miRNA expression and WHO subtypes.
Each patient carried a distinctive miRNA signature composed of a median of 650 miRNAs.
The supervised analysis identified a signature of 24 miRNAs (Table 2) able to discriminate the 4 WHO classes with 62% of accuracy and p-value < < 0.001.
Also, the analysis allowed to select 14 miRNAs frequently expressed in most of the patients of one or more WHO classes (Fig. 1).
Table 2 -- Signature of the twenty-four most frequent miRNAs. Freq is the frequency that indicates the number of times each miRNAs was selected over the number of repetitions of regular and permutation experiments (see Materials and Methods). Here we chose 100 repetitions subdivided in 50 regular and 50 permutation experiments.
Freq.
|
miRNAs
|
96.0
|
miR-1203
|
95.0
|
miR-1292-5p
|
95.0
|
miR-183-5p
|
95.0
|
miR-1197
|
92.0
|
miR-1270
|
92.0
|
miR-1178-3p
|
91.0
|
miR-141-3p
|
90.0
|
miR-100-5p
|
90.0
|
let-7a-3p
|
90.0
|
miR-10b-5p
|
89.0
|
miR-1224-3p
|
86.0
|
miR-10a-3p
|
84.0
|
miR-222-5p
|
84.0
|
miR-1233-3p
|
84.0
|
miR-1179
|
83.0
|
miR-23a-5p
|
82.0
|
miR-1236-3p
|
80.0
|
miR-122-5p
|
79.0
|
miR-129-2-3p
|
79.0
|
miR-145-3p
|
79.0
|
miR-216a-5p
|
79.0
|
miR-1302
|
77.0
|
miR-141-5p
|
75.0
|
miR-1183
|
NGS on bone marrow sample at diagnosis
NGS identified mutations in 43% of patients affected by RAEB, RCMD, and CMML in genes known to be pathogenic and prognostic for MDS such as: TET2, IKZF1, WT1, SH2B3, NF1, ASXL1, RUNX1, BCOR, U2A1F, ABL1, PRPF8, SETBP1, ZRSR2, SH2B3, MFSD11, CSDE1 (figure 2 summarizes their frequency.)
Target Gene Prediction
Based on the miRNA expression result, prediction tools for miRNA target gene prediction were used as explained in the supplemental material.
The target genes identified were compared to the complete list of genes known to be involved in MDS from the related page of the Online Mendelian Inheritance in Man (OMIM).
This latter list is composed of the following three groups of genes: TET2, SF3B1, ASXL1, and GNB1 belong to the group of genes characterized by somatic mutations; GATA2, TERC, and TERT belong to the group of genes characterized by heterozygous germline mutations that predispose patients to MDS and other myeloid disorders and the last group of genes, which are DNMT3A, U2AF1, and JAK2, are characterized by heterozygous somatic mutations.
When we compared the lists of targets, coming from the miRNAs signature identified in the multiclass task, with the above-described OMIM list, we found the first and the last group of genes be involved in disease expression in our cohort of patients.
Our miRNA signature did not identify the genes belonging to germline predisposition.
Functional characterization of the target genes list
Aiming to identify which gene pathways were involved (mostly silenced) by the miRNA signature detected, a functional analysis using the webtoolkit WebGestalt was used19.
Within this tool, we considered KEGG a database of pathways20.
The miRNA enrichment analysis allowed the identifications of genes involved in (i) pathways (some in common and some specific for a WHO class) likely involved in the onset of MDS, (ii) in pathways of diseases related to MDS and disease evolution like the acute myeloid leukemia, as summarized in figure 3.
Through the miRNA signature and its enrichment, it was possible to detect some known gene pathways involved in disease pathogenesis and progression, such as: cAMP signaling, mTOR, hedgehog, TGF-beta signaling, hypoxia-inducible factor, and phospholipase D signaling.
It was also possible to identify other gene pathways that may contribute to some of the symptoms reported by MDS patient; the miRNA signature is suggesting likely the silencing of different genes involved in the T-cell receptor signaling21 (i.e., AKT serine/threonine kinase 1-2-3, CD3e, CD4, CD8a, CD20, and CD40 ligand), FC gamma receptor, calcium signaling, adherens junction, and CD4622.
