Next Article in Journal
Cucurbitacin D Reprograms Glucose Metabolic Network in Prostate Cancer
Next Article in Special Issue
Targeting the mDia Formin-Assembled Cytoskeleton Is an Effective Anti-Invasion Strategy in Adult High-Grade Glioma Patient-Derived Neurospheres
Previous Article in Journal
Integrated Analysis of Germline and Tumor DNA Identifies New Candidate Genes Involved in Familial Colorectal Cancer

Cancers 2019, 11(3), 363; https://doi.org/10.3390/cancers11030363

Review
Triple Negative Breast Cancer Profile, from Gene to microRNA, in Relation to Ethnicity
1
College of Medicine, Qatar University, Doha P. O. Box 2713, Qatar
2
Biomedical Research Centre, Qatar University, Doha P. O. Box 2713, Qatar
*
Author to whom correspondence should be addressed.
Received: 26 January 2019 / Accepted: 6 March 2019 / Published: 13 March 2019

Abstract

:
Breast cancer is the most frequent cause of cancer-related deaths among women worldwide. It is classified into four major molecular subtypes. Triple-negative breast cancers (TNBCs), a subgroup of breast cancer, are defined by the absence of estrogen and progesterone receptors and the lack of HER-2 expression; this subgroup accounts for ~15% of all breast cancers and exhibits the most aggressive metastatic behavior. Currently, very limited targeted therapies exist for the treatment of patients with TNBCs. On the other hand, it is important to highlight that knowledge of the molecular biology of breast cancer has recently changed the decision-making process regarding the course of cancer therapies. Thus, a number of new techniques, such as gene profiling and sequencing, proteomics, and microRNA analysis have been used to explore human breast carcinogenesis and metastasis including TNBC, which consequently could lead to new therapies. Nevertheless, based on evidence thus far, genomics profiles (gene and miRNA) can differ from one geographic location to another as well as in different ethnic groups. This review provides a comprehensive and updated information on the genomics profile alterations associated with TNBC pathogenesis associated with different ethnic backgrounds.
Keywords:
breast cancer; triple negative breast cancer; biomarkers; microarray; gene expression profiling; miRNA

1. Introduction

Breast cancer is the most frequently diagnosed cancer in women worldwide [1]. In 2012, breast cancer accounted for 25% of the prevalent cancer cases worldwide [2]. In developing countries, it is the most common cause of death (14.3%), whereas in developed countries it is the second leading cause of cancer mortality (15.4%) [1].
Various environmental factors contribute to a woman′s risk of developing breast cancer. Increasing age, menarche, high hormonal levels, null-parity, tobacco use, and obesity [3,4,5,6,7,8,9] are risk factors and account for 47% of the breast cancer (BC) cases [10]. Approximately 5–10% of the cases are attributed to genetic factors that include BRCA (BRCA1 and BRCA2) mutations [11,12,13]. BRCA1/2 are autosomal dominant and tumor suppressor genes present on chromosomes 17 and 13, respectively, and are mutated in approximately 30–40% of familial BC cases [14].
On the other hand, oncogenes and tumor suppressor genes are involved in the tumorigenesis of sporadic BC [15]. While most of cancer-related deaths are a result of complications from its metastatic form [16,17]; however, the mechanisms underlying malignant progression in BC are yet to be elucidated. Research has identified numerous genetic changes in malignant tumors, although the frequency of different gene alterations is quite low [18]. Recently, “significantly mutated genes” (SMGs) were identified in the onset of malignant transformation [19] and few of them encode for proteins interacting with BRCA1/2, while others act through different pathways including TP53, PTEN, CHEK2, ATM and PALB2 [20]. Mutations in these genes are suspected to elevate the risk of BC development.
Various prognostic and predictive factors are studied in BC, including estrogen/progesterone receptors (ER/PR) status and HER-2/neu gene amplification [21,22]. Steroid receptor status, HER-2/neu status, nodal status, tumor size, and grade have been used for several years [23], however, none of these factors are reliable predictors of disease outcome.
Gene expression profiling in BC started in the mid-1990s, this technique allowed classification of BC into subtypes via hierarchical clustering of several gene expression profiles of human breast tumors [24,25,26]. BC was first classified into its intrinsic molecular subtypes luminal, Her2, basal-like and normal breast using cDNA microarrays by Perou and colleagues (2000) [27]. Following this study, another study differentiated molecular subtypes linked with different prognosis and further subdivided the luminal group into luminal A and luminal B [28]. Analysis between the subtypes showed the basal-like and the Her2+ subtypes have the shortest overall survival times and relapse-free survival in comparison with the estrogen-receptor positive groups [29]. The study showed that the basal-like subtype potentially represented a different clinical entity linked with shorter survival and a high frequency of TP53 mutations. Genome-wide expression arrays of tumors demonstrated the tumor biology; range in patterns reflected the biological diversity [29]. Based on these subtypes, an Expert Consensus established four clinic-pathological definitions, recommending therapeutic strategies for each group [30]. Further research revealed additional subtypes such as a claudin-low BC, a subtype of basal-like BC [31]. However, a larger cohort of breast tumors needs to be assessed along with comprehensive clinical information to identify clinical phenotypes including resistance and sensitivity to specific therapies, invasiveness, or metastatic potential [29].
In this review, we will focus on the role of microarray molecular profiling (genes and microRNAs) as a prognostic, diagnostic as well as a therapeutic tool for the most aggressive BC phenotype in different ethnic groups, which is triple negative BC.

2. Triple Negative Breast Cancer (TNBC)

Triple negative breast cancer (TNBC) is a subgroup of BC, representing 12–17% of all BCs [32]. TNBCs have a comparatively lower expression of the three receptors: ER, PR and HER-2/neu in comparison with normal tissue as well as other types of BC. It affects more frequently young patients, and is represented by advanced stage, higher proliferative index (measured by mitotic account or Ki-67 proliferative index), higher histologic grade, and significantly higher metastatic rates [33,34,35,36].
TNBCs have a higher prevalence in a distinct group or population [13]; for example, in African-American women the prevalence of TNBCs is very high [37]. TNBC was found to be prevalent in young women of African descent [38]. Environmental as well as genetic factors are known to impact the age of onset and subtype frequency in different populations [38]. In TNBCs, metastatic rates are high to visceral organs [39,40]; in addition, cerebral metastasis is more common [17,41,42,43]. De-novo metastasis plays a key role in cancer mortality with racial/ethnic disparities in the site, frequency, and associated survival [44]. Racial/ethnic differences in BC can partially be due to variations in the biological aggressiveness of TNBC in African women as compared with other racial/ethnic groups [45]. Recent studies in BC patients showed that non-Hispanic blacks largely had metastasis to the bone, brain, or liver, while Hispanics were less likely to have metastasis to the liver in comparison to the non-Hispanic Whites [44].
Sub-classification of TNBCs have been attempted based on several biomarkers including epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF), c-kit and basal cytokeratins (e.g., CK5/6, CK14, CK17), TP53, TOP-2A, Ki67, Cox-2 and heat shock protein 90 [36]. Nevertheless, all TNBCs have a poor clinical prognosis and special pathological characteristics compared to other subtypes of BC. The overall 5-year survival rate for TNBC is 50–60% [37,46,47], with a lower likelihood of developing recurrent tumor over the following 5-years in these patients [37,48]. TNBCs are associated with a higher rate of local recurrence during the first three years after treatment and a high five-year mortality rate compared with other subtypes of BC [49].
Systemic treatment for breast cancer includes the use of cytotoxic, hormonal, and immunotherapeutic agents. To date, cytotoxic chemotherapy is the only approved treatment option for TNBC [36,50,51]. Systemic agents are effective at the beginning of therapy in the majority (90%) of primary and approximately half of metastatic breast cancer cases [52]. However, after a period of time, tumor progression occurs; resistance to therapy is common leading to treatment failure and death in more than 90% of patients with advanced/metastatic disease [52]. Metastasis is a multifarious process in which a primary solid tumor plagues the adjacent tissue and then spreads to the neighboring as well as distant parts of the body [53]. During tumor progression, the cells undergo epithelial-to-mesenchymal transition (EMT), thus enhancing cell invasion and commencing the process of metastasis, one of the hallmarks of cancer [54] (Figure 1).
Generally, breast cancer cells metastasize to the bone, liver, lung and brain [16]. However, there is no efficient targeted therapy available presently for the treatment of patients with TNBCs, especially in its metastatic form [55].
Knowledge of molecular biology in breast cancer has recently introduced new-targeted therapies using cDNA microarray, proteomics, next-generation sequencing (NGS) and miRNA technologies. Among the novel treatment agents for breast cancer are poly (ADP-ribose) polymerase (PARP) inhibitors, angiogenesis inhibitors, EGFR-targeted agents, and src kinase inhibitors [56]. Other favorable molecular targets include the androgen receptor (AR), insulin-like growth factor receptor (IGFR), protein kinase B (AKT), mTOR [57], PI3K [58] and cyclin-dependent kinases [59].
The following sections will present a comprehensive review about gene expression profiling performed on TNBC to identify potential biomarkers related to cancer progression and metastasis in TNBC patients.

