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The expression of miR-513c and miR-3163 was downregulated in tumor tissues compared with normal adjacent tissue of patients with breast cancer

Abstract

Background

Breast cancer (BC) is the most invasive cancer with different subtypes that its metabolism is unique compared with normal cells. Glutamine is considered critical nutrition that many cancer cells, particularly BC cells, are dependent on it for growth and proliferation. Therefore, targeting glutamine metabolism, especially enzymes that are related to this pathway, can be beneficial to design anti-cancer agents. Recent evidence has shown that microRNAs (miRNAs), with a short length and single-strand properties, play a prominent role in regulating the genes related to glutamine metabolism, which may control the development of cancer.

Methods

In silico analysis confirmed that miR-513c and miR-3163 might be involved in glutamine metabolism. The expression level of these two miRNAs was evaluated in eighty BC tissues and normal adjacent tissues. Furthermore, GSE38167, GSE38867, GSE42128, GSE45666, and GSE53179 were employed from gene expression omnibus (GEO). The Limma package was utilized to identify differentially expressed miRNAs (DEMs) of mentioned datasets to evaluate miR-513c and miR-3163 expression. Further, in silico analysis was utilized to predict the potential biological processes and molecular pathways of miR-513c and miR-3163, based on their target genes.

Results

In silico studies revealed top categories of biological processes and cellular pathways that might play a critical role in metabolism reprogramming and cancer development and were target genes for miR-513c and miR-3163. The current study showed that miR-513c (p value = 0.02062 and FC =  − 2.3801) and miR-3163 (p value = 0.02034 and FC =  − 2.3792) were downregulated in tumor tissues compared to normal adjacent tissues. The analysis of GEO microarray datasets showed that miR-513c was downregulated in GSE38167, GSE38867, GSE42128, GSE45666 and GSE53179, whereas there was a significant downregulation of miR-3163 in only two studies, including GSE38867 and GSE42128 that they were in accordance with our experimental results. Furthermore, the subgroup analysis did not show any substantial relationship between expression levels of these two miRNAs and factors such as age, family history of cancer, and abortion history.

Conclusion

MiR-513c and miR-3163 were downregulated in BC tissues, which might serve as tumor suppressors. They are suggested as potential therapeutic targets for patients with BC.

Peer Review reports

Background

Breast cancer (BC) is a major health problem among females worldwide [1]. Based on the world health organization (WHO) statistics, this cancer impacts 2.1 million females every year [2], and causes the greatest number of cancer-related mortality among women [3]. In 2018, it was estimated that 98,755 women died from BC that was nearly 15% of all cancer mortality among females worldwide [4]. Moreover, the mean age of the women identified with this disease has been reduced to lower ages in Iran [5]. Breast cancer has been examined from various aspects, but its exact mechanism is still unclear [6]. Various subtypes of BC and their heterogeneity and complexity deteriorate the early detection and treatment of BC [7, 8]. Many studies have suggested that alteration in glutamine metabolism profile may be one of the unique characteristics of BC cells; consequently targeting glutamine metabolism may be important to design anti-cancer strategies against BC development [8,9,10,11].

Metabolic reprogramming is a unique event in cancer cell development [12, 13] that is utilized to supply the bioenergetic and biosynthetic demands for accelerating growth and proliferation of cancer cells [14]. In this regard, many cancer cells, especially BC cells, are dependent on glutamine, as the major source of energy and building block for growth and proliferation [15, 16]. Moreover, blocking glutamine pathway can severely influence and suppress cell proliferation [9]. Recently, valuable studies and bioinformatics analysis demonstrated that some emerged microRNAs (miRNAs) can efficiently control glutamine metabolism by targeting critical enzymes, such as glutaminase (GLS), which can provide an opportunity for regulating cancer development [17, 18].

MiRNAs are a class of small non-coding RNAs with 18–25 nucleotides in length, which are post-transcriptionally involved in the regulation of gene expression by degrading targeted mRNAs and/or inhibiting their translation [19,20,21,22]. According to bioinformatics and a few experimental analyses, miR-513c and miR-3163 can regulate glutamine metabolism via targeting GLS. Regarding several experimental studies, mir-513c is a tumor suppressor in multiple cancers such as hepatocellular carcinoma [23], prostate cancer [24], glioma [25], neuroblastoma [26], breast cancer [27], and esophageal adenocarcinoma [28], whereas miR-3163 plays vital roles in retinoblastoma cancer stem cell (RCSCs) [29] and non-small cell lung cancer cell (NSCLC) cancers [30]. Therefore, the expression level of these miRNAs can be evaluated to propose potential prognostic markers and their exploitation for control of glutamine metabolism [31]. Since metabolism reprogramming is a common event in cancer cells and this process significantly changes compared with normal cells [32,33,34], in the current study, the expression levels of both miR-513c and miR-3163 were evaluated in tumor and normal adjacent tissues of patients with BC.

