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Details

Autor(en) / Beteiligte
Titel
Limitations of high throughput methods for miRNA expression profiles in non-functioning pituitary adenomas
Ist Teil von
  • Pathology oncology research, 2019-01, Vol.25 (1), p.169-182
Ort / Verlag
Dordrecht: Springer Netherlands
Erscheinungsjahr
2019
Link zum Volltext
Quelle
SpringerLink (Online service)
Beschreibungen/Notizen
  • Microarray, RT-qPCR based arrays and next-generation-sequencing (NGS) are available high-throughput methods for miRNA profiling (miRNome). Analytical and biological performance of these methods were tested in identification of biologically relevant miRNAs in non-functioning pituitary adenomas (NFPA). miRNome of 4 normal pituitary (NP) and 8 NFPA samples was determined by these platforms and expression of 21 individual miRNAs was measured on 30 (20 NFPA and 10 NP) independent samples. Complex bioinformatics was used. 132 and 137 miRNAs were detected by all three platforms in NP and NFPA, respectively, of which 25 were differentially expressed (fold change > 2). The strongest correlation was observed between microarray and TaqMan-array, while the data obtained by NGS were the most discordant despite of various bioinformatics settings. As a technical validation we measured the expression of 21 selected miRNAs by individual RT-qPCR and we were able to validate 35.1%, 76.2% and 71.4% of the miRNAs revealed by SOLiD, TLDA and microarray result, respectively. We performed biological validation using an extended number of samples (20 NFPAs and 8 NPs). Technical and biological validation showed high correlation ( p  < 0.001; R  = 0.96). Pathway and network analysis revealed several common pathways but no pathway showed the same activation score. Using the 25 platform-independent miRNAs developmental pathways were the top functional categories relevant for NFPA genesis. The difference among high-throughput platforms is of great importance and selection of screening method can influence experimental results. Validation by another platform is essential in order to avoid or to minimalize the platform specific errors.

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