Abstract
Recent work has revealed the existence of a class of small non-coding 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.
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Acknowledgements
We thank E. Lander for critical review of the manuscript, S. Ramaswamy for discussions, and J.-P. Brunet, S. Monti, C. Ladd-Acosta and S. Shurtleff for computational help and technical assistance. We also thank J. Jacobson and Luminex Corporation for advice and technical support. E.A.M. was supported by the Howard Hughes Medical Institute. H.R.H., T.J. and T.R.G. are Investigators of the Howard Hughes Medical Institute.
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miRNA expression data have been submitted to the Gene Expression Omnibus under the series accession number GSE2564. Reprints and permissions information is available at npg.nature.com/reprintsandpermissions. The authors declare no competing financial interests.
Supplementary information
Supplementary Figures
Supplementary Figures S1-S8, covering topics from platform validation to data analysis. (PDF 240 kb)
Supplementary Notes
Supplementary Figure Legends, Supplementary Methods, Supplementary Data and additional references. (PDF 153 kb)
Supplementary Table S1
Probe information for the miRNA profiling platform. (PDF 19 kb)
Supplementary Table S2
This file contains information on all samples used in this study. (PDF 22 kb)
Supplementary Table S3
The normal/tumour prediction results of the mouse lung samples. (PDF 4 kb)
Supplementary Table S4
The prediction results of poorly differentiated tumours. (PDF 24 kb)
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Lu, J., Getz, G., Miska, E. et al. MicroRNA expression profiles classify human cancers. Nature 435, 834–838 (2005). https://doi.org/10.1038/nature03702
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DOI: https://doi.org/10.1038/nature03702
