MicroRNA Gene Expression Deregulation in Human Breast Cancer

Marilena V. Iorio(The Ohio State University), Manuela Ferracin(University of Ferrara), Chang‐Gong Liu(The Ohio State University), Angelo Veronese(University of Ferrara), Riccardo Spizzo(University of Ferrara), Silvia Sabbioni(University of Ferrara), Eros Magri(University of Ferrara), Massimo Pedriali(University of Ferrara), Muller Fabbri(The Ohio State University), Manuela Campiglio(Fondazione IRCCS Istituto Nazionale dei Tumori), Sylvie Ménard(Fondazione IRCCS Istituto Nazionale dei Tumori), Juan Palazzo(Thomas Jefferson University), Anne Rosenberg(Thomas Jefferson University), Piero Musiani(University of Chieti-Pescara), Stefano Volinia(The Ohio State University), Italo Nenci(University of Ferrara), George A. Calin(The Ohio State University), Patrizia Querzoli(University of Ferrara), Massimo Negrini(University of Ferrara), Carlo M. Croce(The Ohio State University)
Cancer Research
August 15, 2005
Cited by 4,056

Abstract

MicroRNAs (miRNAs) are a class of small noncoding RNAs that control gene expression by targeting mRNAs and triggering either translation repression or RNA degradation. Their aberrant expression may be involved in human diseases, including cancer. Indeed, miRNA aberrant expression has been previously found in human chronic lymphocytic leukemias, where miRNA signatures were associated with specific clinicobiological features. Here, we show that, compared with normal breast tissue, miRNAs are also aberrantly expressed in human breast cancer. The overall miRNA expression could clearly separate normal versus cancer tissues, with the most significantly deregulated miRNAs being mir-125b, mir-145, mir-21, and mir-155. Results were confirmed by microarray and Northern blot analyses. We could identify miRNAs whose expression was correlated with specific breast cancer biopathologic features, such as estrogen and progesterone receptor expression, tumor stage, vascular invasion, or proliferation index.


Related Papers

No related papers found

Powered by citation graph analysis