Abstract
Background
An important challenge in cancer biology is to understand the complex
aspects of the disease. It is increasingly evident that genes are not
isolated from each other and the comprehension of how different genes
are related to each other could explain biological mechanisms causing
diseases. Biological pathways are important tools to reveal gene
interaction and reduce the large number of genes to be studied by
partitioning it into smaller paths. Furthermore, recent scientific
evidence has proven that a combination of pathways, instead than a
single element of the pathway or a single pathway, could be responsible
for pathological changes in a cell.
Results
In this paper we develop a new method that can reveal miRNAs able to
regulate, in a coordinated way, networks of gene pathways. We applied
the method to subtypes of breast cancer. The basic idea is the
identification of pathways significantly enriched with differentially
expressed genes among the different breast cancer subtypes and normal
tissue. Looking at the pairs of pathways that were found to be
functionally related, we created a network of dependent pathways and we
focused on identifying miRNAs that could act as miRNA drivers in a
coordinated regulation process.
Conclusions
Our approach enables miRNAs identification that could have an important
role in the development of breast cancer.
Electronic supplementary material
The online version of this article (doi:10.1186/s12859-016-1196-1)
contains supplementary material, which is available to authorized
users.
Keywords: miRNAs, Pathway cross-talk, Breast cancer, Network of
pathways
Background
The identification of breast cancer (BC) gene signatures based on
morphology (stage and grade) and two key markers, estrogen receptor
(ER) and human epidermal growth factor receptor 2 (HER2), are a
challenge for current clinical practice [[36]1–[37]5].
However, the landscape of alterations in BC is more complex and
heterogeneous. With the introduction of gene expression microarrays and
next generation sequencing (NGS), additional studies on the molecular
classification of BC were carried out. These have led to the
identification of four molecular subtypes associated with distinct
characteristics, distinct genetic mechanisms of disease and differences
in patient survival [[38]6, [39]7]: Luminal A, Luminal B, Triple
Negative/Basal like and HER2 subtypes [[40]6].
By expression profiling, the large majority of ER+ and/or progesterone
receptor PgR+ tumours are “luminal subtypes” [[41]8, [42]9]: Luminal A
and Luminal B; they have a relatively good prognosis with the former
being typically low grade [[43]10, [44]11]. Luminal A is the most
common subtype and represents 50 %–60 % of all BC and typically highly
expresses regulated gene SLC39A6 (solute carrier family 39 (zinc
transporter), member 6), transcription factors GATA3, FOXA1 and XBP1,
and luminal cytokeratins KRT8 and KRT18 [[45]12, [46]13]. Luminal B
comprises 15 %–20 % of BC and has a more aggressive phenotype and lower
survival rates after relapse [[47]14–[48]17]. It shows an increased
expression of proliferation-related genes such as avian myeloblastosis
viral oncogene homolog (v-MYB), gamma glutamyl hydrolase (GGH),
lysosome-associated transmembrane protein 4-beta (LAPTMB4), nuclease
sensitive element binding protein 1 (NSEP1) and cyclin E1 (CCNE1)
[[49]18].
For a successful discrimination of luminal-B tumours from luminal-A in
clinical practice, Cheang et al. [[50]8] suggested an
immunohistochemistry proliferation marker, the Ki67 hormone receptor.
The authors determined the Ki67 cut off point (14 %) that discriminates
luminal-A from luminal-B tumours. However, Ki67 immunohistochemistry
shows some limitations, such as low intra- and inter-laboratory
reproducibility, the arbitrary selection of standard antibodies for
testing, in addition to potential problems resulting from tumour
heterogeneity [[51]9].
Concerning the response to therapy of BC subtypes, Luminal B responds
better to neoadjuvant chemotherapy, but is less responsive to hormonal
therapy than Luminal A [[52]9]. Potential targets in Luminal B are
insulin-like growth factor 1 (IGF-1) signalling, fibroblast growth
factor (FGF) signalling, Phosphoinositide 3-kinase signalling (PI3K)
[[53]19].
The interplay between ER and insulin-like growth factor 1 receptor
(IGF-1R) shows a critical role in tamoxifen resistance. High
circulating plasma levels of IGF-1, a ligand for IGF-1R, are detected
in women with an increased risk of relapse on adjuvant tamoxifen
[[54]20].
Several studies indicate that the FGF factor, involved in angiogenesis
[[55]21, [56]22], and its receptor FGFR1 are amplified in cells
resistant to endocrine therapy [[57]23, [58]24]. Knockdown of FGFR1
and/or the use of a small molecule FGFR tyrosine kinase inhibitor could
reverse resistance to endocrine therapy [[59]23, [60]24].
Several methods to interrupt IGF-1 signalling, FGF signalling, and PI3K
have been proposed [[61]19, [62]25, [63]26]. Creighton et al. [[64]27]
suggested that the combined effect of endocrine therapy luminal-B BC
cell lines and PI3K inhibitor could increase growth inhibition induced
by the only endocrine therapy. Atzori et al. [[65]26] developed
antibodies against IGF-1R that block IGF-1 ligand-mediated activation
and small-molecule inhibitors of the IGF-1R tyrosine kinase domain.
Some antibodies and small-molecule inhibitors against FGFR are
currently in clinical testing, such as TKI-259 single agent, and
Exemestane [[66]27]. Agents targeting the PI3K pathway comprise
rapamycin analogues or mTOR inhibitors [[67]27].
The basal-like subtype, one of the most clinically aggressive groups
among the different subtypes, represents 8 %–37 % of all BC, and is the
one with highest rate of metastasis to the brain and lung [[68]28]. It
is more commonly negative for all 3 markers—ER, PgR, and HER2—the
“triple-negative” phenotypic classification [[69]16].
There are several other biomarkers associated with the basal subtype as
well as putative candidates suitable for immunohistochemical screening
[[70]29–[71]31].
An association between the basal subtype and germline mutations in the
BRCA1 gene, often termed the “caretaker of the genome”, has been well
described, and it may be speculated that both inherent DNA
damage–sensing processes and DNA repair mechanisms are central in the
development of basal-like tumours [[72]29–[73]31]. However, currently,
there is no specific international agreement on complementary
biomarkers that can define basal-like cancers [[74]32].
Given the lack of validated molecular targets in basal BC, conventional
chemotherapy has been the only therapeutic option for women with this
kind of tumour [[75]33]. Based on BRCA1 mutations, some studies
explored the use of platinum chemotherapy agents (carboplatin,
cisplatin, and others) [[76]33]. Antiangiogenic therapies targeting
VEGF and its receptors have emerged as promising therapies given the
evidence of aberrant VEGF pathway activation in basal BC [[77]34].
Moreover, small-molecule and antibody-based EGFR inhibitors are also
explored as targeted therapies [[78]35].
HER2-positive (HER2+) cancer represents 15–20 % of BC subtypes. HER2+
confers more aggressive biological and clinical behaviour [[79]36].
These tumours present a high expression of the HER2 gene and of other
genes associated with the HER2 pathway [[80]37].
Currently, the treatment in advanced HER2+ BC is the combination of
trastuzumab, pertuzumab, and the chemotherapy agent taxane [[81]37].
High expression of HER2 promotes tamoxifen resistance and the addition
of trastuzumab could improve the tamoxifen response [[82]38].
This BC variety has consequences in the diverse clinical behaviour and
provides critical insight for the development of personalized
therapies.
However, gene expression microarrays and NGS data have many more genes
than number of samples, and methods to reduce the dimension of genes in
functional units, such as gene sets, pathways, and network modules,
have been recently explored [[83]39, [84]40]. These aggregation methods
are based on the statement that the set of genes involved in the same
biological processes are often collaborative in the development and
progression of BC, and show an easier interpretation of the underlying
biology [[85]41, [86]42].
Methodologies to identify the set of genes enriched from a genetic
signature whose combined expression pattern is that uniquely
characteristic of a given phenotype are promising approaches.
Alterations of the interplay among pathways leading to uncontrolled
cellular proliferation, survival, invasion, and metastases are
hallmarks of the BC process.
The mitogen activated protein kinase (MAPK), phosphatidylinositol
3-kinase (PI3K), Akt and nuclear factor kappa B (NF-kB) are commonly
de-regulated in different BC subtypes [[87]36]. Raf/mitogen-activated
and extracellular signal-regulated kinase (MEK)/extracellular
signal-regulated kinase (ERK) are critical for normal human physiology,
and also commonly dysregulated in several human cancers, including BC
[[88]43].
