Serum metabolic fingerprints encode functional biomarkers for ovarian cancer diagnosis: a large-scale cohort studyResearch in context
Summary: Background: 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...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-05-01
|
| Series: | EBioMedicine |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352396425001501 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849712606054449152 |
|---|---|
| author | Wanshan Liu Xiaoxiao Hu Zhouzhou Bao Yanyan Li Juxiang Zhang Shouzhi Yang Yida Huang Ruimin Wang Jiao Wu Xiaoyu Xu Qi Sang Wen Di Huaiwu Lu Xia Yin Kun Qian |
| author_facet | Wanshan Liu Xiaoxiao Hu Zhouzhou Bao Yanyan Li Juxiang Zhang Shouzhi Yang Yida Huang Ruimin Wang Jiao Wu Xiaoyu Xu Qi Sang Wen Di Huaiwu Lu Xia Yin Kun Qian |
| author_sort | Wanshan Liu |
| collection | DOAJ |
| description | Summary: Background: 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. Methods: 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. Findings: 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. Interpretation: 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. Funding: 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). |
| format | Article |
| id | doaj-art-29e63e85b94b45d6aa63a7f24bd2593a |
| institution | DOAJ |
| issn | 2352-3964 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EBioMedicine |
| spelling | doaj-art-29e63e85b94b45d6aa63a7f24bd2593a2025-08-20T03:14:13ZengElsevierEBioMedicine2352-39642025-05-0111510570610.1016/j.ebiom.2025.105706Serum metabolic fingerprints encode functional biomarkers for ovarian cancer diagnosis: a large-scale cohort studyResearch in contextWanshan Liu0Xiaoxiao Hu1Zhouzhou Bao2Yanyan Li3Juxiang Zhang4Shouzhi Yang5Yida Huang6Ruimin Wang7Jiao Wu8Xiaoyu Xu9Qi Sang10Wen Di11Huaiwu Lu12Xia Yin13Kun Qian14Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China; State Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai 200127, PR China; 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; Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR ChinaDepartment of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China; State Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai 200127, PR ChinaDepartment of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China; State Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai 200127, PR ChinaDepartment of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China; State Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai 200127, PR China; 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; Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR ChinaDepartment of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China; State Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai 200127, PR China; 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; Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR ChinaDepartment of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China; State Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai 200127, PR China; 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; Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR ChinaDepartment of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China; State Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai 200127, PR China; 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; Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR ChinaDepartment of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China; State Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai 200127, PR China; 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; Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR ChinaDepartment of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China; State Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai 200127, PR China; 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; Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR ChinaDepartment of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China; State Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai 200127, PR China; 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; Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR ChinaDepartment of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China; State Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai 200127, PR China; 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; Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR ChinaDepartment of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China; State Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai 200127, PR China; Corresponding author. Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China.Department of Gynecologic Oncology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, PR China; Corresponding author.Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China; State Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai 200127, PR China; Corresponding author. Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China.Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China; State Key Laboratory for Oncogenes and Related Genes, Shanghai Key Laboratory of Gynecologic Oncology, Shanghai 200127, PR China; 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; Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China; Corresponding author. Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, PR China.Summary: Background: 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. Methods: 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. Findings: 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. Interpretation: 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. Funding: 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).http://www.sciencedirect.com/science/article/pii/S2352396425001501Ovarian cancerMetabolomicsBiomarkerDiagnosisMachine learning |
| spellingShingle | Wanshan Liu Xiaoxiao Hu Zhouzhou Bao Yanyan Li Juxiang Zhang Shouzhi Yang Yida Huang Ruimin Wang Jiao Wu Xiaoyu Xu Qi Sang Wen Di Huaiwu Lu Xia Yin Kun Qian Serum metabolic fingerprints encode functional biomarkers for ovarian cancer diagnosis: a large-scale cohort studyResearch in context EBioMedicine Ovarian cancer Metabolomics Biomarker Diagnosis Machine learning |
| title | Serum metabolic fingerprints encode functional biomarkers for ovarian cancer diagnosis: a large-scale cohort studyResearch in context |
| title_full | Serum metabolic fingerprints encode functional biomarkers for ovarian cancer diagnosis: a large-scale cohort studyResearch in context |
| title_fullStr | Serum metabolic fingerprints encode functional biomarkers for ovarian cancer diagnosis: a large-scale cohort studyResearch in context |
| title_full_unstemmed | Serum metabolic fingerprints encode functional biomarkers for ovarian cancer diagnosis: a large-scale cohort studyResearch in context |
| title_short | Serum metabolic fingerprints encode functional biomarkers for ovarian cancer diagnosis: a large-scale cohort studyResearch in context |
| title_sort | serum metabolic fingerprints encode functional biomarkers for ovarian cancer diagnosis a large scale cohort studyresearch in context |
| topic | Ovarian cancer Metabolomics Biomarker Diagnosis Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2352396425001501 |
| work_keys_str_mv | AT wanshanliu serummetabolicfingerprintsencodefunctionalbiomarkersforovariancancerdiagnosisalargescalecohortstudyresearchincontext AT xiaoxiaohu serummetabolicfingerprintsencodefunctionalbiomarkersforovariancancerdiagnosisalargescalecohortstudyresearchincontext AT zhouzhoubao serummetabolicfingerprintsencodefunctionalbiomarkersforovariancancerdiagnosisalargescalecohortstudyresearchincontext AT yanyanli serummetabolicfingerprintsencodefunctionalbiomarkersforovariancancerdiagnosisalargescalecohortstudyresearchincontext AT juxiangzhang serummetabolicfingerprintsencodefunctionalbiomarkersforovariancancerdiagnosisalargescalecohortstudyresearchincontext AT shouzhiyang serummetabolicfingerprintsencodefunctionalbiomarkersforovariancancerdiagnosisalargescalecohortstudyresearchincontext AT yidahuang serummetabolicfingerprintsencodefunctionalbiomarkersforovariancancerdiagnosisalargescalecohortstudyresearchincontext AT ruiminwang serummetabolicfingerprintsencodefunctionalbiomarkersforovariancancerdiagnosisalargescalecohortstudyresearchincontext AT jiaowu serummetabolicfingerprintsencodefunctionalbiomarkersforovariancancerdiagnosisalargescalecohortstudyresearchincontext AT xiaoyuxu serummetabolicfingerprintsencodefunctionalbiomarkersforovariancancerdiagnosisalargescalecohortstudyresearchincontext AT qisang serummetabolicfingerprintsencodefunctionalbiomarkersforovariancancerdiagnosisalargescalecohortstudyresearchincontext AT wendi serummetabolicfingerprintsencodefunctionalbiomarkersforovariancancerdiagnosisalargescalecohortstudyresearchincontext AT huaiwulu serummetabolicfingerprintsencodefunctionalbiomarkersforovariancancerdiagnosisalargescalecohortstudyresearchincontext AT xiayin serummetabolicfingerprintsencodefunctionalbiomarkersforovariancancerdiagnosisalargescalecohortstudyresearchincontext AT kunqian serummetabolicfingerprintsencodefunctionalbiomarkersforovariancancerdiagnosisalargescalecohortstudyresearchincontext |