TY - GEN
T1 - A systems biology approach to identify affected regulatory and signaling circuits in protein interaction networks
AU - Gong, Ting
AU - Xuan, Jianhua
AU - Reggins, Rebecca B.
AU - Clarke, Robert
PY - 2009
Y1 - 2009
N2 - Microarray technology and high-throughput proteomics have revolutionized cancer biology by generating vast amount of data for various cancers. To gain insights into the biological processes that drive breast cancer recurrence, we integrate gene expression profiles of breast cancer and protein interaction networks in search for affected regulatory and signaling networks statistically associated with endocrine resistance. To perform this integration systematically, we introduce a systems biology approach to screen related protein-protein interaction networks for active cliques, i.e., connected regions of the network that show significant changes in expression between recurrent and non-recurrent tumors. The experimental results from two breast cancer data sets show that the identified sub-networks can be effectively used to stratify patients treated with endocrine into groups with different outcomes. More importantly, the sub-networks can further help us gain mechanistic insights into estrogen action and endocrine resistance.
AB - Microarray technology and high-throughput proteomics have revolutionized cancer biology by generating vast amount of data for various cancers. To gain insights into the biological processes that drive breast cancer recurrence, we integrate gene expression profiles of breast cancer and protein interaction networks in search for affected regulatory and signaling networks statistically associated with endocrine resistance. To perform this integration systematically, we introduce a systems biology approach to screen related protein-protein interaction networks for active cliques, i.e., connected regions of the network that show significant changes in expression between recurrent and non-recurrent tumors. The experimental results from two breast cancer data sets show that the identified sub-networks can be effectively used to stratify patients treated with endocrine into groups with different outcomes. More importantly, the sub-networks can further help us gain mechanistic insights into estrogen action and endocrine resistance.
KW - Breast cancer
KW - Endocrine resistance
KW - Gene expression profiling
KW - Protein-protein interactions
KW - Systems biology
UR - http://www.scopus.com/inward/record.url?scp=70450194117&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70450194117&partnerID=8YFLogxK
U2 - 10.1109/IJCBS.2009.34
DO - 10.1109/IJCBS.2009.34
M3 - Conference contribution
AN - SCOPUS:70450194117
SN - 9780769537399
T3 - Proceedings - 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, IJCBS 2009
SP - 297
EP - 300
BT - Proceedings - 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, IJCBS 2009
T2 - 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, IJCBS 2009
Y2 - 3 August 2009 through 5 August 2009
ER -