ArticleBMC cancer2019
UPLC-MS based urine untargeted metabolomic analyses to differentiate bladder cancer from renal cell carcinoma.
Article in BMC cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A systematic DoE approach for optimizing urinary LC-HRMS metabolomics: enhancing reliability in bladder cancer profiling.Analytical and bioanalytical chemistry · 2026Article
- Evaluation of FASP for Mass Spectrometry-Based Untargeted Metabolomics Analysis of Urine Samples.Journal of proteome research · 2026Article
- Metabolic Profiling Reveals Insights Into Bladder Cancer Pathogenesis and Recurrence.Advances in urology · 2026Article
- Urinary metabolomic profiling in 22q11.2 deletion syndrome reveals microbial and mitochondrial signatures related to autism and psychosis risk.PCN reports : psychiatry and clinical neurosciences · 2025Article
- Metabolic Reprogramming in Urological Tumors: New Perspectives from Tumor Metabolic Phenotypes to Therapeutic Targets.International journal of biological sciences · 2025Review
- Liquid biopsy in cancer current: status, challenges and future prospects.Signal transduction and targeted therapy · 2024Review
- Urine and serum metabolomic analysis of endometrial cancer diagnosis and classification based on ultra-performance liquid chromatography mass spectrometry.Metabolomics : Official journal of the Metabolomic Society · 2024Article
- Urine metabolomics for assessing fertility-sparing treatment efficacy in endometrial cancer: a non-invasive approach using ultra-performance liquid chromatography mass spectrometry.BMC women's health · 2023Article
- Liquid biopsy at the frontier in renal cell carcinoma: recent analysis of techniques and clinical application.Molecular cancer · 2023Review
- LC-MS based urine untargeted metabolomic analyses to identify and subdivide urothelial cancer.Frontiers in oncology · 2023Article
- UPLC-MS based integrated plasma proteomic and metabolomic profiling of TSC-RAML and its relationship with everolimus treatment.Frontiers in molecular biosciences · 2023Article
- A Comprehensive Metabolomics Analysis of Fecal Samples from Advanced Adenoma and Colorectal Cancer Patients.Metabolites · 2022Article
- Serum metabolomic profiling for patients with adenocarcinoma of the esophagogastric junction.Metabolomics : Official journal of the Metabolomic Society · 2022Article
- A DNA adductome analysis revealed a reduction in the global level of C5-hydroxymethyl-2'-deoxycytidine in the non-tumoral upper urinary tract mucosa of urothelial carcinoma patients.Genes and environment : the official journal of the Japanese Environmental Mutagen Society · 2021Article
- Urinary Metabolic Markers of Bladder Cancer: A Reflection of the Tumor or the Response of the Body?Metabolites · 2021Review
- Multiple Chromatographic Analysis of Urine in the Detection of Bladder Cancer.Diagnostics (Basel, Switzerland) · 2021Article
- Machine Learning-Enabled Renal Cell Carcinoma Status Prediction Using Multiplatform Urine-Based Metabolomics.Journal of proteome research · 2021Article
- Identification of metabolic biomarkers to predict treatment outcome and disease progression in multiple myeloma.American journal of cancer research · 2020Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo discover biomarker panels that could distinguish cancers (BC and RCC) from healthy controls (HCs) and bladder cancers (BC) from renal cell carcinoma (RCC), regardless of whether the patients have haematuria. In addition, we also explored the altered metabolomic pathways of BC and RCC.
methodsIn total, 403 participants were enrolled in our study, which included 146 BC patients (77 without haematuria and 69 with haematuria), 115 RCC patients (94 without haematuria and 21 with haematuria) and 142 sex- and age-matched HCs. Their midstream urine samples were collected and analysed by performing UPLC-MS. The statistical methods and pathway analyses were applied to discover potential biomarker panels and altered metabolic pathways.
resultsThe panel of α-CEHC, β-cortolone, deoxyinosine, flunisolide, 11b,17a,21-trihydroxypreg-nenolone and glycerol tripropanoate could distinguish the patients with cancer from the HCs (the AUC was 0.950) and the external validation also displayed a good predictive ability (the AUC was 0.867). The panel of 4-ethoxymethylphenol, prostaglandin F2b, thromboxane B3, hydroxybutyrylcarnitine, 3-hydroxyphloretin and N'-formylkynurenine could differentiate BC from RCC without haematuria. The AUC was 0.829 in the discovering group and 0.76 in the external validation. The metabolite panel comprising 1-hydroxy-2-oxopropyl tetrahydropterin, 1-acetoxy-2-hydroxy-16-heptadecyn-4-one, 1,2-dehydrosalsolinol and L-tyrosine could significantly discriminate BC from RCC with haematuria (AUC was 0.913). Pathway analyses revealed altered lipid and purine metabolisms between cancer patients and HCs, together with disordered amino acid and purine metabolisms between BC and RCC with haematuria.
conclusionsUPLC-MS urine metabolomic analyses could not only differentiate cancers from HCs but also discriminate BC from RCC. In addition, pathway analyses demonstrated a deeper metabolic mechanism of BC and RCC.
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