Metabolic Network-Based Identification and Prioritization o f Anticancer Targets Based on Expression Data in Hepatocellular Carcinoma
Artikel i vetenskaplig tidskrift, 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

Författare

Gholamreza Bidkhori

Kungliga Tekniska Högskolan (KTH)

Rui Benfeitas

Kungliga Tekniska Högskolan (KTH)

Ezgi Elmas

Kungliga Tekniska Högskolan (KTH)

Meisam Naeimi Kararoudi

Kungliga Tekniska Högskolan (KTH)

Muhammad Arif

Kungliga Tekniska Högskolan (KTH)

Mathias Uhlen

Kungliga Tekniska Högskolan (KTH)

Jens B Nielsen

Chalmers, Biologi och bioteknik, Systembiologi

Adil Mardinoglu

Chalmers, Biologi och bioteknik, Systembiologi

Frontiers in Physiology

1664-042X (ISSN)

Vol. 9 916

Ämneskategorier

Bioinformatik (beräkningsbiologi)

Bioinformatik och systembiologi

Cancer och onkologi

DOI

10.3389/fphys.2018.00916

Mer information

Senast uppdaterat

2018-09-13