Saini et al. [[89]43] suggested, both in vitro and in vivo, that
PI3K/AKT/mTOR and Raf/MEK/ERK cascades are interconnected and the
inhibition of one pathway can still result in a deregulation of the
other. Notch signalling pathway is associated with many oncogenic
signalling pathways, such as developmental signals, i.e., Wnt and
Hedgehog signalling, growth and transcriptional factors, cytokines, and
oncogenic kinases [[90]44]. A review [[91]45] examined the molecular
basis of the collaboration and integration of the ER, and MAPK.
Currently, two main approaches have been proposed to identify
functional units deregulated in a disease. One approach is to identify
de novo functional units from the data. Following this approach, van
Vliet proposed an unsupervised method to identify gene patterns using a
score applied to a Bayes classifier [[92]46]. Ma et al. [[93]47] used
weighted co expression networks and their module to describe the
collaboration among genes.
The other main approaches used existing functional units to build
prognostic or diagnostic analysis. Abraham et al. proposed features
derived from pre-specified gene sets from the Molecular Signatures
Database (MSigDB), and, by using a statistical test aggregating the
expression levels of all genes within a set, they derived prognostic
gene sets [[94]48]. Huang et al. [[95]40] proposed a new pathway-based
de-regulation scoring matrix transforming the gene features in
combination with Cox regression and L1-LASSO regularization to model
survivals. A genomic model consisting of fifteen cancer relevant
pathways was revealed and validated on three independent BCs.
Deregulation of signalling events in a given cancer sample is of great
clinical interest in order to identify candidate drugs developed to
specifically modulate upstream signalling events [[96]49]. Recent
progress in cancer biology has revealed that miRNAs are potential
therapeutic targets suggesting the introduction of the miRNA mimic
oligonucleotides in Phase I cancer clinical trials.
Oncogenic or tumour suppressive miRNAs have been implicated in the
regulation of central cellular pathways, such as differentiation and
apoptosis, across several tumour types [[97]50], but the discovery of
how a miRNA regulates its targets in tumour samples is still
challenging. Recent studies revealed, for instance, that Hsa-miR-21 is
up regulated in BC [[98]51], while Hsa-miR-335 and Hsa-miR-200c have
been shown to inhibit metastatic cell invasion [[99]52].
Emerging evidence demonstrates that miRNAs play an essential role in
controlling stem cell properties by regulating, for instance,
epithelial to mesenchymal transition (EMT) [[100]53]. EMT has a
fundamental role in cancer cells with the loss of intracellular
junctions and epithelial polarity. Several miRNAs, such as Hsa-let-7,
Hsa-miR-10, Hsa-miR-34, Hsa-miR-200, and Hsa-miR-205 are described as
regulator of this process [[101]53].
Other miRNAs have been reported to have an active role in tumour
proliferation control. Hsa-miR-92a can promote tumour proliferation by
controlling the PI3K/Akt/mTOR pathway [[102]54]. Several other miRNAs
were found to be up-regulated in BC; these include the Hsa-miR-221/222
cluster [[103]55], Hsa-miR-9, Hsa-miR10b, Hsa-miR-29a, Hsa-miR-96,
Hsa-miR-146a, Hsa-miR-181, Hsa-miR-373, Hsa-miR-375, Hsa-miR-520c, and
Hsa-miR589 [[104]56], suggesting their potential use for BC diagnosis,
prognosis, and therapeutic studies [[105]55, [106]56].
All these findings demonstrate the ability of miRNAs to regulate the
development of malignancies modulating critical cancer-related genes
and signalling pathways.
While many studies demonstrated the role of miRNA-target interactions
in a single pathway, there are little evidence on the interaction of
specific miRNAs with genes of different pathways.
Hsa-miR-125, whose expression correlates with the HER2 status
[[107]57], has been shown to be significantly down regulated in BC
[[108]58]. Experimentally, the overexpression of Hsa-miR-125 decreases
the expression level of ERBB2 and ERBB3, reducing cell motility and
invasiveness of numerous cancers, including BC [[109]59]. The Let-7
regulatory network suppresses metastasis acting on the
chromatin-remodelling protein HMGA2 and the transcription factor BACH1
[[110]60]. Both targets promote the transcription of pro-invasive genes
that regulate cell invasion and metastasis to the bone [[111]60].
Another important miRNA in BC is Hsa-miR-206. It has been found to be
down-regulated in ERα-positive BC, both in patient samples and BC cell
lines [[112]61], and in lymph node metastatic BC [[113]62]. A critical
role of Hsa-miR-206 has been recently demonstrated in the regulation of
the 3′ UTR of cyclin D1, inducing G1 arrest and a decrease in cell
proliferation in BC cells [[114]63], thus suggesting a potential role
as a tumour suppressor. It has been also shown that Hsa-miR-206
regulates ERα via interaction with its 3′ UTR [[115]64], demonstrating
a specific function in most aggressive types of BC.
In this work we developed a method to detect miRNAs regulating pathway
interactions, based on the integration of gene expression profiles and
biological pathways and miRNAs. We validated the approach in BC
subtypes, obtaining, for each BC subtype, a network of pathways
enriched from differentially expressed genes. We focused on the pairs
of pathways able to differentiate a particular BC subtype with respect
to the normal type. miRNAs significantly enriched from their gene
targets in at least two pathways were found to be key regulators of
interacting pathways.
Methods
Breast cancer subtypes
In our study we focused on four different BC subtypes: luminal A,
luminal B, basal, and HER2 which we compared with normal samples (NS).
We considered the expression level of mRNAs and miRNAs extracted from a
TCGA BC data set. We performed a quantile analysis on TCGA miRNAs and
mRNA, in order to exclude genes and miRNAs with a small variance, thus
obtaining 1046 miRNAs and of 15243 genes. We then used BC matched
samples miRNA-mRNA for all the subsequent analyses.
Luminal A vs. NS
We used 233 BC luminal A samples and 113 NS for mRNA analysis, and 233
BC luminal A samples and 87 NS for miRNA analysis.
Luminal B vs. NS
We used 103 BC luminal B samples and 113 NS for mRNA analysis, and 103
BC luminal A samples and 87 NS for miRNA analysis.
Basal vs. NS
We used 74 BC Basal samples and 113 NS for mRNA analysis, and 74 BC
Basal samples and 87 NS samples for miRNA analysis.
HER2 vs. NS
We used from 43 BC HER2 samples and 113 NS for mRNA analysis, and 43 BC
HER2 samples and 87 NS for miRNA analysis.
Grouping and bootstrapping analysis
We performed an analysis based on several boots, with each boot
consisting of four steps and working on different (randomly selected)
training and testing data sets.
In order to perform bootstrapping, we implemented a classifier based on
Monte Carlo cross validation, that randomly splits a part of the
original data in the training data set (60 % in our case) and the rest
of original data in the testing set (40 % in our case). The first,
second and third step are performed on the training data set, the
fourth step both on the training and testing data set.
In order to avoid problems of unbalanced classes of BC and NS, we
randomly selected classes with the same number of BC and NS in both the
training and testing dataset.
Differentially expressed genes: 1st step
Differentially expressed genes between each subtype class of BC samples
and class of NS were identified by statistical analysis using the
function TCGAanalyze DEA from the package TCGAbiolinks from
Bioconductor. The following parameters were used: quantile-adjusted
conditional maximum likelihood, abs(log fold change) > 1, and
FDR < 0.01 [[116]65]. The obtained p-values were adjusted by using the
Benjamin-Hochberg procedure for multiple testing correction [[117]66].
Pathways enriched from differentially expressed genes: 2nd step
Given 589 pathways derived from the Ingenuity Pathway Analysis (IPA)
database, a pathway enrichment analysis was applied. The enrichment was
evaluated using the Fisher’s Exact Test between differentially
expressed genes and IPA pathways. We considered a pathway to be
enriched if p-value was <0.01.
Interacting pathways: 3rd step
Interactions among the enriched pathways were quantified by an
interaction score (IS), defined as:
[MATH: IS=Mx−My/Sx+Sy :MATH]
where M[X], S[X], M[Y] and S[Y] represent the mean and the standard
deviation of expression levels of genes in pathways X and Y,
respectively. Maximum cross-talk was found for IS near 0.
For every comparison (BC subtype vs NS), we obtained a matrix of IS,
with each raw corresponding to each BC sample and each column
corresponding to IS related to each pair of significantly enriched
pathways.
Identification of the best pathways for breast cancer subtype classification:
4th step
For every comparison (BC subtype vs NS), for the training data set, we
used the matrix of IS to classify the class of BC samples and NS. We
used Random Forest algorithm (RF) from the R-package [[118]67], setting
the following parameters: number of variables randomly sampled at each
split = sqrt(p), p being the number of variables in the matrix of data;
and the number of trees grown = 500. In order to validate the
classifier, we used a k-fold cross-validation (k = 10 except in case of
HER2 vs. normal samples, given the reduced number of samples we used
k = 5) obtaining Area Under the Curve (AUC).