Methods

Selection of miRNAs

Based on recent studies, the glutamine metabolic pathways of cancer cells were reviewed to determine the most prominent enzymes. Furthermore, it has been revealed that upregulation of GLS correlate with progression rate and malignancy of cancers, including BC [35]. Accordingly, miRNAs that are involved in this pathway via targeting GLS, were predicted using mirwalk, miRTarBase 7.0, miRDB, and Target Scan Human 7.2 databases. Further, recent experimental studies were reviewed to confirm these miRNAs. Among numerous miRNAs, miR-513c and miR-3163 were selected because these two miRNAs were confirmed in recent studies and dysregulated in multiple cancers, especially mir-513c in BC cell lines. In this regard, we studied the expression level of miR-513c and miR-3163 in tumor and normal adjacent tissues of patients with BC to determine whether these two miRNAs were dysregulated in this condition.

Clinical specimens

Firstly, 80 samples of tumor and normal adjacent tissues were obtained from patients with BC who underwent surgery at Noor Nejat Hospital, Tabriz. The specimens were provided from mastectomy and lumpectomy in which a portion of the tumor normal adjacent tissue was removed at a distance from the tumor tissue. Thereafter, the spread and invasion of cancer cells were investigated by pathological examination. In addition, tumor margin samples that were considered healthy by a pathologist were examined as the controls. Written informed consent was obtained from all the participants after explaining the study. The study was confirmed by the Ethics Committee (Ethics code: IR.TBZMED.REC.1398.025) of Tabriz University of Medical Sciences, Tabriz, Iran. Patients did not receive chemotherapy or radiotherapy before the surgery. The specimens were collected after surgical resection, were immediately frozen, and stored at − 80 °C. Clinical pathological features are shown in Table 1.

Table 1 Basis characteristics of the patients with breast cancer

Total RNA extraction

For extraction of total RNA, the tissue samples were homogenized with liquid nitrogen and their RNA was extracted by Trizol reagent (Geneall). Then, the quality and quantity of extracted RNAs were assessed using a NanoDrop spectrometer (Thermo Scientific, USA). After extraction, obtained RNAs were eluted in 50 μL of RNase-free water and stored at − 80 °C.

cDNA synthesis and real-time PCR

In this study, cDNA of the miR-3163, miR-513c, and RNU6 were synthesized using reverse transcriptase enzyme (Thermo Fisher, USA), dNTP (Cinnaclon, Iran), and their unique stem-loop-primers. For this purpose, three specific stem-loop primers were designed for miR-3163, miR-513c, and also RNU6 (for normalization) were employed and the conditions of PCR machines were 30 min at 16 °C, 30 min at 42 °C, and 5 min at 75 °C for conducting Real-time PCR reaction, SYBR Green master mix (Amplicon, Denmark), miR-3163, miR-513c specific primers, and dNTP were used. These reactions were performed by MIC PCR bimolecular system in two steps as follows: for miR-513c: 10 min at 94 °C, 40 cycles of 15 s at 94 °C and 30 s at 58 °C. For miR-3163: 10 min at 94 °C, 40 cycles of 15 s at 94 °C and 20 s at 57 °C. Then, for RNU6 amplification: 10 min at 94 °C, 40 cycles in 15 s at 94 °C and 20 s at 56 °C. The results were shown by mic-PCR v1.4.0 software. The sequences of the primers are shown in Table 2.

Table 2 cDNA synthesis specific stem-loop primers and real-time PCR primer sequences

In silico analysis

Firstly, we selected and evaluated some microRNA microarray datasets of BC cancer studies from GEO (Gene Expression Omnibus) to confirm miR-3163 and miR-513c expression. In this regard, we downloaded the expression profiling array data of GSE38167 (67 samples, including 31 primary TNBC, 13 lymph node metastases BC, and 23 matched normal breast tissues), GSE38867 (28 samples, including 7 DCIS BC, 7 invasive BC, 7 metastatic BC, and 7 normal samples), GSE42128 ( 116 samples, including 68 serum samples, 28 BC tissue samples and 20 normal breast tissue samples), GSE45666 (116 samples, including 101 breast tumor samples and 15 from adjacent breast normal tissue samples) and GSE53179 (16 blood samples, including 11 samples from ER+/HER2− advanced breast cancer subjects and 5 age-matched control subjects) from GEO database (https://www.ncbi.nlm.nih.gov/geo/). Then, we normalized the expression array using Quantile Normalization function in Limma package [36]. Using the aggregate function in the S4 Vectors package, which gives an average measure for the probes of each miRNA. The detection of differentially expressed miRNAs (DEMs) between BC and healthy samples was performed Limma package (cut-off: \(\left|{\mathrm{logFC}}\right|\) > 0.5 and p value < 0.05). Then, we performed a survival analysis to evaluate their prognostic performance of miR-513c-5p and miR-3163 based upon mentioned microarray data in BC. Furthermore, we performed a survival analysis to evaluate their prognostic performance of miR-513c-5p and miR-3163 based upon mentioned microarray data in BC.

