Template-driven gene selection procedure

Abstract

The hierarchical clustering and statistical techniques usually used to analyze microarray data do not inherently represent the underlying biology. Herein we present a hybrid approach involving characteristics of both supervised and unsupervised learning. This approach is based on template matching in which the interaction of the variables of inherent malignancy and the ability to express the malignant phenotype are modelled. Immortalized normal urothelial cells and bladder cancer cells of different malignancy were grown in conventional two-dimensional tissue culture and in three dimensions on extracellular matrices that were either permissive or restrictive for expression of the malignant phenotype. The transcriptome represents the effects of two variables--inherent malignancy and the modulatory effect of extracellular matrix. By assigning values to each of the biological variables of inherent malignancy and the ability to express the malignant phenotype, a template was constructed that encapsulated the interaction between them. Gene expression correlating both positively and negatively with the template were observed, but when iterative correlations were carried out, the different models for the template converged to the same actual template. A subset of 21 genes was identified that correlated with two a priori models or an optimized model above the 95% confidence limits identified in a bootstrap resampling with 5,000 permutations of the data set. The correlation coefficients of expression of several genes were > 0.8. Analysis of upstream transcriptional regulatory elements (TREs) confirmed these genes were not a randomly selected set of genes. Several TREs were identified as significantly over-expressed in the sample of 20 genes for which TREs were identified, and the high correlations of several genes were consistent with transcriptional co-regulation. We suggest the template method can be used to identify a unique set of genes for further investigation. KeywordsBladder cancer; phenotype; transcriptomics; extracellular matrix; malignancy * This work was supported in part by NIH grants CA75322 (REH), DK 069808 (REH) and P20 RR1557, P20 RR17703, and P20 RR16478 (MBC) and by a grant from Cook Biotech (REH).

Suppression and Activation of the Malignant Phenotype by Extracellular Matrix in Xenograft Models of Bladder Cancer: A Model for Tumor Cell “Dormancy”

“…Earlier research in our labs suggested that, unlike mammary epithelial cells, α6β4 integrins were not involved [24]. Microarray and proteomic studies implicated complex networks involving TGFβ, cMYC and a series of transcription factors [23], [25], [26]. Because a consistent gene signature could not be identified, it is likely that a protein switch regulates the conversion between the malignant and suppressed phenotypes.…”

Internal standard-based analysis of microarray data2—Analysis of functional associations between HVE-genes

“…Ordinate: the normalized expression level. mRNA for the transcription study was obtained from various samples: ( A ) Samples from healthy controls ( 1–14 ) and TRAPS patients ( 15–28 ). ( B ) Endometrial cells: controls (1–9 and 10–18) and cells transformed to cancer cells by DMBA (19–27 and 28–36).…”

“…The results of two independent experiments are presented. ( C ) Samples from the B cells of healthy donors ( 1–18 ), and B cell chronic lymphocytic leukemia patients: ( 19–34 ) un-mutated, and (35–54) mutated subgroups. ( D ) Whole blood samples from healthy donors ( 1–20 ) and JRA patients (21–40).…”

“…This correlation mosaic method was applied to the analysis of gene expression data and cytokine multiplex data in clinical and experimental samples ( 17–26 ). In the very first example a mouse model of bladder inflammation was used to investigate the role of neurokinin 1 receptors (NK1R) and neprilysin (NEP) in neurogenic inflammation.…”