We thus selected the 10 pairwise pathways with the best AUC in the
classification of the different BC subtypes vs NS.
We finally validated the classification using the top 10 pairwise
pathways and the same k-fold cross-validation on the testing data set
in terms of AUC.
The four steps described above were repeated multiple times (50
bootstraps).
Specifically, each bootstrap generated from a training dataset i) 1
step: a list of differentially expressed genes, ii) 2 step: a list of
pathways significantly enriched by differently expressed genes, iii) 3
step: a subtype-specific matrix of IS for each pair of pathways
significantly enriched, and iii) 4 step: the top 10 pairwise pathways
with the best AUC performance.
In conclusion, for each subtype and for all 50 bootstraps, we obtained
the 10x50 (=500) pairwise pathways with the best AUCs., from which we
selected, by ranking their frequency, the top 10 pairwise pathways.
miRNAs regulating the top 10 pairwise pathways
Mutual Information (MI) was applied between the dataset of 1046 miRNAs
and 15243 genes, providing a linking index between miRNAs and genes. MI
was calculated using entropy estimates from K-nearest neighbour
distances [[119]68] with the R-package parmigene [[120]69]. In this
step we obtained a list of candidate target genes for each miRNA.
In order to link miRNAs with the top 10 pairs of pathways, we applied a
Fisher’s Exact Test between candidate target genes (as obtained from
MI) and genes within each pairwise pathway (when p-value <0.01 in both
pathways). We thus identified a group of miRNAs regulating the top 10
pairwise pathways (miR-r). Then, we focused only on those miR-r
differentially expressed between each BC subtype and NS
(quantile-adjusted conditional maximum likelihood, p-values adjusted
using the Benjamin-Hochberg procedure for multiple testing correction
[[121]66]). Figure [122]1 shows the proposed procedure.
Fig. 1.
Fig. 1
[123]Open in a new tab
Proposed approach
Results
Luminal A vs. NS
After 50 bootstraps, among the 50x10 = 500 pairwise pathways, we found
157 pairwise pathways enriched with 4703 differentially expressed
genes. Indeed, many pathways were found in common among the 10 top
pairs in many bootstraps.
The final top 10 pairwise pathways, selected according to their
frequency in the top 10 in all bootstraps from the 157 pairwise
pathways, are shown in Table [124]1.
Table 1.
Luminal A: frequency of pairwise pathways in the top 10 positions for
all 50 bootstraps
Pairwise pathway Frequency
1) Ethanol Degradation IV;Glioma Invasiveness Signalling 31/50
2) Intrinsic Prothrombin Activation Pathway; Extrinsic Prothrombin
Activation Pathway 28/50
3) Ethanol Degradation IV;Estrogen Receptor Signalling 25/50
4) Axonal Guidance Signalling; Acute Phase Response Signalling 17/50
5) Ethanol Degradation IV;Regulation of Cellular Mechanics by Calpain
Protease 16/50
6) Glioma Invasiveness Signalling; Dopamine Degradation 15/50
7) Glioma Invasiveness Signalling; Fatty Acid oxidation 12/50
8) Glioma Invasiveness Signalling; Tryptophan Degradation X (Mammalian,
via Tryptamine) 12/50
9) Acute Phase Response Signalling; HIF1 Signalling 11/50
10) Glioma Invasiveness Signalling; Oxidative Ethanol Degradation III
10/50
11) Axonal Guidance Signalling; Gs Signalling 9/50
12) HIF1 Signalling; Fatty Acid-oxidation 9/50
13) Oxidative Ethanol Degradation III; Estrogen Receptor Signalling
9/50
14) Retinoate Biosynthesis I; Estrogen Receptor Signalling 8/50
15) Tryptophan Degradation X (Mammalian, via Tryptamine); Glioma
Invasiveness Signalling 8/50
….
50) …. 1/50
[125]Open in a new tab
Dots indicate the other pairs of pathways with minor frequency
Figure [126]2 shows a boxplot with the AUC values for the final 10
pairwise pathways in both the training and testing phase. Both AUC
values are good (median >90 %), although the performance of training is
better.
Fig. 2.
Fig. 2
[127]Open in a new tab
Boxplot of AUC values for the top 10 enriched pairwise pathways in
luminal A, after all 50 bootstraps
Figure [128]3 shows, for each bootstrap, the AUC of the top 10 pairs of
pathways. We can see that some pairwise pathways (e.g. Ethanol
Degradation IV; Glioma Invasiveness Signalling, Intrinsic Prothrombin
Activation Pathway; Extrinsic Prothrombin Activation Pathway) have
excellent AUC in most bootstraps.
Fig. 3.
Fig. 3
[129]Open in a new tab
AUC values representation with the top 10 pairwise pathways for all 50
bootstraps in luminal A. Yellow square indicates AUC values when the
pairwise pathway was included in the top 10 for the corresponding
bootstrap. Red square indicates that the pairwise pathways was not
present in top 10 for that bootstrap
Figure [130]4 shows the inter-pathway coordination among the final top
10 pairwise pathways in luminal A. Among pathways, the role of Glioma
Invasiveness Signalling, hub of a network linking Tryptophan
Degradation X (Mammalian, via Tryptamine), Ethanol Degradation IV,
Dopamine Deregulation, Oxidative Ethanol Degradation III, and Fatty
Acid-oxidation appears dominant.
Fig. 4.
Fig. 4
[131]Open in a new tab
Interaction of the top 10 pairwise pathways in luminal and their
miRNA-r in BC luminal A
We found six pairwise pathways with 11 significant miRNA regulators: 1)
Acute Phase Response Signalling; HIF1 Signalling, 2) Axonal Guidance
Signalling; Acute Phase Response Signalling 3) Ethanol Degradation IV;
Glioma Invasiveness Signalling, 4) Ethanol Degradation IV; Estrogen
Receptor Signalling 5) Glioma Invasiveness Signalling; Oxidative
Ethanol Degradation III, and 6) Extrinsic Prothrombin Activation;
Intrinsic Prothrombin Activation. Four pairwise pathways were not
significantly deregulated by any miRNA.
Table [132]2 lists, for each of the six above mentioned pairwise
pathways, their miRNA-r regulators with, their expression levels in BC
luminal A and in NS, and the statistical significance of the comparison
(in terms of log Fold Change).
Table 2.
For each top 6 pairwise pathway in luminal A: miRNA regulators of
pathways, their expression levels in BC and in NS, and the statistical
significance of the comparison (in terms of log Fold Change)
Pairwise pathways miRNA-r miRNA-r Exp. in BC miRNA-r Exp. in NS
Statistical significance (log Fold Change)
1. a) Acute Phase Response Signalling;
b) HIF1 Signalling Hsa-miR-205 13006.16 25001.9 -1.01017
2. a) Axonal Guidance Signalling;
b) Acute Phase Response Signalling Hsa-miR-452
Hsa-miR-335
Hsa-miR-205
Hsa-miR-99a
Hsa-miR-337
Hsa-miR-1250 101.1974
333.4506
13006.16
4268.871
208.6352
0.746781 720.023
1391.644
25001.9
10953.55
547.3908
0.103448 -3.08782
-2.27528
-1.01017
-1.71181
-1.57547
1.636118
3. a) Ethanol Degradation IV;
b) Glioma Invasiveness Signalling Hsa-miR-3199-1 1.793991 5.45977
-1.79743
4. a) Ethanol Degradation IV;
b) Estrogen Receptor Signalling Hsa-miR-1-1 0.021459 1.62069 -3.82055
5. a) Glioma Invasiveness Signalling;
b) Oxidative Ethanol Degradation III Hsa-miR-3199-1 1.793991 5.45977
-1.79743
6. a) Extrinsic Prothrombin Activation;
b) Intrinsic Prothrombin Activation Hsa-miR-1537
Hsa-miR-210
Hsa-miR-99a
Hsa-miR-381 0.957082
1592.884
4268.871
138.0215 0.333333
317.8046
10953.55
259.954 1.091521
2.397213
-1.71181
-1.25098
[133]Open in a new tab
The results of the MI analysis for the identification of candidate
genes target of miRNAs are shown in the Additional file [134]1.
Luminal B vs. Normal
After 50 bootstraps, we found 129 pairwise pathways enriched with 5590
differentially expressed genes. Similarly to Luminal A. Many pathways
were found in common among the top 10 pairs in many bootstraps.
The final top 10 pairwise pathways selected according to their
frequency in the top 10 in all bootstraps from the 129 pairwise
pathways, are shown in Table [135]3.
