Cross Platform Standardisation of an Experimental Pipeline for Use in the Identification of Dysregulated Human...

Micro RNAs (miRNAs) are a class of highly conserved small non-coding RNAs that play an important part in the post-transcriptional regulation of gene expression. A substantial number of miRNAs have been proposed as biomarkers for diseases. While reverse transcriptase Real-time PCR (RT-qPCR) is considered the gold standard for the evaluation and validation of miRNA biomarkers, small RNA sequencing is now routinely being adopted for the identification of dysregulated miRNAs. However, in many cases where putative miRNA biomarkers are identified using small RNA sequencing, they are not substantiated when RT-qPCR is used for validation. To date, there is a lack of consensus regarding optimal methodologies for miRNA detection, quantification and standardisation when different platform technologies are used.

In this study we present an experimental pipeline that takes into consideration sample collection, processing, enrichment, and the subsequent comparative analysis of circulating small ribonucleic acids using small RNA sequencing and RT-qPCR.

Initially, a panel of miRNAs dysregulated in circulating blood from breast cancer patients compared to healthy women were identified using small RNA sequencing. MiR-320a was identified as the most dysregulated miRNA between the two female cohorts. Total RNA and enriched small RNA populations (<30 bp) isolated from peripheral blood from the same female cohort samples were then tested for using a miR-320a RT-qPCR assay. When total RNA was analysed with this miR-320a RT-qPCR assay, a 2.3-fold decrease in expression levels was observed between blood samples from healthy controls and breast cancer patients. However, upon enrichment for the small RNA population and subsequent analysis of miR-320a using RT-qPCR, its dysregulation in breast cancer patients was more pronounced with an 8.89-fold decrease in miR-320a expression. We propose that the experimental pipeline outlined could serve as a robust approach for the identification and validation of small RNA biomarkers for disease.