advanced computational biology methods identify molecular switches for malignancy in an egf mouse model of liver cancer先进的计算生物学方法确定恶性肿瘤分子开关的egf肝癌的小鼠模型.pdfVIP

Advanced Computational Biology Methods Identify Molecular Switches for Malignancy in an EGF Mouse Model of Liver Cancer 1 1 2,3 1,4,5 1,4,6 Philip Stegmaier , Nico Voss , Tatiana Meier , Alexander Kel , Edgar Wingender , Juergen Borlak2,3* 1 BIOBASE GmbH, Wolfenbuettel, Germany, 2 Department Molecular Medicine and Medical Biotechnology, Fraunhofer Institute of Toxicology and Experimental Medicine, Hannover, Germany, 3 Centre for Pharmacology and Toxicology, Hannover Medical School, Hannover, Germany, 4 GeneXplain GmbH, Wolfenbuettel, Germany, 5 Institute of Chemical Biology and Fundamental Medicine, Novosibirsk, Russia, 6 Department of Bioinformatics, University of Goettingen, Goettingen, Germany Abstract The molecular causes by which the epidermal growth factor receptor tyrosine kinase induces malignant transformation are largely unknown. To better understand EGFs’ transforming capacity whole genome scans were applied to a transgenic mouse model of liver cancer and subjected to advanced methods of computational analysis to construct de novo gene regulatory networks based on a combination of sequence analysis and entrained graph-topological algorithms. Here we identified transcription factors, processes, key nodes and molecules to connect as yet unknown interacting partners at the level of protein-DNA interaction. Many of those could be confirmed by electromobility band shift assay at recognition sites of gene specific promoters and by western blotting of nuclear proteins. A novel cellular regulatory circuitry could therefore be proposed that connects cell cycle regulated genes with components of the EGF signaling pathway. Promoter analysis of differentially expressed genes suggested the majority of regula