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Exploring candidate biomarkers for lung and prostate cancers using gene expression and flux variability analysis

Abstract

Genome-scale metabolic models have provided valuable resources for exploring changes in metabolism under normal and cancer conditions. However, metabolism itself is strongly linked to gene expression, so integration of gene expression data into metabolic models might improve detection of genes involved in the control of tumor progression. Hereunder, we considered gene expression data as extra constraints to enhance the predictive powers of metabolic models. We reconstructed genome-scale metabolic models for lung and prostate, in normal and cancer conditions to detect major genes associated with critical subsystems during tumor development. Furthermore, we utilized gene expression data in combination with the information-theory approach to reconstruct co-expression networks of human lung and prostate in both cohorts. Our results revealed 19 genes as candidate biomarkers for lung and prostate cancer cells. This study also revealed that the development of a complementary approach (integration of gene expression and metabolic profiles) could lead to proposing novel biomarkers and suggesting renovated cancer treatment strategies which have not been possible to detect using either of the methods alone.

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Publication details

The article was received on 08 Aug 2017, accepted on 03 Jan 2018 and first published on 04 Jan 2018


Article type: Paper
DOI: 10.1039/C7IB00135E
Citation: Integr. Biol., 2018, Accepted Manuscript
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    Exploring candidate biomarkers for lung and prostate cancers using gene expression and flux variability analysis

    Y. Asgari, P. khosravi, Z. Zabihinpour and M. Habibi, Integr. Biol., 2018, Accepted Manuscript , DOI: 10.1039/C7IB00135E

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