Thereby prevent tumor cells to proliferate and metastasize. In brief, we report SMAR1 as a transcriptional target of p53. Increased SMAR1 expression in turn results in p53 stabilization. Earlier PTEN-Akt, p14/19 ARF and Rb are reported to positively regulated p53, here we report another protein SMAR1 that can also regulate p53 via positive feed forward loop. SMAR1 is downregulated in Epimedoside-A advanced breast carcinoma stages due to deregulated p53 function that again correlates with the elevated Cyclin D1 expression. Moreover, SMAR1 overexpression in both poorly and highly metastatic breast carcinoma cell lines leads to reduced migration and invasion irrespective of p53 status suggesting that the effect of SMAR1 in regulating genes involved in tumor migration and invasion is downstream of p53 although the wild type expression of SMAR1 is regulated by p53. Doxorubicin mediated upregulation of SMAR1 is p53 dependent and thus it does not function as efficiently in p53 mutated or null breast Homatropine Bromide cancer cell line. Reduced migration and invasion observed in SMAR1 overexpressing cells were thus correlated to downregulated TGFb signaling and inhibition of downstream kinase phosphorylation namely Smad2 phosphorylation. Further, various TGFb target genes were observed to be downregulated such as cutl1 that promote tumor cell metastases. SMAR1 also inhibited the expression of Fibronectin, Vinculin and JAM2 that are involved in promoting cell-extracellular matrix adhesion, cell spreading and migration, suggesting that SMAR1 might prevent tumor cell metastases through negative regulation of these proteins. Thus, SMAR1 is an important anti-tumorigenic protein that regulates cell growth and metastases in breast cancer and acts as a connecting link between p53 and TGFb pathway preventing tumor cells to proliferate and metastasize. Gene networks provide a simple basis for organizing thousands of cellular components and their associations with each other, as well as for generating testable hypotheses about the components and the system as a whole. A number of research efforts have demonstrated that heterogeneous functional genomics and proteomics data can be integrated into gene networks, thus organizing and relating highly complex data sets, as well as simplifying the prediction of new gene functions and associations on basis of the network connections. In such network integration approaches, relationships between genes are detected by various experimental or computational methods, and then combined in a bottom-up fashion in order to build a network model. As high-throughput biological experiments advance, we expect corresponding gains in network models derived from these data. Such improvements, however, are often tempered by the already extreme and growing complexity of the biological data. There are three major problems in integrating diverse genomics data into 
network models. First, the genomics data are heterogeneous in their sensitivity and specificity for relationships between genes. For example, experimental methods such as mass spectrometry preferentially observe abundant proteins, while comparative genomics methods apply only to evolutionarily conserved genes. Increasing the sensitivity of detection usually carries a cost of increasing false positive identifications. Thus, the systematic bias for each method should be understood and considered during data integration. Second, genomics data sets vary widely in their utility for reconstructing gene networks. Thus, we need robust benchmarking methods that can evaluate each data set and allow comparison of their relative merits. Third, data sets are often correlated, complicating integration.