Functional characterization in RAEB patients
A specific focus was directed to those patients affected by RAEB because they had detectable blasts and likely had more DNA damage.
Among the miRNAs expressed in most of the patients belonging to the RAEB class, miR-1270 and miR-1179 emerged because the functional characterization of their target genes achieved good enrichment results. Both are targeting TET2, likely as suppressors of its expression.
The enrichment in KEGG gave a statistically relevant association of 16 genes pathways targeted by miR-1270, whereas miR1179 can influence 19 genes pathways (table 3 and table 4 show which gene pathways are controlled).
Table 3 -- Result of the enrichment analysis in KEGG of the miR-1270 target genes list. The genes predicted to be regulated by miR-1270 were functionally characterized by enrichment analysis in KEGG database. The sixteen pathways shown in this table derived from a selection of the most relevant in the MDS context. FDR, False Discovery Rate. #Genes, number of genes belonging to our list of genes predicted to be regulated by miR-1270.
Name
|
#Genes
|
FDR
|
Signaling pathways regulating pluripotency of stem cells
|
85
|
2.35e-05
|
ErbB signaling pathway
|
57
|
3.47e-05
|
Proteoglycans in cancer
|
112
|
8.59e-05
|
Phospholipase D signaling pathway
|
82
|
1.52e-04
|
Ras signaling pathway
|
121
|
1.52e-04
|
EGFR tyrosine kinase inhibitor resistance
|
51
|
1.52e-04
|
AMPK signaling pathway
|
72
|
1.52e-04
|
Sphingolipid signaling pathway
|
69
|
2.91e-04
|
Focal adhesion
|
107
|
3.13e-04
|
FoxO signaling pathway
|
75
|
3.52e-04
|
Choline metabolism in cancer
|
59
|
4.52e-04
|
Rap1 signaling pathway
|
110
|
4.90e-04
|
AGE-RAGE signaling pathway
|
58
|
8.59e-04
|
mTOR signaling pathway
|
82
|
1.24e-03
|
T cell receptor signaling pathway
|
59
|
1.54e-03
|
Calcium signaling pathway
|
92
|
4.09e-03
|
Table 4 -- Result of the enrichment analysis in KEGG of the miR-1179 target genes list. The genes predicted to be regulated by miR-1179 were functionally characterized by enrichment analysis in KEGG database. The nineteen pathways shown in this table derived from a selection of the most relevant in the MDS context. The pathways in bold are in common with the ones found enriched in the miR-1270 functional analysis. FDR, False Discovery Rate. #Genes, number of genes belonging to our list of genes predicted to be regulated by miR-1179.
Name
|
#Genes
|
FDR
|
Proteoglycans in cancer
|
100
|
2.25e-04
|
AMPK signaling pathway
|
65
|
4.12e-04
|
Longevity regulating pathway
|
52
|
4.12e-04
|
Choline metabolism in cancer
|
82
|
8.56e-04
|
Sphingolipid signaling pathway
|
62
|
8.56e-04
|
P53 signaling pathway
|
39
|
1.3e-03
|
EGFR tyrosine kinase inhibitor resistance
|
44
|
1.55e-03
|
mTOR signaling pathway
|
74
|
1.77e-03
|
Ubiquitin mediated proteolysis
|
66
|
2.72e-03
|
Signaling pathways regulating pluripotency of stem cells
|
68
|
2.72e-03
|
Phosphatidylinositol signaling system
|
50
|
3.47e-03
|
Ras signaling pathway
|
101
|
4.38e-03
|
PI3K-AKT signaling pathway
|
143
|
4.67e-03
|
Rap1 signaling pathway
|
94
|
4.84e-03
|
Transcriptional misregulation in cancer
|
81
|
6.48e-03
|
Fc gamma R-mediated phagocytosis
|
46
|
8.21e-03
|
Adherens junction
|
38
|
8.77e-03
|
Central carbon metabolism in cancer
|
35
|
8.77e-03
|
AGE-RAGE signaling pathway
|
49
|
8.77e-03
|
Among these 16 genes pathways, NF1, ABL1, and TP53 were also detected with our NGS analysis (not all patients with RAEB).
