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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...

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Detalles Bibliográficos
Autores principales: Vojtechova, Zuzana, Zavadil, Jiri, Klozar, Jan, Grega, Marek, Tachezy, Ruth
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
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
Descripción
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.