Comprehensive profiling of circular RNA expressions reveals potential diagnostic and prognostic biomarkers in multiple myeloma

Multiple myeloma (MM) is the second most common hematological malignancy derived from long-lived antibody-producing plasma cells in the bone marrow and is characterized by the presence of monoclonal immunoglobins in the serum and/or urine [1]. Over the past half century, the introduction of novel drugs (such as bortezomib) and application of hematopoietic stem cell transplantation have turned the rapid lethal MM into a chronic and manageable disease with extended survival in most of the patients [2, 3]. However, MM lacks symptoms in early stage, and the identification of the disease onset is difficult to be achieved by current examinations [4]. Moreover, the obstacles in treatment such as relapse and multidrug resistance are still common, contributing to poor prognosis in MM patients [5]. Therefore, it is essential to explore novel biomarkers that would help with diagnosis and improve the survival in MM patients.

Circular RNAs (circRNAs) are a class of non-coding RNAs originated by back-splicing of the precursor messenger RNA and forming a covalent loop with no 5′ to 3′ polarity or polyadenylated tail [6]. CircRNAs are stable, abundant and evolutionarily conserved, and mounting studies have proven that they contain target sites for microRNAs (miRNAs), thereby participate in the pathogenesis of various diseases through disturbing miRNAs signal axis [7]. With the development of circRNA microarrays, our knowledge about circRNA expression patterns has been initially uncovered in various diseases. In cancer research, circRNA expression patterns have been studied in some solid tumors including breast cancer, esophageal squamous cell cancer, epithelial ovarian cancer, etc., and a number of circRNAs are disclosed to involve in the pathophysiological progression of these malignancies [8‐11]. As for hematological malignancies, an extensive analysis of circRNA expression profiles reveals a total of 464 dysregulated circRNAs (147 upregulated and 317 downregulated) in acute myeloid leukemia (AML) patients compared with healthy controls, and among these circRNAs, circ_0004277 is validated to be positively associated with prognosis in AML patients [9]. Whereas in MM, the expression profiles of circRNAs are not yet reported. Concidering that circRNAs are differentially expressed and involve in the pathophysiological progression of solid tumors as well as hematological malignancies, we speculated that they might play critical roles in MM as well.

This present study aimed to investigate the heterogeneity of circRNA expression pattern via microarray, and further evaluate the potential of 10 specific circRNAs as diagnostic and prognostic biomarkers in MM.

From this comprehensive analysis of circRNA expression profiles in MM, (1) we found that circRNA expression patterns were able to distinguish MM patients from HCs, and there were 122 upregulated and 260 downregulated circRNAs in MM compared with HCs, which were implicated in neoplastic signaling pathways such as MAPK signaling pathways and VEGF signaling pathway. (2) In validation stage, 5 out of 10 upregulated and 6 out of 10 downregulated circRNAs by microarray were confirmed by qPCR, and these circRNAs could distinguish MM patients from healthy controls. (3) Circ-PTK2 and circ-RNF217 were correlated with poor treatment response and survival, while circ-AFF2 predicted favorable treatment response and survival in MM patients.

With the rapid progression and wide application of high-throughput sequencing and microarray, the expression pattern of circRNAs in various human diseases are increasingly reported, and the dysregulated expressions of circRNAs are shown to contribute to the pathogenesis of various cancers [7, 9‐11, 14‐19]. For instance, in hepatitis B-related HCC, 189 upregulated circRNAs and 37 downregulated circRNAs were found by circRNA microarray, and circRNA_100,338 is further validated to be associated with metastatic progression by acting as an endogenous sponge for miR-141-3p [7]. Another study identifies 2556 upregulated and 1832 downregulated circRNAs in epithelial ovarian cancer tissues compared with normal ovarian tissues by microarray and bioinformatic analysis [11]. As for hematological malignancies, one previous study reveals the expression patterns of circRNAs in AML and exhibits 147 upregulated and 317 downregulated circRNAs in AML patients compared with healthy controls [10]. These previous studies uncover the expression patterns of circRNAs in several cancers including hematological malignancy, however, there is currently no study on heterogenicity of circRNA expression profiles in MM yet. In our study, we performed circRNA microarray and identified 122 upregulated and 260 downregulated circRNAs in bone marrow plasma cells of MM patients compared with HCs. In addition, these dysregulated circRNAs were shown to be involved in neoplastic signaling pathways including MAPK signaling pathway and VEGF signaling pathway. To our knowledge, this was the first study that investigates the expression patterns of circRNAs in MM, which might serve as valuable reference for further investigation of circRNAs functions in MM.

