Development of a urine-based metabolomics approach for multi-cancer screening and tumor origin prediction

Sep 21, 2026

Urine-based metabolomics is investigated as a noninvasive strategy for multicancer early detection and for predicting tumor origin, addressing the limitations of existing single-cancer screening tests and low-sensitivity blood tumor biomarkers. A total of 911 cancer patients (lung, gastric, colorectal) and 792 non-cancer participants were enrolled and randomly split into discovery and validation cohorts. A urinary panel of 18 metabolites distinguished cancer from healthy controls with AUC 0.96, while additional panels differentiated cancer subtypes and the model predicted origins of lung, gastric, and colorectal cancers with overall accuracy 0.75.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Background

Methods

Results

Biomarker identification and model performance

Differentiation of cancer types and tumor origin prediction

Discussion

Introduction

TYPE Original Research PUBLISHED 13 December 2024 DOI 10.3389/fimmu.2024.1449103 OPEN ACCESS EDITED BY Zhiwei Zhou, University of Texas Southwestern Medical Center, United States REVIEWED BY Daniele Vergara, University of Salento, Italy Murat Akand, University Hospitals Leuven, Belgium *CORRESPONDENCE Yan Li [yan.li@metanotitia.com](yan.li@metanotitia.com)[ ](yan.li@metanotitia.com)Youxiang Chen [chenyx102@126.com](chenyx102@126.com)[ ](chenyx102@126.com)Zhenkun Xia [xwvfye@csu.edu.cn](xwvfye@csu.edu.cn) †These authors have contributed equally to this work RECEIVED 14 June 2024 ACCEPTED 27 November 2024 PUBLISHED 13 December 2024 CITATION Xu X, Zeng C, Qing B, He Y, Song G, Wang J, Yu S, Zhang T, Wei Q, Liu L, Wen H, Hu J, Zhang W, Li Y, Chen Y and Xia Z (2024) Development of a urine-based metabolomics approach for multi-cancer screening and tumor origin prediction. Front. Immunol. 15:1449103 . doi: 10.3389/fimmu.2024.1449103 COPYRIGHT © 2024 Xu, Zeng, Qing, He, Song, Wang, Yu, Zhang, Wei, Liu, Wen, Hu, Zhang, Li, Chen and Xia. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Development of a urine-based metabolomics approach for multi-cancer screening and tumor origin prediction Xinping Xu 1†, Chunyan Zeng 2†, Bei Qing 3†, Yun He 4†, Guodong Song 5, Jiaojiao Wang 4, Shuqi Yu 4, Tao Zhang 4, Qingyan Wei 4, Li Liu 4, He Wen 4, Junyuan Hu 4, Wei Zhang 2, Yan Li 4*, Youxiang Chen 2* and Zhenkun Xia 3* 1Jiangxi Institute of Respiratory Disease, The First Afﬁliated Hospital of Nanchang University, Nanchang, China, 2The First Afﬁliated Hospital of Nanchang University, Nanchang, China, 3The Second Xiangya Hospital of Central South University, Changsha, China, 4 Metanotitia Inc., Shenzhen, China, 5The Second Hospital of Tianjin Medical University, Tianjin, China Background: Cancer remains a leading cause of mortality worldwide. A noninvasive screening solution was required for early diagnosis of cancer. Multicancer early detection (MCED) tests have been considered to address the challenge by simultaneously identifying multiple types of cancer within a single test using minimally invasive blood samples. However, a multi-cancer screening strategy utilizing urine-based metabolomics has not yet been developed. Methods: We enrolled 911 cancer patients with 548 lung cancer (LC), 177 with gastric cancer (GC), and 186 with colorectal cancer (CRC), alongside 563 individuals with non-cancerous benign diseases and 229 healthy controls (HC) and investigated the metabolic proﬁles of urine samples. Participants were randomly allocated to discovery and validation cohorts. The discovery cohort was used for identifying multi-cancer and tissue-speciﬁc signatures to build the cancer screening and tumor origin prediction models, while the validation cohort was employed for assessing the performance of these models. Results: We identiﬁed and annotated a total of 360 metabolites from the urine samples. Using the LASSO regression algorithm, 18 metabolites were characterized as urinary metabolic biomarkers and exhibited excellent discriminative performance between cancer patients and HC with AUC of 0.96 in the validation cohort. In comparison with the performance of traditional tumor markers CEA, the screening model performed higher sensitivity across the cancer stages, with a particularly increase in sensitivity among early-stage cancer patients. Moreover, the screening model also exhibited in high classiﬁcation of cancers from non-cancerous group, comprising with HC and benign disease participants. Furthe

FAQs

What is the main goal of the study?

To develop a urine-based metabolomics approach for multi-cancer screening and for predicting tumor origin.

How were participants organized for analysis?

Participants were enrolled and randomly allocated into discovery and validation cohorts to build and then assess screening and origin-prediction models.

Which metabolites and model performance were reported?

A total of 360 metabolites were identified; 18 urinary metabolites characterized as biomarkers achieved AUC 0.96 for discriminating cancer patients from healthy controls in the validation cohort.

How well did the model predict tumor origin?

The model predicted origins of lung, gastric, and colorectal cancers with overall accuracy 0.75, with AUCs of 0.88, 0.88, and 0.80 for each cancer type, respectively.

Sep 21, 2026

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