Table 3.
Luminal B: frequency of pairwise pathways in the top 10 for all 50
bootstraps
Pairwise pathway Frequency
1) Wnt/catenin Signalling;Mitotic Roles of Polo-Like Kinase 46/50
2) Epithelial Adherens Junction Signalling; Mitotic Roles of Polo-Like
Kinase 39/50
3) Mitotic Roles of Polo-Like Kinase; Growth Hormone Signalling 26/50
4) Wnt/catenin Signalling;Cell Cycle Control of Chromosomal Replication
20/50
5) LPS/IL1 Mediated Inhibition of RXR Function; Assembly of RNA
Polymerase II Complex 11/50
6) Calcium Signalling;Cell Cycle Control of Chromosomal Replication
11/50
7) Mitotic Roles of Polo-Like Kinase;RhoA Signalling 11/50
8) Epithelial Adherens Junction Signalling; Cell Cycle Control of
Chromosomal Replication 10/50
9) Mitotic Roles of Polo Like Kinase; Factor Promoting Cardiogenesis in
Vertebrates 9/50
10) Epithelial Adherens Junction Signalling; EIF2 Signalling 9/50
11) Acute Phase Response Signalling; HIF1 Signalling 9/50
12) Cellular Effects of Sildenafil (Viagra); Cell Cycle Control of
Chromosomal Replication 9/50
13) ILK Signalling;Mitotic Roles of Polo-Like Kinase 9/50
14) Glioblastoma Multiforme Signalling; Mitotic Roles of Polo-Like
Kinase 8/50
15) LPS/IL-1 Mediated Inhibition of RXR Function; linolenate
Biosynthesis II (Animals) 7/50
….
50)… 1/50
[136]Open in a new tab
Dots indicate the other pairs of pathways with minor frequency
Figure [137]5 shows a boxplot with the AUC values for the final 10
pairwise pathways in both the training and testing phase, confirming
the good AUC (median >95 %) both for training and testing.
Fig. 5.
Fig. 5
[138]Open in a new tab
Boxplot of AUC values for the top 10 enriched pairwise pathways in
luminal B, after all 50 bootstraps
Figure [139]6 shows, for each bootstrap, the AUC of the top 10 pairs of
pathways. We can see that some pairwise pathways (e.g. Wnt/ -catenin
Signalling; Mitotic Roles of Polo-Like Kinase and Epithelial Adherens
Junction Signalling; Mitotic Roles of Polo-Like Kinase) have excellent
AUC in most bootstraps.
Fig. 6.
Fig. 6
[140]Open in a new tab
AUC values representation with the top 10 pairwise pathways for all 50
bootstraps in luminal B. Yellow square indicates AUC values when the
pairwise pathway was included in the top 10 for the corresponding. Red
square indicates that the pairwise pathway was not present in top 10
for that bootstrap
Figure [141]7 shows the inter-pathway coordination among the top 10
pairwise pathways in luminal B. We found only 3 miRNAs significantly
deregulating 1 pairwise pathway (Epithelial Adherens Junction
Signalling; EIF2 Signalling) which are also shown. Among pathways, the
role of Mitotic Roles of Polo-like Kinase, hub of a network linking
RhoA Signalling, Epithelial Adherens Junction Signalling, Wnt/catenin
Signalling, Factors Promoting Cardiogenesis in Vertebrates, and Growth
Hormone Signalling appears dominant.
Fig. 7.
Fig. 7
[142]Open in a new tab
Interaction of the top 10 pairwise pathways in luminal B and their
miRNA-r in luminal B BC
Table [143]4 lists, for the above mentioned pairwise pathway, its miRNA
regulators, their expression levels in BC luminal B and in NS, and the
statistical significance of the comparison (in terms of log Fold
Change).
Table 4.
For the top pairwise pathway in luminal B: miRNA regulators of the
pathways, their expression levels in BC and in NS, and the statistical
significance of the comparison (in terms of log Fold Change)
Pairwise pathways miRNA-r miRNA-r Exp. in BC miRNA-r Exp. in NS
Statistical significance (log Fold Change)
1. a) Epithelial Adherens Junction Signalling;
b) EIF2 Signalling Hsa-miR-32
Hsa-miR-3074
Hsa-miR-577 114.3883
77.45631
4.640777 50.54023
33.32184
15.78161 1.579455
1.368266
-1.48828
[144]Open in a new tab
The results of the MI analysis for the identification of candidate
genes target of miRNAs are shown in the Additional file [145]2.
Basal vs. Normal
After 50 bootstraps, we found 74 pairwise pathways enriched with 6011
differentially expressed genes, since many pathways were found in
common among the top 10 pairs in many bootstraps.
The final top 10 pairwise pathways, selected according to their
frequency in the top 10 in all bootstraps from the 74 pairwise
pathways, are shown in Table [146]5.
Table 5.
Basal: frequency of pairwise pathways in the top 10 for all 50
bootstraps
Pairwise pathway Frequency
1) Ethanol Degradation IV; Role of BRCA1 in DNA Damage Response 41/50
2) Putrescine Degradation III; Mismatch Repair in Eukaryotes 40/50
3) Ethanol Degradation IV; Mismatch Repair in Eukaryotes 36/50
4) Role of BRCA1 in DNA Damage Response; Oxidative Ethanol Degradation
III 35/50
5) Ethanol Degradation II; Role of BRCA1 in DNA Damage Response 31/50
6) Role of BRCA1 in DNA Damage Response; Histamine Degradation 24/50
7) Tryptophan Degradation X (Mammalian, via Tryptamine); Role of BRCA1
in DNA Damage Response 24/50
8) Putrescine Degradation III;Role of BRCA1 in DNA Damage Response
23/50
9) Role of BRCA1 in DNA Damage Response; Putrescine Degradation III
18/50
10) Cell Cycle Control of Chromosomal Replication; Cellular Effects of
Sildenafil (Viagra) 17/50
11) Cell Cycle Control of Chromosomal Replication; Colorectal Cancer
Metastasis Signalling 16/50
12) Oxidative Ethanol Degradation III; Role of BRCA1 in DNA Damage
Response 14/50
13) Role of BRCA1 in DNA Damage Response; Ethanol Degradation II 13/50
14) Cell Cycle Control of Chromosomal Replication; eNOS Signalling
12/50
15) Mismatch Repair in Eukaryotes; Fatty Acid -oxidation I 12/50
....
50).... 1/50
[147]Open in a new tab
Dots indicate the other pairs of pathways with minor frequency
Figure [148]8 shows a boxplot with the AUC values for the final 10
pairwise pathways in both the training and testing phase, confirming
the good AUC (median >95 %) both for training and testing.
Fig. 8.
Fig. 8
[149]Open in a new tab
Boxplot of AUC values for the top 10 enriched pairwise pathways in
basal, after all 50 bootstraps
Figure [150]9 shows, for each bootstrap, the AUC of the top 10 pairs of
pathways, confirming also for basal, that some pairwise pathways (e.g.
Ethanol Degradation IV; Role of BRCA1 in DNA Damage Response, and
Putrescine Degradation III; Mismatch Repair in Eukaryotes) have
excellent AUC in most bootstraps.
Fig. 9.
Fig. 9
[151]Open in a new tab
AUC values representation with the top 10 pairwise pathways for all 50
bootstraps in basal. Yellow square indicates AUC values when the
pairwise pathway was included in the top 10 for the corresponding
bootstrap. Red square indicates that the pairwise pathway was not
present in top 10 for that bootstrap
Figure [152]10 shows the inter-pathway coordination among the top 10
pairwise pathways in BC basal. We found only 2 miRNAs significantly
deregulating 3 pairwise pathways: 1) Ethanol Degradation IV; Mismatch
Repair in Eukaryotes, 2) Putrescine Degradation III; Role of BRCA1 in
DNA Damage Response, 3) Tryptophan Degradation X (Mammalian, via
Tryptamine); Role of BRCA1 in DNA Damage Response, which are also
shown. Among pathways, the role of BRCA1 in DNA Damage Response, hub of
a network linking Putrescine Degradation III, Ethanol Degradation IV,
Ethanol Degradation II, Histamine Degradation, Oxidative Ethanol
Degradation III, and Tryptophan Degradation X appears dominant.
Fig. 10.
Fig. 10
[153]Open in a new tab
Interaction of the top 10 pairwise pathways in BC basal and their
miRNA-r in BC basal
Table [154]6 lists, for the 3 above mentioned pairwise pathways, miRNA
regulators of pathways, their expression levels in BC basal and in NS,
and the statistical significance of the comparison (in terms of log
Fold Change).
