Measuring protein-coding gene expression from small RNA experiments: applications to degraded cancer samples

Cancer is a major burden upon society and a leading cause of death world-wide. In trying to understand the development and progression of cancer better, we want to identify the events in a tumour sample that are absent in normal tissue. Our understanding of tumour development and progression could be greatly aided through transcriptomic analysis of matched tumour and normal tissues. However, sequencing of such tissues have been hampered by issues with RNA quality (i.e. samples are often degraded especially samples that have been stored under non-optimal conditions and/or fixed). Such datasets, making use of matched samples are therefore rare. However, as the study of small RNAs became more and more popular, some matched sample analysis exists to study microRNA expression. We hypothesise that degraded RNA from clinical samples may be present in small RNA libraries, and could potentially be used to study the expression level of protein coding transcripts. Small RNA-seq was analysed to see if gene expression changes could be predicted from the degraded RNA present in the small RNA pool. First, I processed small RNA datasets with modified mapping and annotation pipelines to analyse total RNA, and compare their expression values with the measured levels from standard total RNA libraries. Different transformation methods were considered and for all of them I found a strong association between expression levels from total RNA libraries and RNA from small RNA libraries. Through analysis of the degraded RNA from patient tumour samples, I was able to identify genes differentially expressed including FASN, CAMKII, CREB3L4, AR, PAK1, FUT8, FKBP5. The gene expression changes quantified from degraded RNA with alterations were compared with total non-degraded RNA. Some of the genes found to be regulated in the total RNA were also found to be regulated in the degraded RNA including TIMP3, PGR, AREG, AMZ1. The data demonstrated a strong association between gene expression analysis performed on the degraded (small) RNA and total RNA.