3. Gene Expression Profiling of TNBC

Microarray technologies have transformed research, allowing high-throughput whole-genome expression profiling and helped cancer scientists including oncologist to provide insight in a single assay about several diseases as well as create a molecular profile of tumor progression [24,25].
Although on a morphological level TNBC and basal-like breast cancer (BLBC) are comparatively similar in relation to large tumor size, high histologic grade, and substantial metastatic potential [60,61], gene expression profiling classified around 70% of TNBC samples as basal-like [62].
Molecular heterogeneity of TNBC has been recently well characterized at gene expression profiling level. An earlier investigation identified six molecular subtypes of TNBC including basal-like 1, basal-like 2, immunomodulatory, mesenchymal-like, mesenchymal stem-like, and luminal androgen receptor (LAR) subtype [63]. Nevertheless, molecular subtyping of TNBC by gene expression profiling revealed three subtypes, namely luminal androgen receptor, basal-like with low immune response and high M2-like macrophages and, basal-enriched with high immune response and low M2-like macrophages) [64]; which could provide insight for treatment of TNBC.
Both basal-like subtypes (basal-like 1 and basal-like 2) are affected by molecular alterations in cell-cycle, DNA machinery, cell proliferation, glycolysis and gluconeogenesis. These TNBC subtypes were found to be sensitive to cisplatin and PARP inhibitors. However, while, the basal-like 1 subtype displays elevated levels of Ki-67 as well as genes involved in cell division and DNA-damage (ATR, BRCA, Myc, NRAS), basal-like 2 subtype is characterized by high levels of EGFR, MET, EPHA2 and TP53 genes [57].
On the other hand, the immunomodulatory subtype was shown to overexpress genes involved in regulating immune cell signaling such as JAK1/2, STAT1/4, IRF1/7/8 and TNF. Recently, research showed stimulation of the immune signaling pathways including TNF enhanced PD-L1 expression [65]. PD-L1 overexpression is common in basal breast cancers and is linked with high T-cell cytotoxic immune response, better survival and response to chemotherapy [65,66]. The gene expression profile of this subtype was found to be similar to medullary breast cancer [67,68], indicating a good prognosis and a favorable response to both adjuvant and neoadjuvant therapy [69].
Gene expression profile of the other two subtypes (mesenchymal and mesenchymal stem-like) resemble the chemo-resistant metaplastic breast cancer. The mesenchymal subtype shows elevated levels of genes involved in EMT, cell motility, cellular proliferation and differentiation (Wnt, ALK, TGF-β). On the other hand, the mesenchymal stem-like subtype expresses genes involved in angiogenesis, growth factor pathways along with those regulating cellular proliferation and differentiation (EGFR, PDGFR, ERK1/2, VEGFR2) [57]. Moreover, the mesenchymal stem-like subtype shows low-levels of claudins-3,4,7; a characteristic similar to the claudin-low subtype [31]. Furthermore, both subtypes (mesenchymal and mesenchymal stem-like) may respond well to PI3K/mTOR inhibitors as well as abl/src inhibitor (dasatinib) [57].
The last known subtype, luminal androgen receptor (LAR), is found to overlap with the molecular apocrine group (“molecular apocrine breast cancer”/MABC) and is enriched in genes regulating hormone signaling, in particular androgen signaling and synthesis (AR, FOXA1, KRT18, XBP1) [70]. This subtype displays shorter relapse-free survival and plausible therapeutic targets include flutamide, enzalutamide, bicalutamide [71]. However, the LAR/MABC may not be equivalent to invasive apocrine carcinoma as defined by cancer morphology and steroid receptor profile [72].
Research showed that the basal-like 2 subtype has worst survival, whereas, LAR has the best survival rates. Although, molecular subtypes of TNBC are associated with differences in survival and can potentially contribute in treatment selection, the association of patient race or ethnicity with subtypes of TNBC and clinical outcome still lie nascent. A recent study showed that more than half (53%) of Hispanic women had a significantly higher proportion of basal-like 2 subtype, whereas Asians had a lower proportion (19%) and a higher proportion of LAR (38%) compared to the average proportion across all groups [73]. On the other hand, Asian women had a better overall survival compared to other ethnic groups [73]. These variations across racial and ethnic groups in the subtypes may explain differences in their outcomes. Determining TNBC subtypes can help in understanding the heterogeneity of TNBCs and can pave the way for developing subtype-specific therapies and better predictors of TNBC prognosis for all races and ethnicities.
The Cancer Genome Atlas Research Network (TCGA) used genomic DNA copy number arrays, exome sequencing, mRNA arrays and miRNA sequencing in 76 TNBC patients and identified several mutated genes, the most common being TP53 (80%), PIK3CA (9%), MLL3 (5%), AFF2 (4%), RB1 (4%), and PTEN (1%) [58]. Whole genome sequencing analysis of 65 TNBC cases detected six SMGs, of which TP53 was the most frequently mutated gene. Moreover, clonal frequency analysis identified somatic mutations in TP53, PIK3CA and PTEN dominant in the majority of TNBCs [74]. Several other studies have also confirmed that TP53 gene as the most commonly mutated gene (65–80%) in TNBC [58,74]; these mutations result in genetic instability and cytogenetic alterations [75]. Research showed that a loss of TP53 resulted in enhanced metastasis and worse overall survival [76]. Furthermore, the presence of mutations in TP53 can be a predictor of chemo-resistance in breast cancer [77,78] including neoadjuvant chemotherapy; however, larger prospective studies are needed to further analyze its role as a potential therapeutic target in breast cancer as well as other cancers [79]. The other most common gene involved in breast cancer including TNBC is BRCA1/2; more than half of the hereditary TNBC cases (80%) carry mutation in BRCA1, while germ-line mutation in BRCA1 occurs in 15% of TNBC cases [80,81]. Patients lacking BRCA1/2 function are sensitive to platinum derivatives as well as PARP inhibitors [56]. Several investigations have identified and validated potential biomarkers of genomic instability as a response to platinum-based therapy in TNBC [82].
Recently, a tissue microarray study on African-American women displayed a significant link between TNBC and loss of PTEN gene, a negative regulator of the PI3K pathway [83]. They also showed that a loss of PTEN activates the mTOR pathway resulting in a high cellular proliferation leading to a more aggressive cancer phenotype and progression [83]. The study implied mTOR inhibitors as potential therapeutic agents. Similar results were found using tissue microarray in Middle Eastern population, where loss of PTEN occurred at high frequency in TNBC and was associated with poor prognosis [84]; thus it can be used as a predictive factor for a poor clinical response of neoadjuvant chemotherapy in TNBC [85].
Moreover, African-American women with breast cancer showed increased expression of p53, BRCA1, Aurora A, Aurora B and polo-like kinase signaling networks in comparison with European women [38,86]. Additionally, incidence of germline BRCA1 mutations is relatively low in comparison with women of European descent [38]. Furthermore, compared with African Americans, non-Hispanic, non-Jewish [87,88] and the Ashkenazi-Jewish women [87] had higher rates of deleterious BRCA1 mutations. Similarly, less than 20% of African-American women had germline mutations in comparison with Caucasian non-Ashkenazi-Jewish women with TNBC who had at least 50% rate of germline BRCA1 mutations [89], thus, indicating other underlying mechanisms for the onset of TNBC in African-American women. Genes involved in the WNT–β-catenin pathway were significantly deregulated in women of African origin compared with women of European descent, suggesting stimulation of the WNT–β-catenin pathway in the development of the more aggressive phenotype of TNBC in women of African origin [38,90].
Furthermore, phosphatase INPP4B, a negative regulator of the PI3K pathway, was found to be lost in TNBC. Loss of INPP4B was linked with advanced tumor grade, larger tumor size, a loss of hormone receptors and aggressive tumors. Alterations in PIK3CA enhance the PI3K pathway and are present in around 10% of TNBC cases [91]. This data indicates frequent alterations in the PI3K/AKT/mTOR pathway in TNBC and are considered as potential therapeutic targets. INPP4B is a distinctive marker for human basal-like carcinoma and can be a potential candidate for treatment using PI3K pathway inhibitors [92]. Nevertheless, initial clinical data from phase I trials using inhibitors did not show any substantial response rates when used as a single agent therapy [93]. A phase 2 clinical trial demonstrated that ipatasertib, an AKT inhibitor, improved the outcomes in a subset of patients with metastatic TNBC when combined with paclitaxel [94]. In addition, development of novel compounds with distinctive specificity and potency targeting different PI3K/AKT/mTOR components and related molecules are under process as they can provide a huge range of toxic profiles and immediate efficiency [94]. Research is now focusing on analyzing possible inhibitors of PI3K/AKT/mTOR for treating TNBC alone or in combination with other drugs [95]. Moreover, drugs targeting other components of the pathway are being developed and include PDK1 inhibitors, SHIP agonists, and heat shock protein inhibitors [93].
Another study identified six differentially expressed genes (IL32, PTX3, GATA3, TMEM158, ETS1 and MYBL1) in TNBC, which differentiated a subset of TNBC-25 (25 TNBC samples) from other TNBCs, as well as TNBC from normal-like, luminal A, luminal B and HER2 patient samples [96]. In TNBC patients in Mexico, a gene signature with 9 over-expressed genes (PRKX/PRKY, UGT8, HMGA1, LPIN1, HAPLN3, FAM171A1, BCL141A, FOXC1 and ANKRD11) and 1 down-regulated gene (ANX9) involved in metabolism was discovered using microarray gene expression profiling, however, further research needs to be conducted in different populations and geographical areas [97]. In parallel, gene expression analysis along with the Gene Set Enrichment Analysis (GSEA) was used to identify the Yin (upregulated pathway in cancer) and Yang (down-regulated pathway in cancer) in TNBC samples. The analysis showed that while, FOXM1 was upregulated, PPARα was downregulated in TNBC; the Yin and Yang pathways allowed categorization of TNBC further into six sub-groups (C1–C6) each having different clinical outcomes, thus providing insight into TNBC heterogeneity; however, further validation for prognosis and treatment is required [98]. Blocking of FOXM1 induces apoptosis and reduces invasiveness and VEGF expression of TNBC cells; impeding FOXM1 along with cisplatin treatment shows synergistic effect. FOXM1 can serve as a potential target for anticancer activity as well as overcoming cisplatin resistance in TNBC [99,100]. Another transcription factor, FOXA1 can play a role in cellular differentiation; thus, overexpression of FOXA1 is associated with a favorable prognosis [101].
Gene expression analysis along with pathway enrichment analysis identified pathways and genes (SOX8, AR, C9orf152, NRK and RAB30) involved in the onset of TNBC that could be developed as potential therapeutic targets [102]. Two-step genetic screening in TNBC showed loss of ADNP, AP2B1, TOMM70A and ZNF326 in nude mice, of which further research on ZNF326, showed that it regulated tumor cell growth through effects on RNA splicing, epithelial-mesenchymal transition, and cancer stem-cell self-renewal. This study identified novel tumor suppressors in TNBC that can be used as potential targets for therapeutic approach [103]. Loss of expression of these genes lead to cellular migration and invasion (Table 2) and is associated with patient survival [103].
In a Japanese study conducted by Komatsu et al., DNA microarray identified 104 genes that were significantly over-expressed in TNBC and included cancer specific kinases (NEK2, PBK and MELK) as well as genes involved in mitosis (ASPM and CENPK), which can be developed as molecular targets [104]. Deregulation of ASPM, CENPK, MELK, NEK2, PBK genes play a role in tumorigenesis and cell cycle regulation; since they induce programmed cell death, therefore, they can be targeted as novel treatment in TNBCs [104]. On the other hand, androgen receptor (AR) regulates cellular proliferation and differentiation; its presence can indicate a good prognosis [105]. Treatment of both LAR and non-LAR TNBC subtypes using AR inhibitors enzalutamide and bicalutamide in in-vitro and xenograft models showed elevated apoptotic rate and loss of proliferation, anchorage-independent growth, migration, and invasion [106,107]. While, the TBCRC011 study, using bicalutamide in AR-positive patients showed a relatively weak response, with a 6-month clinical benefit rate of 19% [108], a MDV3100-11 study using enzalutamide showed higher clinical activity, with a 6-month clinical benefit rate of 28% [109]. Further research aims on explicating the underlying mechanisms of AR therapy resistance and how to classify patients based on the outcome. Further investigations involve use of CYP17 inhibitors or a combination of AR inhibitors with CDK4/CDK6 inhibitors, PI3K inhibitors or neoadjuvant chemotherapy [110]. AR is an easily detectable marker and can aid in classifying TNBC patients who will derive the least clinical benefit from standard chemotherapy. AR-dependent TNBC patients could gain from targeted therapy based on AR antagonists alone or in combination with other chemotherapeutic agents [111].
Furthermore, in China, potential biomarkers (HORMAD1, ELF5, KLK6, GABRP, AGR2, AGR3, ANKRD30A, NME5 and CYP4Z3P) were identified using gene microarray to characterize TNBC [112]. Anterior Gradient (AGR)-2 and -3 are involved in cellular migration, transformation, metastasis and apoptosis. While overexpression of AGR2 indicates bad prognosis, overexpression of AGR3 can be used as a serum-based biomarker for detecting cancer at early stages [113]. In another study in China, microarray analysis revealed differential gene expression profiles between breast cancer subtypes among which COL4A2, BMF, DUSP1, FOXA1 and MLPH were identified as potential candidate gene targets in TNBCs [114]. Another major study using transcriptome microarrays established a combined mRNA-long non-coding (lnc) RNA signature based on the mRNA species for FCGR1A, RSAD2, CHRDL1 and the lncRNA species for HIF1A-AS2 and AK124454. They further demonstrated that HIF1A-AS2 and AK124454 enhanced cellular growth and invasion in TNBC cells and contributed to a paclitaxel resistance [115]. Another gene expression analysis study was performed to identify prognostic markers for TNBC; the study found that overexpression of EOMES, RASGRP1 and SOD2 were associated with better overall survival, while, loss of FA2H and GSPT1 were linked with better overall survival in TNBC [116].
Furthermore, based on a microarray study, other little-known genes in TNBC were identified; two upregulated (PROM1 and KLK6) and seven downregulated (KRT18, GPR160, CMBL, AGR3, CREB3L4, CRIP1 and SDR16C5) genes that could serve as plausible biomarkers [112]. Moreover, KRT18 is used to determine poor response to chemotherapy [112].
Bioinformatics analysis in TNBC showed the presence of genes (AURKA, BIRC5, BUB1B, BUB1, CCNB1, CDK1, KIF11, MAD2L1, NDC80 and PLK1) involved in cellular proliferation; CCNB1 displayed overexpression and was significantly associated with poor prognosis in TNBC [117]. Although these studies were carried out in South Asian population, different genes were found to be involved in the pathogenesis of TNBC and these could be used as promising therapeutic targets.
Table 1 summarizes list of genes identified in TNBC by gene expression profiling in different geographic regions and Table 2 gives a brief overview of the biological functions of some identified genes in BC.
On the other hand, the initial commercial gene expression signature of BC is MammaPrint® (Agendia, Amsterdam, The Netherlands), measures mRNA of 70 gene expressions as an assay with prognostic value in breast cancer patients. It has been validated for patients with stages I/II and negative or either one or three positive lymph nodes. This gene signature stratifies patients into low-and high-risk groups and identifies patients who can avoid adjuvant chemotherapy [123,124]. Although the stratification is beneficial for ER+ breast cancers, it lacks advantage for ER− cancers, thus making it limited to a substantial proportion of patients [125]. MammaPrint® has been approved by the Food and Drug Administration (FDA) and has been recommended by several guidelines such as St. Gallen′s International Oncology Guidelines for the treatment of early stage breast cancer.
The Oncotype DX® test (Genomic Health, Redwood City, CA, USA) measures 21 gene-expressions (15 tested genes associated with breast cancer plus 6 reference genes). Oncotype DX® test analyzes genes associated with the ER status, proliferating genes, Her2-related genes as well as genes related to cancer invasion. This test provides information whether chemotherapy treatment will be beneficial [126], measures the recurrence risk and classifies them into low-risk, intermediate risk or high risk groups (the Recurrence score is given as a number between 0 and 100) [126]. The Oncotype DX® test may also be utilized for ductal carcinoma in situ (DCIS), the most common form of non-invasive breast carcinoma. This test did not require the FDA approval but has been recommended by various authority bodies and guidelines [127].
The Prediction Analysis of Microarray (PAM) algorithm to a 50-gene set (Prosigna®, Stanford, CA, USA) is a 50-gene signature, with an algorithm for the intrinsic molecular classification of breast cancer. It was introduced to improve immunohistochemical and microarray classification. The PAM50 groups breast cancer patients into luminal A, luminal B, HER2 and basal-like [128]. Based on PAM50 score, a phase II trial in metastatic TNBC treated with platinum monotherapy showed an increased trend toward objective response rate in basal versus non-basal TNBC, however results were not statistically significant [129]. Another study had a neoadjuvant setting and involved pretreatment of tumor samples. The results showed and advantage in the addition of carboplatin in all PAM50 subtypes, including non-basal TNBCs [130]. These studies indicated the limited use of available PAM50 assay in managing several TNBC cases. This test is also validated to predict the risk of metastasis for the postmenopausal patients with ER+, HER2-negative, early breast cancer with negative lymph nodes.
The EndoPredict Test (provided by Myriad Genetics, Inc., Salt Lake City, UT, USA), is another genomic test utilized for patients with newly diagnosed, early-stage (node negative), ER-positive and HER2-negative breast cancer. It includes 12 genes: Eight cancer related genes, three RNA reference genes and one DNA reference gene [131]. EndoPredict calculates a risk score called Endopredict score, which can be used with well-established clinicopathologic variables in predicting patients’ outcome. Although the EndoPredict Test has not been routinely approved by the FDA, some authorities such as ASCO suggested its use to assist in the decision-making regarding adjuvant chemotherapy treatment in patients with early-stage, ER–positive, and HER2-negative breast cancer [131].
Breast Cancer Index (provided by BCI, Biotheranostics, Inc., San Diego, CA, USA) is based on the expression of five proliferation-related genes (molecular grade index (MGI)). It gives the 2-gene ratio HOXB13:IL17BR (H:I) in a linear model. The BCI was developed for the decision-making of adjuvant hormonal therapy in postmenopausal women with early stage, ER-positive BC [132].
As indicated, the TNBC subtype is highly heterogeneous and its classification is routinely based on immunohistochemical biomarkers and limited gene signatures (e.g., PAM50 and Lehmann’s system) [29,57]. Although, these are vital prognostic tools, they are frequently applicable to the luminal subtypes and their use as prognostic tools for TNBC has not been validated yet [133]. Hence, there is an urgent need to develop signatures to aid in the early diagnosis and better treatment stratification of the TNBC patients. Today, with the advancement of genomic techniques and assays, developing novel diagnostic and prognostic biomarkers provide further insights into possible therapeutic targets.
In conclusion, it is evident that gene profiling of BC including TNBC in a specific population of different genetic background can play an important role in developing new biomarkers and gene targets for the management of different types of BC and especially TNBC (Figure 1). In addition, it is important to note that a recent AJCC TNM also incorporated the genomic assays discussed above into the current TNM staging system of BC (eighth edition published in 2017) [131]. However, none of the clinically validated gene expression assays has been approved or recommended for TNBC and HER2-negative patients but for ER+ breast cancers. Therefore, further efforts should be made to accomplish this extremely important task and clinically validate gene expression assays for a more proper management of the patients with these aggressive cancers.
In parallel, it is important to highlight that microRNA profiling can also be essential in the development and management of BC and especially TBNC (Figure 1) which is the topic of the following section.

