title: Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targets.
abstract: BACKGROUND
  Cancer is caused by genetic abnormalities, such as mutations of oncogenes or tumor suppressor genes, which alter downstream signal transduction pathways and protein-protein interactions. Comparisons of the interactions of proteins in cancerous and normal cells can shed light on the mechanisms of carcinogenesis.
  
  
  RESULTS
  We constructed initial networks of protein-protein interactions involved in the apoptosis of cancerous and normal cells by use of two human yeast two-hybrid data sets and four online databases. Next, we applied a nonlinear stochastic model, maximum likelihood parameter estimation, and Akaike Information Criteria (AIC) to eliminate false-positive protein-protein interactions in our initial protein interaction networks by use of microarray data. Comparisons of the networks of apoptosis in HeLa (human cervical carcinoma) cells and in normal primary lung fibroblasts provided insight into the mechanism of apoptosis and allowed identification of potential drug targets. The potential targets include BCL2, caspase-3 and TP53. Our comparison of cancerous and normal cells also allowed derivation of several party hubs and date hubs in the human protein-protein interaction networks involved in caspase activation.
  
  
  CONCLUSIONS
  Our method allows identification of cancer-perturbed protein-protein interactions involved in apoptosis and identification of potential molecular targets for development of anti-cancer drugs.
authors.affiliation: Lab of Control and Systems Biology, National Tsing Hua University, Hsinchu, 300, Taiwan. chulianghui@gmail.com
authors.forename: Liang-Hui
authors.initials: LH
authors.lastname: Chu
authors.forename: Bor-Sen
authors.initials: BS
authors.lastname: Chen
mesh_terms: D017209:Apoptosis / Q000235:genetics*; D053148:Caspase 3 / Q000032:analysis; D030541:Databases, Genetic; D016503:Drug Delivery Systems; D015195:Drug Design; D005260:Female; D005347:Fibroblasts; D006367:HeLa Cells; D006801:Humans; D016013:Likelihood Functions; D009369:Neoplasms / Q000235:genetics* / Q000503:physiopathology*; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D025941:Protein Interaction Mapping / Q000379:methods*; D019253:Proto-Oncogene Proteins c-bcl-2 / Q000032:analysis; D015398:Signal Transduction / Q000235:genetics; D016159:Tumor Suppressor Protein p53 / Q000032:analysis; D020798:Two-Hybrid System Techniques
chemical_list: D019253:Proto-Oncogene Proteins c-bcl-2; C495901:TP53 protein, human; D016159:Tumor Suppressor Protein p53; D053148:Caspase 3