Next Article in Journal
Combination BMSC and Niaspan Treatment of Stroke Enhances White Matter Remodeling and Synaptic Protein Expression in Diabetic Rats
Next Article in Special Issue
Prognostic Discrimination Using a 70-Gene Signature among Patients with Estrogen Receptor-Positive Breast Cancer and an Intermediate 21-Gene Recurrence Score
Previous Article in Journal
The ERK1/2 Signaling Pathway Is Involved in Sulfur Dioxide Preconditioning-Induced Protection against Cardiac Dysfunction in Isolated Perfused Rat Heart Subjected to Myocardial Ischemia/Reperfusion
Previous Article in Special Issue
Aberrant Expression of Posterior HOX Genes in Well Differentiated Histotypes of Thyroid Cancers

Int. J. Mol. Sci. 2013, 14(11), 22202-22220; doi:10.3390/ijms141122202

Review
MicroRNAs and Triple Negative Breast Cancer
Elvira D’Ippolito and Marilena V. Iorio *
Start Up Unit, Experimental Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Via Amadeo 42, Milan 20133, Italy; E-Mail: elvira.dippolito@istitutotumori.mi.it
*
Author to whom correspondence should be addressed; E-Mail: marilena.iorio@istitutotumori.mi.it; Tel.: +39-022-390-5134/5126; Fax: +39-022-390-2692.
Received: 2 September 2013; in revised form: 9 October 2013 / Accepted: 11 October 2013 /
Published: 11 November 2013

Abstract

: Triple Negative Breast Cancer (TNBC) is a very aggressive tumor subtype, which still lacks specific markers for an effective targeted therapy. Despite the common feature of negativity for the three most relevant receptors (ER, PgR and HER2), TNBC is a very heterogeneous disease where different subgroups can be recognized, and both gene and microRNA profiling studies have recently been carried out to dissect the different molecular entities. Moreover, several microRNAs playing a crucial role in triple negative breast cancer biology have been identified, providing the experimental basis for a possible therapeutic application. Indeed, the causal involvement of microRNAs in breast cancer and the possible use of these small noncoding RNA molecules as biomarkers has been extensively studied with promising results. Their application as therapeutic tools might represent an innovative approach, especially for a tumor subgroup still lacking an efficient and specific therapy such as TNBC. In this review, we summarize our knowledge on the most important microRNAs described in TNBC.
Keywords:
microRNA; breast cancer; triple negative

1. Introduction

1.1. MicroRNA Biogenesis

MicroRNAs (miRNAs or miRs) are small non-coding endogenous RNA molecules involved in gene regulation, and are located at introns of protein-coding genes, introns of non-coding genes, or exons of non-coding genes [1,2].

In the canonical biogenesis pathway, microRNAs are transcribed by RNA polymerase II as a long primary transcript (pri-microRNA) and cleaved, in the nucleus, into a 60–70 nt double helix hairpin structure precursor (pre-microRNA) by the RNase III Drosha and its cofactor DGCR8 [3]. After Exportin 5-mediated transfer into the cytoplasm [4], the pre-microRNA is first processed by the RNase III Dicer, in concert with TRBP or PACT cofactors, in an approximately 22 nt dsRNA with two-nucleotide 3′-overhangs [5] called microRNA/microRNA*, and then unwound by cytoplasmic helicase. The strand with the lower stability in the 5′ end (guide strand) is preferentially selected and incorporated in the RISC (RNA-induced Silencing Complex), which includes Argonaute proteins, the microRNA strand and other additional factors, even if in some cases both strands could be incorporated. In this conformation the mature microRNA is directed toward target mRNA.

The binding can occur through a complete or partial base pairing, which respectively leads to degradation, mainly in plant, or translation inhibition, more frequent in mammals and often followed by deadenylation and subsequent degradation of the mRNA [6]. The binding for the most part occurs between the 5′-nucleotides 2–8 of microRNA, the so called “seed” region, and the 3′ untranslated region (3′UTR) of the mRNA, even if also the 5′UTR, promoter and open reading frame regions were demonstrated to be targetable [7,8]. Moreover, microRNAs, as well as dsRNAs, were found to be able to bind promoter regions at the genomic level and induce, rather than repress, gene expression [9,10] by returning in the nucleus, possibly by a hexanucleotide terminal motif mediated transfer [11].

The microRNA scenario is further complicated by the Drosha-independent [12] or Dicer-independent [13,14] mechanism of microRNA biogenesis, or by other kind of RNA molecules which can contribute to the microRNA pool; examples are the so-called mirtrons, RNA structures which resemble the pre-microRNA and do not need Drosha processing [15], or the small nucleolar RNA-derivered RNAs (snRNA) or the tRNA-derived RNAs (tdRNA), which have a totally independent biogenesis pathway [16].

There has been an interesting shift in the study of microRNA biogenesis from the simple elucidation of its key events to the utilization of its potential role as a pharmacological target. AC1MMYR2, a specific small molecule inhibitor of miR-21, for instance, has been designed exploiting the structure conformation of pre-miR-21; the molecule acts by specific blocking of Dicer-mediated miR-21 processing leading to tumor growth suppression and epithelial-mesenchymal transition (EMT) reversion, in epithelial tumor cells and orthotopic nude mouse model [17].

Genetic variants in genes of microRNA biogenesis pathways were associated with the risk and/or survival in various malignancies such as colorectal cancer [18], bladder cancer [19], renal cell carcinoma [20], ovarian cancer [21], head and neck cancer [22,23], prostate cancer [24] and breast cancer [25].

microRNAs can also be secreted in the surrounding region or secreted in biological fluids where they are protected in liposomal-like particles and act on other cell types. Indeed, an increasing body of evidence reveals how circulating miRNAs are associated to MVs (microvescicles), small exosomes/vesicles of endocytic origin released by normal healthy or damaged cell types. Valadi and colleagues demonstrated in 2007 how vesicles released from human and murine mast cell lines contain over 1200 mRNA and approximately 121 miRNA molecules [26], and a year later Taylor and colleagues [27] described a miRNA signature associated to tumor-derived exosomes. Further evidence of this was provided by several other studies.

Furthermore, the small size of miRNAs certainly contributes to a higher stability in comparison with mRNAs, allowing the study of their expression in fixed tissues or other biological material, and thus supporting their possible use as novel, minimally invasive and robust biomarkers: miRNAs can be reliably extracted and detected from frozen and paraffin-embedded tissues, from blood (either total blood, plasma or serum) [28,29], from circulating exosomes [30], and from different biologic fluids as urine [31], saliva [32] and even sputum [33].

1.2. MicroRNAs and Breast Cancer

After the first microRNA signature characteristic of breast carcinoma that our group [34] described in 2005, several of the most significant microRNAs differentially expressed have been extensively studied since their initial discovery and an important role on the biology of breast cancer was revealed. One of the first oncomiRs identified was miR-21, overexpressed in breast carcinoma, which mediates cell survival and proliferation, stimulates invasation, extravasation and metastasis [35] and which has been associated with advanced clinical stage, lymph node metastasis and patient poor prognosis [36,37]. Moreover, mice conditionally expressing miR-21 develop a pre-B malignant lymphoid-like phenotype, thus demonstrating that miR-21 is a genuine oncogene [38].

In contrast, down regulated microRNAs, such as miR-125a and b and miR-205, regulate oncogenes as tyrosine kinase receptors HER2 and HER3, respectively [39,40], whereas let-7 has been described as a new regulator of self-renewal and tumorigenicity of breast cancer cells [41], targeting molecules originally described in lung cancer: RAS, HMGA2 and MYC.

Notably, one of the first “metastamiR”, miRs involved in the metastatic process, has been described in a breast cancer model by Weinberg group, miR-10b [42], for which the same group has also explored a possible therapeutic application, reporting that systemic treatment of tumor-bearing mice with miR-10b antagomirs suppresses breast cancer metastasis [43]. Always focusing on metastasis-inducers microRNAs, the same group (Ma L. and colleagues) [44] described how miR-9 increases the metastatic potential, and miR-373 and miR-520c stimulate the non-metastatic MCF7 cell line to migrate and invade in vitro and in vivo, regulating the cell-surface glycoprotein CD44 [45].

