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Computational metabolomics hints at the relevance of glutamine metabolism in breast cancer

Trilla-Fuertes, L.; Gamez-Pozo, A.; Lopez-Camacho, E.; Prado-Vazquez, G.; Zapater-Moros, A.; Lopez-Vacas, R.; Arevalillo, J. M.; Diaz-Almiron, M.; Navarro, H.; Main, P.; Espinosa, E.; Zamora, P.; Fresno Vara, J. A.

2019-07-11 systems biology
10.1101/370221 bioRxiv
Show abstract

Metabolomics has a great potential in the development of new biomarkers in cancer. In this study, metabolomics and gene expression data from breast cancer tumor samples were analyzed, using (1) probabilistic graphical models to define associations using quantitative data without other a priori information; and (2) Flux Balance Analysis and flux activities to characterize differences in metabolic pathways. On the one hand, both analyses highlighted the importance of glutamine in breast cancer. Moreover, cell experiments showed that treating breast cancer cells with drugs targeting glutamine metabolism significantly affects cell viability. On the other hand, these computational methods suggested some hypotheses and have demonstrated their utility in the analysis of metabolomics data and in associating metabolomics with patients clinical outcome.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.