Alberto Risueño Pérez - Gredos - Universidad de Salamanca

Table 4 Potential microRNA (miRNA)–mRNA interactions deregulated in the multiple myeloma (MM) with monosomy 13 as a unique
abnormality
Deregulated target genes a
GenBank ID TargetScan b
None of the 11 miRNAs downregulated in the total 60 MM
samples has been related to lymphoid cells except for miR-155,
which participates in B-cell differentiation and could act as a
tumor suppressor by reducing potentially oncogenic translocations
generated by AID gene. 41 It has also been demonstrated
that the failure of miR-155-deficient B cells to generate highaffinity
switched antibodies appears not because of a defect in
somatic hypermutation or class-switch recombination, but more
likely to a defect in the differentiation or survival of plasmablasts.
42 The predominance of infra-expressed miRNAs in
myeloma samples is consistent with the global decrease in
miRNA levels observed in other human cancers. 37,43,44 Nevertheless,
Pichiorri et al. 14 have recently reported distinctive
miRNA signatures in MM and MGUS characterized mainly by
the overexpression of miRNAs. Moreover, the deregulated
miRNAs are not the same in the two studies. There are some
miRBD b
Pictar b
miRanda b
No. of sites (predicted
by TargetScan)
hsa-miR-486-5p (MIMAT0002177)
PIM1 NM_002648 0.33 74 - 146 2
hsa-miR-320a (MIMAT0000510)
MLLT3 NM_004529 0.49 87 5.56 295 2
hsa-miR-20a(MIMAT0000075)
CDKN1A NM_078467 0.3 78 5.27 144 2
ADAM9 NM_003816 0.38 67 1.68 159 1
FURIN NM_002569 0.33 50 6.23 153 2
YES1 NM_005433 0.32 0 4.28 152 2
hsa-miR-214 (MIMAT0000271)
RASSF5 NM_182663 0.44 61 4.98 291 3
PLAGL2 NM_002657 0.32 60 6.28 443 3
hsa-miR-19b (MIMAT0000074)
SGK1 NM_005627 0.56 79 6.83 153 2
IGF1 NM_001111283 0.48 71 - 314 2
hsa-miR-15a (MIMAT0000068)
CCND2 NM_001759 0.33 - 3.43 155 3
hsa-miR-10b (MIMAT0000254)
KLF11 NM_003597 0.4 75 2.21 140 1
hsa-miR-186 (MIAMT0000456)
BTBD3 NM_014962 0.78 87 0 582 3
UBE2B NM_003337 0.45 71 0.31 292 1
FBXO33 NM_203301 0.51 88 1.28 289 2
hsa-miR-19a (MIMAT0000073)
KRAS NM_0033360 0.35 0 3.72 152 2
IGF1 NM_001111283 0.48 71 - 314 2
CCND2 NM_001759 0.46 70 2.99 151 2
MAPK6 NM_002748 0.43 77 - 296 1
hsa-miR-let-7b (MIMAT0000063)
MAPK6 NM_002748 0.4 72 - 158 1
LRIG3 NM_153377 0.51 93 4.4 166 1
hsa-miR-125a-5p (MIMAT0000443)
ARID3B NM_006465 0.49 82 20.42 291 3
hsa-miR-223 (MIMAT0000280)
DUSP10 NM_007207 0.57 80 6.99 308 2
STIM1 NM_003156 0.3 52 2.57 159 1
hsa-miR-374a (MIMAT0000727)
GADD45A NM_001924 0.54 78 0.92 162 1
PELI1 NM_020651 0.86 99 3.18 462 3
MAPK6 NM_002748 0.68 92 - 286 2
hsa-miR-let-7c (MIMAT0000064)
LRIG3 NM_153377 0.51 92 4.4 163 1
hsa-miR-130a (MIMAT0000425)
CFL2 NM_138638 0.46 77 9.9 281 2
a All the target genes were upregulated in the gene expression analysis.
b Prediction score.
microRNAs in myeloma
NC Gutiérrez et al
plausible explanations for these discrepancies. First, the
platforms used for miRNA analysis were different and there
are around 110 miRNAs only explored by one of the two
approaches; second, the number of primary myelomas analysed
in this study is higher and the samples are selected according to
the genetic abnormalities, whereas the genetic status of MM
samples from Pichiorri’s study is unknown; and at last, the
statistical design in terms of class comparisons and significance
level is also different.
miRNA target prediction remains a major bioinformatic
challenge, particularly because of false-positive predictions. In
this study, we combined several strategies to identify miRNA–
mRNA interactions more susceptible to a degradation process.
Using these stringent criteria, we found that the miR-135b and
miR-146a (Table 3), both downregulated in MM with t(4;14),
targeted two genes (PELI2 and IRAK1) involved in IL-1 signalling
635
Leukemia