Table 6.
For each top 3 pairwise pathway in BC basal: miRNA regulators of
pathways, their expression levels in BC and in NS, and the statistical
significance of the comparison (in terms of log Fold Change)
Pairwise pathways miRNA-r miRNA-r Exp. in BC miRNA-r Exp. in NS
Statistical significance (log Fold Change)
1. a) Ethanol Degradation IV;
b) Mismatch Repair in Eukaryotes Hsa-miR-135b 342.0541 13.01149
4.96603
2. a) Putrescine Degradation III;
b) Role of BRCA1 in DNA Damage Response Hsa-miR-365-2 153.5541
506.0575 -1.39949
3. a) Tryptophan Degradation X (Mammalian, via Tryptamine);
b) Role of BRCA1 in DNA Damage Response Hsa-miR-365-2 153.5541
506.0575 -1.39949
[155]Open in a new tab
The results of the MI analysis for the identification of candidate
genes target of miRNAs are shown in the Additional file [156]3.
HER2 vs. Normal
After 50 bootstraps, we found 222 pairwise pathways enriched with 4464
differentially expressed genes.
The final top 10 pairwise pathways, selected according to their
frequency in the top 10 in all bootstraps from the 222 pairwise
pathways, are shown in Table [157]7.
Table 7.
HER2: frequency of pairwise pathways in the top 10 for all 50
bootstraps
Pairwise pathway Frequency
1) Axonal Guidance Signalling; CXCR4 Signalling 18/50
2) Atherosclerosis Signalling; Acute Phase Response Signalling 16/50
3) Role of Macrophages, Fibroblasts and Endothelial Cells in Rheumatoid
Arthritis; Growth Hormone Signalling 12 /50
4) HIF1 Signalling; Glioblastoma Multiforme Signalling 11/50
5) Putrescine Degradation III; NAD biosynthesis II (from tryptophan)
11/50
6) HIF1 Signalling; Growth Hormone Signalling 11/50
7) Axonal Guidance Signalling; P2Y Purigenic Receptor Signalling
Pathway 10/50
8) Acute Phase Response Signalling; HIF1 Signalling 8/50
9) Axonal Guidance Signalling; Growth Hormone Signalling 7/50
10) Cellular Effects of Sildenafil (Viagra); tRNA Charging 7/50
11) Hepatic Fibrosis/Hepatic Stellate Cell Activation; Coagulation
System 6/50
12) Acute Phase Response Signalling; Role of Macrophages, Fibroblasts
and Endothelial Cells in Rheumatoid Arthritis 6/50
13) Retinoate Biosynthesis I; Estrogen Receptor Signalling 6/50
14) Factors Promoting Cardiogenesis in Vertebrates; tRNA Charging 6/50
15) Role of Macrophages, Fibroblasts and Endothelial Cells in
Rheumatoid Arthritis; Role of BRCA1 in DNA Damage Response 6/50
…
50).... 1/50
[158]Open in a new tab
Dots indicate the other pairs of pathways with minor frequency
Figure [159]11 shows a boxplot with the AUC values for the final 10
pairwise pathways in both the training and testing phase, confirming
good AUC (median >90 %), both for training and testing.
Fig. 11.
Fig. 11
[160]Open in a new tab
Boxplot of AUC values for the top 10 enriched pairwise pathways in
HER2, after all 50 bootstraps
Figure [161]12 shows, for each boostrap, the AUC of the top 10 pairs of
pathways, confirming that some pairwise pathways (e.g. Axonal Guidance
Signalling;CXCR4 Signalling, Atherosclerosis Signalling; Acute Phase
Response Signalling) have excellent AUC in most bootstraps.
Fig. 12.
Fig. 12
[162]Open in a new tab
AUC values representation with the best top 10 pairwise pathways for
all 50 bootstraps in HER2. Yellow square indicates AUC values when the
pairwise pathway was included in the top 10 for the corresponding
bootstrap. Red square indicates that the pairwise pathway was not
present in top 10 for that bootstrap
Figure [163]13 shows the inter-pathway coordination among the top 10
pairwise pathways in BC HER2. We found 14 miRNAs significantly
deregulating 7 pairwise pathways which are also shown 1) Acute Phase
Response Signalling; HIF1 Signalling, 2) Atherosclerosis Signalling;
Acute Phase Response Signalling, 3) Axonal Guidance Signalling; CXCR4
Signalling, 4) Axonal Guidance Signalling; P2Y Purigenic Receptor
Signalling Pathway 5) HIF1 Signalling; Glioblastoma Multiforme
Signalling 6) HIF1 Signalling; Growth Hormone Signalling, 7) Role of
Macrophages, Fibroblasts and Endothelial Cells in Rheumatoid Arthritis;
Growth Hormone Signalling. Among pathways, the role of Growth Hormone
Signalling, hub of a network linking Axonal Guidance Signalling, HIF1
Signalling, Role of Macrophages, Fibroblasts and Endothelial Cells in
Rheumatoid Arthritis appears dominant.
Fig. 13.
Fig. 13
[164]Open in a new tab
Interaction of the top 10 pairwise pathways in HER2 BC and their
miRNA-r in BC HER2
Table [165]8 lists, for each of the seven pairwise pathways, miRNA
regulators of pathways, their expression levels in BC HER2 and in NS,
and the statistical significance of the comparison (in terms of log
Fold Change).
Table 8.
For each top 7 pairwise pathway in BC HER2: miRNA regulators of
pathways, their expression levels in BC and in NS, and the statistical
significance of the comparison (in terms of log Fold Change)
Pairwise pathways miRNA-r miRNA-r Exp. in BC miRNA-r Exp. in NS logFC
1. a) Acute Phase Response Signalling;
b) HIF1 Signalling Hsa-miR-429
Hsa-miR-3617 434.2326
0.209302 71.18391
0.045977 2.604998
1.078421
2. a) Atherosclerosis Signalling;
b) Acute Phase Response Signalling Hsa-miR-1910 1.581395 0.425287
1.501305
3. a) Axonal Guidance Signalling;
b) CXCR4 Signalling Hsa-miR-584
Hsa-miR-190
Hsa-miR-148b
Hsa-miR-449c 152.5116
11.60465
719.4651
5.651163 482.4253
31.05747
315.1839
0.068966 -1.30985
-1.02572
1.550205
5.430282
4. a) Axonal Guidance Signalling;
b) P2Y Purigenic Receptor Signalling Pathway Hsa-miR-92b
Hsa-miR-190
Hsa-miR-584 531.8605
11.60465
152.5116 171.1494
31.05747
482.4253 1.840728
-1.02572
-1.30985
5. a) HIF1 Signalling;
b) Glioblastoma Multiforme Signalling Hsa-miR-190b
Hsa-miR-1246
Hsa-miR-429 31.90698
0.255814
434.2326 6.137931
0.034483
71.18391 2.680854
1.277809
2.604998
6. a) HIF1 Signalling;
b) Growth Hormone Signalling Hsa-miR-490
Hsa-miR-429 5.395349
434.2326 0.298851
71.18391 4.276816
2.604998
7. a) Role of Macrophages. Fibroblasts and Endothelial Cells in
Rheumatoid Arthritis;
b) Growth Hormone Signalling Hsa-miR-511-2
Hsa-miR-142
Hsa-miR-155 17.69767
11699.28
1931.163 56.51724
3246.46
616.9425 -1.49388
2.248187
1.707846
[166]Open in a new tab
The results of the MI analysis for the identification of candidate
genes target of miRNAs are shown in the Additional file [167]4.
Discussion
In a normal condition the biological pathways act in a coordinated way
to collaborate in a biological process. Cancer can interfere in these
coordinated processes, since alterations in multiple genes that
participate in different pathways result in an uncontrolled growth of
tumour cells, invasion and metastases.
In this work, by assessing the coordination among different pathways
deregulated in BC subtypes, we observed key pairwise pathways for each
BC subtype, which enabled the identification of a network of dependent
pathways characteristic of the disease. Furthermore, we identified
miRNAs that control this network with a potential role in BC. These
miRNAs could be crucial modulators of upstream signalling events linked
by specific subtype of BC.
miRNAs regulating pathways in luminal A
In BC luminal A we identified 11 miRNAs (Hsa-miR-1-1, Hsa-miR-1250,
Hsa-miR-1537, Hsa-miR-205, Hsa-miR-210, Hsa-miR-3199-1, Hsa-miR-335,
Hsa-miR-337, Hsa-miR-381, Hsa-miR-452, and Hsa-miR-99a) which could be
key modulators of six pairs of pathways: 1) Intrinsic Prothrombin
Activation, and Extrinsic Prothrombin Activation; 2) Acute Phase
Response Signalling, and HIF1 Signalling 3) Axonal Guidance Signalling,
and Acute Phase Response Signalling; 4) Ethanol Degradation IV, and
Glioma Invasiveness Signalling; 5) Ethanol Degradation IV, and Estrogen
Receptor Signalling; and 6) Glioma Invasiveness Signalling, and
Oxidative Ethanol Degradation III.
