Cytogenetics and Molecular Genetics

Downregulation of specific miRNAs in hyperdiploid multiple myeloma mimics the oncogenic effect of IgH translocations occurring in the non-hyperdiploid subtype

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Currently, multiple myeloma (MM) patients are broadly grouped into a non-hyperdiploid (nh-MM) group, highly enriched for IgH translocations, or into a hyperdiploid (h-MM) group, which is typically characterized by trisomies of some odd-numbered chromosomes. We compared the micro RNA (miRNA) expression profiles of these two groups and we identified 16 miRNAs that were downregulated in the h-MM group, relative to the nh-MM group. We found that target genes of the most differentially expressed miRNAs are directly involved in the pathogenesis of MM; specifically, the inhibition of hsa-miR-425, hsa-miR-152 and hsa-miR-24, which are all downregulated in h-MM, leads to the overexpression of CCND1, TACC3, MAFB, FGFR3 and MYC, which are the also the oncogenes upregulated by the most frequent IgH chromosomal translocations occurring in nh-MM. Importantly, we showed that the downregulation of these specific miRNAs and the upregulation of their targets also occur simultaneously in primary cases of h-MM. These data provide further evidence on the unifying role of cyclin D pathways deregulation as the key mechanism involved in the development of both groups of MM. Finally, they establish the importance of miRNA deregulation in the context of MM, thereby opening up the potential for future therapeutic approaches based on this molecular mechanism.


Multiple myeloma (MM) is an incurable plasma cell malignancy characterized by clonal proliferation of plasma cells in bone marrow and high variability in clinical features, response to treatment, and survival time.1 Chromosome aberrations are present in almost all patients with patterns that distinguish two broad genetic categories of MM: the hyperdiploid (h-MM) subtype, constituting 40–50% of cases and characterized by the presence of numerous chromosomal trisomies and a low prevalence of IgH translocations; and the non-hyperdiploid (nh-MM) subtype, encompassing 40–50% of cases, including hypodiploid, pseudodiploid and near-tetraploid MM, and highly enriched for IgH translocations.2 Subtype h-MM usually affects older patients, is more often observed in men, is associated with a higher incidence of bone disease, and has a milder clinical course compared with nh-MM.3, 4, 5

We and others have published genomic findings contributing to a better understanding of the biological differences between the two major genetic classes of MM.2, 4, 5, 6 This genomic approach has been partly successful in explaining the distinct features of these two subtypes, showing that, despite gross differences in chromosome dosage and translocations, they have distinct gene expression signatures that can both be ultimately explained by the upregulation of any of the several cyclin D pathways.4, 7, 8 We have recently shown that MYC activation,9 which is more common in h-MM, also displays a specific expression profile and is associated with shorter survival. Other secondary genetic events (that is, deletion 17p) are observed in more advanced disease but are not specifically associated with any of the subtypes.2

Micro RNAs (miRNAs) are small non-coding RNAs that act as negative modulators of their target genes at a post-transcriptional level. They have been linked to hematopoiesis and cancer.10, 11 As the miRNA signatures of the two subtypes of MM have not been directly compared,12 we explored whether differences in miRNA expression could further explain the distinct features of h-MM and nh-MM.

Patients and methods

Patients and cell lines

This study is based on the bone marrow aspirates that were obtained using standard diagnostic procedures from 92 patients with de novo MM. Samples were collected from three different hematology or Clinical Genetics laboratories (Supplementary Table S1). Plasma cells were purified from the bone marrow samples using CD138 immunomagnetic microbeads (Miltenyi Biotec, Bergisch Gladbach, Germany). All patients gave their informed consent in accordance with the Declaration of Helsinki. Seven MM cell lines were also included in the study, six (JJN3, L363, OPM-2, K620, KMS28BM and KMS28PE) representing the nh-MM group and one (OH-2) representing the h-MM group13 (Supplementary Table S1). The same molecular cytogenetic characterization was performed in all cases to classify them as subtype nh-MM (normal karyotype and/or positive for presence of IgH translocation) or h-MM (no IgH translocations and at least two trisomies of the most frequently supernumerary chromosomes: 5, 7 or 9) (Supplementary Table S2). CD138-positive snap-frozen plasma cell pellets (ALLCELLS, Emeryville, CA, USA) were used as a control.