Whereas, metastasis inhibitors are for example miR-126 and miR-335 [46] or miR-200 family members and miR-205, which have been shown to reduce cell migration and invasiveness targeting ZEB transcription factors, known inducers of EMT, “epithelial-mesenchymal transition” [47].

Our group particularly focused on the study of miR-205 involvement in breast cancer biology. Previous studies showed that miR-205 is significantly under-expressed in human breast cancer [34,48] and associated with the absence of vascular invasion [34], although it has also been shown to be up-regulated in other tumors types, such as ovarian cancer [49]. We recently demonstrated [40] that miR-205 is able to interfere with the HER receptor family-mediated survival pathway by directly targeting the HER3 receptor and thus inhibiting its downstream mediator Akt. In addition, other studies indicated that miR-205 is a negative regulator of the EMT, an early phase of the process of metastasis, targeting the transcription factors ZEB1 and ZEB2. Moreover, miR-205 also targets VEGF-A, a factor which plays a key role in the process of invasion and metastasis [48].

1.3. Triple Negative Breast Cancer

Breast cancer is a very heterogeneous disease. Perou et al., in 2000, classified breast cancer into five main categories, according to intrinsic gene signature: Luminal A, estrogen receptor (ER) and/or progesterone receptor (PR) positive, and human epidermal growth factor receptor 2 (HER2) negative; Luminal B, ER or PR positive and HER2 positive; HER2 overexpressing, basal-like and normal-like [50]. Further gene expression studies enriched the sub-classification of breast cancer [51] individuating additional entities such as the Claudin-low subtype, characterized by low expression of claudin proteins, proliferation genes and luminal markers, and high expression of ephitelial-to-mesenchymal transition markers and cancer stem-cell-like features [52]. Moreover gene expression profiling has also been used for the development of prognostic signatures such as Mammaprint and Oncotype DX [5355], which are now enrolled in clinical trials.

However, a gap between achieved molecular knowledge and clinical applications still exists. Indeed, at the time of diagnosis, breast cancer classification and choice of adjuvant treatment still rely on traditional pathological parameters and immunoisthochemical (IHC) analysis of three main markers (ER, PR and HER2), which divide breast cancer into Luminal, HER2 positive, and Triple Negative Breast Cancer [56]. The latter is further stratified based on the expression of basal cytokeratins 5–6 and EGFR [57,58].

Triple Negative Breast Cancer (TNBC) is defined as ER-, PR- and HER2-negative. A first molecular characterization classified TNBCs as basal-like (50%), claudin-low (30%) and luminal A, B and HER2 subtypes for the remaining 20% [59]. Finally, Lehmann et al. in 2011 profiled more than 500 TNBCs classifying them in six different subtypes, according to intrinsic gene signature: basal 1 and 2, mesenchymal and mesenchymal stem cell-like, immunomodulatory and androgen pathway enriched [60]. TNBC accounts for 15%–20% of all breast cancers; it represents the most aggressive subtype with the most dire prognosis. Indeed, the pathological complete response (pCR) is achieved only in 20%–30% of cases after neoadjuvant chemotherapy and, among patients who do not achieve pCR, TNBC patients show poorer outcome compared to non-TNBC patients [61,62]. Moreover, TNBC patients have the highest percentage of early local relapse, especially between the first and third year after diagnosis [58]. Metastases are more aggressive compared to other subtypes and mainly occur in the viscera, in particular the lungs and brain [63], and to a lesser degree in bones [61].

Another major issue with TNBC is the lack of a specific target so that chemotherapy remains the standard of treatment; even if TNBC shows more chemosensitivity compared to ER-positive breast cancer, most of TNBC patients have residual disease in the breast and lymph nodes after neoadjuvant treatment. The absence of a targeted therapy renders these patients more susceptible to relapse and thus to poorer overall survival compared to other breast cancer subtypes.

Improvement in TNBC biology understanding, unraveled molecular pathways deregulated and with potential to be formed into drugs (Supplementary Figure S1). Gene amplification of EGFR and VEGFA and chromosomal deletion of PTEN and RASA1 gene chromosomal region were described in [64,65], whereas the gene profile disclosed overexpression of C-KIT and interferon/immunoglobulin-related genes and low expression of Bcl-2 [66], leading to the development of novel therapeutic approaches. However, the outcome of clinical trials conducted in the last years was controversial: the monoclonal anti-VEGF-A Bevacizumab, for example, appeared as one of the most promising novel agents; however, the promising effect in a neoadjuvant setting, in addiction to chemotherapy, in HER2-negative breast cancer patients [67] was not confirmed by a subsequent clinical study [68]. On the other hand, the encouraging results of the tyrosine kinase inhibitor Sunitinib, when used as single agent in a phase II clinical trial on metastatic breast cancer [69], resulted in totally disappointing results in a phase III clinical trial in a HER2-negative breast cancer population both as a single agent [70] or added to chemotherapy [71]. Unfortunately, also the monoclonal antibody against EGFR cetuximab and the TKI erlotinib only showed marginal effects [72,73].

These results could be in some part explained by differences in classification methods among different institutes and by a nonaccurate selection of patients; this means that patients enrolled in the clinical trials are not always the ones that could really benefit from the specific targeted therapy under investigation. Indeed, these speculations stress the importance of accuracy in patient selection and the need for robust molecular markers able to translate the knowledge coming from preclinical and in silico analyses into the clinics.

2. MicroRNAs and TNBC

2.1. Profiling Studies

Starting from the first profile that identified microRNAs deregulated in breast cancer specimens compared to normal tissues [34], numerous profiles have been performed on tissue samples in an attempt to individuate microRNA signature with clinical value. However, results are often controversial, leaving the question open as to whether microRNA profiling is reliable or not for breast cancer stratification.

Signatures of differentially expressed microRNAs were found between basal and luminal breast cancer subtypes [74,75], and could specifically classify estrogen receptor (ER), progesterone receptor (PR) and HER2/neu receptor status [34,76,77].

In a more recent retrospective analysis on lymph node negative breast cancer tissues, hierarchical clustering based on microRNA profiling was able to identify four main subgroups. However, the TNBC was revealed as the most prominent with a unique microRNA signature. The highest overall classification values by analysis of variance followed by cross-validation was indeed found for cytokeratin 5 and 6, triple negative and estrogen receptor [78].

Farazi et al. in 2011 confirmed in a set of normal, DCIS and invasive breast cancer specimens, the tendency of TNBCs to cluster, according to microRNA profile, as a distinct group with respect to tumor samples found to be positive to one or more IHC marker. However, the strength of this microRNA profiling in breast cancer sub-classification did not compare well with mRNA profiling [79].

Further analysis of Farazi’s dataset by Croce’s group [80] revealed modulation of nine microRNAs differentiating invasive from in situ carcinoma, including miR-210, confirmed activation of miR-17-92 family in IDC TNBC and further identified additional microRNAs differentially expressed in this subtype, such as upregulation of miR-200c. Interestingly, also overexpressed microRNAs have been identified in TNBC, even though the highly undifferentiated nature of this tumor subgroup would suggest a global downregulation of microRNAs [81] (Dvinge et al., Nature 2013).

A more recent study attempted to individuate microRNAs associated to hypoxia and angiogenesis by evaluating microRNA heterogeneously expressed within a tumor, since they could reflect a different microvessel distribution and thus different hypoxic conditions within the tumor itself. They found sets of microRNAs heterogeneously expressed from the edge to the center of the tumor for each clinical-pathological subtype, with a set of 50 microRNAs for TNBC. Among these, miR-20a and miR-20b appeared the most interesting, as they are predicted to target VEGF-A and HIF-1a [82].

