Template-driven gene selection procedure.
The hierarchical clustering and statistical techniques usually used to analyse microarray data do not inherently represent the underlying biology. Herein, a hybrid approach involving characteristics of both supervised and unsupervised learning is presented. 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. Immortalised 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 (ECMs) 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 ECM. By assigning values to each of the biological variables of inherent malignancy and the ability to express the malignant phenotype, a template was constructed, which encapsulated the interaction between them. Gene expression correlating both positively and negatively with the template was observed, but when iterative correlations were carried out, the different models for the template converged on the same actual template. A subset of 21 genes was identified, which correlated with two a priori models or an optimised model above the 95% confidence limits identified in a bootstrap resampling with 5000 permutations of the data set. The correlation coefficients of expression of several genes were > 0.8. Analysis of upstream transcriptional regulatory elements (TREs) confirmed that 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. The authors suggest that the template method can be used to identify a unique set of genes for further investigation.
Knowlton
N
N
Microarray Core Facility, Oklahoma Medical Research Foundation, Oklahoma City 73104, USA.
Dozmorov
I
I
Kyker
K D
KD
Saban
R
R
Cadwell
C
C
Centola
M B
MB
Hurst
R E
RE
D000465:Algorithms; D000818:Animals; D014408:Biomarkers, Tumor / Q000032:analysis*; D045744:Cell Line, Tumor; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D005810:Multigene Family / Q000235:genetics; D009363:Neoplasm Proteins / Q000032:analysis*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D001749:Urinary Bladder Neoplasms / Q000175:diagnosis* / Q000235:genetics / Q000378:metabolism*
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins