The availability of genome-wide biological network data opens up new possibilities to discover novel biomarkers and elucidate cancer-related complex mechanisms at network level. In this paper, we propose a novel module-based feature selection framework, which integrates biological network information and gene expression data to identify biomarkers, not as individual genes but as functional modules. Also, a large-scale analysis of ensemble feature selection concept is presented. The method allows combining features selected from multiple runs with various data subsampling to increase the reliability and classification accuracy of the final set of selected features. The results from four breast cancer studies demonstrate that the identified module biomarkers achieve: i) higher classification accuracy in independent validation datasets; ii) better reproducibility than individual gene biomarkers; iii) improved biological interpretability; and iv) enhanced enrichment in cancer-related "disease drivers".