RNA extraction and miRNA microarray experiments

Total RNA was extracted using the miRNeasy Mini Kit (Qiagen, Valencia, CA, USA) according to the manufacturer's instructions for Purification of total RNA, including Small RNAs from Animal Cells. The quality of the samples was assessed using the 2100 Expert Eukaryotic Total RNA Nano assay (Agilent Technologies, Santa Clara, CA, USA). MiRNA profiling was carried out by labeling 100 ng of total RNA, as recommended by Agilent Technologies (microRNA Microarray System Protocol v.1.5) and using T4 RNA ligase (Amersham Biosciences (Bucks, UK). All samples, including control plasma cells, were labeled and hybridized in duplicate to the ‘8-pack’ Human miRNA Oligo Microarray G4470A (Agilent Technologies), as previously described.11 Hybridization signals were detected with a DNA microarray scanner (Agilent Technologies) and the scanned images were analyzed using Agilent Feature extraction software 9.5. Microarray background subtraction was carried out using normexp method. To normalize the data set, we performed loess within arrays normalization and quantiles between arrays normalization. The miRNA expression data discussed in this article have been deposited in the National Centre for Biotechnology Information’s Gene Expression Omnibus ( and are accessible through Gene Expression Omnibus Series accession number GSE40308. Differentially expressed genes were obtained by applying Significance Analysis of Microarrays (Bioconductor project, using our own previously reported expression data5 (GSE6477 on An additional publicly available MM gene expression data set14 (GSE6401 on was used for validation. To account for multiple hypotheses testing, the estimated significance level (P value) was adjusted using Benjamini and Hochberg false discovery rate (FDR) correction. Those genes with FDR <0.05 were selected as differentially expressed between the nh-MM and the h-MM groups.

Quantitative reverse-transcribed PCR (qRT-PCR)

Six selected miRNAs (hsa-miR-24 (000402), hsa-miR-339 (002184), hsa-miR-425 (001516), hsa-miR-152 (002), hsa-miR-125 (000475), hsa-let-7d (002283)) and the artificial Homo sapiens miRNA control RNU19 (001003) were validated by qRT-PCR using the TaqMan MicroRNA Assay (Applied Biosystems, Foster City, CA, USA), as previously described.15 Total RNA (10 ng) from the case samples and control plasma cells was reverse transcribed using the MicroRNA Reverse Transcription Kit (Applied Biosystems). Three replicates of cDNA were prepared for each sample. qRT-PCR was performed using TaqMan Fast Universal PCR Master Mix on a 7500 Fast Real-Time PCR System following the manufacturer’s protocol. Each independent cDNA was run in triplicate (384-well plate). Data were normalized on the expression of the artificial Homo sapiens miRNA control RNU19 and relative expression was calculated using the comparative Ct method following the manufacturer’s instructions (SDS Program; Applied Biosystems).

Primary samples from h-MM and nh-MM cases from the discovery series were also used to determine the expression levels of selected target genes (TACC3, CCND1, FGFR3 and MAFB) by qRT-PCR. The qRT-PCR was performed on 384-well plates, with each independent cDNA included in triplicate, using TaqMan Fast Universal PCR Master Mix on a 7500 Fast Real-Time PCR System, as previously described. The expression of the endogenous human housekeeping gene GAPDH was used to normalize the data and they are expressed as the mean of 2-ΔCt values obtained for each patient after normalization with normal plasma cells (CD138+). Statistical significances on level of expression of the selected miRNAs and their targets genes were determined with significance levels of P<0.05 and P<0.01 calculated using T-test.

DNA methylation analysis by pyrosequencing

The methylation level of hsa-miR-152 and hsa-miR-339 was determined by pyrosequencing as previously described.16 The primer sequences used for each miRNA are described in the Supplementary Table S3. DNA from healthy CD138+ snap-frozen plasma cell pellets (ALLCELLS, Emeryville, CA, USA) was used as a negative control for methylation-specific assays. Human male genomic DNA universally methylated for all genes (Intergen Company, Purchase, NY, USA) was used as a positive control for methylated alleles.

miRNA target prediction

The predicted mRNA targets of the differentially expressed miRNAs between the two groups studied were separately identified by TargetScan Release 5.1 ( and miRanda (miRBase v12.0).16

miRNA inhibition/overexpression and western blot analysis

293FT and Hela cells were transfected in six-well plates (106 cells per well) with 200 nM miRIDIAN hsa-miR-425, hsa-miR-24 and hsa-miR-152 Hairpin Inhibitors (Dharmacon, Lafayette, CO, USA). The control cells were treated with the transfection reagent alone (mock transfection), miRIDIAN miRNA inhibitor-negative control. Transfection was performed using Effectene Transfection Reagent (Qiagen) according to the manufacturer’s instructions. After 48, 72 and 96 h of transfection, total protein was isolated and the protein expression level of TACC3, cyclin D1, FGFR3 and MAFB was determined by western blot using the following antibodies: anti-TACC3(D-2), anti-cyclin D1 (A-12), anti-FGFR3 (B-9) and anti-MAF B (F-11), respectively (Santa Cruz Biotechnology, Heidelberg, Germany) according to the manufacturer’s instructions. miRNA overexpression was tested in the U266 cell line by transfection with the plasmids pMSCV-425, pMSCV-152 and pMSCV-24 using the X005 mode of Nucleofector (Amaxa, Cologne, Germany), according to the Optimized Protocol. After 24, 48, 72 and 96 h of transfection, total protein was isolated and the protein expression level of cyclin D1, TACC3, FGFR3 and MAFB was analyzed by western blot.

