Cross-study validation and combined analysis of microarray data is critical in genomic analysis. With so much genetic expression data being produced by microarrays in the study of cancer, knowing the extent to which these studies agree can provide valuable insight. Researchers from Johns Hopkins University, Baltimore used Pearson correlation coefficient to develop a system for performing cross-study comparisons of gene expression profiles, found in three separate lung cancer studies, for validation and integration. This paper presents a vector cosine angle method to validate and analyze cross-study of microarray data for cancer and compare the robust of cross-study vector cosine angle validation with that of cross-study Pearson correlation validation. Results show it is effective and robust.