MicroRNA Expression Ratio Is Predictive of Head and Neck Squamous Cell Carcinoma
Open Access
- 15 April 2009
- journal article
- research article
- Published by American Association for Cancer Research (AACR) in Clinical Cancer Research
- Vol. 15 (8) , 2850-2855
- https://doi.org/10.1158/1078-0432.ccr-08-3131
Abstract
Purpose: The involvement of microRNAs in cancer and their potential as biomarkers of diagnosis and prognosis are becoming increasingly appreciated. We sought to identify microRNAs altered in head and neck squamous cell carcinoma (HNSCC) and to determine whether microRNA expression is predictive of disease. Experimental Design: RNA isolated from fresh-frozen primary tumors, fresh-frozen nondiseased head and neck epithelial tissues, and HNSCC cell lines was profiled for the expression of 662 microRNAs by microarray. The microRNAs that were both differentially expressed on the array and by quantitative reverse transcription-PCR were subsequently validated by quantitative reverse transcription-PCR using a total of 99 HNSCC samples and 14 normal epithelia. Results: A marked difference in microRNA expression pattern was observed between tumors and cell lines. Eighteen microRNAs were significantly altered in their expression between normal tissues and tumors. Four of these microRNAs were validated in the larger sample series, and each showed significant differential expression (P < 0.0001). Furthermore, an expression ratio of miR-221:miR-375 showed a high sensitivity (0.92) and specificity (0.93) for disease prediction. Conclusions: These data suggest that cultured tumor cell lines are inappropriate for microRNA biomarker identification and that the pattern of microRNA expression in primary head and neck tissues is reflective of disease status, with certain microRNAs exhibiting strong predictive potential. These results indicate that miR-221 and miR-375 should be evaluated further as diagnostic biomarkers because they may hold utility in defining broadly responsive prevention and treatment strategies for HNSCC.Keywords
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