Intrinsic prothrombin activation and extrinsic prothrombin activation,
regulated by Hsa-miR-99a, Hsa-miR-210, Hsa-miR-381, and Hsa-miR-1537
Intrinsic and extrinsic prothrombin activation pathways perform an
essential role in coagulation, an important process for the
establishment of metastasis also in experimental models of cancer
[[168]70].
Hsa-miR-99a has been already associated with BC; in particular, its up
regulation correlates with cells with stemness properties [[169]71].
Moreover, Hsa-miR-99a has been identified in a screening of miRNA
profiles able to discriminate ductal carcinoma in situ, invasive BC,
metastatic BC and normal tissues. Hsa-miR-99a shows specific
differential expression in the in situ subtype of BC [[170]72]. Among
the other miRNAs regulating the two above mentioned pathways,
Hsa-miR-381 is one of the possible circulating miRNAs able to
discriminate between blood samples of patients with BC and NC
[[171]73]. Hsa-miR-210 was demonstrated to be a potential predictor of
the outcome of different cancers, including BC [[172]74]. Circulating
Hsa-miR-210 levels were associated with trastuzumab sensitivity, tumour
presence, and lymph node metastases, suggesting Hsa-miR-210 as a
predictor and perhaps a monitor of the response to therapies including
trastuzumab [[173]75].
Limited data about Hsa-miR-1537 is available for this miRNA. Few
publications, not related to BC, described this miRNA as altered in the
serum and bile of cholangioma patients [[174]76], where a speculation
of a role for Hsa-miR-1537 in inflammation is proposed.
Acute phase response signalling and HIF1 signalling, regulated by Hsa-miR-205
The acute phase response is a rapid inflammatory response that provides
protection against infections, including cancer [[175]77]. Several
studies found cross-talk between HIF-1 signalling and inflammatory
pathways suggesting that the development of inflammation in response to
hypoxia is clinically relevant [[176]78].
Hsa-miR-205 is an oncosuppressive miRNA lost in BC; it is directly
transactivated by the oncosuppressor p53 [[177]79]. Sempere et al.
[[178]80] showed that Hsa-miR-205 expression is restricted to the
myoepithelial/basal cell compartment of normal mammary ducts and
lobules, and is reduced or completely eliminated in matching tumour
specimens. Hsa-miR-205 regulates a number of important oncogenic
targets as ZEB1, VEGFA, and HER3. Moreover, it may modulate additional
targets, such as HMGB3, showing a potential therapeutic benefit/role
[[179]81].
Axonal guidance signalling and acute phase response signalling, regulated by
Hsa-miR-205, Hsa-miR-99a, Hsa-miR-335, Hsa-miR-337, Hsa-miR-452, and
Hsa-miR-1250
We found that two pathways, Axonal Guidance Signalling and Acute Phase
Response Signalling, are regulated by a group of 6 miRNAs.
There are four families of secreted or membrane-bound factors with
repulsive or attractive activities for growing axons and migrating
neurons (i.e., netrin 1, semaphorine, ephrins, and Slit, all with their
receptors), which have recently emerged as pivotal factors in tumour
progression. Far from being confined to the developing brain, axonal
guidance signalling seems to play an important role in tumour cell
migration, tumour cell survival and tumour angiogenesis [[180]82].
Hsa-miR-205 has already been proposed as a circulating biomarker of the
response of the BC luminal A subtype to neoadjuvant chemotherapy
[[181]83].
Hsa-miR-335 is down regulated in cancer stem cells (CSC) targeting
genes, such as Bmi1 and Suz12 component, Zeb1/2, and Klf4, all
belonging to a regulatory circuit that sustains the breast CSC state
[[182]84]. Hsa-miR-335 could be used as prognostic marker [[183]85] and
could suppress neuroblastoma cell invasiveness by directly targeting
multiple genes from the non-canonical TGF- β signalling pathway
[[184]86].
Hsa-miR-337 plays a role in the reduction of gastric cancer cell
invasion capacity and its loss has been associated with lymph node
metastasis [[185]87]. Furthermore, a study in prostate cancer revealed
Hsa-miR-337 as a potential circulating biomarker able to identify risk
groups [[186]88].
Hsa-miR-452 has been found to be associated with adriamycin-resistance
of BC cells, at least, partially, by targeting the insulin-like growth
factor-1 receptor (IGF-1R) [[187]89], and contributes to the docetaxel
resistance of BC cells [[188]90].
Hsa-miR-1250 has been described in the white matter tracts of the human
brain. Although no publication is available regarding its role in BC,
Hsa-miR-1250 seems to perform a role in oligodendrocyte proliferation
and differentiation [[189]91].
Ethanol degradation IV, and glioma invasiveness signalling regulated by
Hsa-miR-3199-1
Looking to the list of genes in Ethanol Degradation IV, we found that a
lot of these genes belong to the family of ALDH genes, and, although as
they perform a role in ethanol detoxification, they are also considered
biomarkers of CSCs [[190]92].
ER+ cells are able to generate cell progeny of luminal lineage both in
vitro and in vivo. Loss of ALDH isoform, ALDH1A1, plays a role in this
process by weakening cellular differentiation [[191]93]. Several
studies demonstrated that ALDH1A1 correlates with ER status in BC, and
that ALDH1A1 is an independent predictor of poor clinical outcome
[[192]94, [193]95].
Looking at the list of genes involved in glioma invasiveness
signalling, we found several genes such as ITGB5, belonging to the
integrins family, the integrin signalling members (Rhoh, Rhou and VTN)
or some members of phosphatidylinositol 3-kinase (PI3K) signalling
(PIK3C2B, PIK3CB). Integrins comprise a large family of cell surface
receptors and control cell attachment to the extracellular matrix
(ECM), growth, differentiation, apoptosis, cell motility, migration and
survival. A role for integrins in BC development has been already
described [[194]96]. Rhoh and Rhou proteins have a critical role in the
tumour progression and invasion, being important for the transduction
of the signal from integrins to the neighbourhood cell during cell-cell
communication [[195]97, [196]98]. The PI3K signalling pathway in BC is
associated with the poor outcome luminal B subtype, as its activation
leads to the development of endocrine therapy resistance [[197]99].
As regards Hsa-miR-3199-1, no publication is available on BC or on
other cancer types.
Ethanol degradation IV and estrogen receptor signalling, regulated by
Hsa-miR-1-1
We have already previously discussed the role of genes comprised in the
list of Ethanol Degradation IV pathway.
As regards the pathway of Estrogen Receptor Signalling, ERs are
critical regulators of breast epithelial cell proliferation,
differentiation, and apoptosis. Nowadays the role of ER pathway in BC
malignancy development is quite clear. This is the reason why several
therapeutic approaches have been directed against ER+ BC [[198]100].
Hsa-miR-1-1 has been demonstrated to be a tumour suppressor gene that
represses cancer cell proliferation and metastasis and promotes
apoptosis by ectopic expression [[199]101]. Hsa-miR-1-1 regulates
downstream functions of oncogenic signalling pathways such as Met,
HDAC4, PIM-1, Wnt, Cyclin D, FOXP1, Slug, and TAGLN2 [[200]101]. Down
regulation of Hsa-miR-1-1 was found to be associated with colorectal
cancer progression [[201]102].
Glioma Invasiveness Signalling, and Oxidative Ethanol Degradation III
regulated by Hsa-miR-3199-1
As regards the pathway of Glioma Invasiveness Signalling, we have
already discussed the role of its genes previously.
Looking at the list of genes involved in Oxidative Ethanol Degradation
III, regulated by Hsa-miR-3199-1, we found some isoforms of the
Phosphatidylinositol 3-kinase (PI3K) protein. PI3K includes two
subunits, p85α and p110α, that are mediators of the pro-survival
PI3K/Akt pathway signalling. Some isoforms of PI3K as well as p85
subunit have been already described in HER2-positive BC patients,
responding to trastuzumab treatment [[202]103]. Among the other genes,
particularly interesting if the finding of ITGB5, an integrin belonging
to a family of six genes (ITGA3, ITGA6, ITGAv, ITGB3, ITGB4 and ITGB5),
which control cell attachment to the extracellular matrix and play an
important role in mediating cell proliferation, migration and survival
[[203]104]. A strong association between integrin expression, mutation
or polymorphism and BC onset has been already described [[204]104].
