ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2024
Serum Lipidome Profiling Reveals a Distinct Signature of Ovarian Cancer in Korean Women.
Article in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 7 citations in OpenAlex.
- Platelet activation and eicosanoid signaling in ovarian cancer: implications for biomarker development.Molecular biology reports · 2026Review
- Small Extracellular Vesicle and Total Lipid Profiles Provide a Diagnostic Advantage Over CA-125 Across Ovarian Cancer Subtypes.Journal of extracellular vesicles · 2026Article
- Progesterone induces steroidome and lipidome remodeling in a BRCA-resembling mouse model of high-grade serous ovarian cancer.Translational oncology · 2026Article
- Multi-Omics Profiling in a Symptomatic Cohort Identifies Coordinated Biomarker Signatures in Ovarian Cancer Serum.Diagnostics (Basel, Switzerland) · 2026Article
- Machine Learning Framework for Ovarian Cancer Diagnostics Using Plasma Lipidomics and Metabolomics.International journal of molecular sciences · 2025Article
- Machine learning in ovarian cancer: a bibliometric and visual analysis from 2004 to 2024.Discover oncology · 2025Article
- Special Issue "Cancer Biomarker: Current Status and Future Perspectives".International journal of molecular sciences · 2025Article
- Elucidating the role of fatty acid reprogramming in ovarian cancer: insights cross-talk between blood, subcutaneous fat, and ovarian cancer tissues.Frontiers in oncology · 2025Article
- Automated Machine Learning and Explainable AI (AutoML-XAI) for Metabolomics: Improving Cancer Diagnostics.Journal of the American Society for Mass Spectrometry · 2024Article
- Automated machine learning and explainable AI (AutoML-XAI) for metabolomics: improving cancer diagnostics.bioRxiv : the preprint server for biology · 2023Article
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11 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundDistinguishing ovarian cancer from other gynecological malignancies is crucial for patient survival yet hindered by non-specific symptoms and limited understanding of ovarian cancer pathogenesis. Accumulating evidence suggests a link between ovarian cancer and deregulated lipid metabolism. Most studies have small sample sizes, especially for early-stage cases, and lack racial/ethnic diversity, necessitating more inclusive research for improved ovarian cancer diagnosis and prevention.
methodsHere, we profiled the serum lipidome of 208 ovarian cancer, including 93 early-stage patients with ovarian cancer and 117 nonovarian cancer (other gynecological malignancies) patients of Korean descent. Serum samples were analyzed with a high-coverage liquid chromatography high-resolution mass spectrometry platform, and lipidome alterations were investigated via statistical and machine learning (ML) approaches.
resultsWe found that lipidome alterations unique to ovarian cancer were present in Korean women as early as when the cancer is localized, and those changes increase in magnitude as the diseases progresses. Analysis of relative lipid abundances revealed specific patterns for various lipid classes, with most classes showing decreased abundance in ovarian cancer in comparison with other gynecological diseases. ML methods selected a panel of 17 lipids that discriminated ovarian cancer from nonovarian cancer cases with an AUC value of 0.85 for an independent test set.
conclusionsThis study provides a systemic analysis of lipidome alterations in human ovarian cancer, specifically in Korean women. IMPACT: Here, we show the potential of circulating lipids in distinguishing ovarian cancer from nonovarian cancer conditions.
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