The first study totally focused on TNBC with the most consistent results came from a large-scale profile of Cascione et al. in 2013 [83]. In a first set of primary TNBC and normal tissues, the microRNA profile revealed 116 deregulated microRNAs, among which miR-106b, the cluster miR-17/92, miR-200 family (miR-200a, miR-200b and miR-200c), miR-21 and miR-155 were the most up-modulated while let-7b, let-7c, miR-126, miR-145 and miR-205 were the most down-modulated [83]. A second set of TNBC-associated lymph node metastasis and normal tissues allowed instead the identification of a set of six miRNAs differentially expressed in the metastatic tissues (miR-424, miR-125a-5P, miR-627, miR-579, let-7g, miR-101), with miR-424 and miR-125a differentially expressed only in the metastasis vs. primary tumor and in normal tissue vs. metastasis, respectively [83].

Interestingly, some of the miRNAs that associate with the luminal or basal subtype reflect their epithelial and myoepithelial origins, respectively. For instance, the miR-200 family associates with the luminal subtype. This is not surprising since they directly target the EMT regulators ZEB1 and ZEB2 [47], whereas miR-145 and miR-205, preferentially expressed in normal myoepithelial cells, are dramatically reduced in basal-like triple negative tumors (ER-/PR-/HER2-), suggesting that this expression change might be a consequence of disease progression in this subtype [75].

Among the putative mechanisms regulating microRNA expression, abnormalities in the biogenesis machinery have been reported. However, the currently available data about TNBC are both few and controversial: indeed, whereas Dedes K.J. et al. [84] report Dicer mRNA downregolation, consistent with the hypothesis of an oncosuppressive role of the vast majority of microRNAs, Passon N. et al. [85] detected high IHC staining of both Drosha and Dicer. Interestingly, Lin28B, being able to selectively inhibit let-7 processing by Dicer and function, is specifically expressed in TNBC [86], acting as an oncogene. Even microRNAs themselves can control the biogenesis machinery, as the oncogenic miR-103/107 is able to directly target Dicer [87].

The use of in silico bioinformatic prediction tools and integrated microRNA:mRNA computational analysis generated complex networks with the attempt to collocate the microRNAs individuated by profiling studies in cancer-related pathways [81,88]. The potential of these networks to unravel new targets and/or therapeutical approaches is promising but at the same time still far off; indeed, only a small number of all these microRNAs are supported by functional or clinical evidence in TNBC.

2.2. MiR and TNBC—Functional Evidence

For most of the microRNAs that come from profiling studies, only in silico analysis has been reported that describes predictive interactions with the main biological processes related to TNBC biology. However, there is a limited promising number of microRNAs supported by validated experimental data (Table 1, Supplementary Figure S2).

Among the tumor suppressor microRNAs, the miR-200 family, miR-205 and the let-7 family are the most described.

The miR-200 family appears to be one of the most interesting players in TNBC biology; it was previously described as up-modulated in breast cancer where its over-expression was correlated with lymph node positivity and metastasis [102]. Among the different breast cancer subtypes, the miR-200 family was shown to be down-modulated in metaplastic carcinoma [103], one the most mesenchymal and undifferentiated breast cancers characterized by TNBC phenotype. However, functional in vitro evidence that describes the miR-200 family as a tumor suppressor in TNBC, in concomitant with the very low expression levels in the Basal B breast cancer cell lines [104], in vitro model of metaplastic, claudin-low and mesenchymal breast tumors [60], seems to be in contrast with its overexpression in tumor samples [88]. This highlights, therefore, the still controversial role of miR-200 family in breast cancer. Indeed, in concomitant with miR-205, the miR-200 family is a well known negative regulator of the epithelial-mesenchymal transition (EMT), through the direct targeting of Zeb1/Zeb2, two of the key trascriptional factors that guide the process [47]. The MiR-200 family is also down-modulated in stem-cells and breast cancer stem cells [105] and has been demonstrated necessary for the mammary cell differentation process [89]; in particular, downmodulation of miR-200a and miR-200b is observed in a developmental stage of mammary epithelial cell lines which shows correlation in gene expression with basal-like and poor prognosis breast cancer [106]. Furthermore, the miR-200 family, in particular miR-200c, inhibits cancer cell migration [107], invasion [108] and reverts the anoikisis resistance [90,91] that is frequently observed in aggressive carcinoma cells where it correlates with EMT [109,110]. Finally, low expression levels of miR-200c associated with poor response to chemotherapy [111] and radiotherapy [112].

The miR-205 exerts, instead, a clearer tumor-suppressive role. Our group described its down-modulation in TNBC, in particular in the claudin-low subgroup, to be in agreement with other studies [113]. We demonstrated that the ectopic expression of miR-205 reduces proliferation, cell cycle progression and clonogenic potential in vitro, and inhibits tumor growth in vivo, partially by targeting of E2F1, master regulator of cell cycle progression, and LAMC1, the component of extracellular matrix involved in cell adhesion, proliferation and migration. Finally, we demonstrated that the transcription of miR-205 is directly induced by p53 [92].

MiR-203, which has been down regulated in several cancers [114117], also presents low levels in TNBC cell lines. Ectopic expression reduces cell proliferation and migration by targeting BIRC5, which is frequently up-regulated and is associated with poor clinical outcome in breast cancer [118,119], and LASP1, and found to be over-expressed in metastatic breast cancer [120], respectively [93].

The Let-7 family is one of the best known microRNA in the cancer field. It regulates numerous oncogenes, like RAS, MYC and HMGA2 [121], it was found to be down-modulated in different cancer types [122] and restoration in breast cancer inhibited tumor growth [123], validating its role as tumor-suppressor. Moreover, up-modulation of let-7 and miR-200 families was demonstrated to partially mediate the action of garcinol [124], a natural chemical compound with demonstrated anticancer activity [125], in in vivo and in vitro TNBC models. To support the importance of let-7 family in TNBC, recently the KRAS-variant, a germline single nucleotide polymorphism (SNP) mutation in a let-7 complementary site in the 3′UTR of KRAS [126,127], was associated with risk in developing TNBC [128]. Finally, LIN28B, regulator of let-7 biogenesis by blocking the pri-let-7 processing in the nucleus [86], was associated with advanced disease with aggressive and poorly differentiated phenotypes [129131].

Besides the afore-mentioned microRNAs, it is also worth mentioning miR-31, miR-146, miR-146b and miR-34a. MiR-31 plays a specific metastatic suppressor role through the regulation of pro-metastatic oncogenes like WAVE3 [94], RhoA, Radexin [95]. Its expression in breast cancer decreases from early stage to aggressive phenotypes [94] becoming undetectable in metastatic breast cancer. The down-modulation in TNBC cell lines, with particular attention to basal subtype, was partially attributed to epigenetical hypermethylation of LOC554202, the miR-31 host gene [132]. In contrast, in the mouse model, restoration of miR-31 inhibits metastatic potential of TNBC cell lines without affecting primary tumor growth [95]. In addition to its role in the metastastic process, miR-31 overexpression also induces apoptosis and increases chemo- and radiosensitivity in TNBC cell lines by direct inhibition of PRKCE, a positive regulator of Bcl-2 [133]. Inverse correlation between miR-31 and Bcl-2 was found by exploiting a public dataset of breast cancer patients [96]. Finally, miR-34a, one of the most down-modulated by a microRNA profiling study between luminal and Basal-B cell lines, resulted particularly under-expressed in the TNBC cell line where it inversely correlates with AXL and mediates impairment in AXL-mediated migration [97].