Luciferase assay

Luciferase constructs were made following amplification of the 3′UTR of each gene (TACC3, CCND1, FGFR3 and MAFB1) by subcloning in a modified pGL3-control vector (Promega Biotech Ibérica S.L., Alcobendas, Madrid, Spain) with the firefly luciferase gene. PCR products were obtained using the following primers: TACC3, sense 5′-IndexTermCACGAATTCCTGTCAGTGGTCCCAGGTG-3′, antisense 5′-IndexTermTTCGATATCTGAGTCATGTTTTCAAAGCAATC-3′; CCND1, sense 5′-IndexTermCACGAATTCGGAAGTGTTGAAGGGAGGTG-3′, antisense 5′-IndexTermTTCGATATCAACATGCCGGTTACATGTTG-3′; FGFR3, sense 5′-IndexTermCACCCGCGGACTGGTCCCCAACAATGTGA-3′, antisense 5′-IndexTermTTCCATATGCGTCGCTGGGTTAACAAAAT-3′; MAFB, sense 5′-IndexTermCACGAATTCCGTCCCTAGTCCCAGACTACC-3′, antisense 5′-IndexTermTTCGATATCGGAAACCATTTTAATAACCAAAAA-3′. The insert identities were verified by sequencing and the following specific bacterial artificial chromosomes (BACs) were used as a PCR template: RP11–241P10 (TACC3), RP11-681H17 (CCND1), RP11-241P10 (FGFR3) and RP11-755L03 (MAFB). Predicted binding-site variations for each miRNA were introduced into the respective 3′UTR regions by site-directed mutagenesis (DNA EXPRESS; (Supplementary Table S4). A total of 105 HeLa cells were transfected with 500 ng of the firefly luciferase reporter vector containing the TACC3, CCND1, FGFR3 and MAFB1 3′UTR (wild-type and mutated) regions and 200 ng of the control vector containing Renilla luciferase pRL-CMV (Promega), using Effectene (Qiagen). Five hundred nanogram of pMSCV-152, pMSCV-425 and pMSCV-24, or the empty vector were used to analyze the effect of microRNA expression on luciferase signal. Luciferase assays were performed 48 h after transfection using the Dual Luciferase Reporter Assay System (Promega). The results were expressed as relative luciferase activity (%), calculated by normalizing the ratio of firefly luciferase to Renilla luciferase luminiscence. The reporter assay was independently performed four times for each of the experiments, including all samples in triplicate.


16 miRNAs are significantly downregulated in the h-MM subtype

The discovery series was composed of 53 CD138-positive de novo primary cases of multiple myeloma, of which 36 were classified as nh-MM and 17 as h-MM. Also included in this first analysis were 7 MM cell lines, six (JJN3, L363, OPM-2, K620, KMS28BM, KMS28PE) representing the nh-MM subtype17 and one (OH-2) as the model for h-MM.13 All samples, primary cases and cell lines, were profiled to determine the pattern of expression of the 530 miRNAs included in the array. The differentially expressed miRNAs between the nh-MM and the h-MM subtypes were identified by standardized bioinformatics methods. We found that 16 miRNAs were significantly downregulated (FDR<0.05) in the h-MM subtype relative to the nh-MM subtype (Supplementary Table S5). The six miRNAs that showed the greatest differences in expression (FDR <0.01)—hsa-miR-339, hsa-miR-125a, hsa-miR-152, hsa-miR-425-5p, hsa-miR-24, and hsa-let-7d— were selected for further study. We first validated their differential expression by qRT-PCR in the original set of samples (Figure 1a) and then confirmed these differences in a second series of 10 nh-MM and 11 h-MM cases (Supplementary Figure S1).

Figure 1

miRNA expression patterns in MM and target genes validation. (a) Expression levels of the six selected miRNAs in nh-MM cases and h-MM cases were assessed by real-time PCR. The result of the microarray was validated in the same set of MM patients. Data are expressed as 2-ΔCt values obtained by normalization using RNU19 as endogenous control. Error bars represent s.d. (b) Luciferase assay. The luciferase constructs containing, respectively, a wild-type and a mutated 3′UTR regions of the selected target genes (TACC3, CCND1, FGFR3 and MAFB) were co-transfected into Hela cells with the miRNA vector (pMSCV-425, pMSCV-24 or pMSCV-152) and together with Renilla vector for normalization. Luciferase activity was determined 48 h after reporter plasmid transfection in all cases. The reduction in luciferase activity induced by the three miRNAs expression was observed in each case, allowing us to demonstrate that MAFB, CCND1, FGFR3 and TACC3 are real targets of hsa-miR-152, hsa-miR-425 and hsa-miR-24, respectively. Data are presented as mean from four separate experiments with n=3 for each experiment. Error bars represent s.e.m. (Complete experiment in Supplementary Figure 3).

