Comparing Cancer and Normal Gene Regulatory Networks Based on Microarray Data and Transcription Factor Analysis

Microarray is widely used for the cancer research and identifies different expressions for specific genes. We present a computational method for constructing cancer and normal gene regulatory networks from micorarray data based on transcription factor analysis and independency test. The web service technology is used to wrap the bioinformatics toolkits of methods and databases to automatically extract the promoter regions of DNA sequences and predict the transcription factors that regulate gene expressions. After reconstructing the gene regulatory network, the network statistical measure and network motifs extract the potential genes to compare the sub-networks between the cancer and normal gene networks. We adopt the microarray datasets from Stanford microarray database of prostate cancer as a target application to evaluate the methods.