TY - GEN
T1 - Network signatures based on gene pair expression ratios improve classification and the analysis of muscle-invasive urothelial cancer
AU - De Matos Simoes, Ricardo
AU - Mitsiades, Constantine
AU - Williamson, Kate E.
AU - Emmert-Streib, Frank
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/16
Y1 - 2015/12/16
N2 - 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 proteinprotein 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 HNRNPA2Bl). 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.
AB - 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 proteinprotein 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 HNRNPA2Bl). 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.
KW - data feature space inflation
KW - feature selection
KW - gene pair expression ratio
KW - muscle-invasive
KW - non muscleinvasive
KW - Urothelial cancer
UR - https://www.scopus.com/pages/publications/84962439353
U2 - 10.1109/BIBM.2015.7359855
DO - 10.1109/BIBM.2015.7359855
M3 - Conference contribution
AN - SCOPUS:84962439353
T3 - Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
SP - 1216
EP - 1223
BT - Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
A2 - Schapranow, lng. Matthieu
A2 - Zhou, Jiayu
A2 - Hu, Xiaohua Tony
A2 - Ma, Bin
A2 - Rajasekaran, Sanguthevar
A2 - Miyano, Satoru
A2 - Yoo, Illhoi
A2 - Pierce, Brian
A2 - Shehu, Amarda
A2 - Gombar, Vijay K.
A2 - Chen, Brian
A2 - Pai, Vinay
A2 - Huan, Jun
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Y2 - 9 November 2015 through 12 November 2015
ER -