title: In-silico prediction of key metabolic differences between two non-small cell lung cancer subtypes.
abstract: Metabolism expresses the phenotype of living cells and understanding it is crucial for different applications in biotechnology and health. With the increasing availability of metabolomic, proteomic and, to a larger extent, transcriptomic data, the elucidation of specific metabolic properties in different scenarios and cell types is a key topic in systems biology. Despite the potential of the elementary flux mode (EFM) concept for this purpose, its use has been limited so far, mainly because their computation has been infeasible for genome-scale metabolic networks. In a recent work, we determined a subset of EFMs in human metabolism and proposed a new protocol to integrate gene expression data, spotting key 'characteristic EFMs' in different scenarios. Our approach was successfully applied to identify metabolic differences among several human healthy tissues. In this article, we evaluated the performance of our approach in clinically interesting situation. In particular, we identified key EFMs and metabolites in adenocarcinoma and squamous-cell carcinoma subtypes of non-small cell lung cancers. Results are consistent with previous knowledge of these major subtypes of lung cancer in the medical literature. Therefore, this work constitutes the starting point to establish a new methodology that could lead to distinguish key metabolic processes among different clinical outcomes.
authors.affiliation: Department of Bioinformatics, CEIT and TECNUN, University of Navarra, San Sebastián, Spain.
authors.forename: Alberto
authors.initials: A
authors.lastname: Rezola
authors.affiliation: Department of Bioinformatics, CEIT and TECNUN, University of Navarra, San Sebastián, Spain.
authors.forename: Jon
authors.initials: J
authors.lastname: Pey
authors.affiliation: Department of Bioinformatics, CEIT and TECNUN, University of Navarra, San Sebastián, Spain.
authors.forename: Ángel
authors.initials: Á
authors.lastname: Rubio
authors.affiliation: Department of Bioinformatics, CEIT and TECNUN, University of Navarra, San Sebastián, Spain.
authors.forename: Francisco J
authors.initials: FJ
authors.lastname: Planes
mesh_terms: D000465:Algorithms; D002289:Carcinoma, Non-Small-Cell Lung / Q000145:classification / Q000235:genetics / Q000378:metabolism*; D002952:Citric Acid Cycle / Q000235:genetics; D019295:Computational Biology; D003198:Computer Simulation; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006019:Glycolysis / Q000235:genetics; D006801:Humans; D008175:Lung Neoplasms / Q000145:classification / Q000235:genetics / Q000378:metabolism*; D053858:Metabolic Networks and Pathways
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