Circular RNA Profiling Using High-Accuracy Long-Read Nanopore Sequencing of Multiple Myeloma Cell Lines Reveals the Wide Diversity of the Circular Transcriptome

Introduction: Long-read sequencing with nanopore technology is a groundbreaking approach, particularly useful for identifying circular RNAs (circRNAs), which are regulatory molecules regarded as promising non-invasive biomarkers for various malignancies, including multiple myeloma (MM). Despite the breakthroughs of nanopore sequencing in circRNA profiling, there have been no studies so far thoroughly exploring the wealth of the circular transcriptome in MM. Thus, we aimed to identify novel MM-related circRNAs and elucidate their expression patterns, variety in structure and length, splicing patterns, and biomarker potential.

Methods: 8 MM cell lines (L-363, H929, U266, RPMI 8226, KMS-12-BM, LP-1, OPM-2, and SK-MM-2) were propagated. Total RNA was extracted and 1 μg from each cell line was subjected to rRNA depletion; the rRNA-depleted RNAs were treated with RNase R at 37◦C for 15 min to effectively digest linear RNAs, before their rolling-circle reverse transcription using random hexamers. Finally, sequencing libraries were constructed and nanopore sequencing was conducted using PromethION 2, with high-accuracy basecalling enabled. All generated reads were mapped against the GRCh38 reference genome and data were analyzed using CIRI-long 1.1.0 and in-house–developed algorithms. Moreover, bone marrow aspirate (BMA) samples were collected from 145 adult patients with plasma cell disorders. Next, CD138+ selection of plasma cells among mononuclear cells of BMA samples was performed, followed by RNA extraction and cDNA synthesis. A first-round PCR was used to pre-amplify selected circRNAs and GAPDH (reference gene), before their quantification via real-time qPCR. circRNA expression was measured in relative quantification units (RQU).

Results: Through this optimized workflow, the circRNA-enriched RNA fractions from the 8 cell lines were effectively used to characterize full-length circRNA sequences. In particular, the total number of identified circRNAs ranged from 3,795 to 14,612 among all cell lines. Remarkably, the percentage of these circRNAs that were not deposited in databases such as circAtlas, MiOncoCirc, and CSCD2, ranged from 42.2% to 68.8% among all cell lines. Most splice sites at the back-splice junction were canonical; however, non-canonical splice sites were also detected, albeit at smaller frequencies, in all datasets. Moreover, although most circRNAs were exonic, many intronic circRNAs were also identified, in agreement with recent findings that support the substantial inclusion of intronic regions in the sequence of circRNAs. Several circRNAs from MM-associated genes, including TP53, BRAF, EGR1, CYL, and PRDM1, were also identified. Interestingly, 2 novel circRNAs, namely circZFAND6 and circLRCH3, were highly expressed in all cell lines. We successfully developed and applied a quantification assay to determine the expression levels of both these circRNAs in patients’ CD138+ plasma cells; the cohort of patients consisted of 110 newly diagnosed symptomatic MM cases and 35 smoldering MM (SMM) cases. circZFAND6 was expressed in 64 (58.2%) MM samples and 15 (42.9%) SMM samples, while circLRCH3 was expressed in 50 (45.5%) MM samples and 16 (45.7%) SMM samples. Interestingly, both circZFAND6 and circLRCH3 were significantly downregulated in the MM samples, compared to the SMM samples (median circZFAND6 expression = 61.99 RQU in MM vs. 407.16 RQU in SMM samples; median circLRCH3 expression = 3.76 RQU in MM vs. 128.26 RQU in SMM samples). Receiver operating characteristic (ROC) curve analysis showed that both circRNAs could efficiently distinguish MM from SMM cases (circZFAND6: AUC=0.72, 95% CI=0.57-0.88, P=0.004; circ LRCH3: AUC=0.71, 95% CI=0.55-0.87, P=0.0012).

Conclusions: Following our circRNA profiling pipeline, we were able to effectively characterize the landscape of circRNAs in 8 MM cell lines, thus providing a crucial foundation for investigating the role of particular circRNAs in MM pathogenesis. Our findings demonstrate that a wide range of circRNAs are expressed in MM, characterized by diverse splicing events, including the presence of novel exons and/or intron retention. Moreover, the expression levels of 2 newly identified circRNAs, namely circZFAND6 and circLRCH3, differ significantly between SMM and MM patients’ CD138+ plasma cells. Further research is expected to investigate their promising prognostic potential for this disease.