Computational metabolism modeling predicts risk of distant relapse-free survival in breast cancer patients.

Computational metabolism modeling predicts risk of distant relapse-free survival in breast cancer patients.

Publication date: Oct 03, 2019

Aim: Differences in metabolism among breast cancer subtypes suggest that metabolism plays an important role in this disease. Flux balance analysis is used to explore these differences as well as drug response. Materials & methods: Proteomics data from breast tumors were obtained by mass-spectrometry. Flux balance analysis was performed to study metabolic networks. Flux activities from metabolic pathways were calculated and used to build prognostic models. Results: Flux activities of vitamin A, tetrahydrobiopterin and β-alanine metabolism pathways split our population into low- and high-risk patients. Additionally, flux activities of glycolysis and glutamate metabolism split triple negative tumors into low- and high-risk groups. Conclusion: Flux activities summarize flux balance analysis data and can be associated with prognosis in cancer.

Concepts Keywords
Aim Proteomics
Alanine Metabolic networks
Breast Breast cancer
Cancer Glycolysis
Flux Metabolic pathway
Glutamate Metabolism Flux
Glycolysis Biochemistry
Mass Spectrometry Metabolism
Metabolic Networks Branches of biology
Metabolic Pathways Triple negative tumors
Metabolism
Prognosis
Proteomics
Relapse
Tetrahydrobiopterin
Triple Negative
Vitamin

Semantics

Type Source Name
disease DOID cancer
disease MESH tumors
pathway BSID glycolysis
pathway BSID Glycolysis
drug DRUGBANK Sapropterin
drug DRUGBANK Vitamin A
pathway BSID Metabolic pathways
drug DRUGBANK Isoxaflutole
gene UNIPROT DNMT1
gene UNIPROT CD69
pathway BSID Breast cancer
disease DOID breast cancer
disease MESH breast cancer
disease MESH relapse
pathway BSID Metabolism

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