%0 Journal Article
%T Detecting Cancer Outlier Genes with Potential Rearrangement Using Gene Expression Data and Biological Networks
%A Mohammed Alshalalfa
%A Tarek A. Bismar
%A Reda Alhajj
%J Advances in Bioinformatics
%D 2012
%I Hindawi Publishing Corporation
%R 10.1155/2012/373506
%X Gene alterations are a major component of the landscape of tumor genomes. To assess the significance of these alterations in the development of prostate cancer, it is necessary to identify these alterations and analyze them from systems biology perspective. Here, we present a new method (EigFusion) for predicting outlier genes with potential gene rearrangement. EigFusion demonstrated excellent performance in identifying outlier genes with potential rearrangement by testing it to synthetic and real data to evaluate performance. EigFusion was able to identify previously unrecognized genes such as FABP5 and KCNH8 and confirmed their association with primary and metastatic prostate samples while confirmed the metastatic specificity for other genes such as PAH, TOP2A, and SPINK1. We performed protein network based approaches to analyze the network context of potential rearranged genes. Functional gene rearrangement Modules are constructed by integrating functional protein networks. Rearranged genes showed to be highly connected to well-known altered genes in cancer such as AR, RB1, MYC, and BRCA1. Finally, using clinical outcome data of prostate cancer patients, potential rearranged genes demonstrated significant association with prostate cancer specific death. 1. Introduction Genetic alterations in cancer are the most challenging factors that might lead to aggressive behavior of cells. Among the most prevalent forms of genetic alterations observed in cancer cells are gene fusions, gene amplification, and gene deletions. Recurrent translocations generally fall into two categories: functional rearrangements that result in a change in gene's activity due either to a change in protein quality or quantity and the other category is silent translocations that have no effect on gene's activity. Functional translocations can be categorized into two subtypes; one that leads to fused transcripts resulting in new proteins with different activity like BCR-ABL in leukemia [1] and EML4-ALK in lung cancer [2]; on the other hand, it can lead to change in a transcript quantity by translocating a strong gene promoter to the intact coding region of an oncogene like TMPRSS2-ERG [3]. Another functional genomics rearrangement is genomic deletion which results in loss of DNA segment that might harbour functional genes. PTEN is a well-studied genomic deletion in prostate cancer that is anticipated to trigger a cascade of genomic rearrangements [4]. Figure 1 gives a schematic description of the four rearrangement types. Figure 1: Gene rearrangements, common gene rearrangements in
%U http://www.hindawi.com/journals/abi/2012/373506/