Combined label-free quantitative proteomics and microRNA expression analysis of breast cancer unravel molecular differences with clinical implications.

Better knowledge of the biology of breast cancer has allowed the use of new targeted therapies, leading to improved outcome. High-throughput technologies allow deepening into the molecular architecture of breast cancer, integrating different levels of information, which is important if it helps in making clinical decisions. MicroRNA and protein expression profiles were obtained from 71 estrogen receptor-positive and 25 triple-negative breast cancer samples. RNA and proteins obtained from formalin-fixed, paraffin-embedded tumors were analyzed by RT-qPCR and LC-MS/MS respectively. We applied probabilistic graphical models representing complex biological systems as networks, confirming that estrogen receptor-positive and triple-negative breast cancer subtypes are distinct biological entities. The integration of miRNA and protein expression data unravels molecular processes that can be related to differences in the genesis and clinical evolution of these types of breast cancer. Our results confirm that triple-negative breast cancer has a unique metabolic profile that may be exploited for therapeutic intervention.