Introduction

According to global cancer statistics (GLOBOCAN), breast cancer (BC) is the most prevalent cancer among different populations, which can be managed properly if diagnosed early [1]. Due to the molecular heterogeneity, breast cancer can be very diverse in clinical features, prognosis, and response to treatment [2]. Triple-negative breast cancer (TNBC) is a BC subtype lacking the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). This type of BC comprises 15–20% of all breast cancer cases [3]. TNBC has the highest recurrence and mortality rate and is mainly diagnosed in younger women [4]. Conventional chemotherapy is the main treatment for TNBC due to its molecular phenotype characteristics. Therefore, it is crucial to deeper investigate the pathways and mechanisms involved in TNBC malignancy to introduce new probable biomarkers and therapeutic targets.

One of the main reasons for the poor prognosis and more aggressive features of TNBC is the accelerated epithelial-mesenchymal transition (EMT) process [5]. In EMT, breast tumor epithelial cells undergo phenotypic changes, gain higher cell motility, and eventually enter the bloodstream and metastasize to other tissues, including lymph nodes, bones, and the brain [6]. Investigating EMT regulators and contributors can lead us to find more potential therapeutic targets in TNBC. Different studies have indicated the oncogenic role of FOXC1, a key EMT-inducing transcription factor (TF), in aggressive features of TNBC tumors [7, 8]. However, its network of targets, collaborators, and regulators needs to be better studied in TNBC. MMP7 and SNAI1 are among the downstream targets of FOXC1, cooperating in EMT and metastasis of malignant tumors [9]. Matrix metalloproteinase proteins (MMPs) such as MMP7 are the main targets of the FOXC1 transcription factor. Induced upregulation of FOXC1 can stimulate the transcription of MMP7 in breast tumor cells [10]. SNAI1 is another transcription factor with a crucial in initiating EMT alongside SNAI2, TWIST1/2, and TGF-β in different cancer types [11]. It was shown that FOXC1 mainly functions in EMT by regulating the expression of SNAI1 [12].

EMT-related TFs and markers are potential targets of epigenetic regulators such as long non-coding RNAs (lncRNAs) during tumorigenesis [5]. LncRNA translational regulatory RNA (treRNA) is located near the SNAI1 gene and has been confirmed as a transcription enhancer of this gene [13]. Moreover, it is suggested that treRNA regulates the expression of E-cadherin, a biomarker of EMT, at both transcriptional and translational levels during EMT [14]. LncRNA SBF2-AS1, located on the antisense strand of SET binding factor 2 (SBF2), has been suggested to interact with MMP7 through involvement in common signaling pathways, such as MAPK/ERK and TGF-β1, promoting invasion in hepatocellular carcinoma [15, 16]. Although the oncogenic role and prognostic value of SBF2-AS1 and treRNA have been shown in several malignancies such as lung, colorectal and cervical cancer [17], little is known about the function of these lncRNAs in TNBC.

In the present study, we aimed to analyze the expression levels of EMT-related genes and lncRNAs, including MMP7, SNAI1, lncRNA SBF2-AS1, and lncRNA treRNA in TNBC tissues, compared to non-TNBC samples. Moreover, possible correlations between the expression levels of the selected coding genes and lncRNAs were evaluated. To our knowledge, this is the first study to investigate and discuss the role of SBF2-AS1 and treRNA in TNBC pathogenesis.

Materials and methods

Patients and tissue specimens

One hundred fresh frozen breast tumor specimens were referred to the Cancer Institute of Imam Khomeini Hospital, including 50 TNBC and 50 non-TNBC tumors (forty-four luminal A/B and six HER2+). All samples were frozen in liquid nitrogen and immediately stored at -80 °C until further use. None of the subjects performed any chemotherapy or radiotherapy. Informed consent and complete clinicopathological information were received from all participants. The study protocol was approved by the Medical Ethics Committee of SBMU, Tehran, Iran, and was conducted in compliance with Helsinki Declaration.

RNA extraction and cDNA synthesis

Total RNA was extracted from all tissues using RiboEx reagent (GeneAll, Korea) and reverse-transcribed to complementary DNA by RevertAid double-stranded cDNA synthesis kit (Thermo Fisher, USA) following the manufacturer’s protocol. The quality and purity of isolated RNA were evaluated according to the A260/A280 absorption ratio using NanoDrop Spectrophotometer equipment (Aosheng, China).

