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Human epidermal growth factor receptor 2 (HER2)-positive is a particularly aggressive type of the breast cancer. Trastuzumab-based therapy is a standard treatment for HER2-positive breast cancer, but some patients are resistance to the therapy. There are no known diagnostic biomarkers to improve the early diagnosis of HER2-positive breast cancer and the clinical utility of trastuzumab therapy. Using ultrahigh-performance liquid time of flight mass spectrometry (UPLC-TOF-MS)-based serum metabolomics and multivariate statistical analysis, we investigated and identified the circulating metabolites L-arginine and arachidonic acid were elevated in trastuzumab-responsive and trastuzumab-resistant HER2-positive breast cancer patients, and increased until reaching their peaks in trastuzumab-resistant HER2-positive breast cancer patients. Moreover, an equation for assessing the risk scores based on linear logistic regression models involving L-arginine and arachidonic acid was created, which was beneficial for revealing metabolic changes in HER2-positive breast cancer and enhancing current trastuzumab-based therapy. In summary, we develop serum-based metabolic biomarkers for diagnosis of HER2-positive breast cancers and predicts the therapeutic effects of trastuzumab therapy.
Pubmed ID: 34994982
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Software platform for complex network analysis and visualization. Used for visualization of molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data.
View all literature mentionsWeb server for statistical, functional and integrative analysis of metabolomics data. Web based tool suite used for metabolomic data processing, normalization, multivariate statistical analysis, and data annotation, biomarker discovery and classification.
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