We outline a paradigm for meta-microarray database creation and integration with clinical variables. We use as our implementation example a breast cancer database linking RNA expression measurements (by microarray) and clinical variables, such as survival metrics and tumor size. Such an endeavor involves integrating across different microarray datasets as well as clinical parameters. To this end, we created a data curation and processing pipeline, formal database ontology, and SQL schema to optimally query, analyze and visualize data from over 30 publicly available breast cancer microarray studies listed in the Gene Expression Omnibus (GEO). We demonstrate several pilot examples using this database. This methodology serves as a model for future meta-analyses of complex public clinical datasets, in particular those in the field of cancer.