Several recent studies have sought to refine the classification of lung cancer through gene expression profiling, using complementary DNA and oligonucleotide microarray platforms. To initiate the process of cross-validating and integrating the results of these studies, we developed statistical approaches that allow overall assessments to be made of profile similarities, as well as comparisons of individual genes for association with outcomes. Focusing our analysis on three lung cancer-profiling projects, we first compared the data from these studies for consistency of the coexpression relationships among pairs of genes. We computed all possible pairwise correlations within the study and computed the correlation of the resulting values across studies. Using these relationships as reflections of general consistency across projects, we noted that pairwise correlation coefficients ranged from 0.33 to 0.54. While this represents a considerable level of variability across studies, the distribution of gene-pair correlations clearly indicates that subsets of “consistent” genes have reasonably similar coordinate expression patterns across studies.