Further, In silico analysis was performed to evaluate biological processes and cellular pathways that were enriched by targeted genes of miR-513c-5p and miR-3163. The experimentally confirmed target genes of miR-513c-5p and miR-3163 were obtained from the miRTarBase 6.0 database (http://mirtarbase.mbc.nctu.edu.tw/php/index.php) and exposed to overrepresentation enrichment analysis (ORA) based on the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes pathways using the Web Gestalt (http://www.webgestalt.org/option.php) webserver. In both analyses, the reference gene list and the multiple test adjustment method were set to “genome_protein_coding” and Benjamini–Hochberg, respectively. In the GO analysis, the top important categories were selected. Other parameters were set as defaults.

Statistical analysis

All experiments were performed at least two times. Shapiro–Wilk test was checked whether data were normally distributed. Paired T-test and delta-CTs were used for comparison of the gene expressions between tumor and normal adjacent tissues. Two- sample T-test and logarithm2 of FC were applied to compare expression alterations of genes in tumors compared to normal adjacent tissue across subsets of study samples. All statistical analyses were conducted using R software version 0.5. 1. Finally, p < 0.05 was considered statistically significant. Receiver operating characteristics (ROC) curve analysis was performed to evaluate potential biomarker in these miRNAs.

Results

Based on miRTarBase, miRDB, and Target Scan Human 7.2, as miR- related databases, miR-513c and miR-3163 were selected as potential miRNAs that might play a significant role in the regulation of glutamine metabolism pathway via targeting GLS. Based on recent experimental studies, miR-513c has tumor-suppressive roles, while miR-3163 plays diverse roles in several cancers [27, 29, 37]. To determine the roles of miR-513c and miR-3163 in human BC, we compared the expressions of both miRNAs in BC tissues and their normal adjacent tissues.

MiR-513c and miR-3163 were downregulated in BC tissues

MiR-513c

The results of paired T-test showed that the expression level of miR-513c was significantly downregulated in the human BC tissues compared to the normal adjacent tissues (p value = 0.02062) and (FC =  − 2.3801) (Fig. 1a).

Fig. 1
figure1

The expression of miRNAs in tumor tissues and normal adjacent tissues. a The expression of miR-513c in tumor tissues and normal adjacent tissues. b The expression of miR-3163 in tumor tissues and normal adjacent tissues. In both figures, vertical axis, center line, and error bars represent ΔCTs values, median, and interquartile range, respectively

MiR-3163

The results of paired T-test revealed that the expression level of miR-3163 was significantly downregulated in the human BC tissues compared to the normal adjacent tissues (p value = 0.02034) and (FC =  − 2.3792) (Fig. 1b).

Receiver operating characteristics (RUC) curve analysis of MiR-513c and MiR-3163

The curve analysis of both MiR-513c and MiR-3163 showed that AUC (area under the curve) of MiR-513c and MiR-3163 are 0.61 and 0.60 in these samples, respectively; suggesting that both of them could be considered in patients with BC (Fig. 2a, b).

Fig. 2
figure2

The curve analysis of both MiR-3163 (a) and MiR-513c (b) with AUCs (0.61 and 0.60, respectively)

Subgroups analysis

In this study, the mean ± SD of age, age of first menstruation, age of first pregnancy and BMI were 51.28 ± 12.5, 12.28 ± 2.7, 22.41 ± 6.22 and 29.22 ± 5.49, respectively. Additionally, there was no significant difference in the expression level of both MiR-3163 and miR-513c between over 50 and under 50 years, and pre-menopause and post-menopause subjects. Although the expression level of miR-513c was significantly different (p = 0.007) between tumor and normal tissues in patients with grade 2, no significant difference was observed in terms of MiR-3163 (p = 0.21). In addition, there was no significant difference in the expression level of both miRNAs in patients with grade 1 and 3 (p > 0.05). To show the relationship between the expression of miR-513c and miR-3163 with factors such as age, family history cancer, and abortion in BC subgroups, two-sample T-test of logarithm2 of fold changes were employed.