As MM is a hematological neoplasm with an abnormal methylation profile,18 we investigated whether the downregulation of these miRNAs could be epigenetically driven by the methylation of CpG islands located in their promoter regions. Of the six selected miRNAs, only the promoters of miR152 and miR339 were located in a CpG island. We analyzed their methylation status by bisulphite pyrosequencing in an independent series of 18 MM cases (10 nh-MM and 8 h-MM) and 6 MM cell lines. Although the CpG methylation of the promoter region of miR152 could not be demonstrated in the primary samples, the miR339 promoter showed methylated CpGs, although no clear differences were seen when the h-MM and the nh-MM samples were compared (Supplementary Figure S2).

Taken together, our results represent a confirmed pattern of downregulation of miRNAs that is specific to the h-MM subtype. This downregulation could not be explained by the aberrant methylation of the CpG islands in the promoter regions of these miRNAs.

IgH translocations and downregulated miRNAs share genetic targets

We identified the potential mRNA targets of the significant differentially expressed miRNAs computationally and then compared whether the expression of these target genes differed between the two subtypes of MM, using our own previously reported expression data5 as well as other publicly available data set from Agnelli et al.14 For >80% of the predicted target genes, no difference in expression was observed (Supplementary Tables S6 and S7).

We explored explanations for the absence of a differential expression profile that should have been linked to our differentially expressed miRNAs. It has been suggested that deregulation of a cyclin D gene is the unifying oncogenic event for these two subtypes of MM,7, 8, 19 but the mechanisms responsible for this deregulation are not entirely clear, at least for the h-MM subtype. As shown in Table 1, four frequent IgH chromosomal translocations in nh-MM lead to overexpression of six genes directly implicated in cell cycle regulation: CCND1, MYC, MAFB and FGFR3, MMSET and TACC3.20, 21 A critical review of our data revealed that three of the miRNAs downregulated in h-MM (hsa-miR-425, hsa-miR-152 and hsa-miR-24), have five of these genes as predicted targets (Table 1). Supporting this finding, it has recently been demonstrated that one of these miRNAs (hsa-miR-24) is directly involved in the regulation of MYC.22 It therefore seemed reasonable to assume that downregulation of these miRNAs could influence the overexpression of their predicted targets, contributing to the unifying expression profile linked to cell cycle regulation pathways that is observed in the two MM subtypes.

Table 1 Cyclin and oncogene activation mechanisms in MM

To test this hypothesis, we assessed the role of the same three miRNAs (hsa-miR-425, hsa-miR-152 and hsa-miR-24) in the expression of genes related to MM pathobiology. Because the regulation of MYC by hsa-miR-24 has already been demonstrated, we focused our efforts on the study of the other four predicted target genes (CCND1, MAFB, TACC3 and FGFR3), performing a luciferase assay to determine whether these genes are specifically targeted by the miRNAs. In all four experiments, we observed a decrease in luciferase activity when the cells were co-transfected with both the pGL3 vector with the specific 3′UTR cloned and its corresponding miRNA (Figure 1b and Supplementary Figure S3). This inhibition was site-specific as mutated 3′UTR regions that did not allow the miRNA to bind did not respond to the downregulation effect. These results demonstrated that these miRNAs bind specifically to the 3′UTR region of each gene, blocking its transcription.

hsa-miR-24, hsa-miR-152 and hsa-miR-425 target CCND1, FGFR3, TACC3 and MAFB in myeloma cells

We carried out functional assays in myeloma cells, where possible. First, we introduced the three miRNAs into a nh-MM cell line, U266 (Figure 2 and Supplementary Figure S5). All experiments demonstrated that the overexpression of hsa-miR-24, hsa-miR-152 and hsa-miR-425 induced the downregulation of the four targets (CCND1, FGFR3, TACC3 and MAFB), confirming the specificity of these miRNA species and their activity in MM.

Figure 2

Target validation in U266 nh-MM cell line: overexpression of miRNAs. (a) Expression analysis of hsa-miR-152, hsa-miR-425 and hsa-miR-24 in U266 cell line was measured by qRT-PCR 24, 48 and 72 h after their overexpression. Data are expressed as 2-ΔCt values obtained by normalization using RNU19 as endogenous control. Error bars represent s.d. (b) Western blot analysis 24, 48 and 72 h after overexpression of hsa-miR-152, hsa-miR-24 and hsa-miR-425. Total protein lysates were loaded, and the abundance of the four proteins was assessed. GAPDH protein levels were used as load control. We observed a decreased expression of these proteins at 24 or 48 h and in all cases control levels started to be restored at 72 h. (Scramble vector bands are shown separately for illustration; the complete gel and the Densitometric Analysis are in Supplementary Figure S4 and Supplementary Figure S5.)