Moving to oncomiRs, the well-established up-modulated miR-21 and miR-155 still play a marginal role in TNBC. MiR-21, was found to be over-expressed in different cancer types, including breast cancer [34,35,134]. It does not differ according to receptoral status [135], and only in TNBC was a trend between high miR-21 level and poor prognosis described [113]. Regarding miR-155, in breast tumors it induces angiogenesis and tumor growth and its up-regulation is associated with metastasis, late-stage/high grade tumor and poor prognosis [136]. Interestingly, a significant proportion of TNBC show high expression of miR-155 (moreover, it was shown to be epigenetically regulated by BRCA1 and up-regulated in BRCA1-deficient or BRCA-mutant breast cancer [137]) and stimulated by pro-oncogenic stimuli such as hypoxia, both risk factors for TNBC. Even if the exact role of miR-155 in TNBC is still poorly elucidated, this evidence suggests a deeper investigation of its the putative role in TNBC, both as marker and therapeutic target.

Recent data described miR-181a, which is overexpressed in TNBC [98], as metastamiR. It belongs to the miR-181 family, which also comprises miR-181b, -c and -d; all the family members are positively regulated by TGF-β, even if the exact mechanism by which TGF-β regulates each microRNA is not completely understood. Down-modulation of miR-181a inhibits TGF-β mediated EMT, invasion and migration, also reverting anoikisis resistance in breast cancer cells; high levels were then associated with shorter disease-free survival of breast cancer patients, who tested negative for ErbB2 amplification [98]. Moreover miR-181a/b expression sensitizes to cisplatin [138] and to PARP inhibitors [99] by dampening the DNA double-strand-breaks repair, thus representing a possible marker of BRCAness. Quantification of miR-181a levels could then be useful for determining basal-like and TNBC patients’ sensitivity to platinum-derived compounds and who could really benefit from PARP inhibitors treatments.

miR-146 and miR-146b-5p were validated as regulator of BRCA-1 and found up-modulated in TN and basal-like breast cancer cell lines, being responsible for BRCA1-mediated effects on proliferation and homologous recombination [100]. Finally, miR-182 was found up-modulated in TNBC tissues and cell lines, antagonism inhibited cell proliferation and invasion, and induced apoptosis [101], confirming its oncogenic role previously described in other tumor types [139,140].

2.3. MiR and TNBC—Prognostic Value

MicroRNAs have already demonstrated their reliability as biomarkers for prognosis in response to therapy or therapeutic tools in other cancer types. In lung cancer, low levels of let-7a and the high levels of miR-155 are indicative of worse prognosis [141] while a robust 7-miRNA signature can predict overall survival (OS) and relapse-free survival in gastric cancer [142]. In melanomas, low miR-191 and high miR-193a levels were associated with a significantly shorter survival time [143]. In contrast, high levels of miR-21, in addition to being an indicator of poor prognosis in several cancers [115,144,145], is sufficient to predict poor response to adjuvant chemotherapy in adenocarcinoma [115].

With the improvement in stability, specificity and biodistribution of microRNAs in in vivo systems [146148], the use of microRNAs in therapy became more pragmatic. In particular, the miR-34a represents the most suitable therapeutic target [149]. Systemic delivery of miR-34a through liposomal-like vehicle in mouse models led to a huge impact on tumor growth in lung cancer [150] and total regression in hepatocellular carcinoma without side effects. Mirna Therapeutics has already announced the arrival of MRX34, the liposomal-formulated miR-34a mimics used for the study related to HCC, in a Phase I clinical trial.

A recent report by Caldas group [81] describes an integrated analysis of microRNA and mRNA expression with genomic, epigenetic and clinical features in a large set of samples. They showed that, in breast cancer, single microRNAs seem to be poor prognostic indicators, and specific signatures can be identified, but they seem consistently prognostic only in the subgroup of breast cancer devoid of copy number alteration (CNAs), the so called iClust4, as identified in the Metabric cohort [151]. Moreover, this signature mainly includes novel microRNAs, which may reflect their discovery by sequencing high grade tumors.

However, a number of known microRNAs have been identified as prognostic in breast cancer, including the Triple Negative subgroup, where a set of four microRNAs were found to associate with OS. In detail, up-regulation of miR-16, miR-155 or miR-374 correlated with better prognosis while down-modulation of miR-125b correlated with worse prognosis. Furthermore, a set of seven microRNAs were associated with distan disease free survival (DDFS). Cascione et al. identified three “risk-associated” miRs (miR-125b, miR-655 and miR-421) and four “protective” miRNAs (miR-16, miR-374a, miR-374b and miR-497) which negatively and positively correlated with DDFS, respectively [83]. However, these signatures require further validation in a second set on TNBC samples.

MiR-210, over-expressed in TNBC compared to ER+ [113], was described as an independent prognostic factor in TNBC; low levels of miR-210 showed better distant-free-survival (DFS) in TNBC and no relapse in the following 5 years after surgery in node negative TNBC [152]. MiR-34b, a member of the p53-regulated miR-34 family [153], is negatively associated with DFS and OS [154]. However this observation is not consistent with previously reported tumor-suppressor roles of the miR-34 family in cancer. Interestingly, there is a positive correlation of miR-25, miR-10b, miR-130b, miR-1274a, and miR-Plus-1030 and a negative correlation of miR-29c, which is downregulated in ER-/cytokeratin 5 and 6 positive tumors, with proliferation in a set of lymph node negative breast cancer [78], since proliferation represents the strongest single prognostic indicator in this group of patients [155].

3. Conclusions

The molecular and phenotypical heterogeneity that characterizes Triple Negative Breast Cancer has been partially clarified, and the complexity of signal networks driving the biology of a tumor subgroup for which the absence of targets for a specific therapy certainly contributes to a poor clinical outcome has been discussed.

In this scenario, microRNAs might represent not only an additional level of complexity in the molecular portrait of TNBC, contributing to tumor comprehension and subclassification, but, more importantly, they might represent easily detectable biomarkers to predict prognosis and response to therapy. Certainly, their potential as biomarkers needs to be validated in different cohorts of samples, taking into account the evidence that profiling signatures are probably more statistically significant that single miRNAs in predicting outcome.

Moreover, even though the available, up to date data are almost exclusively pre-clinical evidence, the application of miRNAs in therapy as adjuvant tools of targets seems convincing and promising.

Supplementary Information

ijms-14-22202-s001.pdf
Table Table 1. Summary of microRNAs involved in Triple Negative Breast Cancer (TNBC), with relative validated targets and biological functions.

Click here to display table

Table 1. Summary of microRNAs involved in Triple Negative Breast Cancer (TNBC), with relative validated targets and biological functions.
MicroRNAValidated target(s)Main biological function(s) in TNBCReference
Tumor suppressor
miR-200a/bZeb1/Zeb2, Suz 12, EphA2Stimulation of differentiation in undifferentiated mammary epithelial cell line[89]
miR-200cZeb1/Zeb2Inhibition of EMT[47]
MSN; FN1Suppression of migration[90]
TrkBReversion of anoikisis resistance[90,91]
miR-205E2F1; LAMC1Reduction of proliferation, cell cycle and tumor growth[92]
miR-203BIRC5Reduction of proliferation[93]
LASP1Inhibition of migration
miR-31WAVE3; RhoA; RadexinReduction of metastatic potential[94,95]
PRKCEInduction of apoptosis and enhancement of chemo- and radiosensitivity[96]
miR-34aAXLImpairment of migration[97]

OncomiR
miR-181a/bBimInhibition of anoikisis[98]
ATMImpairment of DNA double-strand-breaks repair[99]
MiR-146 and miR-146b-5pBRCA1control of BRCA1-mediated proliferation and homologous recombination[100]
miR-182PFN1Inhibition of cell proliferation and invasion[101]
Induction of apoptosis

