ReviewMetabolic Cancer Biology: Structural-based analysis of cancer as a metabolic disease, new sights and opportunities for disease treatment
Introduction
Cancer is a complex disease which contains multiple types of biological interactions through various physical, sequential, and biological scales. This complexity generates considerable challenges for the description of cancer biology, and inspires the study of cancer in the context of molecular, cellular, and physiological systems. A significant factor contributing to this new synthesis is the observation that several signaling pathways changed in cancer are key regulators of the human metabolic network. This specifies a rational interplay between genetic and metabolic alterations during tumorigenesis without a permanent cause–effect relationship [1]. Otto Warburg first suggested this metabolic modification based on his observations in leukemic cells that altered metabolism of glucose may lead to cancer. This effect is now referred as the “Warburg effect”. Since then, different hypotheses (Fig. 1) have been proposed to find the mechanisms responsible for the Warburg effect [2]. However, the metabolic landscape of cancer is still far from understood, and in particular its regulation. Recently, there has been a resurgence of interest in cancer metabolism [1], [3], [4]. In the last decade, there is a paradigm shift from studying individual enzymes to newer approaches that aims to comprehend altered tumor metabolism as a whole. These new efforts flourish due to increasing availability of high-throughput data from various tumor studies elucidating metabolic concentrations, fluxes and abundance and regulation of the key enzymes. The data can now be analyzed integratively using statistical models to describe cancer metabolism. Beside experimental work, a metabolic network reconstruction is a manually curated, computational framework that empowers the description of gene–protein-reaction relationships [5]. For understanding the metabolic fluxes of a cancer cell, mechanistic genome scale models of cancer metabolism are needed and first attempts are very promising. Mechanistic methods are becoming increasingly feasible not only because of more sophisticated approaches and better data, but also due to hardware improvements enabling to simulate these models on clusters with a couple of hundreds of cores. Several studies have established how such reconstructions of metabolism could guide the development of biological theories and discoveries [6], [7], [8].
In this article we have described recent advances in network-based analysis of cancer as a metabolic disease. In the first section, the topological approach has been explained. In the next section, the constraint-based method (as another network-based approach) has been considered.
Section snippets
Genome-scale metabolic model of human cancer
With the advent of genome-scale metabolic models (GEMs) of various cell types and diseases, a valuable tool to study genetic, epigenetic and metabolic events in combination, has emerged [9]. The convergence of these developments enables the researchers to predict physiological functions and the relevant growth rate of particular human cell types, tissue-specificity and cancer [10], [11], [12]. There are four generic reconstructed genome-scale human metabolic networks: Recon1 [13], Recon2 [14],
Topological approach
The topological analysis of biological networks is an important method in systems biology which allows the investigation of large scale networks such as GEMs [48]. Graph theory is the most useful framework for representation of GEMs. A mathematical representation of a network is a graph G(V,E). Its vertex set (V) consists of all nodes. Two nodes are adjacent if an edge exist between which connects them.
There are many parameters which could be calculated by graph theory method such as clustering
Flux balance analysis
The potential of mathematical computational modeling tools in description, exploration, and prediction of metabolic networks has been realized in recent years [73]. One of the most widely used network analysis approaches is Flux Balance Analysis (FBA), which is based on derivation of the steady-state metabolic capabilities of a system with appropriate constraints and without the need for accurate kinetic data [74]. The main principle of the FBA is that the system will reach a steady-state under
Drug-target prediction and structural analysis
GEMs provide a useful tool for the study of diseases and the development of drugs. Several simulations and modeling methods have been developed to address the issues of drug-target prediction [92], [93], [94], [95], [96]. The structural features of metabolic networks contribute to the robustness and flexibility of the complex biosystems and may explain, in general, the fact that many drug candidates are ineffective (the drug effect is compensated by other pathways in the network) or show
Outline
Aggressive cancers established evidence of a metabolic shift, including upregulation of the pentose phosphate pathway (PPP) and the glutamine transporter genes, downregulation of genes contributed in the TCA cycle, increased acetyl-CoA carboxylase protein, decreased AMPK and PTEN protein levels, and altered promoter methylation of miR-21 and GRB10 [106]. As far as we know, different strategies are available to target metabolic enzymes for cancer therapy including nucleic acid synthesis, amino
Conflict of interest
No conflict of interest exists.
Funding source
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