The two described pathways seem to be controlled by a common miR,
Hsa-miR-3199-1. No publication is currently available about the
function of this miRNA in any biological processes.
miRNAs regulating pathway in luminal B
In BC luminal B, we identified 3 miRNAs (Hsa-miR-32, Hsa-miR-3074, and
Hsa-miR-577), which could be key modulators of the pair of pathways
Epithelial Adherens Junction Signalling - EIF2 Signalling.
Epithelial adherens junction signalling and EIF2 signalling regulated by
Hsa-miR-32, Hsa-miR-3074, and Hsa-miR-577
Adherens junctions are specialist structures for cell-cell adhesion
machinery. The adhesive process is directly related to the
differentiation and normal development of the tissue [[205]105]. The
development of cancer represents a modification of normal tissue
homeostasis and a change in cell-cell interaction. In addition, cancer
metastasis spreads through the circulatory system due to cell adhesion
[[206]105].
EIF2 Signalling is an essential factor for translation initiation and
protein synthesis. No study showed a correlation between these
pathways.
Hsa-miR-32 is located in genomic regions, which might be involved in
malignancies via deletion, amplification, or epigenetic modification
mechanisms [[207]106]. It regulates phosphatase and tensin homologue
(PTEN) expression, and promotes proliferation, migration and invasion
in colorectal cancer [[208]107].
Hsa-miR-3074 has been associated with papillary renal cell carcinoma
[[209]108], but no publication is available about its role in BC.
Hsa-miR-577 is mainly involved in proliferation control in glioblastoma
[[210]109], hepatocellular carcinoma [[211]110] and in esophageal
squamous cell carcinoma [[212]111]. It is possible to hypothesise a
role for Hsa-miR-577 also in BC proliferation control.
miRNAs regulating pathway in basal
In BC basal, we identified 2 miRNAs (Hsa-miR-135b, and Hsa-miR-365-2)
that may play an important role in the regulation of three pairs of
pathways: 1) Ethanol Degradation IV, and Mismatch Repair in Eukaryotes;
2) Putrescine Degradation III, and Role of BRCA1 in DNA Damage
Response, and 3) Tryptophan Degradation X (Mammalian, via Tryptamine),
and Role of BRCA1 in DNA Damage Response.
Ethanol degradation IV and mismatch repair in eukaryotes, regulated by
Hsa-miR-135b
Mismatch Repair plays a key role in maintaining genomic stability.
Cells possess multiple mechanisms to repair DNA damage and thus prevent
mutations [[213]112]. No study revealed a direct interaction between
Ethanol Degradation IV, and Mismatch Repair in BC.
Hsa-miR-135b levels are elevated in a variety of cancers including BC
[[214]113]. Lowery et al. [[215]113] identified a 15-miRNA predictive
signature related to the expression of ER comprising also this miRNA
(Hsa-miR-135b, Hsa-miR-190, Hsa-miR-217, Hsa-miR-218, Hsa-miR-299, and
Hsa-miR-342). Up regulation of Hsa-miR-135b is more robust in highly
invasive than less invasive lines. In colorectal cancer, Hsa-miR-135b
promotes cancer progression by acting as a downstream effector of
oncogenic pathways [[216]114].
Putrescine degradation III, and role of BRCA1 in DNA damage response
regulated by Hsa-miR-365-2
Putrescine is a known metabolite that plays an important role in cancer
and CSCs [[217]115]. Putrescine belongs to the class of polyamine,
involved in numerous processes in normal and cancer cells, such as
proliferation, apoptosis, cell-cell interactions, and angiogenesis
[[218]116]. An association between the basal subtype and BRCA1 gene has
been well described, and it may suggest that both inherent DNA
damage–sensing processes and DNA repair mechanisms are crucial in the
development of basal-like tumours [[219]22, [220]23].
Hsa-miR-365-2 negatively regulates BCL2 protein levels, and its
overexpression combined with the deregulation of other 2 miRNAs have an
apoptotic effect thus suggesting a therapeutic potential [[221]117]. In
pancreatic cancer Hsa-miR-365 was found to induce gemcitabine
resistance by targeting the adaptor protein SHC1 and pro-apoptotic
regulator BAX [[222]118].
Tryptophan degradation X (Mammalian, via Tryptamine), and role of BRCA1 in
DNA damage response regulated by Hsa-miR-365-2
Altered tryptophan metabolism is linked to cancer development and
progression [[223]119, [224]120]. In particular, indoleamine
2,3-dioxygenase 1 (IDO1), an enzyme involved in tryptophan degradation,
has been documented to have therapeutic potential, alone or in
combination with chemotherapy or immunotherapy [[225]121]. Several
studies confirmed its immunosuppressive role and the inhibition of the
IDO1 pathway therefore represents a promising therapeutic approach.
Clinical trials evaluating the first IDO1 inhibitors have already
started [[226]122, [227]123]. The role of Hsa-miR-365 in cancer has
been already reported above.
miRNAs regulating pathway in HER2
In HER2 BC, we identified 14 miRNAs (Hsa-miR-1246, Hsa-miR-142,
Hsa-miR-148b, Hsa-miR-155, Hsa-miR-190, Hsa-miR-190b, Hsa-miR-1910,
Hsa-miR-3617, Hsa-miR-429, Hsa-miR-449c, Hsa-miR-490, Hsa-miR-511-2,
Hsa-miR-584, Hsa-miR-92b) that may have an important role in the
regulation of seven pairs of pathways: 1) Axonal Guidance Signalling;
CXCR4 Signalling; 2) Axonal Guidance Signalling; P2Y Purigenic Receptor
Signalling, 3) Role of Macrophages, Fibroblasts and Endothelial Cells
in Rheumatoid Arthritis; Growth Hormone Signalling, 4) HIF1 Signalling;
Growth Hormone Signalling, 5) HIF1 Signalling; Glioblastoma Multiforme
Signalling, 6) Acute Phase Response Signalling; HIF1 Signalling, and 7)
Atherosclerosis Signalling; Acute Phase Response Signalling.
Axonal guidance signalling and CXCR4 signalling, regulated by Hsa-miR-148b,
Hsa-miR-190, Hsa-miR-449c, Hsa-miR-584
The role of Axonal Guidance Signalling in cancer has already been
mentioned above. CXCR4 Signalling shows a down regulation in
metastasised BC cells [[228]124]. CXCR4, the receptor for
stromal-derived factor-1, is already reported as involved in breast
carcinogenesis and invasion. Recent studies showed that the inhibition
of CXCR4 expression resulted in an anti-invasive effect revealing the
potential for the treatment of BC [[229]125].
CXCR4, the receptor of SDF-1, plays a crucial role in modulating axonal
responsiveness through a cyclic nucleotide-dependent signalling pathway
[[230]126].
Four miRNAs could be important regulator of this interaction.
Hsa-miR-148b was found to be a major coordinator of malignancy
influencing invasion, survival to anoikis, extravasation, lung
metastasis formation, and chemotherapy response [[231]127]. Circulating
Hsa-miR-148b was validated and found elevated in the plasma of BC
patients compared to healthy women [[232]128, [233]129]. Cimino et al.
[[234]127] showed that Hsa-miR-148b expression enhances
chemotherapy-induced apoptosis.
Hsa-miR-190 was associated with lymph node metastasis and its increased
expression inhibited cell migration and invasiveness. The target of
Hsa-miR-190 was protease-activated-receptor 1 (PAR-1), which is a
metastasis promoting protein in several cancers [[235]130].
Hsa-miR-449c showed a decreased expression in human gastric tumours and
induces senescence and apoptosis by activating the p53 pathway
[[236]131]. No information is reported about Hsa-miR-449c and BC.
Hsa-miR-584 was found to be down-regulating TGF-β in BC cells. PHACTR1,
an actin-binding protein, is also regulated by Hsa-miR-584.
Overexpression of Hsa-miR-584 and knockdown of PHACTR1 resulted in a
drastic rearrangement of the actin cytoskeleton and in a loss of
TGF-β-induced cell migration [[237]132].
Axonal guidance signalling and P2Y purigenic receptor signalling pathway,
regulated by Hsa-miR-190, Hsa-miR-584, and Hsa-miR-92b
The role of Axonal Guidance Signalling in cancer has already been
mentioned above. A recent study showed that P2Y Purigenic Receptor
Signalling Pathway is included in a potential pathway signature for
testing Gemcitabine (Gem)-based chemotherapies sensitivity of
gallbladder cancer patients [[238]133].
