ArticlePloS one2024
Combining metabolomics and machine learning to discover biomarkers for early-stage breast cancer diagnosis.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- An ionic liquid-functionalized styrene-maleic anhydride copolymer brush stationary phase coupled with offline two-dimensional LC-MS/MS for comprehensive exosomal phospholipid profiling in breast cancer.Analytical and bioanalytical chemistry · 2026Article
- Dimensionality reduction and metabolite panel derivation in urinary metabolomics based on Random Forest with Gini index feature selection.Analytical and bioanalytical chemistry · 2026Article
- Current and future therapies for triple-negative breast cancer.Journal of hematology & oncology · 2026Review
- Oncometabolites in Cancer Metabolism: Mechanistic Insights and Biomarker Potential- A Narrative Review.Health science reports · 2026Article
- Machine Learning-Driven Metabolomic Biomarker Discovery in Glioblastoma: Advances, Challenges, and Future Directions.International journal of molecular sciences · 2026Review
- Applications of Metabolomics to the Clinical Management of Breast Cancer: New Perspectives for Diagnosis, Treatment and Prognosis.International journal of molecular sciences · 2026Review
- Liquid biopsy, multi-cancer early detection, and artificial intelligence: new frontiers in cancer screening from a technological and immunological perspective.Frontiers in immunology · 2026Review
- Mathematical Modeling in Cancer Metabolism: Tools for Translational Applications in Metabolism-Based Therapy.Advances in experimental medicine and biology · 2026Review
- An update on schistosomicide and tumoricide in the future: arachidonic acid.Lipids in health and disease · 2025Review
- Global research dynamics in metabolic biomarkers and cancer: a bibliometric study from 2015 to 2025.Discover oncology · 2025Review
- Machine Learning-Driven Insights in Cancer Metabolomics: From Subtyping to Biomarker Discovery and Prognostic Modeling.Metabolites · 2025Review
- Review
- Interpretable Machine Learning for Serum-Based Metabolomics in Breast Cancer Diagnostics: Insights from Multi-Objective Feature Selection-Driven LightGBM-SHAP Models.Medicina (Kaunas, Lithuania) · 2025Article
- Lipidomics in Breast Cancer: Decoding Metabolic Reprogramming and Unlocking Therapeutic Opportunities.Current drug targets · 2025Review
- Metabolomic changes associated with treatment response of neoadjuvant chemotherapy with TEC regimen in HER2-negative breast cancer.Frontiers in pharmacology · 2025Article
- Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms andFrontiers in immunology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is an urgent need for better biomarkers for the detection of early-stage breast cancer. Utilizing untargeted metabolomics and lipidomics in conjunction with advanced data mining approaches for metabolism-centric biomarker discovery and validation may enhance the identification and validation of novel biomarkers for breast cancer screening. In this study, we employed a multimodal omics approach to identify and validate potential biomarkers capable of differentiating between patients with breast cancer and those with benign tumors. Our findings indicated that ether-linked phosphatidylcholine exhibited a significant difference between invasive ductal carcinoma and benign tumors, including cases with inconsistent mammography results. We observed alterations in numerous lipid species, including sphingomyelin, triacylglycerol, and free fatty acids, in the breast cancer group. Furthermore, we identified several dysregulated hydrophilic metabolites in breast cancer, such as glutamate, glycochenodeoxycholate, and dimethyluric acid. Through robust multivariate receiver operating characteristic analysis utilizing machine learning models, either linear support vector machines or random forest models, we successfully distinguished between cancerous and benign cases with promising outcomes. These results emphasize the potential of metabolic biomarkers to complement other criteria in breast cancer screening. Future studies are essential to further validate the metabolic biomarkers identified in our study and to develop assays for clinical applications.
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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 Socratic graph.