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Critical Reviews™ in Oncogenesis
SJR: 0.631 SNIP: 0.503 CiteScore™: 2

ISSN Print: 0893-9675
ISSN Online: 2162-6448

Critical Reviews™ in Oncogenesis

DOI: 10.1615/CritRevOncog.2013007207
pages 273-287

Measuring MicroRNA Expression Levels in Oncology: from Samples to Data Analysis

Loris De Cecco
Functional Genomics Core Facility, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
Matteo Dugo
Functional Genomics Core Facility, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
Silvana Canevari
Fondazione IRCCS Istituto Nazionale dei Tumori; Unit of Molecular Therapies, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
Maria Grazia Daidone
Unit of Biomarkers, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
Maurizio Callari
Functional Genomics Core Facility, Fondazione IRCCS Istituto Nazionale dei Tumori; Unit of Biomarkers, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy

ABSTRACT

The relevance of microRNAs (miRNAs) in mammalian species was highlighted in the past decade. Inherent to the rapid advancements in the miRNA field was the growing need for applicable and validated experimental tools that enabled rapid progress from discovery to development of diagnostic and therapeutic applications. In the last few years, different technologies have been developed to measure miRNA expression, and one important challenge is making sense of miRNA profiles. In fact, technical and analytical aspects have substantial impact on subsequent biological interpretations and their translational applications. In the context of the workflow of the entire process of miRNA analysis, we will herein (i) consider some of the more relevant pre-analytical aspects such as study design, sample processing, and miRNA extraction, (ii) describe the available analytical tools for miRNA profiling (reverse transcription quantitative PCR, microarray hybridization, next-generation sequencing) and discuss their strengths and weaknesses, (iii) highlight the available postanalytical strategies for quality control, normalization, and statistical and bioinformatic analyses of miRNA profiling data, and (iv) summarize cross-platform comparison studies.


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