MiR-513c

We did not find any significant changes in the expression level of miR-513c in patients with cancer family history and without cancer family history (p value = 0.0525, F C =  − 1.9859) (Fig. 3a). Additionally, there was no significant relationship between the expression of miR-513c with abortion history (p value = 0.7713, FC = 0.29275) (Fig. 3b) and age subgroups of ≤ 50 years and > 50 years (p value = 0.6758, FC =  − 0.42103) (Fig. 3c).

Fig. 3
figure3

The correlation of miR-513c expression level with age, abortion history, and cancer family history. a LFC-miR-513c in patients with cancer family history and without cancer family history (p value = 0.0525). b LFC-miR-513c in patients with abortion history and without abortion history (p value = 0.7713). c LFC-miR-513c in patients with age ≥ 50 and < 50 (p value = 0.6758). In three figures, vertical axis, center line, and error bars represent LFC (i.e., base 2 logarithm of FC), median, and interquartile range, respectively

MiR-3163

Based on results of paired sample t-test, there was no markedly changes in the expression level of miR-3163 in subgroups of cancer family history (p value = 0.1208, FC =  − 1.5756) (Fig. 4a). Additionally, there was no significant relationship between the expression of miR-3163 with abortion history (p value = 0.5634, FC = 0.58281) (Fig. 4b), and age in patients ≤ 50 and > 50 (p value = 0.3805, FC = 0.8859) (Fig. 4c).

Fig. 4
figure4

The correlation of miR-3163 expression level with age, abortion history, and cancer family history. a LFC-miR-3163 in patients with cancer family history and without cancer family history (p value = 0.1208). b LFC-miR-3163 in patients with abortion history and without abortion history (p value = 0.5634). c LFC-miR-3163 in patients with age < 50 and ≥ 50 (p value = 0.3805). In three figures, vertical axis, center line, and error bars represent LFC (i.e., base 2 logarithm of FC), median, and interquartile range, respectively

In silico analysis

Considering |log2FC| > 0.5 and p < 0.05 as the cut-off, we detected that miR-513c in GSE38167 (\(log FC = - 1.00284\)), GSE38867 (\(logFC=-1.4\)), GSE42128 (\(logFC=-3.1\)), GSE45666 (\(logFC=-1.8\)), and GSE53179 (\(logFC=-0.9\)) were downregulated, whereas there was a significant miR-3163 downregulation in only two array datasets, GSE38867 (\(logFC\)= − 0.6) and GSE42128 (\(logFC=\) − 2.4) that they were in accordance with our experimental results. According to survival plots related to miR-513c-5p and miR-3163 in BC showed that prognostic performance of miR-3163 was higher than miR-513c-5p and therefore, according to array data, the prognostic potency of miR-3163 was higher for assessment of BC risk (Figs. 5, 6).

Fig. 5
figure5

Overall survival analysis of miR-513c-5p in BC from related datasets

Fig. 6
figure6

Overall survival analysis of miR-3163 in BC from related datasets

A total of 98 and 333 experimentally confirmed target genes for miR-513c and miR-3163 were extracted from the miRTarBase database, respectively. Cytoscape V 3.7.1 (Figs. 7, 8) portrayed the miRNA-gene interaction. The ORA showed that significant categories of biological procedures were overrepresented among target genes of miR-513c and miR-3163 (Tables 3, 4). Pathway analysis revealed top biological enriched pathways and processes in target genes of miR-513c and miR-3163 (Tables 5, 6). Based on these analyses, target genes of miR-513c are involved in pathways related to cancer development. Furthermore, substantial biological processes were enriched among target genes of miR-3163, such as negative regulation of nucleobase-containing compound metabolic process, regulation of mRNA metabolic process, positive regulation of nucleobase-containing compound metabolic process, negative regulation of RNA metabolic process, and negative regulation of cellular macromolecule biosynthetic process, are associated with metabolism pathways. Also, MAPK, Hedgehog, and Wnt signaling pathways were the most prominent pathways among several cellular pathways that were considerably influenced by miR-3163. These biological processes and cellular pathways were related to BC progression and cancer metabolism.