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Kim, V.N.; Nam, J.W. Genomics of microRNA. Trends Genet 2006, 22, 165–173. [Google Scholar]
  2. Lin, S.L.; Kim, H. Intron-mediated RNA interference and microRNA (miRNA). Front Biosci 2008, 13, 2216–2230. [Google Scholar]
  3. Lee, Y.; Kim, M. MicroRNA genes are transcribed by RNA polymerase II. EMBO J 2004, 23, 4051–4060. [Google Scholar]
  4. Yi, R.; Qin, Y. Exportin-5 mediates the nuclear export of pre-microRNAs and short hairpin RNAs. Genes Dev 2003, 17, 3011–3016. [Google Scholar]
  5. Hammond, S.M. Dicing and slicing: The core machinery of the RNA interference pathway. FEBS Lett 2005, 579, 5822–5829. [Google Scholar]
  6. Eulalio, A.; Huntzinger, E. Deadenylation is a widespread effect of miRNA regulation. RNA 2009, 15, 21–32. [Google Scholar]
  7. Qin, W.; Shi, Y. miR-24 regulates apoptosis by targeting the open reading frame (ORF) region of FAF1 in cancer cells. PLoS One 2010, 5, e9429. [Google Scholar]
  8. Forman, J.J.; Legesse-Miller, A. A search for conserved sequences in coding regions reveals that the let-7 microRNA targets Dicer within its coding sequence. Proc. Natl. Acad. Sci. USA 2008, 105, 14879–14884. [Google Scholar]
  9. Place, R.F.; Li, L.C. MicroRNA-373 induces expression of genes with complementary promoter sequences. Proc. Natl. Acad. Sci. USA 2008, 105, 1608–1613. [Google Scholar]
  10. Li, L.C.; Okino, S.T. Small dsRNAs induce transcriptional activation in human cells. Proc. Natl. Acad. Sci. USA 2006, 103, 17337–17342. [Google Scholar]
  11. Hwang, H.W.; Wentzel, E.A. A hexanucleotide element directs microRNA nuclear import. Science 2007, 315, 97–100. [Google Scholar]
  12. Chong, M.M.; Zhang, G. Canonical and alternate functions of the microRNA biogenesis machinery. Genes Dev 2010, 24, 1951–1960. [Google Scholar]
  13. Cifuentes, D.; Xue, H.A. Novel miRNA processing pathway independent of Dicer requires Argonaute2 catalytic activity. Science 2010, 328, 1694–1698. [Google Scholar]
  14. Cheloufi, S.; Dos Santos, C.O. A dicer-independent miRNA biogenesis pathway that requires Ago catalysis. Nature 2010, 465, 584–589. [Google Scholar]
  15. Ruby, J.G.; Jan, C.H. Intronic microRNA precursors that bypass Drosha processing. Nature 2007, 448, 83–86. [Google Scholar]
  16. Yang, J.S.; Lai, E.C. Alternative miRNA biogenesis pathways and the interpretation of core miRNA pathway mutants. Mol. Cell 2011, 43, 892–903. [Google Scholar]
  17. Shi, Z.; Zhang, J. AC1MMYR2, an inhibitor of dicer-mediated biogenesis of oncomir miR-21, reverses epithelial-mesenchymal transition and suppresses tumor growth and progression. Cancer Res 2013, 73, 5519–5531. [Google Scholar]
  18. Lee, H.C.; Kim, J.G. Prognostic impact of microRNA related gene polymorphisms on survival of patients with colorectal cancer. J. Cancer Res. Clin. Oncol 2010, 136, 1073–1078. [Google Scholar]
  19. Yang, H.; Dinney, C.P. Evaluation of genetic variants in microRNA-related genes and risk of bladder cancer. Cancer Res 2008, 68, 2530–2537. [Google Scholar]
  20. Lin, J.; Horikawa, Y. Genetic variations in microRNA-related genes are associated with survival and recurrence in patients with renal cell carcinoma. Carcinogenesis 2010, 31, 1805–1812. [Google Scholar]
  21. Liang, D.; Meyer, L. Genetic variants in MicroRNA biosynthesis pathways and binding sites modify ovarian cancer risk, survival, and treatment response. Cancer Res 2010, 70, 9765–9776. [Google Scholar]
  22. Zhang, X.; Yang, H. MicroRNA-related genetic variations as predictors for risk of second primary tumor and/or recurrence in patients with early-stage head and neck cancer. Carcinogenesis 2010, 31, 2118–2123. [Google Scholar]
  23. Ma, H.; Yuan, H. Genetic variations in key microRNA processing genes and risk of head and neck cancer: A case-control study in Chinese population. PLoS One 2012, 7, e47544. [Google Scholar]
  24. Liu, J.; Liu, J. Genetic variants in the microRNA machinery gene GEMIN4 are associated with risk of prostate cancer: A case-control study of the Chinese Han population. DNA Cell Biol 2012, 31, 1296–1302. [Google Scholar]
  25. Sung, H.; Jeon, S. Common genetic polymorphisms of microRNA biogenesis pathway genes and breast cancer survival. BMC Cancer 2012, 12. [Google Scholar] [CrossRef]
  26. Valadi, H.; Ekström, K. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat. Cell Biol 2007, 6, 654–659. [Google Scholar]
  27. Taylor, D.D.; Gercel-Taylor, C. MicroRNA signatures of tumor-derived exosomes as diagnostic biomarkers of ovarian cancer. Gynecol. Oncol 2008, 110, 13–21. [Google Scholar]
  28. Mitchell, P.S.; Parkin, R.K. Circulating microRNAs as stable blood-based markers for cancer detection. Proc. Natl. Acad. Sci. USA 2008, 105, 10513–10518. [Google Scholar]
  29. Schwarzenbach, H.; Hoon, D.S. Cell-free nucleic acids as biomarkers in cancer patients. Nat. Rev. Cancer 2011, 11, 426–437. [Google Scholar]
  30. Hanke, M.; Hoefig, K. A robust methodology to study urine microRNA as tumor marker: MicroRNA-126 and microRNA-182 are related to urinary bladder cancer. Urol. Oncol 2009, 28, 655–661. [Google Scholar]
  31. Park, N.J.; Zhou, H. Salivary microRNA: Discovery, characterization, and clinical utility for oral cancer detection. Clin. Cancer Res 2009, 15, 5473–5477. [Google Scholar]
  32. Michael, A.; Bajracharya, S.D. Exosomes from human saliva as a source of microRNA biomarkers. Oral. Dis 2010, 16, 34–38. [Google Scholar]
  33. Yu, L.; Todd, N.W. Early detection of lung adenocarcinoma in sputum by a panel of microRNA markers. Int. J. Cancer 2010, 127, 2870–2878. [Google Scholar]
  34. Iorio, M.V.; Ferracin, M. MicroRNA gene expression deregulation in human breast cancer. Cancer Res 2005, 65, 7065–7070. [Google Scholar]
  35. Zhu, S.; Wu, H. MicroRNA-21 targets tumor suppressor genes in invasion and metastasis. Cell Res 2008, 18, 350–359. [Google Scholar]
  36. Yan, L.X.; Huang, X.F. MicroRNA miR-21 overexpression in human breast cancer is associated with advanced clinical stage, lymph node metastasis and patient poor prognosis. RNA 2008, 14, 2348–2360. [Google Scholar]
  37. Qian, B.; Katsaros, D. High miR-21 expression in breast cancer associated with poor disease-free survival in early stage disease and high TGF-beta1. Breast Cancer Res. Treat 2009, 117, 131–140. [Google Scholar]
  38. Medina, P.P.; Nolde, M. OncomiR addiction in an in vivo model of microRNA-21-induced pre-B-cell lymphoma. Nature 2010, 467, 86–90. [Google Scholar]
  39. Scott, G.K.; Goga, A. Coordinate suppression of ERBB2 and ERBB3 by enforced expression of micro-RNA miR-125a or miR-125b. J. Biol. Chem 2006, 12, 1479–1486. [Google Scholar]
  40. Iorio, M.V.; Casalini, P. microRNA-205 regulates HER3 in human breast cancer. Cancer Res 2009, 69, 2195–2200. [Google Scholar]