4. MicroRNAs (miRNAs) in TNBC

MicroRNAs (miRNAs) belong to the class of small non-coding RNA, measuring around 25nt in length. miRNAs have distinct functions at the post-transcriptional level [134,135]. Since miRNAs are stable in whole blood, plasma, and serum, circulating miRNAs are being studied in healthy controls and BC patients as a potential diagnostic, predictive and prognostic biomarker for the development of therapeutic strategies [136].
miR-30 expression is associated with ER and PR expression while miR-213 and miR-203 expression are linked with tumor stage. In BC, loss of 29 miRNAs was identified when compared with normal breast tissues [137]. Experimental studies have demonstrated the role of miRNAs in the metastatic process, where few miRNAs are either significantly upregulated or downregulated [138].
A recent study on four ethnic groups identified differential expression of 9 miRNAs. In Nigerian patients, significantly higher levels of miR-140-5p, miR-194 and miR-423-5p were seen in BC compared with other ethnic groups [139]. On the other hand, in Indian patients, miR-101 was overexpressed in BCs [139]. Furthermore, in-silico analysis of miR-423-5p showed that AC genotype was associated with Europeans; while, Asians and Africans displayed the CC and AA genotype, respectively [139]. Another study identified 33 previously undescribed miRNA variants, and 31 miRNA containing variants to be differentially expressed between African and non-African populations [140]. Furthermore, a 26-miRNA panel differentiated TNBC between African American and non-Hispanic White women; however, further validation is needed [141]. A study on Lebanese BC patients showed 21 dysregulated miRNAs and 4 miRNAs with different expression patterns in comparison with American patients; plausible cause for these variations could be age of diagnosis or ethnic variation affecting miRNA epigenetic regulation or sequence of miRNA precursors [142]. Nevertheless, variation in miRNA expression in BCs from different ethnic groups can indicate that specific genetic variants in miRNAs may affect breast cancer risk in these groups.
Various miRNAs were linked with EMT and the development of stem-cell properties. These miRNAs included upregulated expression of miR-10b, miR-21, miR-29, miR-9, miR-221/222, miR-373 as well as downregulated expression of miR-145, miR-199a-5p, miR-200 family, miR-203, miR-205 in TNBC [143,144]. In this regard, tristetraprolin, a target for miR-29a, regulates EMT and metastasis in BC [145].
The miR-200 family including miR-200b, suppress cancer cell growth as well as EMT by targeting ZEB1/2, SIP1, BMI1 proteins and inhibiting PKCα [146,147,148,149,150]. The miR-200 expression was lost in TNBC cells in comparison with other subtypes of breast cancer resulting in increased cellular migration and invasion [43,147,148]. In addition, a loss of miR-200 family was observed in mesenchymal-like TNBC human breast cancer cell lines including MDA-MB-231 [151,152]. The loss of miR-206 in TNBC was shown to promote angiogenesis and invasion in both cell-lines as well as tissue samples [153]. Recently, a study in breast cancer cell lines revealed miR-199/miR-214 as a cluster of miRNAs enhancing cellular motility and aggressiveness via proliferation and EMT [154]. A loss of miR-214 increases the aggressiveness of TNBC via proliferation and EMT, as well as promotes cell growth by enhancing the PTEN-PI3K/AKT signaling pathway. Alterations of miR-10b, miR-21, miR-29, miR-145, miR-200 family, miR-203, miR-221/222 were found to be of prognostic value in TNBC patients [143]. A research study by Kim et al. (2011) analyzed the therapeutic effect of miR-145 against breast cancer and found that adenoviral construct of miR-145 (Ad-miR-145) had the potential to inhibit cell growth and motility both in vitro and in vivo [155]. Furthermore, a combined treatment of Ad-miR-145 and 5-FU showed a remarkable anti-tumor activity when compared to treatment by 5-FU alone [155].
Microarray analysis also revealed deregulation (loss) of miR-205 in cells that undergo EMT in TNBC in response to TGF-β [151,156]. MicroRNA expression profiling in TNBC samples revealed low miR-205 indicating its tumor-suppressive role [157]. P53-stimulation leads to loss of miR-205 in TNBC and its re-expression significantly inhibits cell proliferation, cell cycle progression and tumor growth in vivo [156]. Research showed E2F1 and LAMC1, known regulators of cell cycle progression, adhesion, proliferation and migration as experimentally validated targets for miR-205 [156].
Circulating miR-21 distinguished patients with loco-regional disease from those with metastases [158]. miR-21 promotes metastasis of breast cancer cells by targeting PTEN, TIMP1, TIMP3, PDCD4 [158] which in turn affects the PI3K/AKT/mTOR pathway [159]. In addition, miR-21 sera levels are linked with TNBC phenotype and familial breast cancer along with lymph node metastasis and a higher Ki-67 expression [160,161].
Using qPCR, miR-190a, miR-136-5p, miR-126-5p, miR-135b-5p and miR-182-5p were linked with the pathogenesis of TNBC. MiR-190a plays a tumor-suppressor role preventing metastasis, growth and cell invasion by suppressing VEGF-mediated tumor angiogenesis [162]. On the other hand, miR-135b family plays an oncogenic role regulating the cell cycle, and promoting TNBC cells invasiveness and migration by targeting TGF-beta, WNT and ERBB pathways [163]. A few common genes under the regulation of miR-135b include APC, KLF4, MAFB, CASR, PPP2R5C, SMAD5, LZTS1, MID1, MTCH2, ACVR1B, BMPR2, TGFBR1, IBSP, BGLAP, RUNX2 and, SP7 [162]. MiR-34a/c is a tumor suppressor and induces apoptosis in TNBC cells [164,165]; loss of miR-34a/c [164] and miR-940 [166] in TNBC was linked with tumor progression and poor prognosis.
A panel of several miRNAs were also significantly altered in TNBC, indicating their role as useful prognostic and therapeutic factors in TNBC [167,168,169,170]. While miR-135b, miR-105/93-3p, miR-21, miR-17-5p, miR-27a, miR-95-3p were attributed to the onset, progression and metastases of TNBC [163,171,172,173,174,175], another array of miRNAs unraveled to be linked with chemo-resistance [170,176,177,178]. Thus, up-regulation of miR-155-5p, miR-21-3p, miR-181a-5p, miR-181b-5p, miR-183-5p, miR-105/93-3p and loss of miR-181a, miR-10b-5p, miR-451a, miR-125b-5p, miR-31-5p, miR-195-5p and miR-200c were found to be highly associated with promoting chemo-resistance [146,174,176,179,180,181,182]. MiR-27a plays a role in the onset and progression of tumor cells in TNBC and can predict response to radiotherapy and serve as a prognostic marker [175]. Presently, investigations aim to identify miRNA clusters associated with chemoresistance and to help pave the way for the development of more efficient therapies.
MiRNA profiling by next-generation sequencing (NGS) in TNBCs revealed different expression patterns of miRNAs, of which three miRNAs (miR-224-5p, miR-375 and miR-205-5p) can be used to categorize cancers based on their proliferation, invasion and metastasis. Six miRNAs (high let-7d-3p, miR-203b-5p and miR-324-5p; low miR-30a-3p, miR-30a-5p and miR-199a-5p) were significantly related to decreased overall survival while 5 additional miRNAs (high let-7d-3p; low miR-30a-3p, miR-30a-5p, miR-30c-5p and miR-128-3p) were associated with decreased relapse-free survival [173]. Another study demonstrated that loss of miR-30a in TNBC, which suppresses cell invasion and metastasis of the tumor by directly targeting ROR1; miR-30a is linked with higher histological grade and lymph node metastasis [183]. Moreover, sequencing identified that loss of miR-4319 in TNBC and presence of miR-4319 was shown to reduce malignant potential of TNBC cells as it suppresses the self-renewal and formation of tumor spheres in TNBC through E2F2 as well as inhibits tumor initiation and metastasis [184]. Deep sequencing along with hierarchical clustering analysis exhibited 25 miRNAs signature to distinguish TNBC from normal breast tissue [185]. Genome-wide miRNA profiling showed a panel of 26 miRNAs to help distinguish TNBC in African-American women from the Non-Hispanic White patients [141].
Lack of miR-603 resulted in high eEF2K expression followed by the onset and progression of TNBC [186]. Another miRNA, miR-199a-5p, was found to have a tumor suppressive role in TNBC. High levels of miR-199a-5p in vivo reduced cell motility and invasiveness as well as repressed tumor cell growth [187]. Tissue microarray analysis showed that loss of miR-493 in TNBC patients can be linked with poor disease-free survival, depicting its role as a prognostic factor in TNBC [188]. Using miRNA array analysis, miR-211-5p showed to block proliferation, invasion, migration and metastasis by targeting SETBP1; indicating a tumor suppressive role of miR-211-5p in TNBC; [189]. While, miR-148a [190] and miR-629-3p [191] were identified as promoters of lung metastases; while, miR-141 was identified as an enhancer of brain metastasis; suggesting their roles as biomarkers and latent targets of metastases [192].
Studies have also shown presence of upregulated miRNAs in TNBC. The miR-10 family (miR-10a and miR-10b) is involved in both the progression and metastasis of breast cancer [193]. MiR-10b is one such group of miRNAs, highly elevated in TNBC cell lines MDA-MB-231 and SUM1315 compared with normal mammary epithelial cells HMECS and MCF10A [194,195]. miR-10b is significantly upregulated in metastatic breast cancer cells and initiates cell migration and invasion in murine xenograft model of breast cancer by targeting the HOXD10 gene along with E-cadherin and Tiam1 [196,197,198]. MiR-10b controls cell migration and invasion and regulates the expression of miR-9. MiR-9 is upregulated in TNBC in comparison with the luminal and HER2-enriched breast cancer subtypes [199] and stimulates cell motility and invasion ability by targeting E-cadherin, activating the β-catenin pathway and enhancing VEGF levels [195]. In TNBC, miR-9 was linked with MYC amplification, higher tumor grade, as well as significant metastatic potential leading to poor outcome [195,200]. Moreover, elevated miR-105/93-3p enhances the Wnt/βcatenin signaling by downregulation of SFPR1 leading to chemo-resistance and metastasis [174]. MiR-221/222 [201], miR-761 [202] and miR-373 [165,203,204] are frequently upregulated in TNBC. Research on metastatic samples showed an inverse correlation between miR-373 and CD44; targeting of CD44 by miR-373/520 increases the migratory and invasive ability, both in vitro and in vivo. Clinical metastasis samples also showed an inverse correlation between miR-373 and CD44 expression [204]. High levels of miR-221/222 enhance drug resistance and promote EMT, invasion and cancer cell migration. Additionally, miR-221/222 were also associated with advanced stage, tumor grade and negative hormone receptor status [201,205]. Among Indian women with TNBC, a miRNA signature of 6 different miRNAs (miR-21, miR-221, miR-210, miR-195, miR-145 and let-7a) were associated with an advanced stage, higher tumor grade and negative hormone receptors [205].
miR-21 is the principal miRNA linked with migration and invasion of breast cancer cells and hence plays a critical role in tumor progression and metastasis [206,207]. A report by Iorio et al. (2005) showed that along with miR-125b, miR-145 and miR-155, miR-21 is aberrantly expressed in human breast cancer [137]. Tropomyosin 1 (TPM1) has been discovered as a plausible target of miR-21 [208]. While, miR-21 is inversely associated with PTEN expression in BC [209], which is directly linked with TGF-β [210]. Overexpression of miR-21 leads to an aggressive disease status along with higher tumor grade, negative hormone receptor status and ductal phenotype [210]. A recent investigation conducted in Saudi Arabia identified miR-195 in the plasma of TNBC patients [211].
In summary, a large group of miRNAs has been reported to be implicated in TNBC initiation, progression and/or metastasis. These miRNAs can be differentiated based on their functional characterization in TNBC as tumor suppressors and oncogenes. They may also play both diagnostic and predictive roles. Therefore, we believe that miRNA represent as an important target in the management of BC including TNBCs, however, it is important to highlight that genetic backgrounds of different populations have to be carefully examined in order to identify specific miRNAs associated with populations of various ethnicities (Figure 1).
Table 3 below summarizes key miRNAs with their expression levels and biological functions in TNBC.
Despite the array of miRNAs that have been suggested as plausible biomarkers, their use in clinical practice still remains nascent. One of the major reasons being the challenge in miRNA expression profiling; miRNAs are tiny molecules in which family members display a high degree of homology, and absolute miRNA concentrations in body fluids are relatively low [214]. There are several technological advances for using miRNAs as therapeutic tools for cancers. miRNA expression profiles are correlated with genetic subtype and isotype [215]. Biology and characteristic features of miRNAs have been studied among different cancers. Standardizing expression of down-regulated miRNAs or overexpressed miRNAs can aid to re-balance the expression of genes associated in oncogenesis and tumor progression; hence, targeting miRNAs may provide an important therapeutic strategy for human cancer [196,216]. On the other hand, blocking overexpressed miRNAs was accomplished using anti-miRNA oligonucleotides (AMOs), which are complementary to miRNAs. While, generation of down-regulated miRNAs were accomplished using expression systems that use viral or liposomal delivery systems for the vectors [217,218].
Various miRNAs are validated in preclinical tests and are now under further clinical investigation. In 2013, The first miRNA replacement therapy with MRX34—a liposome-formulated miR-34 mimic was carried out. This study underwent human clinical trials for patients with advanced or metastatic liver cancer by intravenous injection [219]. Moreover, to treat different solid carcinomas including lung and prostate cancer, let-7 mimic was developed [220,221]. For hepatitis C, an antagonist of miR-122 was used and tested in phase II clinical trials [222]. Moreover, an investigation by Di Martino et al. [223] proved that either transient expression of miR-34a synthetic mimics or lentivirus-based stable enforced expression of miR-34a, triggered growth inhibition and apoptosis in MM cells in vitro and in vivo without systemic toxicity. Blocking of miRNA-21 using antisense oligonucleotides reduced growth of MCF7 cells by topotecan by around 40% [224]. Similarly, in lung cancer cell lines, inhibition by AG1478 reduced cellular growth [193,225,226]. Recently, MRG-106, an LNA anti-miR of miRNA-155 entered clinical phase I evaluation. Inhibition of miRNA-155 in lymphoma cells reduced proliferation in-vitro [214]. However, there are several challenges including suboptimal delivery, low bioavailability or long-term safety. Research is focusing presently on latent methods including nanoparticles, polymers and virus-based approaches [227]. Nevertheless, and given the important role of miRNA profiling in personalized medicine, we believe that more studies are necessary to elucidate miRNA profile variations in relation with ethnicity.

5. Conclusions

In BC, gene-expression-based-assays and the classification of patients have a robust clinical impact and help in individualized therapy and personalized cancer management [228]. Therefore, several gene expression-based assays have been clinically validated and utilized for ER+ but not ER- BCs such as TNBC.
Differential gene expression using microarray profiling on a subset of BC including TN from different geographical regions in comparison to a set of normal/benign breast tumors should be performed to further understand the underlying mechanisms of TNBCs.
Numerous challenges hinder treatment of BC, particularly in TN subtype resulting in a high cancer mortality. Genetic markers of women from different ancestries that predispose them to TNBC have not been entirely elucidated. Therefore, biomarkers for TNBC prognosis of specific ethnicities are urgently needed since they can be used as predictive biomarkers as well as tools for targeted therapy in these populations. In short, discovering combined gene and miRNA signatures of TNBC in different populations and ethnicities could help identify new and specific gene targets for this subgroup of cancers and can be regarded as a fertile ground to accomplish a personalized medicine approach, which is the main objective of modern cancer treatment.

Author Contributions

Writing—original draft preparation, I.G; writing—review and editing, R.M.S., I.A.-H., H.A.A.-T., S.V. and A.-E.A.M.; original concept, A.-E.A.M.

Funding

Research of Drs. Al Moustafa’s and Vranic’s labs are supported by grants from Qatar University: QUCG-CMED-2018\2019-3, QUSD-CMED-2018-2 and QUST-1-CMED-2019-18.