Since the TET2 gene is essential in the pathogenesis of MDS and can be prognostic in terms of therapy23, it was assessed the possible interactions of this gene with those belonging to some interesting KEGG pathways listed in Table 3 and 4 using the web tool STRING.
It was possible to detect 85 protein-protein interactions between TET2 and genes of the “Signaling pathways regulating pluripotency of stem cells” (influenced by miR1270 and miR1179) (Figure 4).
This network shows that the interaction between TET2 and TCF3 is experimentally known24 (pink edge), while the interactions between TET2 and the other three proteins, that are JARID2, KRAS, and JAK2, derived from co-expression experiments in homologs proteins in other species.
JARID2 encodes for a transcriptional repressor that interacts with the Polycomb repressive complex 2 (PRC2), which plays an essential role in regulating gene expression during embryonic development. This protein facilitates the recruitment of the PRC2 complex to target genes. Mutations in this gene are associated with myeloid malignancies25.
Shared miRNAs and early steps in MDS pathogenesis.
The miRNA signature and its enrichment also allowed to identify of a list of miRNAs whose targets are involved in regulating some genetic pathways that are in common between the different WHO classes.
This list comprehends eight miRNAs that are miR-10b-5P, miR-100-5p, miR-183-5p, miR-1179, miR-1270, miR-141-3p, miR-1292-5p, miR-1197. AMPK, EGFR, RAS, longevity, RAP1 represent those gene pathways modulated by the list of miRNAs mentioned above.
This result suggests that several genes in different gene pathways may be affected by these eight miRNAs. Table 5 is summarizing which biological processes are influenced.
Table 5 – List of significant GO terms belonging to biological process and molecular function and of significant KEGG pathways related to STRING network shown in Figure 5.. This table show the enrichment in Gene Ontology (GO) and in KEGG related to the gene list gave in input to STRING in order to identify the network shown in figure 5. FDR, False Discovery Rate.
Biological Process
|
Count in gene set
|
FDR
|
hematopoietic or lymphoid organ development
|
10 of 573
|
2.56e-05
|
response to oxygen-containing compound
|
13 of 1427
|
6.26e-05
|
hemopoiesis
|
9 of 526
|
6.26e-05
|
intracellular signal transduction
|
13 of 1528
|
9.07e-05
|
regulation of primary metabolic process
|
23 of 5982
|
0.00025
|
Molecular Function
|
Count in gene set
|
FDR
|
pre-mRNA binding
|
4 of 36
|
0.00012
|
heterocyclic compound binding
|
21 of 5305
|
0.00040
|
organic cyclic compound binding
|
21 of 5382
|
0.00040
|
phosphoprotein binding
|
4 of 80
|
0.00063
|
nucleic acid binding
|
16 of 3332
|
0.00063
|
KEGG pathways
|
Count in gene set
|
FDR
|
RAS signaling pathway
|
12 of 228
|
1.65e-13
|
Glioma
|
6 of 68
|
1.34e-07
|
EGFR tyrosine kinase inhibitor resistance
|
6 of 78
|
1.93e-07
|
Rap1 signaling pathway
|
7 of 203
|
1.29e-06
|
Since these miRNAs are present in all patients, it is reasonable to suggest that these may be the ones to be deranged likely at the onset of disease. Also, they are likely working as post-transcriptional suppressor in gene pathways that have been described to be fundamental for normal haemopoiesis26,27,28 .
STRING tool allowed to detect protein-protein interactions between the genes controlled by the miRNA signature and the genes mutated detected in NGS (figure 5).
Three genes are outliers in STRING: RAPGEF5, CSDE1, MSFD1. This result is suggesting that even if the miRNA signature may negatively influence the expression of these genes, they could be not involved neither in MDS pathogenesis or disease progression.