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.

A sperm-specific proteome-scale metabolic network model identifies non-glycolytic genes for energy deficiency in asthenozoospermia

“…Cell-and tissue-specific metabolic network models have also been generated in a top-down approach by pruning these genome-scale models using proteome and transcriptome data obtained from that tissue [Jerby et al 2010;Lewis et al 2010;Fouladiha et al 2015]. In addition, metabolic network models have been reconstructed to explore the underlying etiology of different tissue-specific disorders [Rezola et al 2014;Salazar et al 2016;Sohrabi-Jahromi et al 2016], resulting in the identification of drug targets and biomarkers [Shlomi et al 2009;Frezza et al 2011;Mardinoglu et al 2014]. Despite the significance of sperm cell metabolism in asthenozoospermia, to the best of our knowledge, no metabolic network model has been reconstructed for this cell.…”

Reconstruction and validation of a constraint‐based metabolic network model for bone marrow‐derived mesenchymal stem cells

“…Reactions, metabolites and enzymes (which are controlled by metabolic genes), are responsible for managing the metabolism of cells . Constraint‐based reconstruction and analysis of metabolic networks is a mathematical approach to provide in silico models of cell metabolism by considering gene‐protein‐reaction relationships .…”

Biomarkers in Lung Cancer Screening: Achievements, Promises, and Challenges

“…80,81 Metabolomics can differentiate between histological subtypes or genetic backgrounds. 82,83 A panel of metabolites excreted in the urine, namely, creatine riboside and N-acetyl neuraminic acid, have been associated with lung cancer risk before clinically detectable disease. 84,85 Panels as well as individual markers in blood, sputum, or EBC have also been proposed to identify high-risk candidates for screening or to discriminate between benign and malignant IPNs.…”