Rochelle D'Mello, Nico Hüttmann, Zoran Minic, Maxim V Berezovski
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引用次数: 0
Abstract
Introduction: Breast Cancer (BC) is one of the most diagnosed malignancies among women and the second leading cause of cancer related death in North America. Triple Negative BC (TNBC), one of the most severe subtypes of BC, is extremely aggressive and has a higher chance of occurrence in women under 50 years of age. Due to a lack of regular mammographic testing in women under 50, many individuals with TNBC are diagnosed late which can decrease their survival rate. Currently, liquid biopsy is being investigated as a potentially less-invasive alternative to traditional breast tissue biopsy, but this approach is not completely reliable. Blood contains extracellular vesicles (EVs), which carry biomolecular cargo and play a role in BC progression and metastasis. Examination of small EVs could potentially yield metabolite biomarkers for early BC diagnosis.
Objective: We aim to study metabolites in small EVs to find biomarkers for BC diagnosis.
Methods: In this work, an untargeted nano-LC MS/MS metabolomics approach was used to analyze metabolites from small EVs derived from metastatic MDA-MB-231 and compare it with a non-cancerous MCF10A cell line.
Results: Two metabolites, LysoPC 22:6/0:0 and N-acetyl-L-Phenylalanine, unique to sEVs of MDA-MB-231, were identified, validated, and proposed as potential BC biomarkers.
Conclusion: Metabolites from sEVs may be used for BC diagnosis.
期刊介绍:
Metabolomics publishes current research regarding the development of technology platforms for metabolomics. This includes, but is not limited to:
metabolomic applications within man, including pre-clinical and clinical
pharmacometabolomics for precision medicine
metabolic profiling and fingerprinting
metabolite target analysis
metabolomic applications within animals, plants and microbes
transcriptomics and proteomics in systems biology
Metabolomics is an indispensable platform for researchers using new post-genomics approaches, to discover networks and interactions between metabolites, pharmaceuticals, SNPs, proteins and more. Its articles go beyond the genome and metabolome, by including original clinical study material together with big data from new emerging technologies.