  41. Yu, F.; Yao, H. let-7 regulates self renewal and tumorigenicity of breast cancer cells. Cell 2007, 131, 1109–1123. [Google Scholar]
  42. Ma, L.; Teruya-Feldstein, J. Tumour invasion and metastasis initiated by microRNA-10b in breast cancer. Nature 2007, 449, 682–688. [Google Scholar]
  43. Ma, L.; Reinhardt, F. Therapeutic silencing of miR-10b inhibits metastasis in a mouse mammary tumor model. Nat. Biotechnol 2010, 28, 341–347. [Google Scholar]
  44. Ma, L.; Young, J. MiR-9, a MYC/MYCN-activated microRNA, regulates E-cadherin and cancer metastasis. Nat. Cell Biol 2010, 12, 247–256. [Google Scholar]
  45. Huang, Q.; Gumireddy, K. The microRNAs miR-373 and miR-520c promote tumour invasion and metastasis. Nat. Cell Biol 2008, 10, 202–210. [Google Scholar]
  46. Tavazoie, S.F.; Alarcon, C. Endogenous human microRNAs that suppress breast cancer metastasis. Nature 2008, 451, 147–152. [Google Scholar]
  47. Gregory, P.A.; Bracken, C.P. MicroRNAs as regulators of epithelial-mesenchymal transition. Cell Cycle 2008, 7, 3112–3118. [Google Scholar]
  48. Wu, H.; Zhu, S. Suppression of cell growth and invasion by miR-205 in breast cancer. Cell Res 2009, 19, 439–448. [Google Scholar]
  49. Iorio, M.V.; Visone, R. MicroRNA signatures in human ovarian cancer. Cancer Res 2007, 67, 8699–8707. [Google Scholar]
  50. Perou, C.M.; Sørlie, T. Molecular portraits of human breast tumours. Nature 2000, 406, 747–752. [Google Scholar]
  51. Teschendorff, A.E.; Miremadi, A. An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer. Genome Biol 2007, 8. [Google Scholar] [CrossRef]
  52. Prat, A.; Parker, J.S. Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast Cancer Res 2010, 12. [Google Scholar] [CrossRef]
  53. Van de Vijver, M.J.; He, Y.D. A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med 2002, 347, 1999–2009. [Google Scholar]
  54. Van’t Veer, L.J.; Dai, H. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002, 415, 530–536. [Google Scholar]
  55. Paik, S.; Shak, S. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N. Engl. J. Med 2004, 351, 2817–2826. [Google Scholar]
  56. Harris, L.; Fritsche, H. American society of clinical oncology. American society of clinical oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J. Clin. Oncol 2007, 25, 5287–5312. [Google Scholar]
  57. Nielsen, T.O.; Hsu, F.D. Immunohistochemical and clinical characterization of the basal-like subtype of invasive breast carcinoma. Clin. Cancer Res 2004, 10, 5367–5374. [Google Scholar]
  58. Blows, F.M.; Driver, K.E. Subtyping of breast cancer by immunohistochemistry to investigate a relationship between subtype and short and long term survival: A collaborative analysis of data for 10,159 cases from 12 studies. PLoS Med 2010, 7, e1000279. [Google Scholar]
  59. Perou, C.M. Molecular stratification of triple-negative breast cancers. Oncologist 2011, 16, 61–70. [Google Scholar]
  60. Lehmann, B.D.; Bauer, J.A. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J. Clin. Invest 2011, 121, 2750–2767. [Google Scholar]
  61. Liedtke, C.; Mazouni, C. Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. J. Clin. Oncol 2008, 26, 1275–1281. [Google Scholar]
  62. Von Minckwitz, G.; Untch, M. Definition and impact of pathologic complete response on prognosis after neoadjuvant chemotherapy in various intrinsic breast cancer subtypes. J. Clin. Oncol 2012, 30, 1796–1804. [Google Scholar]
  63. Heitz, F.; Harter, P. Triple-negative and HER2-overexpressing breast cancers exhibit an elevated risk and an earlier occurrence of cerebral metastases. Eur. J. Cancer 2009, 45, 2792–2798. [Google Scholar]
  64. Andre, F.; Job, B. Molecular characterization of breast cancer with high-resolution oligonucleotide comparative genomic hybridization array. Clin. Cancer Res 2009, 15, 441–451. [Google Scholar]
  65. Hu, X.; Stern, H.M. Genetic alterations and oncogenic pathways associated with breast cancer subtypes. Mol. Cancer Res 2009, 7, 511–522. [Google Scholar]
  66. Bertucci, F.; Finetti, P. How basal are triple-negative breast cancers? Int. J. Cancer 2008, 123, 236–240. [Google Scholar]
  67. Gerber, B.; Loibl, S. Neoadjuvant bevacizumab and anthracycline-taxane-based chemotherapy in 678 triple-negative primary breast cancers: results from geparquinto study (GBG 44). Annal. Oncol 2013. [Google Scholar] [CrossRef]
  68. Bear, H.D.; Tang, G. Bevacizumab added to neoadjuvant chemotherapy for breast cancer. N. Engl. J. Med 2012, 66, 310–320. [Google Scholar]
  69. Burstein, H.J.; Elias, A.D. Phase II study of sunitinib malate, an oral multitargeted tyrosine kinase inhibitor, in patients with metastatic breast cancer previously treated with an anthracycline and a taxane. J. Clin. Oncol 2008, 26, 1810–1816. [Google Scholar]
  70. Barrios, C.H.; Liu, M.C. Phase III randomized trial of sunitinib versus capecitabine in patients with previously treated HER2 negative advanced breast cancer. Breast Cancer Res. Treat 2010, 121, 121–131. [Google Scholar]
  71. Bergh, J.; Bondarenko, I.M. First-line treatment of advanced breast cancer withsunitinib in combination with docetaxel versus docetaxel alone. J. Clin. Oncol 2012, 30, 921–929. [Google Scholar]
  72. Baselga, J.; Gómez, P. Randomize d phase II study of the anti-epidermal growth factor receptor monoclonal antibody cetuximab with cisplatin versus cisplatin alone in patients with metastatic triple-negative breast cancer. J. Clin. Oncol. 2013, 31, 2586–2592. [Google Scholar]
  73. Dickler, M.N.; Rugo, H.S. A phase II trial of erlotinib in combination with bevacizumab in patients with metastatic breast cancer. Clin. Cancer Res 2008, 14, 7878–7883. [Google Scholar]
  74. Blenkiron, C.; Goldstein, L.D. MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype. Genome Biol 2007, 8. [Google Scholar] [CrossRef]
  75. Sempere, L.F.; Christensen, M. Altered MicroRNA expression confined to specific epithelial cell subpopulations in breast cancer. Cancer Res 2007, 67, 11612–11620. [Google Scholar]
  76. Mattie, M.D.; Benz, C.C. Optimized high-throughput microRNA expression profiling provides novel biomarker assessment of clinical prostate and breast cancer biopsies. Mol. Cancer 2006, 5. [Google Scholar] [CrossRef]
  77. Lowery, A.J.; Miller, N. MicroRNA signatures predict oestrogen receptor, progesterone receptor and HER2/neu receptor status in breast cancer. Breast Cancer Res 2009, 11. [Google Scholar] [CrossRef]
  78. Janssen, E.A.; Slewa, A. Biologic profiling of lymph node negative breast cancers by means of microRNA expression. Mod. Pathol 2010, 23, 1567–1576. [Google Scholar]
  79. Farazi, T.A.; Horlings, H.M. MicroRNA sequence and expression analysis in breast tumors by deep sequencing. Cancer Res 2011, 71, 4443–4453. [Google Scholar]