We then investigated whether the inhibition of these three miRNAs leads to the overexpression of the predicted targets. The use of MM cell lines was not possible for several reasons. On the one hand, the only known h-MM cell line (OH-2) already has the selected miRNAs downregulated, making the evaluation of the effect of their inhibition meaningless (Supplementary Figure S6). On the other hand, most nh-MM cell lines already overexpress the predicted targets,17 preventing us from documenting upregulations in the expression levels of their corresponding proteins. As a proof of principle, we assayed the inhibition of hsa-miR-425 in U266 cells and, as expected, we were unable to detect differences in the level of expression of the proteins cyclin D1 and TACC3 compared with the wild type, in which they were already overexpressed (data no shown). As previously reported, other human cell lines such as HeLa or 293FT are commonly accepted biological models for studying inhibition miRNAs in MM.10, 23, 24 After validating by qRT-PCR that CCND1, MAFB and TACC3 were expressed in HeLa cells, that FGFR3 was expressed in 293FT and that the three selected miRNAs (hsa-miR-425, hsa-miR-152 and hsa-miR-24) were also normally expressed in both HeLa and 293FT cells (data no shown), we assessed the effect of the miRNA inhibition on the predicted target protein expression. We observed that the knockdown of hsa-miR-425, hsa-miR-152 and hsa-miR-24 resulted in increased expression of the TACC3, cyclin D1, MAFB and FGFR3 proteins, at 48 and 72 h after the inhibition by transfection with miRIDIAN miRNA-specific inhibitors (Figure 3a and Supplementary Figure S7).

Figure 3

Alternative functional validation: (a) Inhibition of miRNAs in Hela/293FT cell lines: western blot analysis 48, 72 and 96 h after inhibition of has-miR-152, has-miR-24 and has-miR-425 with specific miRIDIAN inhibitors. Total protein lysates was loaded and the abundance of MAFB, Fgfr3, cyclin D1 and TACC3, respectively, was assessed. GAPDH protein levels were used as load control. We observed an increased expression of these proteins at 48 or 72 h and in all cases control levels were restored at 96 h. (Densitometric Analysis in Supplementary Figure S7) (b, c) MM primary samples from patients: 8 h-MM and 7 nh-MM cases from the same set of patients included in the microarray were used to analyze the expression levels of the three miRNAs (hsa-miR-425, hsa-miR-152 and hsa-miR-24) (b) and the four selected target genes (TACC3, CCND1, FGFR3 and MAFB) (c) by qRT-PCR. Data are expressed as the mean of 2-ΔCt values obtained for each patient after normalization with normal plasma cells (CD138+), using RNU19 or GAPDH as endogenous control, for miRNAs and target genes, respectively. Error bars represent s.d. We were able to show that all four genes are significantly overexpressed in the two subtypes of MM compared with normal plasma cells, whereas miRNAs are aberrantly downregulated only in hyperdiploid cases. (Results for each MM case in Supplementary Figure S8.)

Finally, we validated that downregulation of these specific miRNAs actually takes place simultaneously with the overexpression of these relevant genes in primary MM samples. In a subset of samples from the discovery series that included 10 h-MM and 8 nh-MM cases, we studied the relative expression of both the miRNAs and the target genes (Figures 3b and c and Supplementary Figure S8). We observed that although hsa-miR-425, hsa-miR-152 and hsa-miR-24 were significantly downregulated only within the h-MM samples, the target genes were significantly overexpressed in both subtypes of MM. These results indicate that the overexpression of genes that are relevant to MM pathobiology, such as CCND1, MAFB, FGFR3 or TACC3, normally achieved by IgH chromosomal translocations, may also be the molecular result from a defined pattern of dowregulation of specific miRNAs.


MM is an intriguing disease in terms of its genetic and genomic features.25 Although chromosome aberrations are present in almost all patients, these aberrations are present in patterns that robustly differentiate two major subtypes: the h-MM subtype, characterized by the presence of numerous chromosomal trisomies and a low prevalence of IgH translocations; and the nh-MM subtype, highly enriched for IgH translocations.2 Together, these subtypes represent almost 80–90% of cases, with the remaining patients likely belonging to a new subtype characterized by a mixture of IgH translocations and some degree of chromosome numerical aberrations.26 We and others have extensively studied the global expression profiles that are distinctively associated with each subtype.2, 5, 6 A pathway analysis of these profiles suggest that, in spite of the genetic differences between IgH translocations and hiperdiploidy, cyclin D dysregulation constitutes a common early pathogenic event in all MM.4, 25 Still, under current treatments, h-MM has a better prognosis than nh-MM.2, 3, 27 We have recently proposed that the presence of trisomies ameliorates the adverse impact on survival of adverse prognostic markers such as IgH translocations.26

To better understand this apparent paradox (pathogenic cyclin D upregulation common to all MM, but differences in prognosis by subtype), we assessed whether miRNA expression differs between h-MM and nh-MM subtypes, based on our study on a combined total of 92 well-characterized MM samples. We identified 16 miRNAs that were downregulated in the h-MM subtype, relative to the nh-MM subtype. The downregulation of the top six miRNAs (hsa-miR-339, hsa-miR-125a, hsa-miR-152, hsa-miR-425-5p, hsa-miR-24 and hsa-let-7d) was validated in two independent case series. None of these miRNAs has been previously proposed as a differential marker for h-MM. Lionetti et al.11 reported results from a similar comparison, which was inconsistent with ours. However, this discrepancy may be due to the fact that they included some IgH-translocated samples in their h-MM category. Interestingly, we found that hsa-miR-125a, which has been found overexpressed in MM with t(4;14)11 and underexpressed in MM with t(11;14),19 was expressed at a relative higher level in the nh-MM subtype. As overexpression of hsa-miR-125a has also been found to be associated with high-risk MM,28 further study is warranted to determine its potential for clinical practice.