Fig. 7
figure7

The interaction between miR-513c-5p and its target genes

Fig. 8
figure8

The interaction between miR-3163 and its target genes

Table 3 Pathways that are enriched among target genes of mir-513c
Table 4 Pathways that are enriched among target genes of miRNA-3163
Table 5 Top ten enriched biological processes among target genes of miR-513c
Table 6 Top ten enriched biological processes among target genes of miR-3163

Discussion

MiRNAs have been demonstrated as promising biomarkers for diagnosis and prognosis of BC [38, 39]. Irregular expression of miRNAs impacts the processes involved in BC development such as invasion, metastasis, promoting tissue, stimulating anti-apoptotic activity, drug resistance, and metabolism reprogramming [19, 40, 41]. These critical molecules have been found as key players in cancer metabolism by regulating genes related to metabolism pathways [42]. According to bioinformatics databases, miRNAs such as miR-513c and miR-3163 involved in glutamine metabolism were evaluated in the present study. Our findings demonstrated that mir-513c and miR-3163 were significantly downregulated in BC tissues compared to normal adjacent tissues.

To the best of our knowledge, tumor suppressor role of miR-513c in multiple cancers has been established. In R2N1d and MDA-MB-231 BC cell lines, considering treatment with Histone deacetylase inhibitors (HDACi), among the most significantly expressed miRNAs, miR-513c also emerged as the most upregulated gene, and acts as a tumor suppressor in the induction of cell death [27]. Furthermore, miR-513c is markedly downregulated in hepatocellular carcinoma and glioblastoma (GBM) and overexpression of this miRNA prevented the proliferation of these cancer cells through targeting MET and Wnt/β-catenin signaling pathway, respectively [23, 25]. Additionally, miR-513c is markedly downregulated in neuroblastoma. Moreover, miR-513c, as a tumor suppressor, plays an important role in regulating glutamine metabolism by targeting GLS [26].

Since the role of miR-513c-5p has not been fully understood, bioinformatics analysis was done to shed the light on molecular pathways and biological procedures that are potentially impacted by dysregulation of miR-513c-5p. In silico studies indicated that target genes of miR-513c are involved in pathways related to cancer progression. In the present study, miR-513c was downregulated in BC tissues compared to normal adjacent tissues (p value = 0.02062, FC =  − 2.3801). Therefore, it seems miR-513c may serve as a tumor suppressor and may have a considerable role in the development of BC.

In previous studies, the activity and expression of miR-3163 have been evaluated in multiple cancers such as NSCLC and Retinoblastoma (RB). In addition, miR-3163 as a moderator contributes to Meg3 to suppress and regulate the translation of Skp2 in NSCLC. However, miR-3163 expression does not differ in NSCLC compared with normal cells, which suppresses the Skp2 translation and reduction of its level in combination with Meg3 to decrease cell proliferation in NSCLC [30]. Also, the upregulation of miR-3163 via targeting ABCG2 reduced the multidrug resistance and promotion of apoptosis in RCSC [29].

Because the activity of miR-3163 has not been completely understood, bioinformatics analyses were performed to determine a significant relationship between cancer and miR-3163. This analysis provides an insight into molecular pathways and biological processes that are potentially regulated by target genes of miR-3163. Based on this analysis, among important biological processes, negative regulation of cellular macromolecule biosynthetic process (GO: 2000113) is associated with metabolism pathways [43]. Also, significant cellular pathways enriched by miR-3163 are related to cancer cell progression, such as MAPK, Hedgehog, and Wnt signaling pathways are the most important. MAPK and Hedgehog signaling pathways are critical key regulators in cellular functions such as cell differentiation, proliferation, differentiation, survival, and apoptosis. The higher activation of MAPK in subtypes of BC predicts invasive phenotypes and poor prognosis [44,45,46]. Furthermore, these signaling pathways can also affect metabolic pathways by directly regulating the expression of critical enzymes in metabolic pathways [47,48,49]. Also, Wnt signaling pathway, as a master regulator, plays a significant role in the progression and development of BC by metabolism reprogramming, including glutamine metabolism pathway [50, 51]. So, this information may provide a valuable clue about cancer metabolism and our preliminary theory.

Additionally, the GEO microarray datasets analysis showed that both miR-513c and miR-3163 were downregulated in GSE38867 and GSE42128 datasets. Other analyzed array datasets such as GSE38167, GSE45666, and GSE53179 were significant only for downregulated miR-513c. According to survival plots of miR-513c and miR-3163 in BC, it has been demonstrated that miR-3163 may be a potent prognostic risk factor in BC (Figs. 5, 6).

The findings of the present study, contrary to previous studies, demonstrated that the expression of mi-3163 was downregulated in BC tissues compared with normal adjacent tissues (p value = 0.02034, FC =  − 2.3792). Recent reports have shown that miR-3163 plays diverse roles in various cancers; nonetheless, further studies are needed to identify and validate the precise role of this miRNA. According to our results and bioinformatics analyses such as the related data sets, it seems that miR-3163 may play an outstanding role in BC development.

