NDLI: Network signatures based on gene pair expression ratios improve classification and the analysis of muscle-invasive urothelial cancer

Content Provider	IEEE Xplore Digital Library
Author	de Matos Simoes, R. Mitsiades, C. Williamson, K.E. Emmert-Streib, F.
Copyright Year	2015
Description	Author affiliation: Tampere Univ. of Technol., Tampere, Finland (Emmert-Streib, F.) \|\| Harvard Med. Sch., Dana-Farber Cancer Inst., Boston, MA, USA (Williamson, K.E.) \|\| Center for Cancer Res. & Cell Biol., Queen's Univ. Belfast, Belfast, UK (de Matos Simoes, R.; Mitsiades, C.)
Abstract	Urothelial cancer (UC) is highly recurrent and can progress from non-invasive (NMIUC) to a more aggressive muscle-invasive (MIUC) subtype that invades the muscle tissue layer of the bladder. We present a proof of principle study that network-based features of gene pairs can be used to improve classifier performance and the functional analysis of urothelial cancer gene expression data. In the first step of our procedure each individual sample of a UC gene expression dataset is inflated by gene pair expression ratios that are defined based on a given network structure. In the second step an elastic net feature selection procedure for network-based signatures is applied to discriminate between NMIUC and MIUC samples. We performed a repeated random subsampling cross validation in three independent datasets. The network signatures were characterized by a functional enrichment analysis and studied for the enrichment of known cancer genes. We observed that the network-based gene signatures from meta collections of protein-protein interaction (PPI) databases such as CPDB and the PPI databases HPRD and BioGrid improved the classification performance compared to single gene based signatures. The network based signatures that were derived from PPI databases showed a prominent enrichment of cancer genes (e.g., TP53, TRIM27 and HNRNPA2B1). We provide a novel integrative approach for large-scale gene expression analysis for the identification and development of novel diagnostical targets in bladder cancer. Further, our method allowed to link cancer gene associations to network-based expression signatures that are not observed in gene-based expression signatures.
Sponsorship	IEEE
Starting Page	1216
Ending Page	1223
File Size	974492
Page Count	8
File Format	PDF
e-ISBN	9781467367998
DOI	10.1109/BIBM.2015.7359855
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Urothelial cancer gene pair expression ratio muscle-invasive non muscle-invasive data feature space inflation feature selection Cancer
Content Type	Text
Resource Type	Article

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