Benefiting from the stable nature and RNA degradation resistance, circRNAs are considered as prominent and novel biomarkers for many diseases, especially cancer, and there are specific circRNAs whose clinical values have been highlighted in several cancers. For instance, circ-LDLRAD3 is upregulated in pancreatic cancer, and is disclosed to be a potential biomarker in disease diagnosis [20]. In addition, circ_0014130 is positively correlated with TNM stage as well as lymphatic metastasis, and is of good diagnostic potential for NSCLC [21]. As for hematological malignancies, circ_0004277 is downregulated and offers a diagnostic biomarker in AML [10]. These aforementioned studies emphasized the potential of several specific circRNAs as biomarkers in diagnosis of solid tumors and hematological malignancies to a certain extent, however, the diagnostic value of circRNAs in MM is still misty. In addition, circRNA expression profile is a novel concept developed in recent years, and the current comprehensive screening of circRNA expression such as microarray is still limited by the accuracy. In addition, the sample size for microarray was far smaller than that of q-PCR, therefore, it was highly possible that the two analyses yielded deviation in results. CircRNA expression profiles by microarray aimed to give us a macroscopic view about the expression patterns of circRNAs, however, a larger sample size and more accurate tool were needed for a more refined understanding. Therefore, we carried out the Stage II analysis using q-PCR in a larger sample size. We selected the top 10 upregulated and top 10 downregulated circRNAs from the previous bioinformatic analyses, and validated regarding their diagnostic potential in MM with a larger sample size by qPCR. Our analyses revealed that, 5 out of 10 upregulated and 6 out of 10 downregulated circRNAs by microarray were confirmed by qPCR, and these circRNAs could distinguish MM patients from healthy controls. The possible explanations could be: (1) These circRNAs might influence the transcription of their parental genes by acting as restoration pools. For instance, circ-PTK2 serves as restoration pool for its paternal gene PTK2, which was identified as oncogene in MM, and increases the expression of PTK2 gene, thereby increases MM risk [22]. (2) These circRNAs might influence the pathogenesis of MM by sponging their target miRNAs. For example, circ-AFF2 might sponge miR-638 and inhibit the oncogenic function of miR-638 in MM (as shown in circRNA regulation network (Fig. 4)). Additionally, circ-PTK2 might act as sponge for anti-oncogenic miR-1298-5p and promote the neoplastic progression in MM (retrieved from tissue specific circRNA database: http://​gb.​whu.​edu.​cn/​TSCD/​). Although further studies were needed to analyze and demonstrate the detailed mechanisms of these circRNAs in MM, our study still illuminated that circ-PTK2, circ-RERE, circ-AFF2 and circ-WWC3 could serve as novel diagnostic biomarkers in MM.

Although rarely shown, it is still evident from the existing studies that some specific circRNAs are closely correlated with treatment response and may have potential prognostic value in cancer patients. For example, circ_0000285 expression is lower in cisplatin-resistant bladder cancer patients compared to cisplatin-sensitive patients and is independently correlated with poor treatment outcomes in bladder cancer patients [23]. Regarding survival, circ-RAD23B, an oncogene in NSCLC, predicts shorter OS in NSCLC patients [24]. Additionally, circRNA expression profiles display that circ_0001017 and circ_0061276 are correlated with longer OS in gastric cancer patients [25]. As for hematological malignancies, only one study exhibits that circ_100053 contributes to leukemogenesis in chronic myeloid leukemia (CML) and predicts increased resistance to imatinib as well as poor survival in CML patients [26]. Although the correlation of several specific circRNAs with patients’ prognosis in solid tumors as well as hematological malignancy has been reported, the correlation of circRNAs with prognosis in MM is still unknown [27]. In order to get a more profound understanding of the correlation of circRNAs with prognosis in MM, we evaluated the correlation of the top 10 upregulated and top 10 downregulated circRNAs with treatment response as well as survival, and disclosed that circ-PTK2 and circ-RNF217 were correlated with poor treatment response and survival, while circ-AFF2 predicted good treatment response and survival in MM patients. The possible reasons were: (1) According to our analyses, these circRNAs were closely correlated with clinicopathological features in MM patients, therefore, they would affect the prognosis of MM patients via influencing the clinicopathological features such as Durie-Salmon stage and deletion at 17p. (2) These circRNAs might change the cell sensitivity to chemotherapy and develop drug resistance via targeting miRNAs, thereby influence prognosis in cancer patients. For instance, circ-AFF2 might sponge miR-638, which was previously shown to induce drug resistance in human breast cancer, thereby reduced drug resistance and improved prognosis in MM patients [28]. (3) As explained above, these circRNAs might impact the normal function of miRNAs by serving as miRNA sponges in MM (see Fig. 4 for regulation network of candidate circRNAs and the detailed miRNAs), thereby influenced prognosis in MM patients. In addition, the tubular form of potential miRNA targets of the top 10 upregulated and top 10 downregulated circRNAs was shown in Additional file 2: Table S2, and the potential target miRNAs of all the 122 upregulated and 260 downregulated circRNAs were listed in Additional file 3.

This study first revealed the differential expressions of circRNAs and determined circRNAs with diagnostic and prognostic potential in MM, whereas there were still some shortcomings. Firstly, although several circRNAs with potential as diagnostic and prognostic biomarkers for MM were identified, the molecular mechanisms of these circRNAs in MM pathology were not investigated. Secondly, due to the budget, the sample size was relatively small for stage I, and MM patients from different clinical stage were not included for analysis, which could be improved in further studies. Thirdly, we explored the prognostic value of circRNAs but not in a logical approach because it was not the main goal in this study. However, it would be of great clinical significance to further detect the correlation of these circRNAs with prognosis in MM patients in a more logical way in the future. Moreover, the use of circRNAs as biomarkers for cancers is still in the early stage of research, and thorough practical proofs and standards were needed for clinical application. Studies that further validate the feasibility of circRNAs as diagnostic and prognostic biomarkers in cancer are needed to lead the bench side findings to real-life application.

In conclusion, this study provides valuable reference for profound understanding about expression patterns of circRNAs in MM, and validates that circ-PTK2, circ-RNF217 and circ-AFF2 might serve as potential prognostic biomarkers in MM.