Cancer Program Publication

MicroRNA Expression Profiles Classify Human Cancers
ProjectmicroRNA
Abstract 
Recent work has revealed the existence of a class of small noncoding RNA species, known as microRNAs (miRNAs), which have critical functions across various biological processes1,2. Here we use a new, bead-based flow cytometric miRNA expression profiling method to present a systematic expression analysis of 217 mammalian miRNAs from 334 samples, including multiple human cancers. The miRNA profiles are surprisingly informative, reflecting the developmental lineage and differentiation state of the tumours. We observe a general downregulation of miRNAs in tumours compared with normal tissues. Furthermore, we were able to successfully classify poorly differentiated tumours using miRNA expression profiles, whereas messenger RNA profiles were highly inaccurate when applied to the same samples. These findings highlight the potential of miRNA profiling in cancer diagnosis.
AuthorsJun Lu, Gad Getz, Eric A. Miska, Ezequiel A. Alvarez-Saavedra, Justin Lamb, David Peck, Alejandro Sweet-Cordero, Benjamin L. Ebert, Raymond H. Mak, Adolfo A. Ferrando, James R. Downing, Tyler Jacks, H. Robert Horvitz and Todd R. Golub
Publication Date06/09/2005
Contact emails junlu@broad.mit.edu
gadgetz@broad.mit.edu
golub@broad.mit.edu
Publication URLhttp://www.nature.com/nature/journal/v435/n7043/abs/nature03702.html
CitationNature 435, 834-838 (9 June 2005)
KeywordsmicroRNA, miRNA, cancer, tumor, differentiation, poorly differentiated tumor
 
Supplemental Information
URLs
NameURL
FAQhttp://www.broad.mit.edu/mpr/publications/projects/microRNA/FAQ_miGCM.html
Paperhttp://www.broad.mit.edu/mpr/publications/projects/microRNA/Nature_miR_classify_cancer.pdf
Supplementary Noteshttp://www.broad.mit.edu/mpr/publications/projects/microRNA/Supplementary_Notes.pdf
Files
DescriptionFile
Supplementary Table 1, probe informationsupplementary_table_1.xls
Supplementary Table 2, sample informationsupplementary_table_2.xls
Supplementary Table 3, N vs T predition resultsupplementary_table_3.xls
Suppl. Table 4, poorly differentiated tumor prediction resultsupplementary_table_4.xls
microRNA data, miGCM_218 collectionmiGCM_218.gct
microRNA data, acute lymphoblastic leukemiaALL.gct
microRNA data, for samples with both miRNA and mRNA dataCommon_miRNA.gct
microRNA data, mouse lung samplesmLung.gct
microRNA data, poorly differentiated tumorsPDT_miRNA.gct
microRNA data, HL-60 differentiationHL60.gct
microRNA data, erythroid differentiationErythroid.gct
mRNA data, for samples with both miRNA and mRNA dataCommon_Affy.zip
mRNA data, poorly differentiated tumorsPDT_Affy.zip
microRNA data, raw datamiRNA_raw.zip
Expression data in MAGE-ML formatExpression_Data_MAGE_ML.zip
Raw data in MAGE-ML formatmiRNA_raw_MAGE_ML.zip
Frequently Asked QuestionsFAQ_miGCM.html
PaperNature_miR_classify_cancer.pdf
Supplementary NotesSupplementary_Notes.pdf