Metabolic Network-Based Identification and Prioritization o f Anticancer Targets Based on Expression Data in Hepatocellular Carcinoma
Journal article, 2018

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.

cancer

network analysis

hepatocellular carcinoma

gene expression

systems biology and network biology

biological networks

genome-scale metabolic model

protein expression

Author

Gholamreza Bidkhori

Royal Institute of Technology (KTH)

Rui Benfeitas

Royal Institute of Technology (KTH)

Ezgi Elmas

Royal Institute of Technology (KTH)

Meisam Naeimi Kararoudi

Royal Institute of Technology (KTH)

Muhammad Arif

Royal Institute of Technology (KTH)

Mathias Uhlen

Royal Institute of Technology (KTH)

Jens B Nielsen

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Adil Mardinoglu

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Frontiers in Physiology

1664-042X (ISSN)

Vol. 9 916

Subject Categories

Bioinformatics (Computational Biology)

Bioinformatics and Systems Biology

Cancer and Oncology

DOI

10.3389/fphys.2018.00916

More information

Latest update

9/13/2018