Expression analysis

The qRT-PCR was performed in an ABI 7500 Real-Time PCR system (Applied Biosystems, CA) using High ROX PCR Master Mix with SYBR green (Ampliqon, Denmark). Each reaction, with a final volume of 20 µl, consisted of 1 µl of cDNA template, 5 pmol of each forward and reverse primer, 10 µl of SYBR green 2X master mix, and 8 µl of nuclease-free water. All experiments were run in duplicate and normalized to beta-2 microglobulin (β2M) as the reference gene. The relative expression of mRNAs and lncRNAs was quantified using the Pfaffl method. Specific primer sequences used for real-time PCR are presented in Table 1.

Table 1 The sequences of the primers used for qRT-PCR analysis of the selected genes, lncRNAs and the reference gene
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Statistical analysis

The cycle thresholds (Ct) and mean PCR efficiencies for each sample were determined using LinRegPCR software. All the statistical analyses to evaluate the association between clinicopathological parameters of patients and the genes expression levels, including the normality test, unpaired T-test, and the development of the receiver operating characteristic (ROC) curve, were performed by GraphPad Prism 8.0. Pearson correlation coefficient also evaluated the probable correlation between gene expression levels. A P-value less than 0.05 was considered statistically significant.

Results

Expression analysis

Quantitative Real-time PCR results showed the significant upregulation of SNAI1 and its linked lncRNA, treRNA, in triple-negative tumor samples compared to non-triple negative tissues by the p-value of 0.039 and < 0.0001, and fold change (FC) of 1.3 and 2.5, respectively. MMP7 and SBF2-AS1 were also significantly overexpressed in TNBC vs. non-TNBC tumors (by FC of 11 and 5, respectively, and p-value < 0.0001 for both). Moreover, the expression level differences between triple-negative tumors and other distinctive breast cancer subtypes, including luminal and HER2+, were statistically significant for all the studied genes and lncRNAs, except for the SNAI1 in TN vs. HER2+ (Fig. 1).

Fig. 1
figure 1

Relative expression levels (-ΔCt) of (A) SNAI1 in TNBC vs. non-TNBC samples (p = 0.039), (B) SNAI1 in triple-negative subtype compared to luminal (p = 0.01) and HER-2+ (p = 0.08) subtypes, (C) treRNA in TNBC vs. non-TNBC samples (p < 0.0001), (D) treRNA in triple-negative subtype compared to luminal (p < 0.0001) and HER-2+ subtypes (p = 0.0004), (E) MMP7 in TNBC vs. non-TNBC samples (P < 0.0001), (F) MMP7 in triple-negative subtype compared to luminal (p < 0.0001) and HER-2+ subtypes (p = 0.0007), (G) SBF2-AS1 in TNBC vs. non-TNBC samples (p < 0.0001), and (H) SBF2-AS1 in Triple-negative subtype compared to Luminal (p < 0.0001) and HER-2+ subtypes (p = 0.002)

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Investigating the clinical implications of SNAI1, treRNA, MMP7 and SBF2-AS1 expressions in TNBC samples

We performed Receiver operating characteristic (ROC) curve analysis for the studied genes and lncRNAs, to evaluate their power to distinguish TNBC from non-TNBC samples according to AUC (area under the curve) score. As indicated in Fig. 2A, MMP7, treRNA, and SBF2-AS1 have high AUC values (0.80, 0.75, and 0.72, respectively), indicating they have the most potential to discriminate TN tumors from non-TN tumors. Therefore, we carried out a ROC curve analysis for the biomarker panel consisting of these three genes, which resulted in the highest AUC score (AUC = 0.88, p-value < 0.0001) and increased the specificity and sensitivity of the diagnostic power to 76% and 85%, respectively (Fig. 2B).

Fig. 2
figure 2

Receiver operating characteristic (ROC) curve analysis of the studied genes (A) as separate diagnostic biomarkers to distinguish TNBC from non-TNBC subgroup (The specificity and sensitivity of SNAI1 expression to discriminate were 90% and 40%, the specificity and sensitivity of MMP7 expression were 75% and 84%, the specificity and sensitivity of treRNA expression were 63% and 85%, the specificity and sensitivity of SBF2-AS1 expression were 83% and 60%) and (B) as a biomarker panel consisted of MMP7, SBF2-AS1 and treRNA genes to distinguish TNBC from non-TNBC subgroup (The specificity and sensitivity of the panel expression were 77% and 85%). AUC: area under the ROC curve, CI: confidence interval

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To interpret the effect of the studied genes expressions on the clinicopathological features of TNBC patients, we analyzed the association between the genes expression values and age, tumor size, grade, stage, etc., as presented in Tables 2 and 3. Significant associations existed between the higher expression levels of both SNAI1 and SBF2-AS1 genes and larger tumor size in TNBC patients.

