GTA: a game theoretic approach to identifying cancer subnetwork markers.

Abstract:

:The identification of genetic markers (e.g. genes, pathways and subnetworks) for cancer has been one of the most challenging research areas in recent years. A subset of these studies attempt to analyze genome-wide expression profiles to identify markers with high reliability and reusability across independent whole-transcriptome microarray datasets. Therefore, the functional relationships of genes are integrated with their expression data. However, for a more accurate representation of the functional relationships among genes, utilization of the protein-protein interaction network (PPIN) seems to be necessary. Herein, a novel game theoretic approach (GTA) is proposed for the identification of cancer subnetwork markers by integrating genome-wide expression profiles and PPIN. The GTA method was applied to three distinct whole-transcriptome breast cancer datasets to identify the subnetwork markers associated with metastasis. To evaluate the performance of our approach, the identified subnetwork markers were compared with gene-based, pathway-based and network-based markers. We show that GTA is not only capable of identifying robust metastatic markers, it also provides a higher classification performance. In addition, based on these GTA-based subnetworks, we identified a new bonafide candidate gene for breast cancer susceptibility.

journal_name

Mol Biosyst

journal_title

Molecular bioSystems

authors

Farahmand S,Goliaei S,Ansari-Pour N,Razaghi-Moghadam Z

doi

10.1039/c5mb00684h

subject

Has Abstract

pub_date

2016-03-01 00:00:00

pages

818-25

issue

3

eissn

1742-206X

issn

1742-2051

journal_volume

12

pub_type

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