As the most differentially expressed miRNAs were all downregulated in the context of hyperdiploid genomes, we assessed a possible gene-dosage effect by evaluating their chromosome position (Supplementary Table S5). Surprisingly, the most significant miRNAs were located in odd-numbered chromosomes, which are precisely those more frequently involved in the trisomies characteristic of the h-MM group. After ruling out a gene-dosage effect in this scenario, another possible mechanism of simultaneous downregulation, given their distant genomic locations, is a positional effect; these miRNAs could be embedded in clusters that are simultaneously regulated by an unknown common factor due to their specific genomic location. That was not the case: first, none of the differentially expressed miRNAs are clustered together; and second, half of the differentially expressed miRNAs were located in clusters along with other miRNAs which are not regulated in the same direction in h-MM (Supplementary Table S5). Finally, as methylation of CpG islands in miRNA promoter regions seems to have an important role in the molecular mechanisms underlying the regulation of miRNA expression,29, 30 we also explored this mechanism as an alternative explanation. However, we found that methylation of the CpG islands located in the promoters of hsa-miR-339 and hsa-miR-152 could not explain the differences observed in the expression of these miRNAs by MM subtype. Further epigenetic analysis, particularly at the genomic level, will be necessary to completely rule out miRNA aberrant methylation as the cause of this downregulation.

On the basis of published results regarding gene expression in MM,5 we assessed whether the predicted target genes of the miRNAs that are downregulated in the h-MM subtype were also overexpressed in h-MM relative to nh-MM. We performed an additional in silico validation of our analysis. We explored all MM available data set with either global expression or miRNA profiles (Supplementary Table S8) and we selected for this comparison the only publicly available data set that contained accessible information regarding the ploidy status of the samples.14 Both analyses yielded very similar results (Supplementary Tables S6 and S7). The fact that we did not find any substantial gene set meeting these requirements could be partially explained by the fact that, despite the gross differences in chromosome dosage and translocations, both subtypes of MM share a common characteristic of upregulated cyclin D pathways.4, 7, 8, 17, 25 As the mechanisms responsible for this common deregulation are not entirely clear for the h-MM subtype, we conducted a critical review of the predicted targets and found that three of the miRNAs downregulated in h-MM, hsa-miR-425, hsa-miR-152 and hsa-miR-24, target some of the genes that are involved in the IgH translocations that are characteristic of nh-MM (Table 1). MiRNAs have a large number of predicted and established target genes. We found for hsa-miR-24 a set of cell cycle genes with a known role in B-cell neoplasias, including CMYC, E2F2, AURKB, CCNA2, CDC2, CDK4 or FEN1. Interestingly, aurora kinase B (AURKB), functionally involved in chromosome segregation31 and frequently overexpressed in MM,32 is also a predicted target gene for hsa-let-7 (downregulated in h-MM). Among the genes targeted by hsa-miR-152 (also downregulated in h-MM), is the epigenetic regulator DNA (cytosine-5-methyltransferase 1 (DNMT1)). In fact, it has been observed for hepatitis B virus-related hepatocellular carcinoma and cholangiocarcinoma, that the inhibition of hsa-miR-152 causes global DNA hypermethylation and increases the methylation levels of two tumor suppressor genes, glutathione S-transferase pi 1 and E-cadherin 1, via upregulation of DNMT1.33, 34 hsa-miR-425, hsa-miR-152 and hsa-miR-24 are not the only miRNA with target genes related to the pathogenesis of MM; the downregulation of others in h-MM such as hsa-miR-339 and hsa-let7 could also lead to the overexpression of genes such as BCL6, NIK, PIM2 and RAS, which are their predicted targets and are involved in proliferation and tumorigenicity of malignant B cells.11

In this study, we have shown that CCND1, FGFR3, MAFB and TACC3, common targets of IgH translocations (which result in their overexpression), also have binding sequences within their 3′-UTR for three specific miRNAs that regulate the expression and the total protein levels in myeloma cell lines. More importantly, we have shown that the downregulation of the specific miRNAs and the upregulation of their targets also occur simultaneously in primary h-MM. These results provide further evidence of the role of overexpression of these genes (either by IgH translocations or by the downregulation of miRNAs, as shown in Table 1) as a key mechanism in the molecular pathways involved in the development of MM. These findings also partially explain how cyclin D deregulation can become as a common and unifying pathogenic pathway in both subtypes of MM. Finally, they establish the importance of miRNA deregulation in the context of MM, thereby opening up the potential for future therapeutic approaches based on this molecular mechanism.

Accession codes


Gene Expression Omnibus


  1. 1

    Kyle RA, Rajkumar SV . Multiple myeloma. N Engl J Med 2004; 351: 1860–1873.

