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Title

Network analysis of gene lists for finding reproducible prognostic breast cancer gene signatures

 

Authors

Ulykbek Kairov1, 2, Tatyana Karpenyuk1, Erlan Ramanculov2 & Andrei Zinovyev3, 4, 5*

 

Affiliation

1Kazakh National University after Al-Farabi, Almaty, Kazakhstan; 2National Center for Biotechnology of the Republic of Kazakhstan, Astana, Kazakhstan; 3Institute Curie, Paris, France; 4INSERM U900, Paris, France; 5Mines ParisTech, Fontainebleau, France.

 

Email

andrei.zinovyev@curie.fr; *Corresponding author

 

Article Type

Hypothesis

 

Date

Received August 19, 2012; Accepted August 20, 2012; Published August 24, 2012

 

Abstract

Many genome-scale studies in molecular biology deliver results in the form of a ranked list of gene names, accordingly to some scoring method. There is always the question how many top-ranked genes to consider for further analysis, for example, in order to create a diagnostic or predictive gene signature for a disease. This question is usually approached from a statistical point of view, without considering any biological properties of top-ranked genes or how they are related to each other functionally. Here we suggest a new method for selecting a number of genes in a ranked gene list such that this set forms the Optimally Functionally Enriched Network (OFTEN), formed by known physical interactions between genes or their products. The method allows associating a network with the gene list, providing easier interpretation of the results and classifying the genes or proteins accordingly to their position in the resulting network. We demonstrate the method on four breast cancer datasets and show that 1) the resulting gene signatures are more reproducible from one dataset to another compared to standard statistical procedures and 2) the overlap of these signatures has significant prognostic potential. The method is implemented in BiNoM Cytoscape plugin (http://binom.curie.fr).

 

Citation

Kairov et al. Bioinformation 8(16): 773-776 (2012)
 

Edited by

P Kangueane

 

ISSN

0973-2063

 

Publisher

Biomedical Informatics

 

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.