  80. Volinia, S.; Galasso, M. Breast cancer signatures for invasiveness and prognosis defined by deep sequencing of microRNA. Proc. Natl. Acad. Sci. USA 2012, 109, 3024–3029. [Google Scholar]
  81. Dvinge, H.; Git, A. Shaping and functional consequences of the microRNA landscape in breast cancer. Nature 2013, 497, 378–382. [Google Scholar]
  82. Li, J.Y.; Jia, S. Differential distribution of microRNAs in breast cancer grouped by clinicopathological subtypes. Asian Pac. J. Cancer Prev 2013, 14, 3197–3203. [Google Scholar]
  83. Cascione, L.; Gasparini, P. Integrated microRNA and mRNA signatures associated with survival in triple negative breast cancer. PLoS One 2013, 8, e55910. [Google Scholar]
  84. Dedes, K.J.; Natrajan, R. Down-regulation of the miRNA master regulators Drosha and Dicer is associated with specific subgroups of breast cancer. Eur. J. Cancer 2011, 47, 138–150. [Google Scholar]
  85. Passon, N.; Gerometta, A. Expression of Dicer and Drosha in triple-negative breast cancer. J. Clin. Pathol 2012, 65, 320–326. [Google Scholar]
  86. Piskounova, E.; Polytarchou, C. Lin28A and Lin28B inhibit let-7 microRNA biogenesis by distinct mechanisms. Cell 2011, 147, 1066–1079. [Google Scholar]
  87. Martello, G.; Rosato, A. A MicroRNA targeting dicer for metastasis control. Cell 2010, 141, 1195–1207. [Google Scholar]
  88. Buffa, F.M.; Camps, C. microRNA-associated progression pathways and potential therapeutic targets identified by integrated mRNA andmicroRNA expression profiling in breast cancer. Cancer Res 2011, 71, 5635–5645. [Google Scholar]
  89. Aydo˘du, E.; Katchy, A. MicroRNA-regulated gene networks during mammary cell differentiation are associated with breast cancer. Carcinogenesis 2012, 33, 1502–1511. [Google Scholar]
  90. Howe, E.N.; Cochrane, D.R. Targets of miR-200c mediate suppression of cell motility and anoikis resistance. Breast Cancer Res 2011, 13. [Google Scholar] [CrossRef]
  91. Howe, E.N.; Cochrane, D.R. miR-200c targets a NF-κB up-regulated TrkB/NTF3 autocrine signaling loop to enhance anoikis sensitivity in triplenegative breast cancer. PLoS One 2012, 7, e49987. [Google Scholar]
  92. Piovan, C.; Palmieri, D. Oncosuppressive role of p53-induced miR-205 in triple negative breast cancer. Mol. Oncol 2012, 6, 458–472. [Google Scholar]
  93. Wang, C.; Zheng, X. MicroRNA-203 suppresses cell proliferation and migration by targeting BIRC5 and LASP1 in human triple-negative breast cancer cells. J. Exp. Clin. Cancer Res 2012, 31. [Google Scholar] [CrossRef]
  94. Sossey-Alaoui, K.; Downs-Kelly, E. WAVE3, an actin remodeling protein, is regulated by the metastasis suppressor microRNA, miR-31, during the invasion-metastasis cascade. Int. J. Cancer 2011, 129, 1331–1343. [Google Scholar]
  95. Valastyan, S.; Reinhardt, F. A pleiotropically acting microRNA, miR-31, inhibits breast cancer metastasis. Cell 2009, 137, 1032–1046. [Google Scholar]
  96. Körner, C.; Keklikoglou, I. MicroRNA-31 sensitizes human breast cells to apoptosis by direct targeting of protein kinase C epsilon (PKCepsilon). J. Biol. Chem 2013, 288, 8750–8761. [Google Scholar]
  97. Mackiewicz, M.; Huppi, K. Identification of the receptor tyrosine kinase AXL in breast cancer as a target for the human miR-34a microRNA. Breast Cancer Res. Treat 2011, 130, 663–679. [Google Scholar]
  98. Taylor, M.A.; Sossey-Alaoui, K. TGF-β upregulates miR-181a expression to promote breast cancer metastasis. J. Clin. Invest 2013, 123, 150–163. [Google Scholar]
  99. Bisso, A.; Faleschini, M. Oncogenic miR-181a/b affect the DNA damage response in aggressive breast cancer. Cell Cycle 2013, 12, 1679–1687. [Google Scholar]
  100. Garcia, A.I.; Buisson, M. Down-regulation of BRCA1 expression by miR-146a and miR-146b-5p in triple negative sporadic breast cancers. EMBO Mol. Med 2011, 3, 279–290. [Google Scholar]
  101. Liu, H.; Wang, Y. Expression and regulatory function of miRNA-182 in triple-negative breast cancer cells through its targeting of profiling 1. Tumour Biol 2013, 34, 1713–1722. [Google Scholar]
  102. Wang, J.; Zhao, H. Overexpressions of MicroRNA-9 and MicroRNA-200c in human breast cancers are associated with lymph node metastasis. Cancer Biother. Radiopharm 2013, 28, 283–288. [Google Scholar]
  103. Castilla, M.Á.; Díaz-Martín, J. MicroRNA-200 family modulation in distinct breast cancer phenotypes. PLoS One 2012, 7, e47709. [Google Scholar]
  104. Blick, T.; Hugo, H. Epithelial mesenchymal transition traits in human breast cancer cell lines parallel the CD44(hi/)CD24 (lo/-) stem cell phenotype in human breast cancer. J. Mammary Gland Biol. Neoplasia 2010, 15, 235–252. [Google Scholar]
  105. Shimono, Y.; Zabala, M. Downregulation of miRNA-200c links breast cancer stem cells with normal stem cells. Cell 2009, 138, 592–603. [Google Scholar]
  106. Williams, C.; Helguero, L. Gene expression in murine mammary epithelial stem cell-like cells shows similarities to human breast cancer gene expression. Breast Cancer Res 2009, 11. [Google Scholar] [CrossRef]
  107. Korpal, M.; Lee, E.S. The miR-200 family inhibits epithelial-mesenchymal transition and cancer cell migration by direct targeting of E-cadherin transcriptional repressors ZEB1 and ZEB2. J. Biol. Chem 2008, 283, 14910–14914. [Google Scholar]
  108. Burk, U.; Schubert, J. A reciprocal repression between ZEB1 and members of the miR-200 family promotes EMT and invasion in cancer cells. EMBO Rep 2008, 9, 582–589. [Google Scholar]
  109. Smit, M.A.; Geiger, T.R. A Twist-Snail axis critical for TrkB-induced epithelial-mesenchymal transition-like transformation, anoikis resistance, and metastasis. Mol. Cell Biol 2009, 29, 3722–3737. [Google Scholar]
  110. Smit, M.A.; Peeper, D.S. Zeb1 is required for TrkB-induced epithelial-mesenchymal transition, anoikis resistance and metastasis. Oncogene 2011, 30, 3735–3744. [Google Scholar]
  111. Chen, J.; Tian, W. Down-regulation of microRNA-200c is associated with drug resistance in human breast cancer. Med. Oncol 2012, 29, 2527–2534. [Google Scholar]
  112. Lin, J.; Liu, C. miR-200c enhances radiosensitivity of human breast cancer cells. J. Cell Biochem 2013, 114, 606–615. [Google Scholar]
  113. Radojicic, J.; Zaravinos, A. MicroRNA expression analysis in triple-negative (ER, PR and Her2/neu) breast cancer. Cell Cycle 2011, 10, 507–517. [Google Scholar]
  114. Furuta, M.; Kozaki, K.I. miR-124 and miR-203 are epigenetically silenced tumor-suppressive microRNAs in hepatocellular carcinoma. Carcinogenesis 2010, 31, 766–776. [Google Scholar]
  115. Schetter, A.J.; Leung, S.Y. MicroRNA expression profiles associated with prognosis and therapeutic outcome in colon adenocarcinoma. JAMA 2008, 299, 425–436. [Google Scholar]
  116. Boll, K.; Reiche, K. MiR-130a, miR-203 and miR-205 jointly repress key oncogenic pathways and are downregulated in prostatecarcinoma. Oncogene 2013, 32, 277–285. [Google Scholar]