  2. 2

    Fonseca R, Bergsagel PL, Drach J, Shaughnessy J, Gutierrez N, Stewart AK et al. International Myeloma Working Group molecular classification of multiple myeloma: spotlight review. Leukemia 2009; 23: 2210–2221.

  3. 3

    Chng WJ, Santana-Davila R, Van Wier SA, Ahmann GJ, Jalal SM, Bergsagel PL et al. Prognostic factors for hyperdiploid-myeloma: effects of chromosome 13 deletions and IgH translocations. Leukemia 2006; 20: 807–813.

  4. 4

    Bergsagel PL, Kuehl WM, Zhan F, Sawyer J, Barlogie B, Shaughnessy J . Cyclin D dysregulation: an early and unifying pathogenic event in multiple myeloma. Blood 2005; 106: 296–303.

  5. 5

    Chng WJ, Kumar S, Vanwier S, Ahmann G, Price-Troska T, Henderson K et al. Molecular dissection of hyperdiploid multiple myeloma by gene expression profiling. Cancer Res 2007; 67: 2982–2989.

  6. 6

    Largo C, Saez B, Alvarez S, Suela J, Ferreira B, Blesa D et al. Multiple myeloma primary cells show a highly rearranged unbalanced genome with amplifications and homozygous deletions irrespective of the presence of immunoglobulin-related chromosome translocations. Haematologica 2007; 92: 795–802.

  7. 7

    Zhou Y, Barlogie B, Shaughnessy JD . The molecular characterization and clinical management of multiple myeloma in the post-genome era. Leukemia 2009; 23: 1941–1956.

  8. 8

    Broyl A, Hose D, Lokhorst H, de Knegt Y, Peeters J, Jauch A et al. Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients. Blood 116: 2543–2553.

  9. 9

    Chng WJ, Huang GF, Chung TH, Ng SB, Gonzalez-Paz N, Troska-Price T et al. Clinical and biological implications of MYC activation: a common difference between MGUS and newly diagnosed multiple myeloma. Leukemia 25: 1026–1035.

  10. 10

    Pichiorri F, Suh SS, Ladetto M, Kuehl M, Palumbo T, Drandi D et al. MicroRNAs regulate critical genes associated with multiple myeloma pathogenesis. Proc Natl Acad Sci USA 2008; 105: 12885–12890.

  11. 11

    Lionetti M, Biasiolo M, Agnelli L, Todoerti K, Mosca L, Fabris S et al. Identification of microRNA expression patterns and definition of a microRNA/mRNA regulatory network in distinct molecular groups of multiple myeloma. Blood 2009; 114: e20–e26.

  12. 12

    Pichiorri F, De Luca L, Aqeilan RI . MicroRNAs: new players in multiple myeloma. Front Genet 2: 22.

  13. 13

    Vatsveen TK, Tian E, Kresse SH, Meza-Zepeda LA, Gabrea A, Glebov O et al. OH-2, a hyperdiploid myeloma cell line without an IGH translocation, has a complex translocation juxtaposing MYC near MAFB and the IGK locus. Leuk Res 2009; 33: 1670–1677.

  14. 14

    Agnelli L, Fabris S, Bicciato S, Basso D, Baldini L, Morabito F et al. Upregulation of translational machinery and distinct genetic subgroups characterise hyperdiploidy in multiple myeloma. Br J Haematol 2007; 136: 565–573.

  15. 15

    Frenquelli M, Muzio M, Scielzo C, Fazi C, Scarfo L, Rossi C et al. MicroRNA and proliferation control in chronic lymphocytic leukemia: functional relationship between miR-221/222 cluster and p27. Blood 115: 3949–3959.

  16. 16

    Vilas-Zornoza A, Agirre X, Martin-Palanco V, Martin-Subero JI, San Jose-Eneriz E, Garate L et al. Frequent and simultaneous epigenetic inactivation of TP53 pathway genes in acute lymphoblastic leukemia. PLoS One 6: e17012.

  17. 17

    Largo C, Alvarez S, Saez B, Blesa D, Martin-Subero JI, Gonzalez-Garcia I et al. Identification of overexpressed genes in frequently gained/amplified chromosome regions in multiple myeloma. Haematologica 2006; 91: 184–191.

  18. 18

    Martin-Subero JI, Ammerpohl O, Bibikova M, Wickham-Garcia E, Agirre X, Alvarez S et al. A comprehensive microarray-based DNA methylation study of 367 hematological neoplasms. PLoS One 2009; 4: e6986.

  19. 19

    Gutierrez NC, Sarasquete ME, Misiewicz-Krzeminska I, Delgado M, De Las Rivas J, Ticona FV et al. Deregulation of microRNA expression in the different genetic subtypes of multiple myeloma and correlation with gene expression profiling. Leukemia 24: 629–637.

  20. 20

    Stewart JP, Thompson A, Santra M, Barlogie B, Lappin TR, Shaughnessy J . Correlation of TACC3, FGFR3, MMSET and p21 expression with the t(4;14)(p16.3;q32) in multiple myeloma. Br J Haematol 2004; 126: 72–76.

