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High-throughput sequencing (HTS) offers a detailed view of microRNA expression, outperforming microarrays. New HTS analysis methods correlate well with qPCR, identifying key microRNA biomarkers for follicular thyroid cancer.

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Area of Science:

  • Molecular Biology
  • Genomics
  • Biotechnology

Background:

  • High-throughput sequencing (HTS) is increasingly utilized for digital gene expression profiling.
  • Novel analysis strategies for HTS data, including mapping to miRBase and sequence read grouping, enable hypothesis-free detection of differential microRNA (miRNA) expression.

Purpose of the Study:

  • To introduce and evaluate new analysis options for HTS data in miRNA expression profiling.
  • To compare HTS data analysis with traditional microarray and quantitative PCR (qPCR) methods.
  • To identify potential miRNA biomarkers for distinguishing between benign and malignant follicular thyroid tumors.

Main Methods:

  • Utilized Illumina platforms for microarray and miRNA sequencing of 20 thyroid tissue samples (benign and malignant).
  • Applied three distinct HTS data analysis strategies to identify miRNA biomarkers.
  • Compared HTS results with microarray and qPCR data from the same RNA samples.

Main Results:

  • Demonstrated high correlation between qPCR and HTS data using the proposed analysis methods.
  • Highlighted the broader dynamic range of HTS compared to microarrays for small RNA studies.
  • Confirmed hsa-miR-197-3p, hsa-miR-221-3p, hsa-miR-222-3p, and both hsa-miR-144-3p/5p as potential biomarkers for follicular thyroid cancer.

Conclusions:

  • HTS provides a more comprehensive and detailed global miRNA expression profile than microarrays.
  • Summarizing HTS reads into isoform groups or functional criteria (seed analysis) offers promising new analysis avenues that correlate well with qPCR.
  • The findings suggest limitations of qPCR in validating HTS data and open future research perspectives for HTS in miRNA analysis.