakyat (MARA) 330408376064Shandong Province "Double-Hundred Talent Plan" Teams WSR2023049
6 · The paper itself

Abstract

backgroundBreast 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.

methodsMetabolite 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.

resultsPartial 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.

conclusionThis 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.

Indexed as

Biomarkers, TumorBreast NeoplasmsMetabolomeMetabolomicsAdultAgedCase-Control StudiesFemaleHumansMiddle AgedBiomarkers, Tumorbenign breast diseasebreast cancerplasmaserumsymptom controlsurine

Identifiers

PMID42271577
PMCPMC13253611

What Socratic holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.