  21. 21

    Hideshima T, Mitsiades C, Tonon G, Richardson PG, Anderson KC . Understanding multiple myeloma pathogenesis in the bone marrow to identify new therapeutic targets. Nat Rev Cancer 2007; 7: 585–598.

  22. 22

    Lal A, Navarro F, Maher CA, Maliszewski LE, Yan N, O’Day E et al. miR-24 Inhibits cell proliferation by targeting E2F2, MYC, and other cell-cycle genes via binding to "seedless" 3'UTR microRNA recognition elements. Mol Cell 2009; 35: 610–625.

  23. 23

    Lerner M, Harada M, Loven J, Castro J, Davis Z, Oscier D et al. DLEU2, frequently deleted in malignancy, functions as a critical host gene of the cell cycle inhibitory microRNAs miR-15a and miR-16-1. Exp Cell Res 2009; 315: 2941–2952.

  24. 24

    Wong KY, Liang R, So CC, Jin DY, Costello JF, Chim CS . Epigenetic silencing of MIR203 in multiple myeloma. Br J Haematol 154: 569–578.

  25. 25

    Morgan GJ, Walker BA, Davies FE . The genetic architecture of multiple myeloma. Nat Rev Cancer 12: 335–348.

  26. 26

    Kumar S, Fonseca R, Ketterling RP, Dispenzieri A, Lacy MQ, Gertz MA et al. Trisomies in multiple myeloma: impact on survival in patients with high-risk cytogenetics. Blood 119: 2100–2105.

  27. 27

    Zhan F, Huang Y, Colla S, Stewart JP, Hanamura I, Gupta S et al. The molecular classification of multiple myeloma. Blood 2006; 108: 2020–2028.

  28. 28

    Zhou Y, Chen L, Barlogie B, Stephens O, Wu X, Williams DR et al. High-risk myeloma is associated with global elevation of miRNAs and overexpression of EIF2C2/AGO2. Proc Natl Acad Sci USA 107: 7904–7909.

  29. 29

    Bandres E, Agirre X, Bitarte N, Ramirez N, Zarate R, Roman-Gomez J et al. Epigenetic regulation of microRNA expression in colorectal cancer. Int J Cancer 2009; 125: 2737–2743.

  30. 30

    Fabbri M, Calin GA . Epigenetics and miRNAs in human cancer. Adv Genet 70: 87–99.

  31. 31

    Farag SS . The potential role of Aurora kinase inhibitors in haematological malignancies. Br J Haematol 155: 561–579.

  32. 32

    Chng WJ, Braggio E, Mulligan G, Bryant B, Remstein E, Valdez R et al. The centrosome index is a powerful prognostic marker in myeloma and identifies a cohort of patients that might benefit from aurora kinase inhibition. Blood 2008; 111: 1603–1609.

  33. 33

    Braconi C, Huang N, Patel T . MicroRNA-dependent regulation of DNA methyltransferase-1 and tumor suppressor gene expression by interleukin-6 in human malignant cholangiocytes. Hepatology 51: 881–890.

  34. 34

    Huang J, Wang Y, Guo Y, Sun S . Down-regulated microRNA-152 induces aberrant DNA methylation in hepatitis B virus-related hepatocellular carcinoma by targeting DNA methyltransferase 1. Hepatology 52: 60–70.

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This work was partially funded by INTRASALUD PI 08-0440 (to JCC), Fondo de Investigaciones Sanitarias (Spain). We thank the staff of the Molecular Cytogenetics Group at the CNIO (Madrid, Spain), the Department of Genetics, University of Navarra (Pamplona, Spain) and the Division of Hematology–Oncology, Mayo Clinic (Scottsdale, Arizona, USA) for their support in the molecular cytogenetic characterization of the samples.

Author contributions

JCC designed the study and wrote the paper; AR-M, BF, TH and XA performed the experiments; GG-L, BIF, AR-M, SA, SR-P and JCC analyzed the data; FP, MJC, JM and RF provided key reagents and materials.

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Correspondence to J C Cigudosa.

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Competing interests

Relevant to this work Dr Fonseca holds a patent for the prognostication of MM based on the genetic categorization of the disease. Not relevant to this work, Dr Fonseca has received consulting fees from Medtronic, Otsuka, Celgene, Genzyme, BMS and AMGEN. He has also received funding for research from Cylene and Onyx. The remaining authors declare no conflicts of interest.

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Supplementary Information accompanies the paper on the Leukemia website

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Rio-Machin, A., Ferreira, B., Henry, T. et al. Downregulation of specific miRNAs in hyperdiploid multiple myeloma mimics the oncogenic effect of IgH translocations occurring in the non-hyperdiploid subtype. Leukemia 27, 925–931 (2013) doi:10.1038/leu.2012.302

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  • microRNA profile
  • aneuploidy
  • trisomies
  • chromosomal rearrangements.

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