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

Angelo Gámez-Pozo, Julia Berges-Soria, Jorge M. Arevalillo, Paolo Nanni, Rocío López-Vacas, Hilario Navarro, Jonas Grossmann, Carlos A. Castaneda, Paloma Main, Mariana Díaz-Almirón, Enrique Espinosa, Eva Ciruelos, Juan Ángel Fresno Vara

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

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.

Original languageEnglish
Pages (from-to)2243-2253
Number of pages11
JournalCancer Research
Volume75
Issue number11
DOIs
StatePublished - 1 Jun 2015

Bibliographical note

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© 2015 AACR.

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