| Literature DB >> 25883093 |
Angelo Gámez-Pozo1, Julia Berges-Soria1, Jorge M Arevalillo2, Paolo Nanni3, Rocío López-Vacas1, Hilario Navarro2, Jonas Grossmann3, Carlos A Castaneda4, Paloma Main5, Mariana Díaz-Almirón6, Enrique Espinosa7, Eva Ciruelos8, Juan Ángel Fresno Vara9.
Abstract
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 (miRNA) and protein expression profiles were obtained from 71 estrogen receptor-positive (ER(+)) and 25 triple-negative breast cancer (TNBC) 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 biologic systems as networks, confirming that ER(+) and TNBC subtypes are distinct biologic 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 TNBC has a unique metabolic profile that may be exploited for therapeutic intervention. ©2015 American Association for Cancer Research.Entities:
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Year: 2015 PMID: 25883093 DOI: 10.1158/0008-5472.CAN-14-1937
Source DB: PubMed Journal: Cancer Res ISSN: 0008-5472 Impact factor: 12.701