Acknowledgments

The authors would like to thank Mrs. A. Kassab for her critical reading of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ferlay, J.S.I.; Ervik, M.; Dikshit, R.; Eser, S.; Mathers, C.; Rebelo, M.; Parkin, D.M.; Forman, D.; Bray, F. GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11. Available online: http://globocan.iarc.fr (accessed on 13 November 2014).
  2. Bray, F.R.J.; Masuyer, E.; Ferlay, J. Estimates of global cancer prevalence for 27 sites in the adult population in 2008. Int. J. Cancer 2013, 132, 12. [Google Scholar] [CrossRef]
  3. Wass, J.; Finer, N. Action on obesity: Comprehensive care for all. Clin. Med. 2013, 13, 4–5. [Google Scholar] [CrossRef]
  4. Lakhtakia, R. Conspicuous Consumption and Sedentary Living: Is this our legacy to our children? Sultan Qaboos Univ. Med. J. 2013, 13, 336–340. [Google Scholar] [CrossRef]
  5. McKenzie, F.; Ellison-Loschmann, L.; Jeffreys, M.; Firestone, R.; Pearce, N.; Romieu, I. Cigarette Smoking and Risk of Breast Cancer in a New Zealand Multi-Ethnic Case-Control Study. PLoS ONE 2013, 8, e63132. [Google Scholar] [CrossRef] [PubMed]
  6. Lee, I.M.; Shiroma, E.J.; Lobelo, F.; Puska, P.; Blair, S.N.; Katzmarzyk, P.T. Effect of physical inactivity on major non-communicable diseases worldwide: An analysis of burden of disease and life expectancy. Lancet 2012, 380, 219–229. [Google Scholar] [CrossRef]
  7. Fioretti, F.; Tavani, A.; Bosetti, C.; Vecchia, C.L.; Negri, E.; Barbone, F.; Talamini, R.; Franceschi, S. Risk factors for breast cancer in nulliparous women. Br. J. Cancer 1999, 79, 1923–1928. [Google Scholar] [CrossRef] [PubMed]
  8. Sieri, S.; Krogh, V.; Bolelli, G.; Abagnato, C.A.; Grioni, S.; Pala, V.; Evangelista, A.; Allemani, C.; Micheli, A.; Tagliabue, G.; et al. Sex Hormone Levels, Breast Cancer Risk, and Cancer Receptor Status in Postmenopausal Women: The ORDET Cohort. Cancer Epidemiol. Biomark. Prev. 2009, 18, 169–176. [Google Scholar] [CrossRef] [PubMed]
  9. Colombo, N.; Van Gorp, T.; Parma, G.; Amant, F.; Gatta, G.; Sessa, C.; Vergote, I. Ovarian cancer. Crit. Rev. Oncol./Hematol. 2006, 60, 159–179. [Google Scholar] [CrossRef] [PubMed]
  10. Virnig, B.A.; Tuttle, T.M.; Shamliyan, T.; Kane, R.L. Ductal Carcinoma In Situ of the Breast: A Systematic Review of Incidence, Treatment, and Outcomes. J. Natl. Cancer Inst. 2010, 102, 170–178. [Google Scholar] [CrossRef] [PubMed]
  11. Claus, E.B.; Schildkraut, J.M.; Thompson, W.D.; Risch, N.J. The genetic attributable risk of breast and ovarian cancer. Cancer 1996, 77, 2318–2324. [Google Scholar] [CrossRef]
  12. Lynch, H.T.; Casey, M.J.; Snyder, C.L.; Bewtra, C.; Lynch, J.F.; Butts, M.; Godwin, A.K. Hereditary ovarian carcinoma: Heterogeneity, molecular genetics, pathology, and management. Mol. Oncol. 2009, 3, 97–137. [Google Scholar] [CrossRef] [PubMed]
  13. Shiovitz, S.; Korde, L.A. Genetics of breast cancer: A topic in evolution. Ann. Oncol. 2015, 26, 1291–1299. [Google Scholar] [CrossRef] [PubMed]
  14. Perry, C.W.P.B. Quick Review: Breast Cancer. Intern. J. Oncol. 2002, 1, 1–5. [Google Scholar]
  15. Gupta, I.; Burney, I.; Al-Moundhri, M.S.; Tamimi, Y. Molecular genetics complexity impeding research progress in breast and ovarian cancers. Mol. Clin. Oncol. 2017, 7, 3–14. [Google Scholar] [CrossRef] [PubMed]
  16. Weigelt, B.; Peterse, J.L.; van’t Veer, L.J. Breast cancer metastasis: Markers and models. Nat. Rev. Cancer 2005, 5, 591–602. [Google Scholar] [CrossRef] [PubMed]
  17. Al Moustafa, A.E.; Yasmeen, A.; Ghabreau, L.; Mohamed, A.H.; Achkhar, A. Brain Metastases Progression of Breast Cancer. In Breast Cancer; Mehmet, G., Esra, G., Eds.; Intech Open: London, UK, 2011. [Google Scholar]
  18. Macdonald, F.; Ford, C.H.J.; Casson, A.G. Molecular Biology of Cancer, 2nd ed.; BIOS Scientific: London, UK, 2004. [Google Scholar]
  19. Watson, I.R.; Takahashi, K.; Futreal, P.A.; Chin, L. Emerging patterns of somatic mutations in cancer. Nat. Rev. Genet. 2013, 14, 703–718. [Google Scholar] [CrossRef] [PubMed]
  20. Economopoulou, P.; Dimitriadis, G.; Psyrri, A. Beyond BRCA: New hereditary breast cancer susceptibility genes. Cancer Treat. Rev. 2015, 41, 1–8. [Google Scholar] [CrossRef] [PubMed]
  21. Zhao, D.; Zhang, F.; Zhang, W.; He, J.; Zhao, Y.; Sun, J. Prognostic role of hormone receptors in ovarian cancer: A systematic review and meta-analysis. Int. J. Gynecol. Cancer 2013, 23, 25–33. [Google Scholar] [CrossRef]
  22. Gusterson, B.A.; Gelber, R.D.; Goldhirsch, A.; Price, K.N.; Säve-Söderborgh, J.; Anbazhagan, R.; Styles, J.; Rudenstam, C.M.; Golouh, R.; Reed, R. Prognostic importance of c-erbB-2 expression in breast cancer. International (Ludwig) Breast Cancer Study Group. J. Clin. Oncol. 1992, 10, 1049–1056. [Google Scholar] [CrossRef]
  23. Carter, C.L.; Allen, C.; Henson, D.E. Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases. Cancer 1989, 63, 181–187. [Google Scholar] [CrossRef]
  24. Maughan, N.J.; Lewis, F.A.; Smith, V. An introduction to arrays. J. Pathol. 2001, 195, 3–6. [Google Scholar] [CrossRef] [PubMed]
  25. Wildsmith, S.E.; Elcock, F.J. Microarrays under the microscope. Mol. Pathol. 2001, 54, 8–16. [Google Scholar] [CrossRef] [PubMed]
  26. Reis-Filho, J.S.; Westbury, C.; Pierga, J.Y. The impact of expression profiling on prognostic and predictive testing in breast cancer. J. Clin. Pathol. 2006, 59, 225–231. [Google Scholar] [CrossRef] [PubMed]
  27. Perou, C.M.; Sørlie, T.; Eisen, M.B.; Van De Rijn, M.; Jeffrey, S.S.; Rees, C.A.; Pollack, J.R.; Ross, D.T.; Johnsen, H.; Akslen, L.A.; et al. Molecular portraits of human breast tumours. Nature 2000, 406, 747–752. [Google Scholar] [CrossRef] [PubMed]
  28. Sotiriou, C.; Neo, S.-Y.; McShane, L.M.; Korn, E.L.; Long, P.M.; Jazaeri, A.; Martiat, P.; Fox, S.B.; Harris, A.L.; Liu, E.T. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc. Natl. Acad. Sci. USA 2003, 100, 10393–10398. [Google Scholar] [CrossRef] [PubMed]
  29. Sørlie, T.; Perou, C.M.; Tibshirani, R.; Aas, T.; Geisler, S.; Johnsen, H.; Hastie, T.; Eisen, M.B.; van de Rijn, M.; Jeffrey, S.S.; et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl. Acad. Sci. USA 2001, 98, 10869–10874. [Google Scholar] [CrossRef]
  30. Goldhirsch, A.; Wood, W.C.; Coates, A.S.; Gelber, R.D.; Thürlimann, B.; Senn, H.-J.; Panel members. Strategies for subtypes—dealing with the diversity of breast cancer: Highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann. Oncol. 2011, 22, 1736–1747. [Google Scholar] [CrossRef] [PubMed]
  31. Prat, A.; Parker, J.S.; Karginova, O.; Fan, C.; Livasy, C.; Herschkowitz, J.I.; He, X.; Perou, C.M. Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Cancer Res. 2010, 12, R68. [Google Scholar] [CrossRef]
  32. Foulkes, W.; Smith, I.E.; Reis-Filho, J.S. Triple-negative breast cancer. N. Engl. J. Med. 2010, 363, 1938–1948. [Google Scholar] [CrossRef]
  33. Irvin, W.; Carey, L.A. What is triple-negative breast cancer? Eur. J. Cancer 2008, 44, 2799–2805. [Google Scholar] [CrossRef]
  34. Stockmans, G.; Deraedt, K.; Wildiers, H.; Moerman, P.; Paridaens, R. Triple-negative breast cancer. Curr. Opin. Oncol. 2008, 20, 614–620. [Google Scholar] [CrossRef] [PubMed]
  35. Thike, A.; Cheok, P.Y.; Jara-Lazaro, A.R.; Tan, B.; Tan, P.; Tan, P.H. Triple-negative breast cancer: Clinicopathological characteristics and relationship with basal-like breast cancer. Mod. Pathol. 2010, 23, 123–133. [Google Scholar] [CrossRef]
  36. Yadav, B.S.; Chanana, P.; Jhamb, S. Biomarkers in triple negative breast cancer: A review. World J. Clin. Oncol. 2015, 6, 252–263. [Google Scholar] [CrossRef] [PubMed]
  37. Bauer, K.; Brown, M.; Cress, R.D.; Parise, C.A.; Caggiano, V. Descriptive analysis of estrogen receptor (ER)-negative, progesterone receptor (PR)-negative, and HER2-negative invasive breast cancer, the so-called triple-negative phenotype: A population-based study from the California cancer Registry. Cancer 2007, 109, 1721–1728. [Google Scholar] [CrossRef] [PubMed]
  38. Dietze, E.C.; Sistrunk, C.; Miranda-Carboni, G.; O’Regan, R.; Seewaldt, V.L. Triple-negative breast cancer in African-American women: Disparities versus biology. Nat. Rev. Cancer 2015, 15, 248–254. [Google Scholar] [CrossRef] [PubMed]
  39. Smid, M.; Wang, Y.; Zhang, Y.; Sieuwerts, A.M.; Yu, J.; Klijn, J.G.; Foekens, J.A.; Martens, J.W. Subtypes of breast cancer show preferential site of relapse. Cancer Res. 2008, 68, 3018–3114. [Google Scholar] [CrossRef] [PubMed]
  40. Lin, N.; Claus, E.; Sohl, J.; Razzak, A.R.; Arnaout, A.; Winer, E.P. Sites of distant recurrence and clinical outcomes in patients with metastatic triple-negative breast cancer: High incidence of central nervous system metastases. Cancer 2008, 113, 2638–2645. [Google Scholar] [CrossRef] [PubMed]
  41. Haffty, B.; Yang, Q.; Reiss, M.; Kearney, T.; Higgins, S.A.; Weidhaas, J.; Harris, L.; Hait, W.; Toppmeyer, D. Locoregional relapse and distant metastasis in conservatively managed triple negative early-stage breast cancer. J. Clin. Oncol. 2006, 24, 5652–5657. [Google Scholar] [CrossRef] [PubMed]
  42. Heitz, F.; Harter, P.; Lueck, H.J.; Fissler-Eckhoff, A.; Lorenz-Salehi, F.; Scheil-Bertram, S.; Traut, A.; du Bois, A. Triple-negative and HER2-overexpressing breast cancers exhibit an elevated risk and an earlier occurrence of cerebral metastases. Eur. J. Cancer 2009, 45, 2792. [Google Scholar] [CrossRef]
  43. Kaplan, H.; Malmgren, J.A.; Atwood, M. T1N0 triple negative breast cancer: Risk of recurrence and adjuvant chemotherapy. Breast J. 2009, 15, 454–460. [Google Scholar] [CrossRef]
  44. Akinyemiju, T.; Sakhuja, S.; Waterbor, J.; Pisu, M.; Altekruse, S.F. Racial/ethnic disparities in de novo metastases sites and survival outcomes for patients with primary breast, colorectal, and prostate cancer. Cancer Med. 2018, 7, 1183–1193. [Google Scholar] [CrossRef] [PubMed]
  45. Iqbal, J.; Ginsburg, O.; Rochon, P.A.; Sun, P.; Narod, S.A. Differences in breast cancer stage at diagnosis and cancer-specific survival by race and ethnicity in the United States. JAMA 2015, 313, 165–173. [Google Scholar] [CrossRef] [PubMed]
  46. Dong, G.; Wang, D.; Liang, X.; Gao, H.; Wang, L.; Yu, X.; Liu, J. Factors related to survival rates for breast cancer patients. Int. J. Clin. Exp. Med. 2014, 7, 3719–3724. [Google Scholar] [PubMed]
  47. Liedtke, C.; Rody, A.; Gluz, O.; Baumann, K.; Beyer, D.; Kohls, E.-B.; Lausen, K.; Hanker, L.; Holtrich, U.; Becker, S.; et al. The prognostic impact of age in different molecular subtypes of breast cancer. Breast Cancer Res. Treat. 2015, 152, 667–673. [Google Scholar] [CrossRef]
  48. Skandan, S.P. 5 year Overall survival of triple negative breast cancer: A single institution experience. J. Clin. Oncol. 2016, 34, e12580. [Google Scholar] [CrossRef]
  49. Patil, V.; Singhai, R.; Patil, A.V.; Gurav, P.D. Triple-negative (ER, PgR, HER-2/neu) breast cancer in Indian women. Breast Cancer 2011, 3, 9–19. [Google Scholar]
  50. Liedtke, C.; Mazouni, C.; Hess, K.R.; André, F.; Tordai, A.; Mejia, J.A.; Symmans, W.F.; Gonzalez-Angulo, A.M.; Hennessy, B.; Green, M.; et al. Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. J. Clin. Oncol. 2008, 26, 1275–1281. [Google Scholar] [CrossRef]
  51. Kassam, F.E.K.; Dent, R.; Dranitsaris, G.; Myers, J.; Flynn, C.; Fralick, M.; Kumar, R.; Clemons, M. Survival outcomes for patients with metastatic triple-negative breast cancer: Implications for clinical practice and trial design. Clin. Breast Cancer 2009, 9, 29–33. [Google Scholar] [CrossRef]
  52. Gonzalez-Angulo, A.M.; Morales-Vasquez, F.; Hortobagyi, G.N. Overview of resistance to systemic therapy in patients with breast cancer. Adv. Exp. Med. Biol. 2007, 608, 21. [Google Scholar]
  53. Tavazoie, S.F.; Alarcón, C.; Oskarsson, T.; Padua, D.; Wang, Q.; Bos, P.D.; Gerald, W.L.; Massagué, J. Endogenous human microRNAs that suppress breast cancer metastasis. Nature 2008, 451, 147–152. [Google Scholar] [CrossRef]
  54. Chaffer, C.L.; Weinberg, R.A. A Perspective on Cancer Cell Metastasis. Science 2011, 331, 1559–1564. [Google Scholar] [CrossRef] [PubMed]
  55. Al Moustafa, A. Epithelial-mesenchymal transition and its regulators are major targets of triple-negative breast cancer. Cell Adhes. Migr. 2013, 7, 424–425. [Google Scholar] [CrossRef] [PubMed]
  56. Plummer, R. Poly(ADP-ribose) polymerase inhibition: A new direction for BRCAand triple-negative breast cancer? Breast Cancer Res. 2011, 13, 218. [Google Scholar] [CrossRef]
