The high-throughput gene expression profiling by microarray analysis has enabled researchers to compare the relative expression levels of thousands of genes in diseases, including cancer. By identifying how these genes cluster in different carcinomas, these profiling techniques can improve the accuracy of classifying subtypes of tumors and their prognoses and could help determine which therapy is appropriate for each patient with cancer. These efforts aim to provide more effective personalized medicine. To reach this goal, the analysis of microarray data has also evolved and become more sophisticated and complex. Herein, using kidney cancer as an example, we demonstrate the use of microarray data for different bases of analysis, ie, direct differential expression, deduced chromosomal alteration, and pathways signature. We believe combining these will enhance the value of microarray studies and will better serve the goals we try to achieve using these data.