Table 2 Association of SNAI1 and lnc-treRNA expressions with clinicopathological features of TNBC patients
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Table 3 Association of MMP7 and lnc-SBF2-AS1 expressions with clinicopathological features of TNBC patients
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Correlation study of SNAI1, treRNA, MMP7 and SBF2-AS1 genes expressions in TNBC

Pearson correlation analysis was applied between expression levels of the studied genes and lncRNAs to evaluate the hypothesis of the synergic effect of the studied genes in TNBC. The results showed weak positive correlations between SNAI1/MMP7, SNAI1/treRNA, and MMP7/SBF2-AS1 expression levels (r = 0.17, 0.11, and 0.26 respectively). However, as displayed in Fig. 3, only the correlation between SNAI1 and treRNA expression levels was statistically significant (r = 0.26, p-value = 0.01). Therefore, these two variables have a positive linear relationship and change in the same direction.

Fig. 3
figure 3

Pearson correlation analysis displayed in linear scatterplot showing a weak positive correlation between the expression level of SNAI1 and related lncRNA, treRNA, in TNBC samples (r = 0 to 1 indicates Positive correlation, r = 0 to -1 indicates Negative correlation and r = 0 indicates no correlation)

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Discussion

Overregulated EMT is a prerequisite for promoting invasion, metastasis, recurrence, and other hallmarks of TNBC [5]. As a complex and multi-stage process, EMT is controlled by different regulatory mechanisms, including transcription factors, non-coding RNAs, and various post-translational and epigenetic changes [6]. In the present study, we confirmed the overexpression of EMT-related genes and lncRNAs, including MMP7, SNAI1, lncRNA SBF2-AS1, and lncRNA treRNA in TNBC tissues compared to non-TNBC samples.

Our previous study showed the significant upregulation of FOXC1, a key EMT-inducing transcription factor, and its associated lncRNAs in TNBC compared to other breast cancer subtypes [18]. Studies have shown that SNAI1, the downstream target of FOXC1, was overexpressed during the EMT stage in different cancers [12, 19]. SNAI1 suppresses epithelial cell marker CDH1 (CADHERIN 1) expression by occupying its promoter [20, 21]. On the other hand, lnc-treRNA was first identified to enhance the transcription of its neighboring genes, including SNAI1 [22]. Also, it was reported that treRNA could inhibit the translation of E-cadherin directly by establishing a ribonucleoprotein (RNP) complex in primary breast tumors and metastasized lymph nodes [14].

The observed overexpression of SNAI1 in TNBC tumors vs. non-TNBC tumors and its association with larger tumor size in the present research was consistent with the former studies indicating the contribution of this gene to the malignant features of TNBC [23, 24]. We also showed that the lnc-treRNA expression was significantly higher in triple-negative tumors compared to both Luminal and HER2 + subtypes. The expression of this lncRNA was positively correlated to SNAI1 expression, meaning that the expression level of SNAI1 increases when the expression of treRNA increases and vice versa, which is consistent with the nature of their “enhancer-gene” relationship. Besides, we showed the high diagnostic value of treRNA to distinguish TN tumors from other BC subtypes, proposing this lncRNA as a potential diagnostic biomarker of the TNBC subgroup. A review of previous studies shows that lnc-treRNA can employ various molecular mechanisms to promote EMT, remarkably depending on its cellular location. As illustrated in Fig. 4, it was confirmed that lnc-treRNA could suppress the expression of CDH1 on the transcriptional level by both enhancing the expression of SNAI1, known as the direct repressor of CDH1 transcription, and also by recruiting EZH2 protein, resulting in promoter methylation and epigenetic silencing of CDH1 in tumors [13, 25]. At the translational level, lnc-treRNA can form an RNP complex and attach to CDH1 mRNA transcripts to interfere with the ribosome binding and translation process [14]. Figure 4 shows different pathways that treRNA can act through to decrease the expression of E-cadherin1 and promote EMT in TN tumors, in detail.