P2Y receptors (e.g., P2Y1, P2Y2) have strong direct effects on the
tumour by modulating cell growth. In vivo data support in vitro
evidence that lowering the intratumour adenosine concentration and
targeting the P2X7 receptor have a strong antitumour effect [[239]134].
No study showed a direct interaction between Axonal Guidance Signalling
and P2Y Purigenic Receptor Signalling.
Hsa-miR-190 has already been discussed above. As regards Hsa-miR-584,
the involvement of this miRNA in the pathway controlled by TGF-β
[[240]132] has been already described. In particular, TGF-β is able to
decrease the expression of Hsa-miR-584. This in turn leads to the
increase of protein phosphatase and actin regulator 1 (PHACTR1), a
protein required for TGF-β-induced cell migration of breast cancer
cells [[241]132]. The drastic reorganization of the actin cytoskeleton
is important in axonal guidance signalling, playing a role in tumour
cell migration, tumour cell survival and tumour angiogenesis.
Hsa-miR-92b, regulating these pairwise pathways, was found
over-expressed in brain primary tumours, suggesting a functional link
between neuronal stem cells and brain tumourigenesis [[242]135]. The
involvement of this miRNA in radiation resistance was also found
[[243]136].
Role of macrophages, fibroblasts and endothelial cells in rheumatoid
arthritis and growth hormone signalling, regulated by Hsa-miR-142,
Hsa-miR-155, Hsa-miR-511-2
Tumours comprise proliferating tumour cells and stromal cells,
including endothelial cells, inflammatory cells, and fibroblasts
[[244]137].
Macrophages play a crucial role in the innate and adaptive response to
pathogens. Recently, it was also found that tumour-associated
macrophages interact with CSCs thus leading to tumourigenesis,
metastasis, and drug resistance [[245]138]. As regards Hsa-miR-511-2,
several publications demonstrated that this miRNA plays an important
role in modulating tumour-associated macrophages. The upregulation of
Hsa-miR-511 affects the pro-tumoural gene signature of
tumour-associated macrophages, which are endowed with
tissue-remodelling, proangiogenic, and protumoural activity [[246]139,
[247]140].
Hormones play an important role for normal development and possibly
also for tumour formation in the mammary gland. Human growth hormone
could also stimulate the tumour initiating capacity and metastasis of
estrogen receptor-negative BC [[248]141]. Hsa-miR-142 plays a role as
potent inhibitor of human growth hormone signalling in normal and
cancer cells thus suggesting the development of miRNA inhibitors as
therapeutic agents in growth hormone-related disease, including cancer
[[249]142].
Hsa-miR-155, described as oncomiR, is implicated in EMT, cell
migration, and invasion control. Roth et al. [[250]143] found
Hsa-miR-155 in the serum of patients with BC and not in healthy
controls; this miRNA has been used to monitor the effect of taxane
treatment on BC. Sun et al. observed the decreased expression of
Hsa-miR-155 in serum after chemotherapy, which reached levels
comparable to those of healthy subjects [[251]144].
HIF1 signalling and growth hormone signalling, regulated by Hsa-miR-490,
Hsa-miR-429
Resistance to hormonal therapy is still unknown, but hypoxia could play
an important role, for instance, in down-regulating ER-alpha expression
as well as ER-alpha function in BC cells [[252]145]. Furthermore,
hypoxia and estrogen are interchangeable as both similarly modulate
epithelial-endothelial cell interaction [[253]146].
Previous studies showed the role of Hsa-miR-490 as potential drug
resistance in ovarian cancer [[254]147] and as a potential novel
biomarker for diagnosing of colorectal cancer [[255]148].
Down regulation of Hsa-miR-429 was highlighted in the 3D
culture-specific miRNA profile better than that in the 2D
culture-specific profile, by correlating with the 3D invasive capacity
of the MDA-MB-231 BC cell line [[256]149].
Hsa-miR-429 could be also a regulator of HIF1 Signalling, Glioblastoma
Multiforme, Acute Phase Response Signalling and HIF1 Signalling.
HIF1 signalling and glioblastoma multiforme signalling regulated by
Hsa-miR-190b, Hsa-miR-1246, Hsa-miR-429
The role of HIF1 Signalling in cancer has already been mentioned above.
As regards Glioblastoma multiforme Signalling pathway, among all the
altered genes in common with HIF1 Signalling pathway, the main genes
are those of the Ras family (i.e., KRas and NRas), already found
mutated in triple-negative BC [[257]150], the genes of the PI3K pathway
(i.e., PIK3C3, PIK3CA), which has been already found silenced or
mutated in aggressive BC [[258]151, [259]152], and those of the
serine/threonine protein kinase family, like ATM, already associated
with hormone negative early stage BC [[260]153].
As regards miRNAs able to regulate these couples of pathways, we
identified Hsa-miR-429 (already described above), Hsa-miR-190b and
Hsa-miR-1246. Hsa-miR-190b is indicated as a higher discriminating
miRNA between ER+ and ER- BC. This miRNA has also an impact on
metastasis-free survival and event-free survival rates, independently
of ER status [[261]154]. Hsa-miR-1246 was included in a 5-miRNA
signature with good diagnostic features, able to discriminate between
healthy and early stage BC samples [[262]155].
Acute phase response signalling and HIF1 Signalling regulated by Hsa-miR-429,
Hsa-miR-3617
The role of HIF1 Signalling in cancer has already been mentioned above.
Acute Phase Response Signalling has a clear role in both ER+ and triple
negative BC [[263]156]. Looking to the genes involved in Acute Phase
Response Signalling in common with those of HIF1 Signalling, they
belong to the MAP kinase pathway (i.e. MAPK8, MAPK14) or to the RAS
protein family (i.e. NRas), as discussed above. Hsa-miR-429 has already
been discussed. As regards Hsa-miR-3617, no publication is currently
available about the function of this miRNA in any biological processes.
Atherosclerosis signalling and acute phase response signalling regulated by
Hsa-miR-1910
The Acute Phase Response Signalling plays a clear role in BC, as
already mentioned. There are only a few publications that associate
Atherosclerosis Signalling to BC. However, one of the main molecule
involved in tissue remodelling and in atherosclerosis is tenascin-C.
Its serum level of expression has no predictive or prognostic ability
in BC, although it is elevated in BC patients [[264]157]. As regards
miRNAs involved in the control of this couple of pathways, we
identified a single miRNA, Hsa-miR-1910. This miRNA is included in a
group of 8 miRNAs, whose silencing by methylation leads to the onset of
BC [[265]158].
Conclusions
We identified pairwise pathways for BC subtypes able to discriminate BC
vs. normal samples. From these pairs, we created a network of pathways
specific for each subtype. Following an enrichment analysis, we focused
on miRNAs with an important role in the regulation of the network.
In the network of pathways for BC luminal A, we found 11 miRNAs:
Hsa-miR-1-1, Hsa-miR-1250, Hsa-miR-1537, Hsa-miR-205, Hsa-miR-210,
Hsa-miR-3199-1, Hsa-miR-335, Hsa-miR-337, Hsa-miR-381, Hsa-miR-452, and
Hsa-miR-99a. Among them, Hsa-miR-210, and Hsa-miR-205 have a potential
therapeutic role, acting as biomarkers of the response to trastuzumab,
and to neoadjuvant chemotherapy, respectively.
In the network of pathways for BC luminal B, we found 3 miRNAs:
Hsa-miR-32, Hsa-miR-3074, and Hsa-miR-577. Among them, Hsa-miR-32 has
been already associated with cancer progression.
In the network of pathways for BC basal we found 2 miRNAs:
Hsa-miR-135b, and Hsa-miR-365-2. Among them, Hsa-miR-365-2 showed an
apoptotic role and could play a therapeutic role.
In the network of pathways for HER2 BC, we found 14 miRNAs:
Hsa-miR-1246, Hsa-miR-142, Hsa-miR-148b, Hsa-miR-155, Hsa-miR-190,
Hsa-miR-190b, Hsa-miR-1910, Hsa-miR-3617, Hsa-miR-429, Hsa-miR-449c,
Hsa-miR-490, Hsa-miR-511-2, Hsa-miR-584, Hsa-miR-92b. Among them,
Hsa-miR-148b, Hsa-miR-92b, Hsa-miR-142, Hsa-miR-155 are interesting for
drug design, as a role in the response to different therapeutic
strategies has been already described.
The identification of a network of dependent pathways and their
regulatory miRNAs is a current challenge in order to have an overview
of a complex disease such as cancer. In particular, miRNAs, once
validated in a laboratory assay, could be suitable for translation to a
clinical environment. The low-cost procedures and the possibility to be
measured by non-invasive tests make miRNAs important diagnostic and
therapeutic tools for further studies.
Acknowledgments