cancer network

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.

Game theoretic approach (GTA)

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 this approach, the identified subnetwork markers were compared with gene-based, pathway-based and network-based markers.

Authors 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, they identified a new bonafide candidate gene for breast cancer susceptibility.

Open references

 A pan-cancer modular regulatory network analysis to identify common and cancer-specific network components. Knaack SA, Siahpirani AF, Roy S.
Cancer Inform. 2014 Oct 28;13(Suppl 5):69-84. doi : 10.4137/CIN.S14058. eCollection 2014 PMID: 25374456 Free

Paywall references

 GTA: a game theoretic approach to identifying cancer subnetwork markers. Farahmand S, Goliaei S, Ansari-Pour N, Razaghi-Moghadam Z. Mol Biosyst. 2016 Mar;12(3):818-25. doi : 10.1039/c5mb00684h PMID: 26750920