Background: transcriptome data provide a valuable resource for the study of cancer molecular mechanisms, but technical biases, samples’ heterogeneity and small sample sizes result in poorly reproducible lists of regulated genes. Additionally, the presence of multiple cellular components contributing to cancer development complicate the interpretation of bulk transcriptomic profiles. Methods: we collected 48 microarray datasets of laser capture microdissected breast tumors, and performed a meta-analysis to identify robust lists of genes differentially expressed in these tumors. We created a database with carefully harmonized metadata to be used as a resource for the research community. Results: combining the results of multiple datasets improved the statistical power, and the analysis of stroma and epithelium separately allows identifying genes with different contribution in each compartment. Conclusions: our database can profitably help biomarkers’ discovery and is readily accessible through a user-friendly web interface (https://aurorasavino.shinyapps.io/metalcm/).