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Metabolic Network-Based Identification and Prioritization o f Anticancer Targets Based on Expression Data in Hepatocellular Carcinoma
KTH, Centra, Science for Life Laboratory, SciLifeLab.
KTH, Centra, Science for Life Laboratory, SciLifeLab.
KTH, Centra, Science for Life Laboratory, SciLifeLab.
KTH, Centra, Science for Life Laboratory, SciLifeLab.
Vise andre og tillknytning
2018 (engelsk)Inngår i: Frontiers in Physiology, ISSN 1664-042X, E-ISSN 1664-042X, Vol. 9, artikkel-id 916Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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.

sted, utgiver, år, opplag, sider
Frontiers Media S.A., 2018. Vol. 9, artikkel-id 916
Emneord [en]
hepatocellular carcinoma, genome-scale metabolic model, network analysis, biological networks, cancer, gene expression, protein expression, systems biology and network biology
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-232768DOI: 10.3389/fphys.2018.00916ISI: 000438974200001Scopus ID: 2-s2.0-85050120958OAI: oai:DiVA.org:kth-232768DiVA, id: diva2:1236788
Forskningsfinansiär
Knut and Alice Wallenberg FoundationScience for Life Laboratory - a national resource center for high-throughput molecular bioscience
Merknad

QC 20180807

Tilgjengelig fra: 2018-08-06 Laget: 2018-08-06 Sist oppdatert: 2019-08-20bibliografisk kontrollert

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Bidkhori, GholamrezaBenfeitas, RuiUhlén, MathiasMardinoglu, Adil

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Bidkhori, GholamrezaBenfeitas, RuiElmas, EzgiKararoudi, Meisam NaeimiArif, MuhammadUhlén, MathiasMardinoglu, Adil
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