  57. Lehmann, B.D.; Bauer, J.A.; Chen, X.; Sanders, M.E.; Chakravarthy, A.B.; Shyr, Y.; Pietenpol, J.A. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J. Clin. Investig. 2011, 121, 2750–2767. [Google Scholar] [CrossRef] [PubMed]
  58. The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumors. Nature 2012, 490, 61–70. [Google Scholar] [CrossRef]
  59. Roberts, P.J.; Bisi, J.E.; Strum, J.C.; Combest, A.J.; Darr, D.B.; Usary, J.E.; Zamboni, W.C.; Wong, K.-K.; Perou, C.M.; Sharpless, N.E. Multiple Roles of Cyclin-Dependent Kinase 4/6 Inhibitors in Cancer Therapy. JNCI J. Natl. Cancer Inst. 2012, 104, 476–487. [Google Scholar] [CrossRef]
  60. Rakha, E.A.; Elsheikh, S.E.; Aleskandarany, M.A.; Habashi, H.O.; Green, A.R.; Powe, D.G.; El-Sayed, M.E.; Benhasouna, A.; Brunet, J.-S.; Akslen, L.A.; et al. Triple-Negative Breast Cancer: Distinguishing between Basal and Nonbasal Subtypes. Clin. Cancer Res. 2009, 15, 2302–2310. [Google Scholar] [CrossRef]
  61. Rebecca, D.; Trudeau, M.; Pritchard, K.I.; Hanna, W.M.; Kahn, H.K.; Sawka, C.A.; Lickley, L.A.; Rawlinson, E.; Sun, P.; Narod, S.A. Triple-Negative Breast Cancer: Clinical Features and Patterns of Recurrence. Clin. Cancer Res. 2007, 13, 4429–4434. [Google Scholar]
  62. François, B.; Finetti, P.; Cervera, N.; Esterni, B.; Hermitte, F.; Viens, P.; Birnbaum, D. How basal are triple-negative breast cancers? Int. J. Cancer 2008, 123, 236–240. [Google Scholar]
  63. Chen, X.; Li, J.; Gray, W.H.; Lehmann, B.D.; Bauer, J.A.; Shyr, Y.; Pietenpol, J.A. TNBCtype: A Subtyping Tool for Triple-Negative Breast Cancer. Cancer Inform. 2012, 11, 147–156. [Google Scholar] [CrossRef]
  64. Jézéquel, P.; Loussouarn, D.; Guérin-Charbonnel, C.; Campion, L.; Vanier, A.; Gouraud, W.; Lasla, H.; Guette, C.; Valo, I.; Verrièle, V.; et al. Gene-expression molecular subtyping of triple-negative breast cancer tumours: Importance of immune response. Breast Cancer Res. 2015, 17, 43. [Google Scholar] [CrossRef] [PubMed]
  65. Sabatier, R.; Finetti, P.; Mamessier, E.; Adelaide, J.; Chaffanet, M.; Ali, H.R.; Viens, P.; Caldas, C.; Birnbaum, D.; Bertucci, F. Prognostic and predictive value of PDL1 expression in breast cancer. Oncotarget 2014, 6, 5449–5464. [Google Scholar] [CrossRef] [PubMed]
  66. Beckers, R.K.; Selinger, C.I.; Vilain, R.; Madore, J.; Wilmott, J.S.; Harvey, K.; Holliday, A.; Cooper, C.L.; Robbins, E.; Gillett, D.; et al. Programmed death ligand 1 expression in triple-negative breast cancer is associated with tumour-infiltrating lymphocytes and improved outcome. Histopathology 2016, 69, 25–34. [Google Scholar] [CrossRef] [PubMed]
  67. Bertucci, F.; Finetti, P.; Cervera, N.; Charafe-Jauffret, E.; Mamessier, E.; Adélaïde, J.; Debono, S.; Houvenaeghel, G.; Maraninchi, D.; Viens, P.; et al. Gene expression profiling shows medullary breast cancer is a subgroup of basal breast cancers. Cancer Res. 2006, 66, 4636–4644. [Google Scholar] [CrossRef] [PubMed]
  68. Lehmann, B.D.; Pietenpol, J.A. Identification and use of biomarkers in treatment strategies for triple-negative breast cancer subtypes. J. Pathol. 2014, 232, 142–150. [Google Scholar] [CrossRef] [PubMed]
  69. Denkert, C.; von Minckwitz, G.; Brase, J.C.; Sinn, B.V.; Gade, S.; Kronenwett, R.; Pfitzner, B.M.; Salat, C.; Loi, S.; Schmitt, W.D.; et al. Tumor-infiltrating lymphocytes and response to neoadjuvant chemotherapy with or without carboplatin in human epidermal growth factor receptor 2-positive and triple-negative primary breast cancers. J. Clin. Oncol. 2015, 33, 983–991. [Google Scholar] [CrossRef] [PubMed]
  70. Farmer, P.; Bonnefoi, H.; Becette, V.; Tubiana-Hulin, M.; Fumoleau, P.; Larsimont, D.; MacGrogan, G.; Bergh, J.; Cameron, D.; Goldstein, D.; et al. Identification of molecular apocrine breast tumours by microarray analysis. Oncogene 2005, 24, 4660. [Google Scholar] [CrossRef] [PubMed]
  71. Rampurwala, M.; Wisinski, K.B.; O’Regan, R. Role of the androgen receptor in triple-negative breast cancer. Clin. Adv. Hematol. Oncol. 2016, 14, 186–193. [Google Scholar]
  72. Vranic, S.; Schmitt, F.; Sapino, A.; Costa, J.L.; Reddy, S.; Castro, M.; Gatalica, Z. Apocrine carcinoma of the breast: A comprehensive review. Histol. Histopathol. 2013, 28, 1393–1409. [Google Scholar]
  73. Ding, Y.C.; Steele, L.; Warden, C.; Wilczynski, S.; Mortimer, J.; Yuan, Y.; Neuhausen, S.L. Molecular subtypes of triple-negative breast cancer in women of different race and ethnicity. Oncotarget 2019, 10, 198–208. [Google Scholar] [CrossRef]
  74. Shah, S.P.; Roth, A.; Goya, R.; Oloumi, A.; Ha, G.; Zhao, Y.; Turashvili, G.; Ding, J.; Tse, K.; Haffari, G.; et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 2012, 486, 395–399. [Google Scholar] [CrossRef] [PubMed]
  75. Olivier, M.; Taniere, P. Somatic mutations in cancer prognosis and prediction: Lessons from TP53 and EGFR genes. Curr. Opin. Oncol. 2011, 23, 88–92. [Google Scholar] [CrossRef] [PubMed]
  76. Powell, E.; Shao, J.; Yuan, Y.; Chen, H.-C.; Cai, S.; Echeverria, G.V.; Mistry, N.; Decker, K.F.; Schlosberg, C.; Do, K.-A.; et al. p53 deficiency linked to B cell translocation gene 2 (BTG2) loss enhances metastatic potential by promoting tumor growth in primary and metastatic sites in patient-derived xenograft (PDX) models of triple-negative breast cancer. Breast Cancer Res. 2016, 18, 13. [Google Scholar] [CrossRef] [PubMed]
  77. Geisler, S.; Lønning, P.E.; Aas, T.; Johnsen, H.; Fluge, O.; Haugen, D.F.; Lillehaug, J.R.; Akslen, L.A.; Børresen-Dale, A.L. Influence of TP53 Gene Alterations and c-erbB-2 Expression on the Response to Treatment with Doxorubicin in Locally Advanced Breast Cancer. Cancer Res. 2001, 61, 2505–2512. [Google Scholar] [PubMed]
  78. Chae, B.J.; Bae, J.S.; Lee, A.; Park, W.C.; Seo, Y.J.; Song, B.J.; Kim, J.S.; Jung, S.S. p53 as a Specific Prognostic Factor in Triple-negative Breast Cancer. Jpn. J. Clin. Oncol. 2009, 39, 217–224. [Google Scholar] [CrossRef] [PubMed]
  79. Chen, M.-B.; Zhu, Y.-Q.; Xu, J.-Y.; Wang, L.-Q.; Liu, C.-Y.; Ji, Z.-Y.; Lu, P.-H. Value of TP53 Status for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer: A Meta-Analysis. PLoS ONE 2012, 7, e39655. [Google Scholar] [CrossRef] [PubMed]
  80. Engel, C.; Rhiem, K.; Hahnen, E.; Loibl, S.; Weber, K.E.; Seiler, S.; Zachariae, S.; Hauke, J.; Wappenschmidt, B.; Waha, A.; et al. Prevalence of pathogenic BRCA1/2 germline mutations among 802 women with unilateral triple-negative breast cancer without family cancer history. BMC Cancer 2018, 18, 265. [Google Scholar] [CrossRef]
  81. Couch, F.J.; Hart, S.N.; Sharma, P.; Toland, A.E.; Wang, X.; Miron, P.; Olson, J.E.; Godwin, A.K.; Pankratz, V.S.; Olswold, C.; et al. Inherited Mutations in 17 Breast Cancer Susceptibility Genes Among a Large Triple-Negative Breast Cancer Cohort Unselected for Family History of Breast Cancer. J. Clin. Oncol. 2015, 33, 304–311. [Google Scholar] [CrossRef]
  82. Anders, C.; Abramson, V.; Tan, T.; Dent, R. The Evolution of Triple-Negative Breast Cancer: From Biology to Novel Therapeutics. Am. Soc. Clin. Oncol. Educ. Book 2016, 35, 34–42. [Google Scholar] [CrossRef]
  83. Khan, F.; Esnakula, A.; Ricks-Santi, L.J.; Zafar, R.; Kanaan, Y.; Naab, T. Loss of PTEN in high grade advanced stage triple negative breast ductal cancers in African American women. Pathol. Res. Pract. 2018, 214, 673–678. [Google Scholar] [CrossRef]
  84. Beg, S.; Siraj, A.K.; Prabhakaran, S.; Jehan, Z.; Ajarim, D.; Al-Dayel, F.; Tulbah, A.; Al-Kuraya, K.S. Loss of PTEN expression is associated with aggressive behavior and poor prognosis in Middle Eastern triple-negative breast cancer. Breast Cancer Res. Treat. 2015, 151, 541–553. [Google Scholar] [CrossRef] [PubMed]
  85. Djawaria, F.P.; Saputra, H.; Susraini, A.A. Loss of PTEN Expression as a Predictive Factor for Poor Clinical Response of Neoadjuvant Chemotherapy in Triple Negative Breast Cancer. IJSR 2018, 7, 1693–1697. [Google Scholar]
  86. Stewart, P.A.; Luks, J.; Roycik, M.D.; Sang, Q.-X.A.; Zhang, J. Differentially expressed transcripts and dysregulated signaling pathways and networks in African American breast cancer. PLoS ONE 2013, 8, e82460. [Google Scholar] [CrossRef] [PubMed]
  87. Nanda, R.; Schumm, L.P.; Cummings, S.; Fackenthal, J.D.; Sveen, L.; Ademuyiwa, F.; Cobleigh, M.; Esserman, L.; Lindor, N.M.; Neuhausen, S.L.; et al. Genetic testing in an ethnically diverse cohort of high-risk women: A comparative analysis of BRCA1 and BRCA2 mutations in American families of European and African ancestry. JAMA 2005, 294, 1925–1933. [Google Scholar] [CrossRef] [PubMed]
  88. Olopade, O.I.; Fackenthal, J.D.; Dunston, G.; Tainsky, M.A.; Collins, F.; Whitfield-Broome, C. Breast cancer genetics in African Americans. Cancer 2003, 97, 236–245. [Google Scholar] [CrossRef]
  89. Greenup, R.; Buchanan, A.; Lorizio, W.; Rhoads, K.; Chan, S.; Leedom, T.; King, R.; McLennan, J.; Crawford, B.; Kelly Marcom, P.; et al. Prevalence of BRCA Mutations Among Women with Triple-Negative Breast Cancer (TNBC) in a Genetic Counseling Cohort. Ann. Surg. Oncol. 2013, 20, 3254–3258. [Google Scholar] [CrossRef]
  90. Getz, J.; Ahearn, M.E.; Gomez, C.; Pegram, M.; Bird, P.; Carpten, J.; Baumbach-Reardon, L.L. Abstract 2368: Differential gene expression in key oncolytic pathways between node-matched Caucasian-American, African-American and East African triple-negative breast cancer patients. Cancer Res. 2014, 74, 2368. [Google Scholar] [CrossRef]
  91. Lehmann, B.D.; Bauer, J.A.; Schafer, J.M.; Pendleton, C.S.; Tang, L.; Johnson, K.C.; Chen, X.; Balko, J.M.; Gómez, H.; Arteaga, C.L.; et al. PIK3CA mutations in androgen receptor-positive triple negative breast cancer confer sensitivity to the combination of PI3K and androgen receptor inhibitors. Breast Cancer Res. 2014, 16, 406. [Google Scholar] [CrossRef]
  92. Tokunaga, E.; Yamashita, N.; Kitao, H.; Tanaka, K.; Taketani, K.; Inoue, Y.; Saeki, H.; Oki, E.; Oda, Y.; Maehara, Y. Biological and clinical significance of loss of heterozygosity at the INPP4B gene locus in Japanese breast cancer. Breast 2016, 25, 62–68. [Google Scholar] [CrossRef]
  93. Markman, B.; Dienstmann, R.; Tabernero, J. Targeting the PI3K/Akt/mTOR pathway—Beyond rapalogs. Oncotarget 2010, 1, 530–543. [Google Scholar]
  94. Costa, R.L.B.; Han, H.S.; Gradishar, W.J. Targeting the PI3K/AKT/mTOR pathway in triple-negative breast cancer: A review. Breast Cancer Res. Treat. 2018, 169, 397–406. [Google Scholar] [CrossRef] [PubMed]
  95. Dey, N.; De, P.; Leyland-Jones, B. PI3K-AKT-mTOR inhibitors in breast cancers: From tumor cell signaling to clinical trials. Pharmacol. Ther. 2017, 175, 91–106. [Google Scholar] [CrossRef] [PubMed]
  96. Player, A.; Abraham, N.; Burrell, K.; Bengone, I.O.; Harris, A.; Nunez, L.; Willaims, T.; Kwende, S.; Walls, W. Identification of candidate genes associated with triple negative breast cancer. Genes Cancer 2017, 7, 659–672. [Google Scholar]
  97. Santuario-Facio, S.K.; Cardona-Huerta, S.; Perez-Paramo, Y.X.; Trevino, V.; Hernandez-Cabrera, F.; Rojas-Martinez, A.; Uscanga-Perales, G.; Martinez-Rodriguez, J.L.; Martinez-Jacobo, L.; Padilla-Rivas, G.; et al. A New Gene Expression Signature for Triple-Negative Breast Cancer Using Frozen Fresh Tissue before Neoadjuvant Chemotherapy. Mol. Med. 2017, 23, 101–111. [Google Scholar] [CrossRef] [PubMed]
  98. Narrandes, S.; Huang, S.; Murphy, L.; Xu, W. The exploration of contrasting pathways in Triple Negative Breast Cancer (TNBC). BMC Cancer 2018, 18, 22. [Google Scholar] [CrossRef] [PubMed]
  99. Tan, Y.; Wang, Q.; Xie, Y.; Qiao, X.; Zhang, S.; Wang, Y.; Yang, Y.; Zhang, B. Identification of FOXM1 as a specific marker for triple-negative breast cancer. Int. J. Oncol. 2018, 54, 87–97. [Google Scholar] [CrossRef] [PubMed]
  100. Yom, C.K.; Lee, K.-M.; Han, W.; Kim, S.-W.; Moon, H.-G.; Noh, D.-Y. FoxM1 as a potential therapeutic target for triple-negative breast cancer. J. Clin. Oncol. 2013, 31, e22063. [Google Scholar]
  101. Augello, M.A.; Hickey, T.E.; Knudsen, K.E. FOXA1: Master of steroid receptor function in cancer. EMBO J. 2011, 30, 3885–3894. [Google Scholar] [CrossRef] [PubMed]
  102. Dong, P.; Yu, B.; Pan, L.; Tian, X.; Liu, F. Identification of Key Genes and Pathways in Triple-Negative Breast Cancer by Integrated Bioinformatics Analysis. BioMed Res. Int. 2018, 2018, 10. [Google Scholar] [CrossRef]
