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  • Metabolic Network-Based Identification and Prioritization of Anticancer Targets Based on Expression Data in Hepatocellular Carcinoma.

Metabolic Network-Based Identification and Prioritization of Anticancer Targets Based on Expression Data in Hepatocellular Carcinoma.

Frontiers in physiology (2018-08-02)
Gholamreza Bidkhori, Rui Benfeitas, Ezgi Elmas, Meisam Naeimi Kararoudi, Muhammad Arif, Mathias Uhlen, Jens Nielsen, Adil Mardinoglu
ABSTRACT

Hepatocellular carcinoma (HCC) is a deadly form of liver cancer with high mortality worldwide. Unfortunately, the large heterogeneity of this disease makes it difficult to develop effective treatment strategies. Cellular network analyses have been employed to study heterogeneity in cancer, and to identify potential therapeutic targets. However, the existing approaches do not consider metabolic growth requirements, i.e., biological network functionality, to rank candidate targets while preventing toxicity to non-cancerous tissues. Here, we developed an algorithm to overcome these issues based on integration of gene expression data, genome-scale metabolic models, network controllability, and dispensability, as well as toxicity analysis. This method thus predicts and ranks potential anticancer non-toxic controlling metabolite and gene targets. Our algorithm encompasses both objective-driven and-independent tasks, and uses network topology to finally rank the predicted therapeutic targets. We employed this algorithm to the analysis of transcriptomic data for 50 HCC patients with both cancerous and non-cancerous samples. We identified several potential targets that would prevent cell growth, including 74 anticancer metabolites, and 3 gene targets (PRKACA, PGS1, and CRLS1). The predicted anticancer metabolites showed good agreement with existing FDA-approved cancer drugs, and the 3 genes were experimentally validated by performing experiments in HepG2 and Hep3B liver cancer cell lines. Our observations indicate that our novel approach successfully identifies therapeutic targets for effective treatment of cancer. This approach may also be applied to any cancer type that has tumor and non-tumor gene or protein expression data.

MATERIALS
Product Number
Brand
Product Description

Sigma-Aldrich
Minimum Essential Medium Eagle, With Earle′s salts, L-glutamine and sodium bicarbonate, liquid, sterile-filtered, suitable for cell culture
Sigma-Aldrich
Fetal Bovine Serum, USA origin, sterile-filtered, suitable for cell culture, suitable for hybridoma
Sigma-Aldrich
StableCell RPMI-1640, With stable glutamine and sodium bicarbonate, liquid, sterile-filtered, suitable for cell culture
Sigma-Aldrich
(Tyr[SO3H]27)Cholecystokinin fragment 26-33 Amide, ≥97% (HPLC), powder