Conclusion

Altogether, mir-513c and mir-3163 expression were downregulated in BC tissues in comparison to normal adjacent tissues. Therefore, it seems that mir-513c and mir-3163 may serve as tumor suppressors in patients with BC. Furthermore, no significant association was found between miR-513c and miR-3163 expression and variables such as age, cancer family history, and abortion. Although these miRNAs may be suggested as therapeutic targets, further studies are needed to elucidate molecular mechanisms and validate the predicted findings using bioinformatics studies.

Availability of data and materials

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions e.g. containing information that could compromise the privacy of research participants.

Abbreviations

BC:

Breast cancer

GLS:

Glutaminase

HCC:

Hepatocellular carcinoma

LPR6:

Low-density lipoprotein (LDL) receptor-related protein-6

RB:

Retinoblastoma

GBM:

Glioblastoma

ABCG2:

ATP binding cassette subfamily G member 2

MEG3:

Maternally expressed 3

LFC:

Logarithm fold change

RCSCs:

Retinoblastoma cancer stem cells

NSCLC:

Non-small cell lung cancer

HDACi:

Histone deacetylase inhibitor

WHO:

World Health Organization

References

  1. 1.

    Zare M, et al. Aberrant miRNA promoter methylation and EMT-involving miRNAs in breast cancer metastasis: diagnosis and therapeutic implications. J Cell Physiol. 2018;233(5):3729–44.

    CAS  PubMed  Article  Google Scholar 

  2. 2.

    Bray F, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424.

    Article  Google Scholar 

  3. 3.

    Dafni U, Tsourti Z, Alatsathianos I. Breast cancer statistics in the European Union: incidence and survival across European countries. Breast Care. 2019;14(6):344–53.

    PubMed  PubMed Central  Article  Google Scholar 

  4. 4.

    Cardoso F, et al. Early breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2019;30(8):1194–220.

    CAS  PubMed  Article  Google Scholar 

  5. 5.

    Baradaran B, Hajiasgharzadeh K. Breast cancer among young women in Iran. Int J Women’s Health Reprod Sci. 2019;7(2):140.

    Article  Google Scholar 

  6. 6.

    Fuentes P, et al. ITGB3-mediated uptake of small extracellular vesicles facilitates intercellular communication in breast cancer cells. Nat Commun. 2020;11(1):1–15.

    Article  CAS  Google Scholar 

  7. 7.

    Ng CK, Pemberton HN, Reis-Filho JS. Breast cancer intratumor genetic heterogeneity: causes and implications. Expert Rev Anticancer Ther. 2012;12(8):1021–32.

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Delgir S, et al. The pathways related to glutamine metabolism, glutamine inhibitors and their implication for improving the efficiency of chemotherapy in triple-negative breast cancer. Mutat Res Rev Mutat Res. 2021;787:108366.

    CAS  Article  Google Scholar 

  9. 9.

    El Ansari R, et al. Altered glutamine metabolism in breast cancer; subtype dependencies and alternative adaptations. Histopathology. 2018;72(2):183–90.

    PubMed  Article  Google Scholar 

  10. 10.

    Kim S, et al. Expression of glutamine metabolism-related proteins according to molecular subtype of breast cancer. Endocr Relat Cancer. 2013;20(3):339–48.

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Lampa M, et al. Glutaminase is essential for the growth of triple-negative breast cancer cells with a deregulated glutamine metabolism pathway and its suppression synergizes with mTOR inhibition. PLoS ONE. 2017;12(9):e0185092.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  12. 12.

    Bacci M, et al. miR-155 drives metabolic reprogramming of ER+ breast cancer cells following long-term estrogen deprivation and predicts clinical response to aromatase inhibitors. Can Res. 2016;76(6):1615–26.

    CAS  Article  Google Scholar 

  13. 13.

    Ilkhani K, et al. The engaged role of tumor microenvironment in cancer metabolism: focusing on cancer-associated fibroblast and exosome mediators. Anti Cancer Agents Med Chem. 2020;21:254–66.

    Article  CAS  Google Scholar 

  14. 14.

    Hensley CT, Wasti AT, DeBerardinis RJ. Glutamine and cancer: cell biology, physiology, and clinical opportunities. J Clin Investig. 2013;123(9):3678–84.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Korangath P, et al. Targeting glutamine metabolism in breast cancer with aminooxyacetate. Clin Cancer Res. 2015;21(14):3263–73.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Marshall A, et al. ASCT2 regulates glutamine uptake and cell growth in endometrial carcinoma. Oncogenesis. 2017;6(7):e367–e367.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Gao P, et al. c-Myc suppression of miR-23a/b enhances mitochondrial glutaminase expression and glutamine metabolism. Nature. 2009;458(7239):762–5.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Pahlavan Y, et al. Prominent roles of microRNA-142 in cancer. Pathol Res Pract. 2020;216:153220.