  103. Rangel, R.; Guzman-Rojas, L.; Kodama, T.; Kodama, M.; Newberg, J.Y.; Copeland, N.G.; Jenkins, N.A. Identification of New Tumor Suppressor Genes in Triple-Negative Breast Cancer. Cancer Res. 2017, 77, 4089–4101. [Google Scholar] [CrossRef]
  104. Komatsu, M.; Yoshimaru, T.; Matsuo, T.; Kiyotani, K.; Miyoshi, Y.; Tanahashi, T.; Rokutan, K.; Yamaguchi, R.; Saito, A.; Imoto, S.; et al. Molecular features of triple negative breast cancer cells by genome-wide gene expression profiling analysis. Int. J. Oncol. 2013, 42, 478–506. [Google Scholar] [CrossRef] [PubMed]
  105. Iacopetta, D.; Rechoum, Y.; Fuqua, S.A. The Role of Androgen Receptor in Breast Cancer. Drug Discov. Today Dis. Mech. 2012, 9, e19–e27. [Google Scholar] [CrossRef] [PubMed]
  106. Barton, V.N.; D’Amato, N.C.; Gordon, M.A.; Lind, H.T.; Spoelstra, N.S.; Babbs, B.L.; Heinz, R.E.; Elias, A.; Jedlicka, P.; Jacobsen, B.M.; et al. Multiple molecular subtypes of triple-negative breast cancer critically rely on androgen receptor and respond to enzalutamide in vivo. Mol. Cancer Ther. 2015, 14, 769–778. [Google Scholar] [CrossRef] [PubMed]
  107. Zhu, A.; Li, Y.; Song, W.; Xu, Y.; Yang, F.; Zhang, W.; Yin, Y.; Guan, X. Antiproliferative Effect of Androgen Receptor Inhibition in Mesenchymal Stem-Like Triple-Negative Breast Cancer. Cell. Physiol. Biochem. 2016, 38, 1003–1014. [Google Scholar] [CrossRef] [PubMed]
  108. Gucalp, A.; Tolaney, S.; Isakoff, S.J.; Ingle, J.N.; Liu, M.C.; Carey, L.A.; Blackwell, K.; Rugo, H.; Nabell, L.; Forero, A.; et al. Phase II trial of bicalutamide in patients with androgen receptor-positive, estrogen receptor-negative metastatic Breast Cancer. Clin. Cancer Res. 2013, 19, 5505–5512. [Google Scholar] [CrossRef] [PubMed]
  109. Traina, T.A.; Miller, K.; Yardley, D.A.; Eakle, J.; Schwartzberg, L.S.; O’Shaughnessy, J.; Gradishar, W.; Schmid, P.; Winer, E.; Kelly, C.; et al. Enzalutamide for the Treatment of Androgen Receptor–Expressing Triple-Negative Breast Cancer. J. Clin. Oncol. 2018, 36, 884–890. [Google Scholar] [CrossRef] [PubMed]
  110. Mina, A.; Yoder, R.; Sharma, P. Targeting the androgen receptor in triple-negative breast cancer: Current perspectives. OncoTargets Ther. 2017, 10, 4675–4685. [Google Scholar] [CrossRef]
  111. Sporikova, Z.; Koudelakova, V.; Trojanec, R.; Hajduch, M. Genetic Markers in Triple-Negative Breast Cancer. Clin. Breast Cancer 2018, 18, e841–e850. [Google Scholar] [CrossRef] [PubMed]
  112. Guo, J.; Gong, G.; Zhang, B. Screening and identification of potential biomarkers in triple-negative breast cancer by integrated analysis. Oncol. Rep. 2017, 38, 2219–2228. [Google Scholar] [CrossRef]
  113. Salmans, M.L.; Zhao, F.; Andersen, B. The estrogen-regulated anterior gradient 2 (AGR2) protein in breast cancer: A potential drug target and biomarker. Breast Cancer Res. BCR 2013, 15, 204. [Google Scholar] [CrossRef]
  114. He, J.; Yang, J.; Chen, W.; Wu, H.; Yuan, Z.; Wang, K.; Li, G.; Sun, J.; Yu, L. Molecular Features of Triple Negative Breast Cancer: Microarray Evidence and Further Integrated Analysis. PLoS ONE 2015, 10, e0129842. [Google Scholar] [CrossRef] [PubMed]
  115. Jiang, Y.-Z.; Liu, Y.-R.; Xu, X.-E.; Jin, X.; Hu, X.; Yu, K.-D.; Shao, Z.-M. Transcriptome Analysis of Triple-Negative Breast Cancer Reveals an Integrated mRNA-lncRNA Signature with Predictive and Prognostic Value. Cancer Res. 2016, 76, 2105–2114. [Google Scholar] [CrossRef] [PubMed]
  116. Wang, S.; Beeghly-Fadiel, A.; Cai, Q.; Cai, H.; Guo, X.; Shi, L.; Wu, J.; Ye, F.; Qiu, Q.; Zheng, Y.; et al. Gene expression in triple-negative breast cancer in relation to survival. Breast Cancer Res. Treat. 2018, 171, 199–207. [Google Scholar] [CrossRef] [PubMed]
  117. Li, M.-X.; Jin, L.-T.; Wang, T.-J.; Feng, Y.-J.; Pan, C.-P.; Zhao, D.-M.; Shao, J. Identification of potential core genes in triple negative breast cancer using bioinformatics analysis. OncoTargets Ther. 2018, 11, 4105–4112. [Google Scholar] [CrossRef]
  118. Phuah, S.-Y.; Looi, L.-M.; Hassan, N.; Rhodes, A.; Dean, S.; Taib, N.A.M.; Yip, C.-H.; Teo, S.-H. Triple-negative breast cancer and PTEN (phosphatase and tensin homologue)loss are predictors of BRCA1 germline mutations in women with early-onset and familial breast cancer, but not in women with isolated late-onset breast cancer. Breast Cancer Res. 2012, 14, R142. [Google Scholar] [CrossRef]
  119. Fedele, C.G.; Ooms, L.M.; Ho, M.; Vieusseux, J.; O’Toole, S.A.; Millar, E.K.; Lopez-Knowles, E.; Sriratana, A.; Gurung, R.; Baglietto, L.; et al. Inositol polyphosphate 4-phosphatase II regulates PI3K/Akt signaling and is lost in human basal-like breast cancers. Proc. Natl. Acad. Sci. USA 2010, 107, 22231–22236. [Google Scholar] [CrossRef]
  120. Gordon, V.; Banerji, S. Molecular Pathways: PI3K Pathway Targets in Triple-Negative Breast Cancers. Clin. Cancer Res. 2013, 19, 3738–3744. [Google Scholar] [CrossRef]
  121. Obacz, J.; Brychtova, V.; Podhorec, J.; Fabian, P.; Dobes, P.; Vojtesek, B.; Hrstka, R. Anterior gradient protein 3 is associated with less aggressive tumors and better outcome of breast cancer patients. OncoTargets Ther. 2015, 8, 1523–1532. [Google Scholar]
  122. Li, J.; Chen, Y.; Yu, H.; Tian, J.; Yuan, F.; Fan, J.; Liu, Y.; Zhu, L.; Wang, F.; Zhao, Y.; et al. DUSP1 promoter methylation in peripheral blood leukocyte is associated with triple-negative breast cancer risk. Sci. Rep. 2017, 7, 43011. [Google Scholar] [CrossRef]
  123. van’t Veer, L.J.; Dai, H.; van de Vijver, M.J.; He, Y.D.; Hart, A.A.M.; Mao, M.; Peterse, H.L.; van der Kooy, K.; Marton, M.J.; Witteveen, A.T.; et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002, 415, 530–536. [Google Scholar] [CrossRef]
  124. Mook, S.; Schmidt, M.K.; Viale, G.; Pruneri, G.; Eekhout, I.; Floore, A.; Glas, A.M.; Bogaerts, J.; Cardoso, F.; Piccart-Gebhart, M.J.; et al. The 70-gene prognosis-signature predicts disease outcome in breast cancer patients with 1–3 positive lymph nodes in an independent validation study. Breast Cancer Res. Treat. 2009, 116, 295–302. [Google Scholar] [CrossRef] [PubMed]
  125. Knauer, M.; Mook, S.; Rutgers, E.J.T.; Bender, R.A.; Hauptmann, M.; van de Vijver, M.J.; Koornstra, R.H.T.; Bueno-de-Mesquita, J.M.; Linn, S.C.; van’t Veer, L.J. The predictive value of the 70-gene signature for adjuvant chemotherapy in early breast cancer. Breast Cancer Res. Treat. 2010, 120, 655–661. [Google Scholar] [CrossRef] [PubMed]
  126. Paik, S.; Shak, S.; Tang, G.; Kim, C.; Baker, J.; Cronin, M.; Baehner, F.L.; Walker, M.G.; Watson, D.; Park, T.; et al. A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer. N. Engl. J. Med. 2004, 351, 2817–2826. [Google Scholar] [CrossRef] [PubMed]
  127. Trosman, J.R.; Van Bebber, S.L.; Phillips, K.A. Coverage policy development for personalized medicine: Private payer perspectives on developing policy for the 21-gene assay. J. Oncol. Pract. 2010, 6, 238–242. [Google Scholar] [CrossRef] [PubMed]
  128. Parker, J.S.; Mullins, M.; Cheang, M.C.U.; Leung, S.; Voduc, D.; Vickery, T.; Davies, S.; Fauron, C.; He, X.; Hu, Z.; et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 2009, 27, 1160–1167. [Google Scholar] [CrossRef] [PubMed]
  129. Isakoff, S.J.; Mayer, E.L.; He, L.; Traina, T.A.; Carey, L.A.; Krag, K.J.; Rugo, H.S.; Liu, M.C.; Stearns, V.; Come, S.E.; et al. TBCRC009: A Multicenter Phase II Clinical Trial of Platinum Monotherapy With Biomarker Assessment in Metastatic Triple-Negative Breast Cancer. J. Clin. Oncol. 2015, 33, 1902–1909. [Google Scholar] [CrossRef]
  130. Sikov, W.M.; Berry, D.A.; Perou, C.M.; Singh, B.; Cirrincione, C.T.; Tolaney, S.M.; Kuzma, C.S.; Pluard, T.J.; Somlo, G.; Port, E.R.; et al. Impact of the Addition of Carboplatin and/or Bevacizumab to Neoadjuvant Once-per-Week Paclitaxel Followed by Dose-Dense Doxorubicin and Cyclophosphamide on Pathologic Complete Response Rates in Stage II to III Triple-Negative Breast Cancer: CALGB 40603 (Alliance). J. Clin. Oncol. 2015, 33, 13–21. [Google Scholar]
  131. Vieira, A.F.; Schmitt, F. An Update on Breast Cancer Multigene Prognostic Tests-Emergent Clinical Biomarkers. Front. Med. 2018, 5, 248. [Google Scholar] [CrossRef]
  132. Sgroi, D.C.; Carney, E.; Zarrella, E.; Steffel, L.; Binns, S.N.; Finkelstein, D.M.; Szymonifka, J.; Bhan, A.K.; Shepherd, L.E.; Zhang, Y.; et al. Prediction of late disease recurrence and extended adjuvant letrozole benefit by the HOXB13/IL17BR biomarker. J. Natl. Cancer Inst. 2013, 105, 1036–1042. [Google Scholar] [CrossRef]
  133. Fumagalli, D.; Desmedt, C.; Ignatiadis, M.; Loi, S.; Piccart, M.; Sotiriou, C. Gene Profiling Assay and Application: The Predictive Role in Primary Therapy. JNCI Monogr. 2011, 2011, 124–127. [Google Scholar] [CrossRef]
  134. de Planell-Saguer, M.; Rodicio, M.C. Detection methods for microRNAs in clinic practice. Clin. Biochem. 2013, 46, 869–878. [Google Scholar] [CrossRef] [PubMed]
  135. Molnár, V.; Tamási, V.; Bakos, B.; Wiener, Z.; Falus, A. Changes in miRNA expression in solid tumors: An miRNA profiling in melanomas. Sem. Cancer Biol. 2008, 18, 111–122. [Google Scholar] [CrossRef] [PubMed]
  136. van Schooneveld, E.; Wildiers, H.; Vergote, I.; Vermeulen, P.B.; Dirix, L.Y.; Van Laere, S.J. Dysregulation of microRNAs in breast cancer and their potential role as prognostic and predictive biomarkers in patient management. Breast Cancer Res. 2015, 17, 21. [Google Scholar] [CrossRef] [PubMed]
  137. Iorio, M.V.; Ferracin, M.; Liu, C.-G.; Veronese, A.; Spizzo, R.; Sabbioni, S.; Magri, E.; Pedriali, M.; Fabbri, M.; Campiglio, M.; et al. MicroRNA Gene Expression Deregulation in Human Breast Cancer. Cancer Res. 2005, 65, 7065–7070. [Google Scholar] [CrossRef] [PubMed]
  138. Wang, L.; Wang, J. MicroRNA-mediated breast cancer metastasis: From primary site to distant organs. Oncogene 2011, 31, 2499. [Google Scholar] [CrossRef] [PubMed]
  139. Pollard, J.; Burns, P.A.; Hughes, T.A.; Ho-Yen, C.; Jones, J.L.; Mukherjee, G.; Omoniyi-Esan, G.O.; Titloye, N.A.; Speirs, V.; Shaaban, A.M. Differential Expression of MicroRNAs in Breast Cancers from Four Different Ethnicities. Pathobiology 2018, 85, 220–226. [Google Scholar] [CrossRef] [PubMed]
  140. Rawlings-Goss, R.A.; Campbell, M.C.; Tishkoff, S.A. Global population-specific variation in miRNA associated with cancer risk and clinical biomarkers. BMC Med. Genom. 2014, 7, 53. [Google Scholar] [CrossRef]
  141. Sugita, B.; Gill, M.; Mahajan, A.; Duttargi, A.; Kirolikar, S.; Almeida, R.; Regis, K.; Oluwasanmi, O.L.; Marchi, F.; Marian, C.; et al. Differentially expressed miRNAs in triple negative breast cancer between African-American and non-Hispanic white women. Oncotarget 2016, 7, 79274–79291. [Google Scholar] [CrossRef]
  142. Nassar, F.J.; Talhouk, R.; Zgheib, N.K.; Tfayli, A.; El Sabban, M.; El Saghir, N.S.; Boulos, F.; Jabbour, M.N.; Chalala, C.; Boustany, R.-M.; et al. microRNA Expression in Ethnic Specific Early Stage Breast Cancer: An Integration and Comparative Analysis. Sci. Rep. 2017, 7, 16829. [Google Scholar] [CrossRef]
  143. Piasecka, D.; Braun, M.; Kordek, R.; Sadej, R.; Romanska, H. MicroRNAs in regulation of triple-negative breast cancer progression. J. Cancer Res. Clin. Oncol. 2018, 144, 1401–1411. [Google Scholar] [CrossRef]
  144. Shimono, Y.; Mukohyama, J.; Nakamura, S.-I.; Minami, H. MicroRNA Regulation of Human Breast Cancer Stem Cells. J. Clin. Med. 2015, 5, 2. [Google Scholar] [CrossRef] [PubMed]
  145. Gebeshuber, C.A.; Zatloukal, K.; Martinez, J. miR-29a suppresses tristetraprolin, which is a regulator of epithelial polarity and metastasis. EMBO Rep. 2009, 10, 400–405. [Google Scholar] [CrossRef] [PubMed]
  146. Korpal, M.; Lee, E.S.; Hu, G.; Kang, Y. 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] [CrossRef] [PubMed]
  147. Mekala, J.R.; Naushad, S.M.; Ponnusamy, L.; Arivazhagan, G.; Sakthiprasad, V.; Pal-Bhadra, M. Epigenetic regulation of miR-200 as the potential strategy for the therapy against triple-negative breast cancer. Gene 2018, 641, 248–258. [Google Scholar] [CrossRef]
  148. Humphries, B.; Wang, Z.; Oom, A.L.; Fisher, T.; Tan, D.; Cui, Y.; Jiang, Y.; Yang, C. MicroRNA-200b targets protein kinase Cα and suppresses triple-negative breast cancer metastasis. Carcinogenesis 2014, 35, 2254–2263. [Google Scholar] [CrossRef] [PubMed]
