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Comparison of the miRNA expression profiles in fresh frozen and formalin-fixed paraffin-embedded tonsillar tumors
MicroRNAs are considered as promising prognostic and diagnostic biomarkers of human cancer since their profiles differ between tumor types. Most of the tumor profiling studies were performed on rarely available fresh frozen (FF) samples. Alternatively, archived formalin-fixed paraffin-embedded (FFPE...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5482461/ https://www.ncbi.nlm.nih.gov/pubmed/28644855 http://dx.doi.org/10.1371/journal.pone.0179645 |
Sumario: | MicroRNAs are considered as promising prognostic and diagnostic biomarkers of human cancer since their profiles differ between tumor types. Most of the tumor profiling studies were performed on rarely available fresh frozen (FF) samples. Alternatively, archived formalin-fixed paraffin-embedded (FFPE) tissue samples are also well applicable to larger-scale retrospective miRNA profiling studies. The aim of this study was to perform systematic comparison of the miRNA expression profiles between FF and macrodissected FFPE tonsillar tumors using the TaqMan Low Density Array system, with the data processed by different software programs and two types of normalization methods. We observed a marked correlation between the miRNA expression profiles of paired FF and FFPE samples; however, only 27-38% of the differentially deregulated miRNAs overlapped between the two source systems. The comparison of the results with regard to the distinct modes of data normalization revealed an overlap in 58–67% of differentially expressed miRNAs, with no influence of the choice of software platform. Our study highlights the fact that for an accurate comparison of the miRNA expression profiles from published studies, it is important to use the same type of clinical material and to test and select the best-performing normalization method for data analysis. |
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