ArticleBMC bioinformatics2010
Multivariate classification of urine metabolome profiles for breast cancer diagnosis.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- Exploring the complex relationship between metabolomics and breast cancer early detection (Review).Molecular and clinical oncology · 2025Review
- Metabolomics-Based Machine Learning Models Accurately Predict Breast Cancer Estrogen Receptor Status.International journal of molecular sciences · 2024Article
- Identification of a Novel Biomarker Panel for Breast Cancer Screening.International journal of molecular sciences · 2024Article
- Omics-Based Investigations of Breast Cancer.Molecules (Basel, Switzerland) · 2023Review
- Applications of machine learning in metabolomics: Disease modeling and classification.Frontiers in genetics · 2022Review
- Cancer metabolomics: A tool of clinical utility for early diagnosis of gynaecological cancers.The Indian journal of medical research · 2021Review
- Review
- Untargeted UrinaryMetabolites · 2019Article
- Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition).European journal of immunology · 2019Article
- Review and Comparison of Cancer Biomarker Trends in Urine as a Basis for New Diagnostic Pathways.Cancers · 2019Review
- Metabolomic profiles in breast cancer:a pilot case-control study in the breast cancer family registry.BMC cancer · 2018Article
- Metabolomics for the masses: The future of metabolomics in a personalized world.New horizons in translational medicine · 2017Review
- A characteristic biosignature for discrimination of gastric cancer from healthy population by high throughput GC-MS analysis.Oncotarget · 2016Article
- Differential distribution improves gene selection stability and has competitive classification performance for patient survival.Nucleic acids research · 2016Article
- A dried blood spot mass spectrometry metabolomic approach for rapid breast cancer detection.OncoTargets and therapy · 2016Article
- Use of Charged Nanoparticles in NMR-Based Metabolomics for Spectral Simplification and Improved Metabolite Identification.Analytical chemistry · 2015Article
- Analytical methods in untargeted metabolomics: state of the art in 2015.Frontiers in bioengineering and biotechnology · 2015Review
- Review of mass spectrometry-based metabolomics in cancer research.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2013Review
- The significance and robustness of a plasma free amino acid (PFAA) profile-based multiplex function for detecting lung cancer.BMC cancer · 2013Article
- An expert support system for breast cancer diagnosis using color wavelet features.Journal of medical systems · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiagnosis techniques using urine are non-invasive, inexpensive, and easy to perform in clinical settings. The metabolites in urine, as the end products of cellular processes, are closely linked to phenotypes. Therefore, urine metabolome is very useful in marker discoveries and clinical applications. However, only univariate methods have been used in classification studies using urine metabolome. Since multiple genes or proteins would be involved in developments of complex diseases such as breast cancer, multiple compounds including metabolites would be related with the complex diseases, and multivariate methods would be needed to identify those multiple metabolite markers. Moreover, because combinatorial effects among the markers can seriously affect disease developments and there also exist individual differences in genetic makeup or heterogeneity in cancer progressions, single marker is not enough to identify cancers.
resultsWe proposed classification models using multivariate classification techniques and developed an analysis procedure for classification studies using metabolome data. Through this strategy, we identified five potential urinary biomarkers for breast cancer with high accuracy, among which the four biomarker candidates were not identifiable by only univariate methods. We also proposed potential diagnosis rules to help in clinical decision making. Besides, we showed that combinatorial effects among multiple biomarkers can enhance discriminative power for breast cancer.
conclusionsIn this study, we successfully showed that multivariate classifications are needed to precisely diagnose breast cancer. After further validation with independent cohorts and experimental confirmation, these marker candidates will likely lead to clinically applicable assays for earlier diagnoses of breast cancer.
Indexed as
Identifiers
What OpenQuestion holds
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.