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Serum metabolic fingerprints encode functional biomarkers for ovarian cancer diagnosis: a large-scale cohort study

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收录情况： ◇ SCIE

作者：
Liu Wanshan[1,2,3,4] * ; Hu Xiaoxiao[1,2] * ; Bao Zhouzhou[1,2] * ; Li Yanyan[1,2,3,4] ; Zhang Juxiang[1,2,3,4] ; Yang Shouzhi[1,2,3,4] ; Huang Yida[1,2,3,4] ; Wang Ruimin[1,2,3,4] ; Wu Jiao[1,2,3,4] ; Xu Xiaoyu[1,2,3,4] ; Sang Qi[1,2,3,4] ; Di Wen[1,2] ; Lu Huaiwu[5] ; Yin Xia[1,2] ; Qian Kun[1,2,3,4] ;
机构： [1]Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China [2]State Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai 200127, PR China [3]State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai 200030, PR China [4]Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China [5]Department of Gynecologic Oncology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, PR China
出处：
DOI： 10.1016/j.ebiom.2025.105706

关键词： Ovarian cancer Metabolomics Biomarker Diagnosis Machine learning

摘要：
Ovarian cancer (OC) ranks as the most lethal gynaecological malignancy worldwide, with early diagnosis being crucial yet challenging. Current diagnostic methods like transvaginal ultrasound and blood biomarkers show limited sensitivity/specificity. This study aimed to identify and validate serum metabolic biomarkers for OC diagnosis using the largest cohort reported to date.We constructed a large-scale OC-associated cohort of 1432 subjects, including 662 OC, 563 benign ovarian disease, and 207 healthy control subjects, across retrospective (n = 1073) and set-aside validation (n = 359) cohorts. Serum metabolic fingerprints (SMFs) were recorded using nanoparticle-enhanced laser desorption/ionization mass spectrometry (NELDI-MS). A diagnostic panel was developed through machine learning of SMFs in the discovery cohort and validated in independent verification and set-aside validation cohorts. The identified metabolic biomarkers were further validated using liquid chromatography MS and their biological functions were assessed in OC cell lines.We identified a metabolic biomarker panel including glucose, histidine, pyrrole-2-carboxylic acid, and dihydrothymine. This panel achieved consistent areas under the curve (AUCs) of 0.87-0.89 for distinguishing between malignant and benign ovarian masses across all cohorts, and improved to AUCs of 0.95-0.99 when combined with risk of ovarian malignancy algorithm (ROMA). In vitro validation provided initial biological context for the metabolic alterations observed in our diagnostic panel.Our study established a reliable serum metabolic biomarker panel for OC diagnosis with potential clinical translations. The NELDI-MS based approach offers advantages of fast analytical speed (∼30 s/sample) and low cost (∼2-3 dollars/sample), making it suitable for large-scale clinical applications.MOST (2021YFA0910100), NSFC (82421001, 823B2050, 824B2059, and 82173077), Medical-Engineering Joint Funds of Shanghai Jiao Tong University (YG2021GD02, YG2024ZD07, and YG2023ZD08), Shanghai Science and Technology Committee Project (23JC1403000), Shanghai Institutions of Higher Learning (2021-01-07-00-02-E00083), Shanghai Jiao Tong University Inner Mongolia Research Institute (2022XYJG0001-01-16), Sichuan Provincial Department of Science and Technology (2024YFHZ0176), Innovation Research Plan by the Shanghai Municipal Education Commission (ZXWF082101), Innovative Research Team of High-Level Local Universities in Shanghai (SHSMU-ZDCX20210700), Basic-Clinical Collaborative Innovation Project from Shanghai Immune Therapy Institute, Guangdong Basic and Applied Basic Research Foundation (2024A1515013255).Copyright © 2025 The Author(s). Published by Elsevier B.V. All rights reserved.

基金：
The authors gratefully acknowledge financial support from Project 2021YFA0910100 by MOST, Projects 82421001, 823B2050, 824B2059,and 82173077 by NSFC, Medical-Engineering Joint Funds of Shanghai Jiao Tong University (YG2021GD02, YG2024ZD07, and YG2023ZD08), Project 23JC1403000 by Shanghai Science and Technology Committee Project, and Project 2021-01-07-00-02-E00083 by Shanghai Institutions of Higher Learning. This work was also sponsored by the Project 2022XYJG0001-01-16 by Shanghai Jiao Tong University Inner Mongolia Research Institute, Project 2024YFHZ0176 by Sichuan Provincial Department of Science and Technology, Innovation Research Plan by the Shanghai Municipal Education Commission (ZXWF082101), Innovative Research Team of High-Level Local Universities in Shanghai (SHSMU-ZDCX20210700), Basic-Clinical Collaborative Innovation Project from Shanghai Immune Therapy Institute, Guangdong Basic and Applied Basic Research Foundation (2024A1515013255).
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外文
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出版当年[2025]版：
大类 | 1 区 医学
小类 | 1 区 医学：研究与实验
最新[2025]版：
大类 | 1 区 医学
小类 | 1 区 医学：研究与实验
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出版当年[2025]版：
无
最新[2023]版：
Q1 MEDICINE, RESEARCH & EXPERIMENTAL

影响因子： 最新[2023版] 最新五年平均 出版当年[2024版] 出版当年五年平均 出版前一年[2024版]

第一作者：
第一作者机构： [1]Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China [2]State Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai 200127, PR China [3]State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai 200030, PR China [4]Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China
共同第一作者：
通讯作者：
通讯机构： [1]Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China [2]State Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai 200127, PR China [3]State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai 200030, PR China [4]Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China
推荐引用方式(GB/T 7714)：
Liu Wanshan,Hu Xiaoxiao,Bao Zhouzhou,et al.Serum metabolic fingerprints encode functional biomarkers for ovarian cancer diagnosis: a large-scale cohort study[J].Ebiomedicine.2025,115:105706.doi:10.1016/j.ebiom.2025.105706.
APA：
Liu Wanshan,Hu Xiaoxiao,Bao Zhouzhou,Li Yanyan,Zhang Juxiang...&Qian Kun.(2025).Serum metabolic fingerprints encode functional biomarkers for ovarian cancer diagnosis: a large-scale cohort study.Ebiomedicine,115,
MLA：
Liu Wanshan,et al."Serum metabolic fingerprints encode functional biomarkers for ovarian cancer diagnosis: a large-scale cohort study".Ebiomedicine 115.(2025):105706

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