We have observed a similar phenomenon in colorectal carcinoma cell lines expressing heterozygous APC mutations, where exposure to increasing amounts of wnt3a leads to a dose-dependent increase of LGR5 RNA in the range of 20�C70 ng/ml, but no significant increase in LGR5 is detected when wnt3a is used at 200 ng/ml. We propose that these findings have implications for the role of LGR5 in colorectal cancer progression: induction of wnt SCH772984 activity by APC mutation or b-catenin mutation would maintain LGR5 expression outside the stem cell niche, however overstimulation of wnt signalling at the invasive front of a tumour would be expected to reduce LGR5 expression, thus facilitating wnt-stimulated EMT and favouring invasiveness. Thus the transition from adenoma to carcinoma may involve selective loss of LGR5 in areas of wnt hyperactivation, contributing to EMT and invasiveness. This hypothesis needs to be directly investigated by co-staining a large set of primary colorectal cancer specimens, including the invasive front, for LGR5, b-catenin, wnt pathway target proteins and markers of cell adhesion or EMT. The highly conserved basic helix-loop-helix transcription factor Twist1 was first identified in Drosophila as a critical regulator of mesoderm formation and specification. In mouse and avian embryos, Twist1 functions in mesenchymal precursors of the developing pharyngeal arches, limb, cranial sutures, and heart valve endocardial cushions . Within these cell populations Twist1 promotes cell proliferation, migration, and expression of primitive extracellular matrix, thus promoting an BAY 43-9006 undifferentiated state. In humans, highly metastatic and chemotherapeutic resistant cancers including breast, glioma, prostate, melanoma, and neuroblastoma express high levels of TWIST1. TWIST1 expression is also upregulated in human diseased aortic valves that have increased expression of mesenchymal markers of valve progenitor cells. The correlation of Twist1 expression with increased cell proliferation and migration of cancer cells, and also in diseased heart valves, is likely to be related to its functions in embryonic mesenchymal populations, including ECC mesenchymal cells. However, the underlying mechanisms by which Twist1 promotes proliferation and migration of mesenchymal cells during heart valve development and disease are largely unknown. Mesenchymal valve progenitor cells of the ECCs are highly proliferative, migratory, and express ECM genes that encode the relatively unstructured and open matrix of the ECCs. As heart valve development progresses the valve progenitor cells begin to differentiate, which is marked by decreased proliferation, decreased migration, and expression of genes that encode the complex stratified ECM of the mature valves. Within the mesenchymal cell population several factors, including Twist1, that promote cell proliferation and migration have been identified through both in vivo and in vitro studies.

In order to predict more complex RNA-to-RNA relationships in the siRNA-treated A375 cells, including upstream regulators of the co-expressed clusters described above, and the putative direction of RNA-to-RNA relationships. Reassuringly, 226 of the 327 combined children of these three E2F transcription factors have E2F binding sites in their promoters, a significantly greater proportion than would be expected due to chance. As well as identifying hubs, Bayesian gene networks also identify clusters of co-expressed RNAs, which are downstream of the same hub. Identifying these clusters may be seen as a more conservative use of this network method than identifying directional edges, and is the primary use made of Bayesian gene networks in this paper. Reassuringly, every one of the 200 clusters identified by the hierarchical clustering method above had at least 70% of their members included among clusters identified by the gene network method. We wished to determine whether the Bayesian gene network hubs and clusters identified from the A375 microarray data were associated with prognosis. Therefore, we used the ��Survival�� package in R to generate Cox proportional Hazards models to estimate the association between the abundance of RNAs in tumours and the survival of melanoma patients. Two survival models were generated: based on gene expression in metastatic melanomas using an Affymetrix microarray dataset and based on gene expression in primary melanomas using an Agilent microarray dataset, which was mapped to Affymetrix probe IDs using Entrez gene ID annotations. We then used this melanoma microarray survival information to assess whether gene network hubs and clusters were significantly associated with patient survival. Firstly, to establish a baseline, we considered whether the abundance of RNAs that encoded AZ 960 proteins with particular classes of functional annotation were significantly associated with patient survival. We hypothesised that RNAs Screening Libraries clinical trial encoding the types of proteins that perform important oncogenic functions may be more strongly associated with the survival of patients than the abundance of RNAs that encode proteins that do not play known roles in cancer. For both primary tumours and metastatic tumours, no one functional category was clearly more or less associated with patient survival than all RNAs taken together. This analysis was repeated for all Bayesian gene network hubs with $50 downstream children but again it did not identify any particular functional category with strong patient survival associations. We then repeated this analysis focussing on hubs with children that encoded proteins of common function. We used the GATHER web tool to identify hubs with children significantly enriched for GO paths.