Using a process to which we refer as """"""""reverse engineering"""""""", we propose to reconstruct signaling pathways in benign, aggressive, and metastatic prostate cancer cells from integrated microarray data. Current reverse-engineering signaling pathways have been an ill-posed problem because the signaling pathway topology is difficult, if not impossible, to directly observe in a single experiment, and high experimental costs prohibit collecting sufficient samples in a single study. As a result, current research often relies upon overly stringent, sometimes unrealistic, biological, statistical or computational assumptions. In this proposal, we aim to develop a new informatics paradigm to reconstruct cancer signaling pathways, an approach that will overcome these limitations. The specific hypotheses underlying the proposed research are: 1. Signaling pathways are composed of entangled information flows of overlapping gene sets. 2. The """"""""Information Flow Gene Set"""""""" (IFGS) is the basic functional unit of the signaling pathway, and many IFGS's execute distinct biological endpoint functions. 3. The signaling pathways in benign tissue, in primary cancer tissue and in metastatic cancer tissue are composed of differentially regulated information flows and may be dissected by analysis of the IFGS's. To test these hypotheses we will carry out the following specific aims to reconstruct and compare signaling pathways in different prostate cancer types: SA1. Using integrated gene expression data, we will identify unordered, overlapping and biologically relevant IFGS's for each prostate tissue type. SA2. We will reconstruct realistic signaling pathways from overlapping IFGS's and determine differentially activated/inhibited information flows corresponding to prostate cancer progression.

Public Health Relevance

Among all cancer types, prostate cancer is the most common non-skin cancer. Its etiology remains largely unknown in that more than half of American men have some cancerous cells in their prostate, but most show no signs of disease. Deciphering signaling pathways in benign, primary, and metastatic prostate cancer tissues will help us to understand the mechanisms of carcinogenesis and develop therapeutic approaches based upon the biology of the tumor

National Institute of Health (NIH)
National Library of Medicine (NLM)
Exploratory/Developmental Grants (R21)
Project #
Application #
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Ye, Jane
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Louisiana State University-University of New Orleans
Biostatistics & Other Math Sci
Schools of Arts and Sciences
New Orleans
United States
Zip Code
Judeh, Thair; Johnson, Cole; Kumar, Anuj et al. (2013) TEAK: topology enrichment analysis framework for detecting activated biological subpathways. Nucleic Acids Res 41:1425-37
Acharya, Lipi R; Judeh, Thair; Wang, Guangdi et al. (2012) Optimal structural inference of signaling pathways from unordered and overlapping gene sets. Bioinformatics 28:546-56
Acharya, Lipi; Judeh, Thair; Duan, Zhansheng et al. (2012) GSGS: a computational approach to reconstruct signaling pathway structures from gene sets. IEEE/ACM Trans Comput Biol Bioinform 9:438-50
Deng, Nan; Puetter, Adriane; Zhang, Kun et al. (2011) Isoform-level microRNA-155 target prediction using RNA-seq. Nucleic Acids Res 39:e61
Xu, Guorong; Fewell, Claire; Taylor, Christopher et al. (2010) Transcriptome and targetome analysis in MIR155 expressing cells using RNA-seq. RNA 16:1610-22
Lin, Zhen; Xu, Guorong; Deng, Nan et al. (2010) Quantitative and qualitative RNA-Seq-based evaluation of Epstein-Barr virus transcription in type I latency Burkitt's lymphoma cells. J Virol 84:13053-8
Zhang, Wensheng; Edwards, Andrea; Fan, Wei et al. (2010) svdPPCS: an effective singular value decomposition-based method for conserved and divergent co-expression gene module identification. BMC Bioinformatics 11:338
Zhang, Kun; Fan, Wei; Deininger, Prescott et al. (2009) Breaking the computational barrier: a divide-conquer and aggregate based approach for Alu insertion site characterisation. Int J Comput Biol Drug Des 2:302-22