  149. Kolacinska, A.; Morawiec, J.; Fendler, W.; Malachowska, B.; Morawiec, Z.; Szemraj, J.; Pawlowska, Z.; Chowdhury, D.; Choi, Y.E.; Kubiak, R.; et al. Association of microRNAs and pathologic response to preoperative chemotherapy in triple negative breast cancer: Preliminary report. Mol. Biol. Rep. 2014, 41, 2851–2857. [Google Scholar] [CrossRef]
  150. Rhodes, L.V.; Martin, E.C.; Segar, H.C.; Miller, D.F.B.; Buechlein, A.; Rusch, D.B.; Nephew, K.P.; Burow, M.E.; Collins-Burow, B.M. Dual regulation by microRNA-200b-3p and microRNA-200b-5p in the inhibition of epithelial-to-mesenchymal transition in triple-negative breast cancer. Oncotarget 2015, 6, 16638–16652. [Google Scholar] [CrossRef]
  151. Gregory, P.A.; Bert, A.G.; Paterson, E.L.; Barry, S.C.; Tsykin, A.; Farshid, G.; Vadas, M.A.; Khew-Goodall, Y.; Goodall, G.J. The miR-200 family and miR-205 regulate epithelial to mesenchymal transition by targeting ZEB1 and SIP1. Nat. Cell Biol. 2008, 10, 593. [Google Scholar] [CrossRef]
  152. Sun, X.; Li, Y.; Zheng, M.; Zuo, W.; Zheng, W. MicroRNA-223 Increases the Sensitivity of Triple-Negative Breast Cancer Stem Cells to TRAIL-Induced Apoptosis by Targeting HAX-1. PLoS ONE 2016, 11, e0162754. [Google Scholar] [CrossRef]
  153. Liang, Z.; Bian, X.; Shim, H. Downregulation of MicroRNA-206 Promotes Invasion and Angiogenesis of Triple Negative Breast Cancer. Biochem. Biophys. Res. Commun. 2016, 477, 461–466. [Google Scholar] [CrossRef]
  154. Cantini, L.; Bertoli, G.; Cava, C.; Dubois, T.; Zinovyev, A.; Caselle, M.; Castiglioni, I.; Barillot, E.; Martignetti, L. Identification of microRNA clusters cooperatively acting on Epithelial to Mesenchymal Transition in Triple Negative Breast Cancer. bioRxiv 2018. [Google Scholar] [CrossRef]
  155. Kim, S.-J.; Oh, J.-S.; Shin, J.-Y.; Lee, K.-D.; Sung, K.W.; Nam, S.J.; Chun, K.-H. Development of microRNA-145 for therapeutic application in breast cancer. J. Control. Release 2011, 155, 427–434. [Google Scholar] [CrossRef] [PubMed]
  156. Piovan, C.; Palmieri, D.; Di Leva, G.; Braccioli, L.; Casalini, P.; Nuovo, G.; Tortoreto, M.; Sasso, M.; Plantamura, I.; Triulzi, T.; et al. Oncosuppressive role of p53-induced miR-205 in triple negative breast cancer. Mol. Oncol. 2012, 6, 458–472. [Google Scholar] [CrossRef] [PubMed]
  157. Huo, L.; Wang, Y.; Gong, Y.; Krishnamurthy, S.; Wang, J.; Diao, L.; Liu, C.-G.; Liu, X.; Lin, F.; Symmans, W.F.; et al. MicroRNA expression profiling identifies decreased expression of miR-205 in inflammatory breast cancer. Mod. Pathol. 2016, 29, 330. [Google Scholar] [CrossRef] [PubMed]
  158. Asaga, S.; Kuo, C.; Nguyen, T.; Terpenning, M.; Giuliano, A.E.; Hoon, D.S.B. Direct Serum Assay for MicroRNA-21 Concentrations in Early and Advanced Breast Cancer. Clin. Chem. 2011, 57, 84–91. [Google Scholar] [CrossRef] [PubMed]
  159. Frankel, L.B.; Christoffersen, N.R.; Jacobsen, A.; Lindow, M.; Krogh, A.; Lund, A.H. Programmed Cell Death 4 (PDCD4) Is an Important Functional Target of the MicroRNA miR-21 in Breast Cancer Cells. J. Biol. Chem. 2008, 283, 1026–1033. [Google Scholar] [CrossRef] [PubMed]
  160. Yang, L.; Feng, Y.; Qi, P.; Xu, S.; Zhou, Y. Mechanism of serum miR-21 in the pathogenesis of familial and triple negative breast cancer. J. Biol. Regul. Homeost. Agents 2016, 30, 1041–1045. [Google Scholar]
  161. Song, N.; Liang, B.; Wang, D. The function of MiR-21 expression differences and pathogenesis on familial and triple negative breast Cancer serum. Pak. J. Pharm. Sci. 2016, 29, 679–684. [Google Scholar] [PubMed]
  162. Sylwia, P.; Gabło, N.; Barnaś, E.; Szybka, M.; Morawiec, J.; Kołacińska, A.; Zawlik, I. Dysregulation of microRNAs in triple-negative breast cancer. Ginekol. Pol. 2017, 88, 530–536. [Google Scholar]
  163. Uva, P.; Cossu-Rocca, P.; Loi, F.; Pira, G.; Murgia, L.; Orrù, S.; Floris, M.; Muroni, M.R.; Sanges, F.; Carru, C.; et al. miRNA-135b Contributes to Triple Negative Breast Cancer Molecular Heterogeneity: Different Expression Profile in Basal-like Versus non-Basal-like Phenotypes. Int. J. Med. Sci. 2018, 15, 536–548. [Google Scholar] [CrossRef] [PubMed]
  164. Zeng, Z.; Chen, X.; Zhu, D.; Luo, Z.; Yang, M. Low Expression of Circulating MicroRNA-34c is Associated with Poor Prognosis in Triple-Negative Breast Cancer. Yonsei Med. J. 2017, 58, 697–702. [Google Scholar] [CrossRef] [PubMed]
  165. Eichelser, C.; Flesch-Janys, D.; Chang-Claude, J.; Pantel, K.; Schwarzenbach, H. Deregulated Serum Concentrations of Circulating Cell–Free MicroRNAs miR-17, miR-34a, miR-155, and miR-373 in Human Breast Cancer Development and Progression. Clin. Chem. 2013, 59, 1489–1496. [Google Scholar] [CrossRef] [PubMed]
  166. Hou, L.; Chen, M.; Yang, H.; Xing, T.; Li, J.; Li, G.; Zhang, L.; Deng, S.; Hu, J.; Zhao, X.; et al. MiR-940 Inhibited Cell Growth and Migration in Triple-Negative Breast Cancer. Med. Sci. Monit. 2016, 22, 3666–3672. [Google Scholar] [CrossRef]
  167. Dong, G.; Liang, X.; Wang, D.; Gao, H.; Wang, L.; Wang, L.; Liu, J.; Du, Z. High expression of miR-21 in triple-negative breast cancers was correlated with a poor prognosis and promoted tumor cell in vitro proliferation. Med. Oncol. 2014, 31, 57. [Google Scholar] [CrossRef] [PubMed]
  168. Lü, L.; Mao, X.; Shi, P.; He, B.; Xu, K.; Zhang, S.; Wang, J. MicroRNAs in the prognosis of triple-negative breast cancer. Medicine 2017, 96, e7085. [Google Scholar] [CrossRef] [PubMed]
  169. Liu, Y.; Zhang, Y.; Li, Q.; Li, J.; Ma, X.; Xing, J.; Rong, S.; Wu, Z.; Tian, Y.; Li, J.; et al. MiRNAs Predict the Prognosis of Patients with Triple Negative Breast Cancer: A Meta-Analysis. PLoS ONE 2017, 12, e0170088. [Google Scholar] [CrossRef] [PubMed]
  170. Kleivi Sahlberg, K.; Bottai, G.; Naume, B.; Burwinkel, B.; Calin, G.A.; Børresen-Dale, A.-L.; Santarpia, L. A Serum MicroRNA Signature Predicts Tumor Relapse and Survival in Triple-Negative Breast Cancer Patients. Clin. Cancer Res. 2015, 21, 1207–1214. [Google Scholar] [CrossRef] [PubMed]
  171. Jin, Y.-Y. Developing microRNA Therapeutics for Triple Negative Breast Cancer; Thomas Jefferson University: Philadelphia, PA, USA, 2016. [Google Scholar]
  172. Li, J.; Lai, Y.; Ma, J.; Liu, Y.; Bi, J.; Zhang, L.; Chen, L.; Yao, C.; Lv, W.; Chang, G.; et al. miR-17-5p suppresses cell proliferation and invasion by targeting ETV1 in triple-negative breast cancer. BMC Cancer 2017, 17, 745. [Google Scholar] [CrossRef] [PubMed]
  173. Turashvili, G.; Lightbody, E.D.; Tyryshkin, K.; SenGupta, S.K.; Elliott, B.E.; Madarnas, Y.; Ghaffari, A.; Day, A.; Nicol, C.J.B. Novel prognostic and predictive microRNA targets for triple-negative breast cancer. FASEB J. 2018, 29, fj201800120R. [Google Scholar] [CrossRef] [PubMed]
  174. Li, H.-Y.; Liang, J.-L.; Kuo, Y.-L.; Lee, H.-H.; Calkins, M.J.; Chang, H.-T.; Lin, F.-C.; Chen, Y.-C.; Hsu, T.-I.; Hsiao, M.; et al. miR-105/93-3p promotes chemoresistance and circulating miR-105/93-3p acts as a diagnostic biomarker for triple negative breast cancer. Breast Cancer Res. BCR 2017, 19, 133. [Google Scholar] [CrossRef]
  175. Wu, J.; Sun, Z.; Sun, H.; Li, Y. MicroRNA-27a promotes tumorigenesis via targeting AKT in triple negative breast cancer. Mol. Med. Rep. 2018, 17, 562–570. [Google Scholar] [CrossRef] [PubMed]
  176. Ouyang, M.; Li, Y.; Ye, S.; Ma, J.; Lu, L.; Lv, W.; Chang, G.; Li, X.; Li, Q.; Wang, S.; et al. MicroRNA Profiling Implies New Markers of Chemoresistance of Triple-Negative Breast Cancer. PLoS ONE 2014, 9, e96228. [Google Scholar] [CrossRef] [PubMed]
  177. Gasparini, P.; Cascione, L.; Fassan, M.; Lovat, F.; Guler, G.; Balci, S.; Irkkan, C.; Morrison, C.; Croce, C.M.; Shapiro, C.L.; et al. microRNA expression profiling identifies a four microRNA signature as a novel diagnostic and prognostic biomarker in triple negative breast cancers. Oncotarget 2014, 5, 1174–1184. [Google Scholar] [CrossRef] [PubMed]
  178. Shen, S.; Sun, Q.; Liang, Z.; Cui, X.; Ren, X.; Chen, H.; Zhang, X.; Zhou, Y. A Prognostic Model of Triple-Negative Breast Cancer Based on miR-27b-3p and Node Status. PLoS ONE 2014, 9, e100664. [Google Scholar] [CrossRef] [PubMed]
  179. Kong, W.; He, L.; Richards, E.J.; Challa, S.; Xu, C.X.; Permuth-Wey, J.; Lancaster, J.M.; Coppola, D.; Sellers, T.A.; Djeu, J.Y.; et al. Upregulation of miRNA-155 promotes tumour angiogenesis by targeting VHL and is associated with poor prognosis and triple-negative breast cancer. Oncogene 2014, 33, 679–689. [Google Scholar] [CrossRef] [PubMed]
  180. Liu, P.; Tang, H.; Chen, B.; He, Z.; Deng, M.; Wu, M.; Liu, X.; Yang, L.; Ye, F.; Xie, X. miR-26a suppresses tumour proliferation and metastasis by targeting metadherin in triple negative breast cancer. Cancer Lett. 2015, 357, 384–392. [Google Scholar] [CrossRef]
  181. Fkih M’hamed, I.; Privat, M.; Trimeche, M.; Penault-Llorca, F.; Bignon, Y.-J.; Kenani, A. miR-10b, miR-26a, miR-146a And miR-153 Expression in Triple Negative Vs Non Triple Negative Breast Cancer: Potential Biomarkers. Pathol. Oncol. Res. 2017, 23, 815–827. [Google Scholar] [CrossRef]
  182. Chen, J.; Tian, W.; Cai, H.; He, H.; Deng, Y. Down-regulation of microRNA-200c is associated with drug resistance in human breast cancer. Med. Oncol. 2012, 29, 2527–2534. [Google Scholar] [CrossRef]
  183. Wang, X.; Qiu, H.; Tang, R.; Song, H.; Pan, H.; Feng, Z.; Chen, L. miR-30a inhibits epithelial-mesenchymal transition and metastasis in triple-negative breast cancer by targeting ROR1. Oncol. Rep. 2018, 39, 2635–2643. [Google Scholar] [CrossRef]
  184. Chu, J.; Li, Y.; Fan, X.; Ma, J.; Li, J.; Lu, G.; Zhang, Y.; Huang, Y.; Li, W.; Huang, X.; et al. MiR-4319 Suppress the Malignancy of Triple-Negative Breast Cancer by Regulating Self-Renewal and Tumorigenesis of Stem Cells. Cell. Physiol. Biochem. 2018, 48, 593–604. [Google Scholar] [CrossRef]
  185. Chang, Y.-Y.; Lai, L.-C.; Tsai, M.-H.; Chuang, E.Y. Deep Sequencing Reveals a MicroRNA Expression Signature in Triple-Negative Breast Cancer. In MicroRNA and Cancer: Methods and Protocols; Wu, W., Ed.; Springer: New York, NY, USA, 2018; pp. 99–111. [Google Scholar]
  186. Bayraktar, R.; Pichler, M.; Kanlikilicer, P.; Ivan, C.; Bayraktar, E.; Kahraman, N.; Aslan, B.; Oguztuzun, S.; Ulasli, M.; Arslan, A.; et al. MicroRNA 603 acts as a tumor suppressor and inhibits triple-negative breast cancer tumorigenesis by targeting elongation factor 2 kinase. Oncotarget 2017, 8, 11641–11658. [Google Scholar] [CrossRef] [PubMed]
  187. Chen, J.; Shin, V.Y.; Siu, M.T.; Ho, J.C.W.; Cheuk, I.; Kwong, A. miR-199a-5p confers tumor-suppressive role in triple-negative breast cancer. BMC Cancer 2016, 16, 887. [Google Scholar] [CrossRef] [PubMed]
  188. Yao, L.; Liu, Y.; Cao, Z.; Li, J.; Huang, Y.; Hu, X.; Shao, Z. MicroRNA-493 is a prognostic factor in triple-negative breast cancer. Cancer Sci. 2018, 109, 2294–2301. [Google Scholar] [CrossRef] [PubMed]
  189. Chen, L.-L.; Zhang, Z.-J.; Yi, Z.-B.; Li, J.-J. MicroRNA-211-5p suppresses tumour cell proliferation, invasion, migration and metastasis in triple-negative breast cancer by directly targeting SETBP1. Br. J. Cancer 2017, 117, 78–88. [Google Scholar] [CrossRef]
  190. Xu, X.; Zhang, Y.; Jasper, J.; Lykken, E.; Alexander, P.B.; Markowitz, G.J.; McDonnell, D.P.; Li, Q.-J.; Wang, X.-F. MiR-148a functions to suppress metastasis and serves as a prognostic indicator in triple-negative breast cancer. Oncotarget 2016, 7, 20381–20394. [Google Scholar] [CrossRef] [PubMed]
  191. Wang, J.; Song, C.; Tang, H.; Zhang, C.; Tang, J.; Li, X.; Chen, B.; Xie, X. miR-629-3p may serve as a novel biomarker and potential therapeutic target for lung metastases of triple-negative breast cancer. Breast Cancer Res. BCR 2017, 19, 72. [Google Scholar] [CrossRef] [PubMed]
  192. Debeb, B.G.; Lacerda, L.; Anfossi, S.; Diagaradjane, P.; Chu, K.; Bambhroliya, A.; Huo, L.; Wei, C.; Larson, R.A.; Wolfe, A.R.; et al. miR-141-Mediated Regulation of Brain Metastasis From Breast Cancer. JNCI J. Natl. Cancer Inst. 2016, 108, djw026. [Google Scholar] [CrossRef] [PubMed]
  193. Zhang, L.; Huang, J.; Yang, N.; Greshock, J.; Megraw, M.S.; Giannakakis, A.; Liang, S.; Naylor, T.L.; Barchetti, A.; Ward, M.R.; et al. microRNAs exhibit high frequency genomic alterations in human cancer. Proc. Natl. Acad. Sci. USA 2006, 103, 9136–9141. [Google Scholar] [CrossRef]
  194. Edmonds, M.D.; Hurst, D.R.; Vaidya, K.S.; Stafford, L.J.; Chen, D.; Welch, D.R. Breast cancer metastasis suppressor 1 coordinately regulates metastasis-associated microRNA expression. Int. J. Cancer J. Int. Cancer 2009, 125, 1778–1785. [Google Scholar] [CrossRef]
