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  3. Analysis of the interaction of extracellular matrix and phenotype of bladder cancer cells

The authors thank Sarah Boyle for assistance with figure, critical reading of and constructive feedback on this manuscript. Bacac M, Stamenkovic I. Metastatic cancer cell.

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Analysis of the interaction of extracellular matrix and phenotype of bladder cancer cells

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Extracellular Matrix Remodeling during Cancer Progression and Metastasis (Manu Platt)

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One strategy for analyzing the significance of gene clusters identified by correlational clustering is based upon the assumption that co-expressed genes are likely to share common regulatory motifs [ 21 ]. The results depend upon the reference chosen thereby reflecting Bayesian probability. If the entire genome is selected as the reference, the results will emphasize TREs common to bladder cells and would therefore identify tissue-specific TREs.

Alternately, if the list of all genes expressed on a given matrix is selected as the reference, then the results should be enriched in those TREs responsible for the clustering rather than tissue-specific promoters. If the reference is the list of hypervariable genes, then significance of the partitioning of the hypervariable genes by clusters and TREs is identified. The PAINT analysis was performed against each of the indicated references, but only the reference against all hypervariable genes was reported.

Venn diagram of hypervariable genes expressed on different matrixes. The size of circles does not represent relative number of genes in each group. X-ray repair complementing defective repair in Chinese hamster cells 5 double-strand-break rejoining; Ku autoantigen, 80 kDa.

Hierarchical clustering of hypervariable genes across different cell lines on different matrixes. Hierarchical clustering of genes up- red color or downregulated green color on the given 6 cell lines. Colored bars outline individual clusters used for subsequent analysis. Driving signatures and ontologies of gene clusters on different matrixes. Driving signature. Top Functions. Contained in the 33 genes that were hypervariable on both ECM preparations but not on plastic were two networks of 15 and 11 genes respectively.

The 11 gene network consisted of a c-Myc response network and EGF signaling. These genes are also listed in Table 4 and the pathways are presented [see Additional file 6 ]. Filtered maps of TREs on gene clusters of hypervariable genes identified in cells growing on different matrixes. Colored bars represent individual gene clusters. In this study we sought to identify genes that modulate the malignant phenotype of bladder cancer. The experimental design made genes modulating the phenotype hypervariable as six different cell lines with different inherent malignancies were grown on plastic and two different extracellular matrices.

Because varying the substrate on which the cells are grown changes the malignant phenotype, genes that modulate the phenotype were therefore brought to the fore. Our approach for analysis of this noisy dataset first consisted of identifying the subset of hypervariable genes because this subset will contain the set of genes encapsulating the relevant biological effect [ 14 ]. This step is designed to minimize false negatives for further in silico analysis.

The subsequent steps sought to generate testable hypotheses with minimal false positives. By finding gene ontologies, pathways and TRE motifs that were significantly enriched within individual clusters, genes that potentially functioned together in pathways were identified. Because microarray studies are weighted toward high expression genes, these approaches, particularly pathways and TRE analysis can identify effects due to low expression genes such as transcription factors whose expression may be too low to be detected with the microarray.

Because these determinations are made with reference to statistical probability, the only step in the analysis process that does not contain a quantitative estimate of statistical significance is the clustering. Except for clusters M4 and P7 the driving force for clustering appeared to represent the behavior of individual cell lines rather than overall malignancy. However, these two clusters identify genes generally expressed at higher levels in the malignant cells than in the HUC cells.

Of particular interest is the apparent activation of pro-survival AKT1 signaling in the malignant cells on Matrigel [see Additional file 6 ]. Other ontologies in these two clusters clearly showed connections to cancer and apoptosis. While the mechanisms identified as operating only within a single cell line are probably less interesting than those operating in several, identification of a range of behaviors in bladder cancers could become useful if pathway-specific drugs are developed.

Some 20 genes were identified as being hypervariable, regardless the substrate. These represent a group of genes that are not modulated by the ECM but are differentially expressed among the different cell lines. Examples include Prohibitin PHB , a survival gene, which is entirely matrix-independent. The connection with AKT1 survival pathway is clear. AKT1 can cause resistance to therapy in other cancers [ 25 ]. Interestingly, genes coding for two polysaccharide-binding proteins were also present. That these two would be differentially expressed by different cell types growing on polysaccharide-containing gels and the minimal endogenous matrix secreted by cells growing on plastic is consistent with the role of these molecules in differentiation and growth [ 26 ].

Additionally, 33 genes were identified as being hypervariable on both Matrigel and SISgel, in spite of the suppression of many features of the malignant phenotype on the latter. These genes probably represent a "core" set of cancer genes. Ontologies for this cluster of genes were cancer, cell morphology and cell cycle networks. Eleven of these 33 genes are interconnected within the MYC network.

Deregulation of MYC genes is associated with several malignancies [ 29 ]. Although the ECM can have major effects on the biology of individual cancers, a core of oncogenes involved in the p53 and MYC networks is unaffected. Interestingly, a set of signature genes for the suppression of the malignant phenotype by SISgel was not observed.

Either the correct probes are not on the array, or each cell type respond uniquely to a changing matrix. Previous results showed this response was not due to integrin signaling [ 9 ] as has been reported by Weaver, et. The TRE analysis confirmed the uniqueness of the clustering and suggests that a limited set of TREs may be driving gene expression in this study. Notable is Hand1, which appears in most of the genes. This gene has been identified as playing a role in heart development [ 31 ], but the abundance in these genes that are expressed in bladder suggests it may also act as a bladder-specific factor.

The corresponding transcription factor has been shown to promote cell growth in common human carcinomas [ 32 ] and to be dependent on the p53 pathway [ 32 , 33 ] supporting its role in upregulating the cluster in cancer cells. This combination suggests loss of epithelial differentiation. Bladder cancer cells were earlier demonstrated to contain both functional androgen and estrogen receptors without regard to the sex of the donor [ 34 , 35 ] while myogenin was primary expressed in rhabdomyosarcomas [ 36 , 37 ]. Identification of myogenin expression in bladder cancer cell lines is novel, and further research will be needed to determine if this transcription factor is actually active.

Comparisons to other microarray studies are, in general, less informative because of wide differences among array technologies [ 38 ] and criteria to identify "significant" genes. Nonetheless the composition of Cluster M4, which is comprised of genes that are under-expressed in HUC cells in comparison to all the cancer cells, shows 3 of the 28 genes in the cluster corresponded to 3 of the 29 genes identified as being diagnostic for TCC in patient samples [ 39 ]. This association suggests this cluster might provide additional diagnostic genes which is strongly supported by the finding of the presponsive gene network.

In addition, although the comparison was made difficult by a lack of accession numbers or universal gene symbols, considerable correspondence between the 32 genes identified as hypervariable in 3-dimensional growth was noted with the set of genes reported to differentially expressed between superficial and invasive TCC [ 40 ].

Activation of the AKT1 survival pathway on Matrigel suggests this pathway could be relevant to clinical bladder cancer. The p53 response network is confirmed to play a central role, as does c-MYC signaling. Free Preview. Will discuss how integrins and extracellular matrix components control cancer initiation, progression and metastasis. Buy eBook. Buy Hardcover. Buy Softcover. FAQ Policy. About this book Cells require interactions with extracellular matrix ECM components in order to undergo normal morphogenesis with respect to organogenesis.