ArticleOncotarget2017
Metabolite marker discovery for the detection of bladder cancer by comparative metabolomics.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
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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
41 citing papers in PubMed, 1 synthesis or guideline pooled it, 68 citations in OpenAlex.
- AI predicting recurrence in non-muscle-invasive bladder cancer: systematic review with study strengths and weaknesses.Frontiers in oncology · 2024Pooled it
- Bladder cancer recurrence surveillance by urine metabolomics analysis.Scientific reports · 2018Trial
- A systematic DoE approach for optimizing urinary LC-HRMS metabolomics: enhancing reliability in bladder cancer profiling.Analytical and bioanalytical chemistry · 2026Article
- A multi-modal approach for decision making in bladder cancer.Nature reviews. Urology · 2026Review
- The role of spectral characteristics of urine in bladder cancer diagnostics.Scientific reports · 2025Article
- Machine Learning-Driven Insights in Cancer Metabolomics: From Subtyping to Biomarker Discovery and Prognostic Modeling.Metabolites · 2025Review
- Intervention of machine learning in bladder cancer research using multi-omics datasets: systematic review on biomarker identification.Discover oncology · 2025Review
- Integrated microbiome and metabolome analysis reveals a novel interplay between gut microbiota and metabolites in differentiated thyroid carcinoma.BMC microbiology · 2025Article
- Artificial Intelligence in Diagnostics: Enhancing Urine Test Accuracy Using a Mobile Phone-Based Reading System.Annals of laboratory medicine · 2025Article
- New Challenges in Bladder Cancer Diagnosis: How Biosensing Tools Can Lead to Population Screening Opportunities.Sensors (Basel, Switzerland) · 2024Review
- Insight into Fructose-to-Sucrose Ratio as the Potential Target of Urinalysis in Bladder Cancer.Metabolites · 2024Article
- Metabolomics for the diagnosis of bladder cancer: A systematic review.Asian journal of urology · 2024Review
- Artificial intelligence application in the diagnosis and treatment of bladder cancer: advance, challenges, and opportunities.Frontiers in oncology · 2024Review
- Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement.Diagnostics (Basel, Switzerland) · 2023Review
- Urinary Metabolomic Analysis of Prostate Cancer by UPLC-FTMS and UPLC-Ion Trap MSDiagnostics (Basel, Switzerland) · 2023Article
- Untargeted urinary metabolomics for bladder cancer biomarker screening with ultrahigh-resolution mass spectrometry.Scientific reports · 2023Article
- New bladder cancer non-invasive surveillance method based on voltammetric electronic tongue measurement of urine.iScience · 2022Article
- The Role of Novel Bladder Cancer Diagnostic and Surveillance Biomarkers-What Should a Urologist Really Know?International journal of environmental research and public health · 2022Review
- Urinary metabolomics for discovering metabolic biomarkers of bladder cancer by UPLC-MS.BMC cancer · 2022Article
- Machine Learning in Prediction of Bladder Cancer on Clinical Laboratory Data.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bladder cancer is one of the most common urinary tract carcinomas in the world. Urine metabolomics is a promising approach for bladder cancer detection and marker discovery since urine is in direct contact with bladder epithelia cells; metabolites released from bladder cancer cells may be enriched in urine samples. In this study, we applied ultra-performance liquid chromatography time-of-flight mass spectrometry to profile metabolite profiles of 87 samples from bladder cancer patients and 65 samples from hernia patients. An OPLS-DA classification revealed that bladder cancer samples can be discriminated from hernia samples based on the profiles. A marker discovery pipeline selected six putative markers from the metabolomic profiles. An LLE clustering demonstrated the discriminative power of the chosen marker candidates. Two of the six markers were identified as imidazoleacetic acid whose relation to bladder cancer has certain degree of supporting evidence. A machine learning model, decision trees, was built based on the metabolomic profiles and the six marker candidates. The decision tree obtained an accuracy of 76.60%, a sensitivity of 71.88%, and a specificity of 86.67% from an independent test.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.