ArticleFrontiers in oncology2022
Multi-Omic Profiling of Multi-Biosamples Reveals the Role of Amino Acid and Nucleotide Metabolism in Endometrial 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 19 citations in OpenAlex.
- Advances in urinary biomarkers for endometrial cancer detection.British journal of cancer · 2026Review
- GOT1 promotes endometrial cancer progression by regulating ferroptosis through the STAT3-GPX4 axis and attenuating medroxyprogesterone acetate sensitivity.Cancer gene therapy · 2026Article
- Integrating artificial intelligence and multi-omics data for precision oncology in endometrial cancer: a narrative review.Functional & integrative genomics · 2026Review
- Artificial intelligence-driven integration of multi-biofluid omics and clinical phenotype enables stratification of endometrial cancer.Cell reports. Medicine · 2026Article
- Ensemble learning on serum metabolic fingerprints for early detection of lung adenocarcinoma.NPJ precision oncology · 2026Article
- Liquid Biopsy and Multi-Omic Biomarkers in Breast Cancer: Innovations in Early Detection, Therapy Guidance, and Disease Monitoring.Biomedicines · 2025Review
- Unlocking the Potential of Liquid Biopsy: A Paradigm Shift in Endometrial Cancer Care.Diagnostics (Basel, Switzerland) · 2025Review
- Review
- Article
- Liquid chromatography-high-resolution mass spectrometry-based metabolomics revealing the effects of zearalenone and alpha-zearalenol on human endometrial cancer cells.Toxicology research · 2024Article
- Article
- Cervicovaginal Metabolome and Tumor Characteristics for Endometrial Cancer Detection and Risk Stratification.Clinical cancer research : an official journal of the American Association for Cancer Research · 2024Article
- Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review.Frontiers in oncology · 2023Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Endometrial cancer (EC) is one of the most common gynecological cancers. The traditional diagnosis of EC relies on histopathology, which, however, is invasive and may arouse tumor spread. There have been many studies aiming to find the metabolomic biomarkers of EC to improve the early diagnosis of cancer in a non-invasive or minimally invasive way, which can also provide valuable information for understanding the disease. However, most of these studies only analyze a single type of sample by metabolomics, and cannot provide a comprehensive view of the altered metabolism in EC patients. Our study tries to gain a pathway-based view of multiple types of samples for understanding metabolomic disorders in EC by combining metabolomics and proteomics. Methods: Forty-four EC patients and forty-three controls were recruited for the research. We collected endometrial tissue, urine, and intrauterine brushing samples. Untargeted metabolomics and untargeted proteomics were both performed on the endometrial tissue samples, while only untargeted metabolomics was performed on the urine and intrauterine brushing samples. Results: By integrating the differential metabolites and proteins between EC patients and controls detected in the endometrial tissue samples, we identified several EC-related significant pathways, such as amino acid metabolism and nucleotide metabolism. The significance of these pathways and the potential of metabolite biomarker-based diagnosis were then further verified by using urine and intrauterine brushing samples. It was found that the regulation of metabolites involved in the significant pathways showed similar trends in the intrauterine brushings and the endometrial tissue samples, while opposite trends in the urine and the endometrial tissue samples. Conclusions: With multi-omics characterization of multi-biosamples, the metabolomic changes related to EC are illustrated in a pathway-based way. The network of altered metabolites and related proteins provides a comprehensive view of altered metabolism in the endometrial tissue samples. The verification of these critical pathways by using urine and intrauterine brushing samples provides evidence for the possible non-invasive or minimally invasive biopsy for EC diagnosis in the future.
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