Serum Fingerprinting-Based Integrative Dual-Omics Machine Learning for Endometriosis-Associated Ovarian Cancer

Man Zhang, Zhang M, Yunqiang Zhang, Yanchao Zhang, Zhang Y, Zhang W, Wantong Zhang, Xiangmin Zhang, Jingxin Ding, Ding J, Nianrong Sun, Chunhui Deng
Analytical chemistry · 2025 · vol. 97(42) , pp. 23622–23630 · doi:10.1021/acs.analchem.5c05483 · PMID:41099367 · W4415244370
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⚙ AI-generated summary by gemini-2.5-flash-lite, 2026-06-13 ⓘ

This study developed a dual-omics platform integrating serum metabolic and peptide fingerprints using machine learning, which significantly improved the accuracy of screening and subtyping endometriosis-associated ovarian cancer compared to single omics approaches.

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This study developed a functionalized mesoporous nanoparticle-coupled laser desorption/ionization mass spectrometry platform to generate serum metabolic fingerprints and serum peptide fingerprints from trace serum samples within 50 seconds, then used integrative machine learning with identified 6 metabolites and 6 peptides. In distinguishing endometriosis-associated ovarian cancer from benign controls, dual-omics substantially improved performance (AUC 0.989, accuracy 93.1%) versus metabolomics alone and peptidomics alone, and it also outperformed single-omics approaches for subtype classification. The key limitation explicitly noted in the abstract is that model performance comparisons are reported on the study’s dataset without detailing independent external validation in the provided text. This paper is centrally about endometriosis-associated ovarian cancer — it uses dual-omics serum fingerprinting to screen and subtype malignancy linked to endometriosis.

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Abstract

Dual-omics, by integrating molecular information from two distinct dimensions, can offer more comprehensive perspective for complex disease. Herein, we developed an efficient functionalized mesoporous nanoparticle-coupled laser desorption/ionization mass spectrometry (fMNPLDI-MS) platform capable of extracting high-quality serum metabolic fingerprints (SMFs) and serum peptide fingerprints (SPFs) from trace serum samples within 50 s. Integrating these SMFs and SPFs markedly improved early screening and subtyping of endometriosis-associated ovarian cancer (EAOC) more than single omics. Specifically, leveraging machine learning on the identified 6 metabolites and 6 peptides, the integration strategy attained an area under the receiver operating characteristic curve (AUC) value of 0.989 and accuracy of 93.1%, compared with 0.964/89.7% for metabolomics alone and 0.960/87.9% for peptidomics alone in distinguishing EAOC from benign controls. Similarly, the dual-omics integration strategy achieved an AUC value of 0.875 and accuracy of 86.7%, surpassing individual metabolomics (AUC: 0.869, accuracy: 83.3%) and peptidomics (AUC: 0.733, accuracy: 76.7%), in subtype classification. This high-throughput fMNPLDI-MS dual-omics platform provides a powerful tool for EAOC screening and subtyping, paving the way toward early detection and precision management of this malignancy.
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Serum Fingerprinting-Based Integrative Dual-Omics Machine Learning for Endometriosis-Associated Ovarian CancerClick to copy article linkArticle link copied! - Man ZhangMan ZhangDepartment of Chemistry, Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai 200433, ChinaMore by Man Zhang - Yunqiang ZhangYunqiang ZhangObstetrics & Gynecology Hospital of Fudan University, Shanghai Key Lab of Reproduction and Development, Shanghai Key Lab of Female Reproductive Endocrine Related Diseases, Shanghai 200433, ChinaMore by Yunqiang Zhang - Yanchao ZhangYanchao ZhangDepartment of Chemistry, Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai 200433, ChinaMore by Yanchao Zhang - Wantong ZhangWantong ZhangDepartment of Chemistry, Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai 200433, ChinaMore by Wantong Zhang - Xiangmin ZhangXiangmin ZhangDepartment of Chemistry, Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai 200433, ChinaMore by Xiangmin Zhang - Jingxin Ding*Jingxin Ding*Email: [email protected]Obstetrics & Gynecology Hospital of Fudan University, Shanghai Key Lab of Reproduction and Development, Shanghai Key Lab of Female Reproductive Endocrine Related Diseases, Shanghai 200433, ChinaMore by Jingxin Ding - Nianrong Sun*Nianrong Sun*Email: [email protected]Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai 200032, ChinaMore by Nianrong Sun - Chunhui Deng*Chunhui Deng*Email: [email protected]Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Department of Chemistry, Institutes of Biomedical Sciences, Fudan University, Shanghai 201399, ChinaMore by Chunhui Deng Abstract Dual-omics, by integrating molecular information from two distinct dimensions, can offer more comprehensive perspective for complex disease. Herein, we developed an efficient functionalized mesoporous nanoparticle-coupled laser desorption/ionization mass spectrometry (fMNPLDI-MS) platform capable of extracting high-quality serum metabolic fingerprints (SMFs) and serum peptide fingerprints (SPFs) from trace serum samples within 50 s. Integrating these SMFs and SPFs markedly improved early screening and subtyping of endometriosis-associated ovarian cancer (EAOC) more than single omics. Specifically, leveraging machine learning on the identified 6 metabolites and 6 peptides, the integration strategy attained an area under the receiver operating characteristic curve (AUC) value of 0.989 and accuracy of 93.1%, compared with 0.964/89.7% for metabolomics alone and 0.960/87.9% for peptidomics alone in distinguishing EAOC from benign controls. Similarly, the dual-omics integration strategy achieved an AUC value of 0.875 and accuracy of 86.7%, surpassing individual metabolomics (AUC: 0.869, accuracy: 83.3%) and peptidomics (AUC: 0.733, accuracy: 76.7%), in subtype classification. This high-throughput fMNPLDI-MS dual-omics platform provides a powerful tool for EAOC screening and subtyping, paving the way toward early detection and precision management of this malignancy. Cited By This article is cited by 2 publications. - Man Zhang, Shun Shen, Fangying Shi, Chunhui Deng. Engineering inorganic nanomaterials for precision metabolic profiling via laser desorption/ionization mass spectrometry. Chinese Chemical Letters 2026, 99 , 112659. https://doi.org/10.1016/j.cclet.2026.112659 - Chencheng Dai, Yiwei Cao, Yiran Xu, Moyuan Li, Guangquan Liu, Sujuan Xu, Nuo Ye, Changxiang Shi, Tiantian Fan, Pengfei Xu, Xuemei Jia. Serum metabolic fingerprinting for diagnosis and therapeutic applications of ovarian endometriosis. iScience 2026, 29 (3) , 114887. https://doi.org/10.1016/j.isci.2026.114887 Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days. Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts. The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.

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endometriosis

MeSH descriptors

Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis

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