Noncoding circular RNAs (circRNAs) are a class of single-stranded noncoding RNAs formed in a circular form during non-canonical splicing or back-splicing [1]. Initially thought to be the result of splicing errors, circRNAs are now widely recognized as important gene expression regulators, although very little is known about their specific roles in or outside the cell. Previous studies have shown that most of the currently annotated circRNAs are involved in various pathologies and in the carcinogenesis as well, being potential cancer biomarkers and, thus, the attractive research target [2]. However, their molecular analyses are not perfected, yet.
In the present study, we aimed to evaluate several steps of RNA sample preparation for circRNA analysis in order to determine the optimal workflow. The yield and quality of the purified RNA were compared among three different RNA extraction kits (coded M, P, and Z). Also, circular and linear transcripts of the same genes (13 transcripts in total) were quantified in two different cellular fractions: nuclear and cytoplasmic RNA. The efficacy of RNase R treatment step was assessed by comparing quantities of linear transcripts. The selected circRNA and linear targets were analyzed by means of real-time PCR using the QuantStudioTM 7 Pro Real-Time PCR System (Applied BiosystemsTM, Thermo Fisher Scientific). All analyses were performed using six cancer cell lines.
Our preliminary results revealed that although the yield and quality of purified RNA were comparable among the kits, relatively higher quantities of the same circRNA targets were obtained with the P kit. Among the analyzed circRNA targets, the majority were detected in higher relative amounts in the cytoplasmic RNA fraction than in the nuclear one (e.g. circFOXO3 and circHIPK3). However, some circRNAs were found in similar quantities in both cellular fractions (e.g. circNRIP1). The majority of the circRNA targets were detected in higher relative amounts when the RNase R treatment step was performed as compared to untreated samples (e.g. circUBAP2 and circCSNK1G3). However, quantities of several other circRNAs showed the opposite tendency (e.g. circFAT1 and circNRIP1).
In conclusion, our preliminary data indicate no significant differences in the yield and quality of the circRNA fraction comparing the three RNA extraction kits. The RNase R treatment reduced the amount of linear transcripts in the total RNA fraction, but not all the analyzed circRNAs were enriched uniformly, raising concern for biased data.