    CAS  PubMed  Article  Google Scholar 

  19. 19.

    Safi A, et al. miRNAs modulate the dichotomy of cisplatin resistance or sensitivity in breast cancer: an update of therapeutic implications. Anti Cancer Agents Med Chem. 2020;21:1069–81.

    Article  CAS  Google Scholar 

  20. 20.

    Nejati K, Alivand M, Arabzadeh A. MicroRNA-22 in female malignancies: focusing on breast, cervical, and ovarian cancers. Pathol Res Pract. 2021;21:153452.

    Article  CAS  Google Scholar 

  21. 21.

    Soheilifar MH, et al. Concomitant overexpression of mir-182-5p and mir-182-3p raises the possibility of IL-17–producing Treg formation in breast cancer by targeting CD3d, ITK, FOXO1, and NFATs: A meta-analysis and experimental study. Cancer Sci. 2021;112(2):589.

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Khoshmirsafa M, et al. Elevated expression of miR-21 and miR-155 in peripheral blood mononuclear cells as potential biomarkers for lupus nephritis. Int J Rheum Dis. 2019;22(3):458–67.

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Zhang K, et al. LncRNA FLVCR1-AS1 acts as miR-513c sponge to modulate cancer cell proliferation, migration, and invasion in hepatocellular carcinoma. J Cell Biochem. 2018;119(7):6045–56.

    CAS  PubMed  Article  Google Scholar 

  24. 24.

    Wang B-D, et al. Identification and functional validation of reciprocal microRNA–mRNA pairings in African American prostate cancer disparities. Clin Cancer Res. 2015;21(21):4970–84.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. 25.

    Xu J, Sun T, Hu X. microRNA-513c suppresses the proliferation of human glioblastoma cells by repressing low-density lipoprotein receptor-related protein 6. Mol Med Rep. 2015;12(3):4403–9.

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Xia H-L, et al. MiR-513c suppresses neuroblastoma cell migration, invasion, and proliferation through direct targeting glutaminase (GLS). Cancer Biomark. 2017;20(4):589–96.

    CAS  PubMed  Article  Google Scholar 

  27. 27.

    Hsieh T-H, et al. HDAC inhibitors target HDAC5, upregulate microRNA-125a-5p, and induce apoptosis in breast cancer cells. Mol Ther. 2015;23(4):656–66.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Yang H, et al. MicroRNA expression signatures in Barrett’s esophagus and esophageal adenocarcinoma. Clin Cancer Res. 2009;15(18):5744–52.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Jia M, et al. Silencing of ABCG2 by microRNA-3163 inhibits multidrug resistance in retinoblastoma cancer stem cells. J Korean Med Sci. 2016;31(6):836–42.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Su L, et al. Skp2 regulates non-small cell lung cancer cell growth by Meg3 and miR-3163. Tumor Biol. 2016;37(3):3925–31.

    CAS  Article  Google Scholar 

  31. 31.

    Subramaniam S, et al. Emergence of MicroRNAs as key players in cancer cell metabolism. Clin Chem. 2019;65(9):1090–101.

    CAS  PubMed  Article  Google Scholar 

  32. 32.

    Hsu PP, Sabatini DM. Cancer cell metabolism: Warburg and beyond. Cell. 2008;134(5):703–7.

    CAS  PubMed  Article  Google Scholar 

  33. 33.

    Ilkhani K, et al. Clinical and in silico outcomes of miR-130a-5p and miR-615-3p expression in tumor compared with non-tumor adjacent tissues of patients with BC. Anti Cancer Agents Med Chem. 2021;21:927–35.

    CAS  Article  Google Scholar 

  34. 34.

    Katt WP, Cerione RA. Glutaminase regulation in cancer cells: a druggable chain of events. Drug Discov Today. 2014;19(4):450–7.

    CAS  PubMed  Article  Google Scholar 

  35. 35.

    Masisi BK, et al. The role of glutaminase in cancer. Histopathology. 2020;76(4):498–508.

    PubMed  Article  Google Scholar 

  36. 36.

    Rahmati Y, Alivand M, Mollanoori H. MiR-330-3p and miR-485-5p as biomarkers for glioblastoma: an integrated bioinformatics and experimental study. Comput Biol Chem. 2021;92:107458.

    CAS  PubMed  Article  Google Scholar 

  37. 37.

    Xiong YZ, Wang J, Cao X. MiR-513c suppresses neuroblastoma cell migration, invasion, and proliferation through direct targeting glutaminase (GLS). Cancer Biomark. 2018;23(311):311.

