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

BioRxiv : the Preprint Server for Biology
Lucia Trilla-FuertesJuan Angel Fresno Vara

Abstract

Aims: 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 beta-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. Conclusions: Flux activities summarize Flux Balance Analysis data and can be associated with prognosis in cancer.

Related Concepts

Beta-Alanine
Malignant Neoplasm of Breast
Malignant Neoplasms
Glycolysis
Metabolism
Mass Spectrometry
Vitamin A
Glutamate
Subtype (Attribute)
Membrane Flux

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