  195. Ma, L.; Young, J.; Prabhala, H.; Pan, E.; Mestdagh, P.; Muth, D.; Teruya-Feldstein, J.; Reinhardt, F.; Onder, T.T.; Valastyan, S.; et al. miR-9, a MYC/MYCN-activated microRNA, regulates E-cadherin and cancer metastasis. Nat. Cell Biol. 2010, 12, 247–256. [Google Scholar] [CrossRef]
  196. Ma, L.; Teruya-Feldstein, J.; Weinberg, R.A. Tumour invasion and metastasis initiated by microRNA-10b in breast cancer. Nature 2007, 449, 682. [Google Scholar] [CrossRef] [PubMed]
  197. Liu, Y.; Zhao, J.; Zhang, P.-Y.; Zhang, Y.; Sun, S.-Y.; Yu, S.-Y.; Xi, Q.-S. MicroRNA-10b targets E-cadherin and modulates breast cancer metastasis. Med. Sci. Monit. 2012, 18, BR299–BR308. [Google Scholar] [CrossRef] [PubMed]
  198. Moriarty, C.H.; Pursell, B.; Mercurio, A.M. miR-10b targets Tiam1: Implications for Rac activation and carcinoma migration. J. Biol. Chem. 2010, 285, 20541–20546. [Google Scholar] [CrossRef] [PubMed]
  199. D’Ippolito, E.; Plantamura, I.; Bongiovanni, L.; Casalini, P.; Baroni, S.; Piovan, C.; Orlandi, R.; Gualeni, A.V.; Gloghini, A.; Rossini, A.; et al. miR-9 and miR-200 Regulate PDGFRβ-Mediated Endothelial Differentiation of Tumor Cells in Triple-Negative Breast Cancer. Cancer Res. 2016, 76, 5562–5572. [Google Scholar] [CrossRef] [PubMed]
  200. Jang, M.H.; Kim, H.J.; Gwak, J.M.; Chung, Y.R.; Park, S.Y. Prognostic value of microRNA-9 and microRNA-155 expression in triple-negative breast cancer. Hum. Pathol. 2017, 68, 69–78. [Google Scholar] [CrossRef] [PubMed]
  201. Stinson, S.; Lackner, M.R.; Adai, A.T.; Yu, N.; Kim, H.-J.; O’Brien, C.; Spoerke, J.; Jhunjhunwala, S.; Boyd, Z.; Januario, T.; et al. miR-221/222 Targeting of Trichorhinophalangeal 1 (TRPS1) Promotes Epithelial-to-Mesenchymal Transition in Breast Cancer. Sci. Signal. 2011, 4, pt5. [Google Scholar] [CrossRef]
  202. Guo, G.-C.; Wang, J.-X.; Han, M.-L.; Zhang, L.-P.; Li, L. microRNA-761 induces aggressive phenotypes in triple-negative breast cancer cells by repressing TRIM29 expression. Cell. Oncol. 2017, 40, 157–166. [Google Scholar] [CrossRef] [PubMed]
  203. Chen, D.; Dang, B.-L.; Huang, J.-z.; Chen, M.; Wu, D.; Xu, M.-L.; Li, R.; Yan, G.-R. MiR-373 drives the epithelial-to-mesenchymal transition and metastasis via the miR-373-TXNIP-HIF1α-TWIST signaling axis in breast cancer. Oncotarget 2015, 6, 32701–32712. [Google Scholar]
  204. Huang, Q.; Gumireddy, K.; Schrier, M.; le Sage, C.; Nagel, R.; Nair, S.; Egan, D.A.; Li, A.; Huang, G.; Klein-Szanto, A.J.; et al. The microRNAs miR-373 and miR-520c promote tumour invasion and metastasis. Nat. Cell Biol. 2008, 10, 202. [Google Scholar] [CrossRef]
  205. Thakur, S.; Grover, R.K.; Gupta, S.; Yadav, A.K.; Das, B.C. Identification of Specific miRNA Signature in Paired Sera and Tissue Samples of Indian Women with Triple Negative Breast Cancer. PLoS ONE 2016, 11, e0158946. [Google Scholar] [CrossRef]
  206. Han, M.; Liu, M.; Wang, Y.; Chen, X.; Xu, J.; Sun, Y.; Zhao, L.; Qu, H.; Fan, Y.; Wu, C. Antagonism of miR-21 reverses epithelial-mesenchymal transition and cancer stem cell phenotype through AKT/ERK1/2 inactivation by targeting PTEN. PLoS ONE 2012, 7, e39520. [Google Scholar] [CrossRef] [PubMed]
  207. Han, M.; Liu, M.; Wang, Y.; Mo, Z.; Bi, X.; Liu, Z.; Fan, Y.; Chen, X.; Wu, C. Re-expression of miR-21 contributes to migration and invasion by inducing epithelial-mesenchymal transition consistent with cancer stem cell characteristics in MCF-7 cells. Mol. Cell. Biochem. 2012, 363, 427–436. [Google Scholar] [CrossRef] [PubMed]
  208. Zhu, S.; Si, M.-L.; Wu, H.; Mo, Y.-Y. MicroRNA-21 Targets the Tumor Suppressor Gene Tropomyosin 1 (TPM1). J. Biol. Chem. 2007, 282, 14328–14336. [Google Scholar] [CrossRef] [PubMed]
  209. Yan, L.-X.; Huang, X.-F.; Shao, Q.; Huang, M.-Y.; Deng, L.; Wu, Q.-L.; Zeng, Y.-X.; Shao, J.-Y. 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] [CrossRef] [PubMed]
  210. Qian, B.; Katsaros, D.; Lu, L.; Preti, M.; Durando, A.; Arisio, R.; Mu, L.; Yu, H. High miR-21 expression in breast cancer associated with poor disease-free survival in early stage disease and high TGF-β1. Breast Cancer Res. Treat. 2009, 117, 131–140. [Google Scholar] [CrossRef] [PubMed]
  211. Qattan, A.; Intabli, H.; Alkhayal, W.; Eltabache, C.; Tweigieri, T.; Amer, S.B. Robust expression of tumor suppressor miRNA’s let-7 and miR-195 detected in plasma of Saudi female breast cancer patients. BMC Cancer 2017, 17, 799. [Google Scholar] [CrossRef]
  212. Zhao, G.; Han, C.; Zhang, Z.; Wang, L.; Xu, J. Increased expression of microRNA-31-5p inhibits cell proliferation, migration, and invasion via regulating Sp1 transcription factor in HepG2 hepatocellular carcinoma cell line. Biochem. Biophys. Res. Commun. 2017, 490, 371–377. [Google Scholar] [CrossRef]
  213. Paszek, S.; Gabło, N.; Barnaś, E.; Szybka, M.; Morawiec, J.; Kołacińska, A.; Zawlik, I. Dysregulation of micrornas in triple-negative breast cancer. Ginekol. Pol. 2017, 88, 530–536. [Google Scholar] [CrossRef]
  214. Kreth, S.; Hübner, M.; Hinske, L.C. MicroRNAs as Clinical Biomarkers and Therapeutic Tools in Perioperative Medicine. Anesth. Analges. 2018, 126, 670–681. [Google Scholar] [CrossRef]
  215. Chi, J.; Ballabio, E.; Chen, X.-H.; Kušec, R.; Taylor, S.; Hay, D.; Tramonti, D.; Saunders, N.J.; Littlewood, T.; Pezzella, F.; et al. MicroRNA expression in multiple myeloma is associated with genetic subtype, isotype and survival. Biol. Direct 2011, 6, 23. [Google Scholar] [CrossRef]
  216. Lujambio, A.; Calin, G.A.; Villanueva, A.; Ropero, S.; Sánchez-Céspedes, M.; Blanco, D.; Montuenga, L.M.; Rossi, S.; Nicoloso, M.S.; Faller, W.J.; et al. A microRNA DNA methylation signature for human cancer metastasis. Proc. Natl. Acad. Sci. USA 2008, 105, 13556–13561. [Google Scholar] [CrossRef] [PubMed]
  217. Zhang, W.; Dahlberg, J.E.; Tam, W. MicroRNAs in tumorigenesis: A primer. Am. J. Pathol. 2007, 171, 728–738. [Google Scholar] [CrossRef] [PubMed]
  218. Suárez, Y.; Sessa, W.C. MicroRNAs as novel regulators of angiogenesis. Circul. Res. 2009, 104, 442–454. [Google Scholar] [CrossRef] [PubMed]
  219. Beg, M.S.; Brenner, A.J.; Sachdev, J.; Borad, M.; Kang, Y.-K.; Stoudemire, J.; Smith, S.; Bader, A.G.; Kim, S.; Hong, D.S. Phase I study of MRX34, a liposomal miR-34a mimic, administered twice weekly in patients with advanced solid tumors. Investig. New Drugs 2017, 35, 180–188. [Google Scholar] [CrossRef] [PubMed]
  220. Wu, X.-Q.; Huang, C.; Liu, X.-H.; Li, J. MicroRNA let-7a: A novel therapeutic candidate in prostate cancer. Asian J. Androl. 2014, 16, 327–328. [Google Scholar] [PubMed]
  221. Yang, G.; Zhang, W.; Yu, C.; Ren, J.; An, Z. MicroRNA let-7: Regulation, single nucleotide polymorphism, and therapy in lung cancer. J. Cancer Res. Ther. 2015, 11, C1–C6. [Google Scholar] [PubMed]
  222. Janssen, H.L.A.; Reesink, H.W.; Lawitz, E.J.; Zeuzem, S.; Rodriguez-Torres, M.; Patel, K.; van der Meer, A.J.; Patick, A.K.; Chen, A.; Zhou, Y.; et al. Treatment of HCV Infection by Targeting MicroRNA. N. Engl. J. Med. 2013, 368, 1685–1694. [Google Scholar] [CrossRef] [PubMed]
  223. Di Martino, M.T.; Leone, E.; Amodio, N.; Foresta, U.; Lionetti, M.; Pitari, M.R.; Cantafio, M.E.G.; Gullà, A.; Conforti, F.; Morelli, E.; et al. Synthetic miR-34a mimics as a novel therapeutic agent for multiple myeloma: In vitro and in vivo evidence. Clin. Cancer Res. 2012, 18, 6260–6270. [Google Scholar] [CrossRef]
  224. Voorhoeve, P.M.; le Sage, C.; Schrier, M.; Gillis, A.J.M.; Stoop, H.; Nagel, R.; Liu, Y.-P.; van Duijse, J.; Drost, J.; Griekspoor, A.; et al. A Genetic Screen Implicates miRNA-372 and miRNA-373 As Oncogenes in Testicular Germ Cell Tumors. Cell 2006, 124, 1169–1181. [Google Scholar] [CrossRef]
  225. Wurdinger, T.; Costa, F.F. Molecular therapy in the microRNA era. Pharm. J. 2006, 7, 297. [Google Scholar] [CrossRef]
  226. Ma, L.; Yan, H.; Zhou, Q. AG1478 inhibits the migration and invasion of cisplatin-resistant human lung adenocarcinoma cells via the cell cycle regulation by matrix metalloproteinase-9. Oncol. Lett. 2014, 8, 921–927. [Google Scholar] [CrossRef] [PubMed]
  227. Ji, W.; Sun, B.; Su, C. Targeting MicroRNAs in Cancer Gene Therapy. Genes 2017, 8, 21. [Google Scholar] [CrossRef] [PubMed]
  228. Prat, A.; Ellis, M.J.; Perou, C.M. Practical implications of gene-expression-based assays for breast oncologists. Nat. Rev. Clin. Oncol. 2012, 9, 48–57. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Schematic outline showing normal and abnormal genes and miRNA profiles of normal mammary and breast cancer. It is evident that there are variations in gene expressions and miRNA profiles from normal to non-invasive and invasive cancer, in which epithelial-mesenchymal transition (EMT) is the main hallmark. Thus, combined gene and miRNA profiles can be used as novel Biomarkers and therapy targets for each step of cancer progression. However, it is important to highlight that Gene and miRNA profiles can differ from one geographic location to another as well as between different ethnic groups.
Figure 1. Schematic outline showing normal and abnormal genes and miRNA profiles of normal mammary and breast cancer. It is evident that there are variations in gene expressions and miRNA profiles from normal to non-invasive and invasive cancer, in which epithelial-mesenchymal transition (EMT) is the main hallmark. Thus, combined gene and miRNA profiles can be used as novel Biomarkers and therapy targets for each step of cancer progression. However, it is important to highlight that Gene and miRNA profiles can differ from one geographic location to another as well as between different ethnic groups.
Cancers 11 00363 g001
Table 1. List of Genes involved in Progression of Triple-Negative Breast Cancer Identified by Gene Expression Profiling.
Table 1. List of Genes involved in Progression of Triple-Negative Breast Cancer Identified by Gene Expression Profiling.
GeneCountryMethodReference
PTENUSA, Middle EastTissue microarray[83,84]
PIK3CAUSAReverse phase protein array[91]
ADNP, AP2B1, TOMM70A, ZNF326USATwo-step genetic screening[103]
ANKRD11, BCL141A, FAM17IAI, FOXC1, HAPLN3, HMGT8, HMGA1, LPIN1, PRKX, PRKY, UGT8MexicoMicro-array gene expression profile[96]
FOXM1, PPARCanada, United KingdomGene enrichment analysis (GSEA)
Gene expression analysis
[98]
SOX8, AR, C9/F152, EOMES, FA2H, GSPT1, NPK, RAB30, RASGRP1, SOD2ChinaGene enrichment analysis (GSEA)
Gene expression analysis
[102,116]
BMF, COL4A2, DUSP1, FOXA1, FCGR1A, HIF1A-AS2, MLPHChinaMicroarray analysis[114]
RSAD2, AK124454ChinaTranscriptome microarrays[115]
AGR2, AGR3, ANKRD30A, CMBL, CREB3L4, CRIP1, CYP4Z3P, ELF5, GABRP, GPR160, HORMAD1, KLK6, KRT18, NME5, PROM1, SDR16C5ChinaGene microarray[112]
CCNB1GEO database
China
Bio-informatics analysis[117]
ASPM, CENPK, MELK, NEK2, PBKJapanDNA microarray[104]
Table 2. List of Genes and their role in TNBC.
Table 2. List of Genes and their role in TNBC.
Biological FunctionsGenesReferences
Cell ProliferationPTEN
INPP4B
PIK3CA
FOXM1
AR
AGR3
DUSP1
[118]
[119]
[120]
[99]
[105]
[121]
[122]
Tumor Metastases and ProgressionFOXM1
AGR2
[99]
[113,121]
Cell Cycle RegulationCCNB1
ASPM, CENPK, MELK, NEK2, PBK
FOXM1
[117]
[104]
[99]
ApoptosisDUSP1
AGR3
[122]
[121]
Table 3. List of miRNAs and their Roles in TNBC.
Table 3. List of miRNAs and their Roles in TNBC.
Biological FunctionsmiRNAsReferences
StimulateInhibit
Cell ProliferationmiR-155-5p, miR-199, miR-761, miR-27a, miR-224-5p, miR-375, miR-205-5pmiR-940, miR-211-5p, miR-148a[166,173,189,190]
Tumor Metastases and ProgressionmiR-21, miR-21-3p, miR-135b, miR-205-5p, miR-135b-5p, miR-224-5p, miR-375, miR-629-3p, miR-141, miR-10b, miR-105/miR-93-3p, miR-761, miR-181a, miR-181a-5p, miR-181b-5p, miR-183-5pmiR-190a, miR-30a, miR-4319, miR-200, miR-214, miR-31-5p, miR-211-5p, miR-148a, miR-373[146,147,148,151,154,158,160,161,162,165,166,171,173,174,176,183,184,189,190,191,192,194,196,199,200,202,203,204,212]
Cell Cycle RegulationmiR-135b, miR-135b-5p [163,213]
Cell ApoptosismiR-31-5pmiR-21, miR-23p, miR-27a[158,160,161,167,171,175,205,212]
Resistance to TherapymiR-21, miR-21-3p, miR-155-5p, miR-195-5p, miR-210, miR-221/222miR-10b-5p, miR-125b-5p, miR-35p, miR-451a, miR-200c[146,158,160,161,167,171,176,179,201,205]
EMTmiR-155, miR-199, miR-221/222miR-200, miR-200b, miR-200c, miR-206, miR-373[146,147,148,149,150,151,157,165,199,203,204]

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Back to TopTop