    Google Scholar 

  38. 38.

    Zografos E, et al. Prognostic role of microRNAs in breast cancer: a systematic review. Oncotarget. 2019;10(67):7156.

    PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Ilkhani K, et al. Clinical and in silico outcomes of the expression of miR-130a-5p and miR-615-3p in tumor compared with non-tumor adjacent tissues of patients with BC. Anti Cancer Agents Med Chem (Former Curr Med Chem Anti Cancer Agents). 2021;21(7):927–35.

    CAS  Google Scholar 

  40. 40.

    Zhang L-F, Jiang S, Liu M-F. MicroRNA regulation and analytical methods in cancer cell metabolism. Cell Mol Life Sci. 2017;74(16):2929–41.

    CAS  PubMed  Article  Google Scholar 

  41. 41.

    Zhan M-N, et al. MicroRNA-494 inhibits breast cancer progression by directly targeting PAK1. Cell Death Dis. 2018;8(1):e2529–e2529.

    Article  CAS  Google Scholar 

  42. 42.

    Pedroza-Torres A, et al. microRNAs in tumor cell metabolism: Roles and therapeutic opportunities. Front Oncol. 2019;9:1404.

    PubMed  PubMed Central  Article  Google Scholar 

  43. 43.

    Lin D, et al. Metabolic heterogeneity signature of primary treatment-naive prostate cancer. Oncotarget. 2017;8(16):25928.

    PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Mittal S, et al. Cooperation of Notch and Ras/MAPK signaling pathways in human breast carcinogenesis. Mol Cancer. 2009;8(1):128.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  45. 45.

    Jia Y, et al. KLF4 overcomes tamoxifen resistance by suppressing MAPK signaling pathway and predicts good prognosis in breast cancer. Cell Signal. 2018;42:165–75.

    CAS  PubMed  Article  Google Scholar 

  46. 46.

    Mirzoeva OK, et al. Basal subtype and MAPK/ERK kinase (MEK)-phosphoinositide 3-kinase feedback signaling determine susceptibility of breast cancer cells to MEK inhibition. Cancer Res. 2009;69(2):565–72.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Thangavelu K, et al. Structural basis for the allosteric inhibitory mechanism of human kidney-type glutaminase (KGA) and its regulation by Raf–Mek–Erk signaling in cancer cell metabolism. Proc Natl Acad Sci. 2012;109(20):7705–10.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Souzaki M, et al. Hedgehog signaling pathway mediates the progression of non-invasive breast cancer to invasive breast cancer. Cancer Sci. 2011;102(2):373–81.

    CAS  PubMed  Article  Google Scholar 

  49. 49.

    Li J, et al. Regulation of hepatic stellate cell proliferation and activation by glutamine metabolism. PLoS ONE. 2017;12(8):e0182679.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  50. 50.

    Mo Y, et al. The role of Wnt signaling pathway in tumor metabolic reprogramming. J Cancer. 2019;10(16):3789.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Koval A, Katanaev VL. Dramatic dysbalancing of the Wnt pathway in breast cancers. Sci Rep. 2018;8(1):1–10.

    CAS  Article  Google Scholar 

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Acknowledgements

We would like to express our gratitude to Immunology Research Center for funding and personnel of medical genetic lab at department of Medical Genetics.

Funding

This study was supported by Immunology Research Center (Grant No. 61270) of Tabriz University of Medical Sciences. It should be noted that the funding body was not involved in study design, collection, analysis, and interpretation of data and writing of the study.

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Authors

Contributions

SD, MB and MRA: substantial contribution to the conception, designed the work, collected the samples, and supervised the study. MRA wrote the first draft of the manuscript. AS, KI, VM and ZZ-K: acquisition, analysis, and interpretation of the data, respectively. FS and YR revised the manuscript for important intellectual content. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Mohammad Reza Alivand.

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Ethics approval and consent to participate

This study was approved by the Ethics Committee of Tabriz University of Medical Sciences, Tabriz, Iran with Ethics code IR.TBZMED.REC.1398.025. After explaining the study to participants, the written informed consent was obtained from all of them.

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Not Applicable.

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The authors declare no conflict of interest.

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Delgir, S., Ilkhani, K., Safi, A. et al. The expression of miR-513c and miR-3163 was downregulated in tumor tissues compared with normal adjacent tissue of patients with breast cancer. BMC Med Genomics 14, 180 (2021). https://doi.org/10.1186/s12920-021-01029-3

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Keywords

  • Breast cancer
  • Glutamine metabolism
  • Glutaminase
  • Fold change
  • MicroRNA
  • miR-513c
  • miR-3163