Fig. 4
figure 4

Schematic illustration of the proposed cooperation between SNAI1 AND treRNA to promote EMT in TNBC. TreRNA can regulate the expression of EMT-related marker (E-cadherin1) by three different mechanisms, which include: enhancing SNAI1 expression to occupy the promotor of CDH1, recruiting of EZH2 for epigenetic silencing of CDH1, and forming the treRNP complex to bind to the CDH1 mRNA transcripts and prevent the translation process. treRNP: treRNA nucleoprotein, EMT: epithelial-mesenchymal transition

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MMPs, such as MMP7, create a proper tissue environment for the EMT process by destructing extracellular matrix proteins [26]. Studies have reported the dependence of expression and function between FOXC1 and MMP7 and the direct effect of MMP7 on invasion and migration in both TNBC and BLBC (Basal-like breast cancer) tumor cells [7, 10].

Lnc-SBF2-AS1, which recently has been noted to have a pivotal role in EMT and invasion, has been mentioned to function in the same signaling pathways as MMP7, including MAPK/ERK and TGF-β1 [27]. Zhang and colleagues showed that the knockdown of SBF2-AS1 reduced the expression of main EMT markers, E-cadherin and Vimentin, resulting in EMT regression in hepatocellular carcinoma (HPC) cells [28]. Furthermore, SBF2-AS1 has been shown to participate in the proliferation, invasion, and migration of breast tumor cells by sponging miR-143 and repressing the RRS1 (Ribosome biogenesis regulatory protein homolog) complex [29]. So far, many studies have proved the oncogenic role of SBF2-AS1 in diverse cancer types but not in TNBC tumors specifically [27].

In the present study, we confirmed the significant upregulation of MMP7 and SBF2-AS1 in TNBC tumors compared to non-TNBC samples. We revealed the diagnostic value of both MMP7 and SBF2-AS1 in discriminating triple-negative tumors from non-triple negative tumors. Also, an association between SBF2-AS1 expression level and TN tumor size was found. Altogether, our results were in accordance with previous studies indicating the association of SBF2-AS1 with undesirable clinicopathological features of tumors. We propose that SBF2-AS1 be added to the list of lncRNAs with differential expression in TNBC compared to luminal A/B and HER2+ subgroups. lnc-SBF2-AS1 mainly functions in tumorigenesis by establishing a ceRNA network and regulates some oncogenic signaling pathways [17]. Considering that the biological functions of both SBF2-AS1 and MMP7 are primarily related to tumor invasion, metastasis, EMT, and angiogenesis, some regulatory pathways might provide this gene-lncRNA cooperation. For example, it was suggested that the overexpression of SBF2-AS1 in glioblastoma can trigger MAPK/ERK signaling cascade and invasive angiogenesis by increasing the expression level of EGFL7 (EGF-like domain multiple 7) [15]. As we know, activating the MAPK/ERK signaling pathway can enhance tumor invasion and metastasis via upregulating MMPs expression per se [30], and ERK/MMP7 axis is one of the approved pathways in the invasion process in many tumors [31, 32].

Nevertheless, another probable pathway is the TGF-β1 signaling pathway, a confirmed downstream target of SBF2-AS1 in HPC [16, 33]. Several studies have reported the involvement of the TGF-β1/MMP7 axe in cell migration and EMT in tumors [34]. For instance, Zeng et al. showed that TGF-β1 was essential to establish the MMP7/Syndecan-1/TGF-β autocrine loop and promoting EMT in HPC [35]. Figure 5 summarizes the possible interactions and cooperation for MMP7 and SBF2-AS1 duo during EMT and metastasis. In general, various regulatory mechanisms for EMT can co-exist in one tumor. Identifying the exact interactions between EMT-related transcription factors, lncRNAs, and related pathways needs further molecular experiments.

Fig. 5
figure 5

Schematic illustration of the proposed cooperation between MMP7 and SBF2-AS1 to promote EMT in TNBC. SBF2-AS1 can function in cancer and upregulate the EMT process by regulating the same oncogenic signaling pathways that MMP7 contributes to, such as ERK/MAPK and TGF-β1 pathways; and by forming ceRNA networks consisting of regulatory non-coding RNAs and EMT-related target genes. ceRNA: competitive endogenous RNA, EMT: epithelial-mesenchymal transition

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Overall, we presented the first study to confirm the overexpression of SNAI1, MMP7, lnc-SBF2-AS1 and lnc-treRNA in TNBC tumors vs. non-TNBC samples. Considering our results, we suggest SBF2-AS1 and treRNA as new potential diagnostic biomarkers to distinguish TNBC from non-TNBC subtypes. Although, further investigations and functional studies are needed to clarify the exact effects of these EMT-related lncRNAs on TNBC aggressive behavior.