ArticleFrontiers in oncology2022
Complex metabolic interactions between ovary, plasma, urine, and hair in ovarian cancer.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 21 citations in OpenAlex.
- A Urinary Three-Metabolite Signature Enables Noninvasive Identification of Patients with High-Risk Ovarian Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Two cycles of chemotherapy modulate itaconic acid levels via ATF3/TET2/NF-κB pathways in ovarian cancer among Han Chinese women.Journal of ovarian research · 2026Article
- Metabolomic and transcriptomic analyses identify metabolic alterations and immune suppression in ovarian cancer.Scientific reports · 2026Article
- Effective energy harvesting of Yak by regulating the glucose and lipid metabolism in liver to adapt to the plateau environment.Scientific reports · 2025Article
- Sequential Plasma Metabolome and Proteome Analyses to Develop a Novel Monitoring Strategy for Patients with Epithelial Ovarian Cancer: A Pilot Study.International journal of molecular sciences · 2025Article
- Metabolic signature of renal cell carcinoma tumours and its correlation with the urinary metabolome.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- A metabolic fingerprint of ovarian cancer: a novel diagnostic strategy employing plasma EV-based metabolomics and machine learning algorithms.Journal of ovarian research · 2025Article
- Metabolomic Analysis of Histological Composition Variability of High-Grade Serous Ovarian Cancer UsingInternational journal of molecular sciences · 2024Article
- Worm-Based Diagnosis Combining Microfluidics toward Early Cancer Screening.Micromachines · 2024Review
- Selpercatinib combination with the mitochondria-targeted antioxidant MitoQ effectively suppresses RET-mutant thyroid cancer.NPJ precision oncology · 2024Article
- Characterization of Hair Metabolome in 5xFAD Mice and Patients with Alzheimer's Disease Using Mass Spectrometry-Based Metabolomics.ACS chemical neuroscience · 2024Article
- Critical Factors in Sample Collection and Preparation for Clinical Metabolomics of Underexplored Biological Specimens.Metabolites · 2024Review
- MACdb: A Curated Knowledgebase for Metabolic Associations across Human Cancers.Molecular cancer research : MCR · 2023Article
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Authors and funding
13 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ovarian cancer (OC) is the third most common malignant tumor of women accompanied by alteration of systemic metabolism, yet the underlying interactions between the local OC tissue and other system biofluids remain unclear. In this study, we recruited 17 OC patients, 16 benign ovarian tumor (BOT) patients, and 14 control patients to collect biological samples including ovary plasma, urine, and hair from the same patient. The metabolic features of samples were characterized using a global and targeted metabolic profiling strategy based on Gas chromatography-mass spectrometry (GC-MS). Principal component analysis (PCA) revealed that the metabolites display obvious differences in ovary tissue, plasma, and urine between OC and non-malignant groups but not in hair samples. The metabolic alterations in OC tissue included elevated glycolysis (lactic acid) and TCA cycle intermediates (malic acid, fumaric acid) were related to energy metabolism. Furthermore, the increased levels of glutathione and polyunsaturated fatty acids (linoleic acid) together with decreased levels of saturated fatty acid (palmitic acid) were observed, which might be associated with the anti-oxidative stress capability of cancer. Furthermore, how metabolite profile changes across differential biospecimens were compared in OC patients. Plasma and urine showed a lower concentration of amino acids (alanine, aspartic acid, glutamic acid, proline, leucine, and cysteine) than the malignant ovary. Plasma exhibited the highest concentrations of fatty acids (stearic acid, EPA, and arachidonic acid), while TCA cycle intermediates (succinic acid, citric acid, and malic acid) were most concentrated in the urine. In addition, five plasma metabolites and three urine metabolites showed the best specificity and sensitivity in differentiating the OC group from the control or BOT groups (AUC > 0.90) using machine learning modeling. Overall, this study provided further insight into different specimen metabolic characteristics between OC and non-malignant disease and identified the metabolic fluctuation across ovary and biofluids.
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