In order to summarize the results obtained in this study we have build a hypothetical model-diagram of the EGF/IGF-2 regulatory circuit functioning during the transition from transgenic to tumor state. This model combines two signal NVP-BEZ235 transduction networks of the signal flow from EGF and IGF-2 reaching a number of transcription factors, that in turn, regulate expression of several important genes that encode components of the upstream network. Thus created feedback loops should play an important role in emerging as well as in stabilizing the cancer state of the cells, In this model, we can propose multiple paths of signals initially coming massively from EGF in the transgenic cells and triggering activity of several TFs, such as C/EBP-alpha, GR and HNF4alpha, that down-regulate expression of their target gene encoding EGF as well as Cav1, thus trying to compensate the excess of the EGF stimulus in the cell. At the same time, through parallel signaling cascades and activation of a number of other TFs, such as c-Ets-1, PPAR-gamma, STAT family factors, cMyb and others, upregulation of expression of Igf2 gene as well as Igfbp6 and Pparg can be achieved. Due, to several feedback loops on different levels of the network coming from these genes, we can speculate that a steady signal for upregulation of the Igf2 gene leads eventually to a sharp elevation of its expression with the consequence of increase of mitogenic activity of the cells, which marks the transition to the carcinogenic state. It was reported previously, that Igf2 gene is located in an imprinted area of genome and is repressed in most of tissues of the adult organism. Loss of imprinting of the Igf2 gene is one of the most common observations in cancers. It was shown that the imprinting status is maintained by binding of CTCF repressor to an intergenic area of the Igf2 gene and loss of this binding can lead to 10-fold elevation of Igf2 expression. We propose a model where the feedback mechanisms involved in the Igf2 epigenetic control through multiple transcription factors, activators and repressors, play the major role in the switching the cells to malignant transformation. In conclusion, promoter analysis of the differentially expressed genes enabled us to identify transcription factor binding sites. Such integration of sequence information into signal transduction networks enabled an identification of key nodes upstream of the identified transcription factors. By searching for pairs of TF sites and integration of this information into the network analysis robust information can be retrieved in an unbiased manner that clearly identifies keynodes and molecules acting in concert in defined biological conditions. Therefore, we propose a sequence of events whereby the insulin-like growth factor pathway represents an important molecular switch in malignantly transformed liver cells.