  117. Bian, K.; Fan, J. MicroRNA-203 leads to G1 phase cell cycle arrest in laryngeal carcinoma cells by directly targeting survivin. FEBS Lett 2012, 586, 804–809. [Google Scholar]
  118. Petrarca, C.R.; Brunetto, A.T. Survivin as a predictive biomarker of complete pathologic response to neoadjuvant chemotherapy in patients with stage II and stage III breast cancer. Clin. Breast Cancer 2011, 11, 129–134. [Google Scholar]
  119. Kennedy, S.M.; O’Driscoll, L. Prognostic importance of survivin in breast cancer. Br. J. Cancer 2003, 88, 1077–1083. [Google Scholar]
  120. Grunewald, T.G.; Kammerer, U. Silencing of LASP-1 influences zyxin localization, inhibits proliferation and reduces migration in breast cancer cells. Exp. Cell Res 2006, 312, 974–982. [Google Scholar]
  121. Büssing, I.; Slack, F.J. Let-7 microRNAs in development, stem cells and cancer. Trends Mol. Med 2008, 14, 400–409. [Google Scholar]
  122. Boyerinas, B.; Park, S.M. The role of let-7 in cell differentiation and cancer. Endocr. Relat. Cancer 2010, 17, F19–F36. [Google Scholar]
  123. Barh, D.; Malhotra, R. MicroRNA let-7: An emerging next-generation cancer therapeutic. Curr. Oncol 2010, 17, 70–80. [Google Scholar]
  124. Ahmad, A.; Sarkar, S.H. Garcinol regulates EMT and Wnt signaling pathways in vitro and in vivo, leading to anticancer activity against breast cancer cells. Mol. Cancer Ther 2012, 11, 2193–2201. [Google Scholar]
  125. Ahmad, A.; Wang, Z. Apoptosis-inducing effect of garcinol is mediated by NF-kappaB signaling in breast cancer cells. J. Cell Biochem 2010, 109, 1134–1141. [Google Scholar]
  126. Kundu, S.T.; Nallur, S. KRAS alleles: The LCS6 3′UTR variant and KRAS coding sequence mutations in the NCI-60 panel. Cell Cycle 2012, 11, 361–366. [Google Scholar]
  127. Chin, L.J.; Ratner, E. A SNP in a let-7 microRNA complementary site in the KRAS 3′ untranslated region increases non-small cell lung cancer risk. Cancer Res 2008, 68, 8535–8540. [Google Scholar]
  128. Paranjape, T.; Heneghan, H. A 3′-untranslated region KRAS variant and triple-negative breast cancer: A case-control and genetic analysis. Lancet Oncol 2011, 12, 377–386. [Google Scholar]
  129. Lu, L.; Katsaros, D. Pluripotent factor lin-28 and its homologue lin-28b in epithelial ovarian cancer and their associations with diseaseoutcomes and expression of let-7a and IGF-II. Eur. J. Cancer 2009, 45, 2212–2218. [Google Scholar]
  130. Viswanathan, S.R.; Powers, J.T. Lin28 promotes transformation and is associated with advanced human malignancies. Nat. Genet 2009, 41, 843–848. [Google Scholar]
  131. Wang, Y.C.; Chen, Y.L. Lin-28B expression promotes transformation and invasion in human hepatocellular carcinoma. Carcinogenesis 2010, 31, 1516–1522. [Google Scholar]
  132. Augoff, K.; McCue, B. miR-31 and its host gene lncRNA LOC554202 are regulated by promoter hypermethylation in triple-negative breast cancer. Mol. Cancer 2012, 11. [Google Scholar] [CrossRef]
  133. Shankar, E.; Krishnamurthy, S. PKCepsilon induces Bcl-2 by activating CREB. Int. J. Oncol 2010, 36, 883–888. [Google Scholar]
  134. Krichevsky, A.M.; Gabriely, G. miR-21: A small multi-faceted RNA. J. Cell Mol. Med 2009, 13, 9–53. [Google Scholar]
  135. Savad, S.; Mehdipour, P. Expression analysis of MiR-21, MiR-205, and MiR-342 in breast cancer in Iran. Asian Pac. J. Cancer Prev 2012, 13, 873–877. [Google Scholar]
  136. Kong, W.; He, L. Upregulation of miRNA-155 promotes tumour angiogenesis by targeting VHL and is associated with poor prognosis and triple-negative breast cancer. Oncogene 2013. [Google Scholar] [CrossRef]
  137. Chang, S.; Wang, R.H. Tumor suppressor BRCA1 epigenetically controls oncogenic microRNA-155. Nat. Med 2011, 17, 1275–1282. [Google Scholar]
  138. Galluzzi, L.; Morselli, E. miR-181a and miR-630 regulate cisplatin-induced cancer cell death. Cancer Res 2010, 70, 1793–1803. [Google Scholar]
  139. Guan, P.; Yin, Z. Meta-analysis of human lung cancer microRNA expression profiling studies comparing cancer tissues with normal tissues. J. Exp. Clin. Cancer Res 2012, 31. [Google Scholar] [CrossRef]
  140. Liu, Z.; Liu, J. MiR-182 overexpression in tumourigenesis of high-grade serous ovarian carcinoma. J. Pathol 2012, 228, 204–215. [Google Scholar]
  141. Yanaihara, N.; Caplen, N. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 2006, 9, 189–198. [Google Scholar]
  142. Li, X.; Zhang, Y. Survival prediction of gastric cancer by a seven-microRNA signature. Gut 2010, 59, 579–585. [Google Scholar]
  143. Caramuta, S.; Egyházi, S. MicroRNA expression profiles associated with mutational status and survival in malignant melanoma. J. Invest Dermatol 2010, 130, 2062–2070. [Google Scholar]
  144. Dillhoff, M.; Liu, J. MicroRNA-21 is overexpressed in pancreatic cancer and a potential predictor of survival. J. Gastrointest. Surg 2008, 12, 2171–2176. [Google Scholar]
  145. Rossi, S.; Shimizu, M. microRNA fingerprinting of CLL patients with chromosome 17p deletion identify a miR-21 score that stratifies early survival. Blood 2010, 116, 945–952. [Google Scholar]
  146. Bader, A.G.; Brown, D. Developing therapeutic microRNAs for cancer. Gene Ther 2011, 18, 1121–1126. [Google Scholar]
  147. Bader, A.G.; Brown, D. The promise of microRNA replacement therapy. Cancer Res 2010, 70, 7027–7030. [Google Scholar]
  148. Stenvang, J.; Petri, A. Inhibition of microRNA function by antimiR oligonucleotides. Silence 2012, 3. [Google Scholar] [CrossRef]
  149. Bader, A.G. miR-34—A microRNA replacement therapy is headed to the clinic. Front Genet 2012, 3. [Google Scholar] [CrossRef]
  150. Trang, P.; Wiggins, J.F. Systemic delivery of tumor suppressor microRNA mimics using a neutral lipid emulsion inhibits lung tumors in mice. Mol. Ther 2011, 19, 1116–1122. [Google Scholar]
  151. Curtis, C.; Shah, S.P. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 2012, 486, 346–352. [Google Scholar]
  152. Toyama, T.; Kondo, N. High expression of microRNA-210 is an independent factor indicating a poor prognosis in Japanese triple-negative breast cancer patients. Jpn. J. Clin. Oncol 2012, 42, 256–263. [Google Scholar]
  153. Yamakuchi, M.; Lowenstein, C.J. MiR-34, SIRT1 and p53: The feedback loop. Cell Cycle 2009, 8, 712–715. [Google Scholar]
  154. Svoboda, M.; Sana, J. MiR-34b is associated with clinical outcome in triple-negative breast cancer patients. Diagn Pathol 2012, 7. [Google Scholar] [CrossRef]
  155. Baak, J.P.; van Diest, P.J. Prospective multicenter validation of the independent prognostic value of the mitotic activity index in lymph node-negative breast cancer patients younger than 55 years. J. Clin. Oncol 2005, 23, 5993–6001. [Google Scholar]
Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert