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Network-based Identification of Novel Cancer Genes
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
2010 (English)In: Molecular & Cellular Proteomics, ISSN 1535-9476, E-ISSN 1535-9484, Vol. 9, no 4, 648-655 p.Article in journal (Refereed) Published
Abstract [en]

Genes involved in cancer susceptibility and progression can serve as templates for searching protein networks for novel cancer genes. To this end, we introduce a general network searching method, MaxLink, and apply it to find and rank cancer gene candidates by their connectivity to known cancer genes. Using a comprehensive protein interaction network, we searched for genes connected to known cancer genes. First, we compiled a new set of 812 genes involved in cancer, more than twice the number in the Cancer Gene Census. Their network neighbors were then extracted. This candidate list was refined by selecting genes with unexpectedly high levels of connectivity to cancer genes and without previous association to cancer. This produced a list of 1891 new cancer candidates with up to 55 connections to known cancer genes. We validated our method by cross-validation, Gene Ontology term bias, and differential expression in cancer versus normal tissue. An example novel cancer gene candidate is presented with detailed analysis of the local network and neighbor annotation. Our study provides a ranked list of high priority targets for further studies in cancer research. Supplemental material is included.

Place, publisher, year, edition, pages
2010. Vol. 9, no 4, 648-655 p.
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry with Emphasis on Theoretical Chemistry
Identifiers
URN: urn:nbn:se:su:diva-49937DOI: 10.1074/mcp.M900227-MCP200ISI: 000276379400005OAI: oai:DiVA.org:su-49937DiVA: diva2:379935
Note

authorCount :3

Available from: 2010-12-20 Created: 2010-12-20 Last updated: 2017-12-11Bibliographically approved
In thesis
1. Data integration for robust network-based disease gene prediction
Open this publication in new window or tab >>Data integration for robust network-based disease gene prediction
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

For many complex diseases the cause/mechanism can be tied not to a single gene and in order to cope with the complexity a systems wide approach is needed. By combining evidence indicative of functional association it is possible to infer networks of protein functional coupling. The reliability of these networks is dependent on having sufficient data and on the data being informative.

By combining evidence from multiple species, functional coupling networks can reach higher coverage and accuracy. Genes in different species derived from the same gene by a speciation event are orthologous and likely to have a conserved function. In order to enable the transfer of information across species we inferred orthology with the InParanoid algorithm and made the inferences available to the public in the associated database.

Identification of genes involved in diseases is an important biomedical goal. Based on the "guilt by association" principle, we implemented an approach, Maxlink, for identifying and prioritizing novel disease genes. By searching the FunCoup network for genes functionally coupled to cancer genes we identified some 1800 novel cancer gene candidates showing characteristics of cancer genes.

While proteins are the active components, mRNA is often used as a proxy due to the difficulty of measuring protein abundance. We examined the relationship between mRNA and protein, using properties of expression profiles to identify subsets of genes with higher mRNA-protein concordance.

If technical and biological differences between patient/control studies of gene expression have a large impact, the results of studies of the same disease might be inconsistent. To determine this impact we examined the consistency in differential (co)expression between different studies of cancer, as well as non-cancer studies. Such consistency could generally be found, even between studies of different diseases, but only when common pitfalls of gene expression analysis are avoided.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2013. 71 p.
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry with Emphasis on Theoretical Chemistry
Identifiers
urn:nbn:se:su:diva-87962 (URN)978-91-7447-629-3 (ISBN)
Public defence
2013-04-12, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 5: Manuscript.

Available from: 2013-03-21 Created: 2013-02-27 Last updated: 2013-03-18Bibliographically approved

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Östlund, GabrielSonnhammer, Erik L. L.
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