ArticleCancers2021
Urine-Based Metabolomics and Machine Learning Reveals Metabolites Associated with Renal Cell Carcinoma Stage.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
15 citing papers in PubMed, 21 citations in OpenAlex.
- Urinary Biomarkers in Urological Oncology: From Biological Origin to Clinical Decision-Making-A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Differences in the Fecal Metabolome of Autoimmune Pancreatitis Patients.United European gastroenterology journal · 2025Article
- Untargeted metabolomic profiling of serum and urine in kidney cancer: a non-invasive approach for biomarker discovery.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Urinary metabolites in association with kidney cancer risk.Carcinogenesis · 2025Article
- Abnormal changes in metabolites caused by mJournal of advanced research · 2025Review
- Renal Cell Carcinoma Discrimination through Attenuated Total Reflection Fourier Transform Infrared Spectroscopy of Dried Human Urine and Machine Learning Techniques.International journal of molecular sciences · 2024Article
- Serum and Urine Metabolic Fingerprints Characterize Renal Cell Carcinoma for Classification, Early Diagnosis, and Prognosis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Amino acid metabolomics and machine learning for assessment of post-hepatectomy liver regeneration.Frontiers in pharmacology · 2024Article
- Liquid-based biomarkers in breast cancer: looking beyond the blood.Journal of translational medicine · 2023Review
- The Effects of Two Kinds of Dietary Interventions on Serum Metabolic Profiles in Haemodialysis Patients.Biomolecules · 2023Article
- An Individualized Prognostic Model in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma Based on Serum Metabolomic Profiling.Life (Basel, Switzerland) · 2023Article
- Liquid biopsy at the frontier in renal cell carcinoma: recent analysis of techniques and clinical application.Molecular cancer · 2023Review
- KYNU as a Biomarker of Tumor-Associated Macrophages and Correlates with Immunosuppressive Microenvironment and Poor Prognosis in Gastric Cancer.International journal of genomics · 2023Article
- Article
- Renal Cell Carcinoma as a Metabolic Disease: An Update on Main Pathways, Potential Biomarkers, and Therapeutic Targets.International journal of molecular sciences · 2022Review
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
Urine metabolomics profiling has potential for non-invasive RCC staging, in addition to providing metabolic insights into disease progression. In this study, we utilized liquid chromatography-mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and machine learning (ML) for the discovery of urine metabolites associated with RCC progression. Two machine learning questions were posed in the study: Binary classification into early RCC (stage I and II) and advanced RCC stages (stage III and IV), and RCC tumor size estimation through regression analysis. A total of 82 RCC patients with known tumor size and metabolomic measurements were used for the regression task, and 70 RCC patients with complete tumor-nodes-metastasis (TNM) staging information were used for the classification tasks under ten-fold cross-validation conditions. A voting ensemble regression model consisting of elastic net, ridge, and support vector regressor predicted RCC tumor size with a
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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.