Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Metabolic Network-Based Identification and Prioritization o f Anticancer Targets Based on Expression Data in Hepatocellular Carcinoma
KTH, Centres, Science for Life Laboratory, SciLifeLab.
KTH, Centres, Science for Life Laboratory, SciLifeLab.
KTH, Centres, Science for Life Laboratory, SciLifeLab.
KTH Royal Inst Technol, Sci Life Lab, Stockholm, Sweden..
Show others and affiliations
2018 (English)In: Frontiers in Physiology, ISSN 1664-042X, E-ISSN 1664-042X, Vol. 9, article id 916Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2018. Vol. 9, article id 916
Keywords [en]
hepatocellular carcinoma, genome-scale metabolic model, network analysis, biological networks, cancer, gene expression, protein expression, systems biology and network biology
National Category
Physiology
Identifiers
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
Funder
Knut and Alice Wallenberg FoundationScience for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20180807

Available from: 2018-08-06 Created: 2018-08-06 Last updated: 2018-11-23Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Bidkhori, GholamrezaBenfeitas, RuiUhlén, MathiasMardinoglu, Adil

Search in DiVA

By author/editor
Bidkhori, GholamrezaBenfeitas, RuiElmas, EzgiKararoudi, Meisam NaeimiArif, MuhammadUhlén, MathiasMardinoglu, Adil
By organisation
Science for Life Laboratory, SciLifeLab
In the same journal
Frontiers in Physiology
Physiology

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 58 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf