Expression of miR-34c induces G2/M cell cycle arrest in breast cancer cells
© Achari et al.; licensee BioMed Central Ltd. 2014
Received: 3 February 2014
Accepted: 17 July 2014
Published: 26 July 2014
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© Achari et al.; licensee BioMed Central Ltd. 2014
Received: 3 February 2014
Accepted: 17 July 2014
Published: 26 July 2014
MicroRNA-34 is a family of three miRNAs that have been reported to function as tumor suppressor miRNAs and show decreased expression in various cancers. Here, we examine functions of miR-34c in basal-like breast cancer cells.
Data from The Cancer Genome Atlas (TCGA) were used for evaluation of expression in primary breast cancers. Cellular processes affected by miR-34c were investigated by thymidine incorporation, Annexin V-assays and cell cycle analysis using breast cancer cell lines. Effects on potential targets were analyzed with qPCR and Western blot.
TCGA data revealed that miR-34c was expressed at lower levels in basal-like breast cancer tumors and low expression was associated with poor prognosis. Ectopic expression of miR-34c in basal-like breast cancer cell lines resulted in suppressed proliferation and increased cell death. Additionally, miR-34c influenced the cell cycle mainly by inducing an arrest in the G2/M phase. We found that expression levels of the known cell cycle-regulating miR-34 targets CCND1, CDK4 and CDK6, were downregulated upon miR-34c expression in breast cancer cell lines. In addition, the levels of CDC23, an important mediator in mitotic progression, were suppressed following miR-34c expression, and siRNAs targeting CDC23 mimicked the effect of miR-34c on G2/M arrest. However, protein levels of PRKCA, a predicted miR-34c target and a known regulator of breast cancer cell proliferation were not influenced by miR-34c.
Together, our results support the role of miR-34c as a tumor suppressor miRNA also in breast cancer.
MicroRNAs (miRNAs) are small (~22 nt) non-coding RNAs of importance for protein level regulation. They act by interacting with the 3’UTR of the target mRNA which may cause mRNA degradation or translational inhibition [1, 2]. Several miRNAs have been associated with processes involved in cancer progression, e.g. proliferation, differentiation, apoptosis and tumorigenesis  and miRNAs have been classified as both oncogenic and tumor suppressive .
The miR-34 family consists of three homologous miRNAs located at chromosome 1 (miR-34a) and chromosome 11 (miR-34b/c) at positions frequently deleted in solid tumors, e.g. neuroblastoma, breast, prostate and lung cancer [5–9]. Several reports have also pointed out a decreased expression of miR-34 in numerous malignancies, such as miR-34c in prostate cancer , miR-34a and -34c in colon  and lung cancer , miR-34a in neuroblastoma , and miR-34a and -34b in breast cancer [14, 15]. Many studies report tumor suppressor-like effects of miR-34, for instance in ovarian cancer , prostate cancer , and neuroblastoma cells , putatively by regulating the expression of common miR-34 targets such as CCND1 , CCNE2 , CDK4 [18, 19], CDK6 [17, 20], MET [18, 19, 21, 22] and E2F3 [5, 20].
A recent study with prostate cancer PC3 cells revealed that miR-34c expression also resulted in downregulation of protein kinase Cα (PKCα) mRNA . In addition, five target prediction tools (MiRanda , DIANAmT , miRWALK , PICTAR5  and Targetscan  predict PRKCA as a putative miR-34c target. From a breast cancer perspective this could be of relevance since PKCα expression has been reported to be important for optimal breast cancer cell proliferation [28, 29], support a cancer stem cell-like breast cancer cell population  and to predict poorer survival .
Taken together, these facts led us to investigate putative suppressive effects of miR-34c on growth properties of breast cancer cells. We found that miR-34c overexpression both blocks the proliferation of cultured basal-like breast cancer cells and induces cell death, although this was not mediated by PKCα downregulation.
All cell lines were obtained from American Type Culture Collection. MDA-MB-231, MDA-MB-468, BT-549 and T47D breast cancer cells were maintained in RPMI 1640 medium (HyClone, Thermo Scientific) supplemented with 10% fetal bovine serum (Saveen & Werner AB), 1 mM sodium pyruvate (HyClone, Thermo Scientific) and 100 IU/ml penicillin-streptomycin solution (HyClone, Thermo Scientific). The media for BT-549 cells were additionally supplemented with 0.01 mg/ml insulin (Novo Nordisk A/S) and for T47D with 1% glucose.
Control 48% GC
Cells were seeded in triplicates at a density of 5 × 104 cells per well in 12-well plates and transiently transfected for 5 hours. Cells were incubated with 1 μCi/ml [3H]-thymidine for 6 hours before harvesting the cells with 10 mM EDTA. The amount of radioactivity was measured with a Tri-carb 2810TR liquid scintillation analyzer (Perkin Elmer).
MDA-MB-231, MDA-MB-468 and BT-549 cells were seeded at a density of 150,000 cells per 35-mm cell culture dish and transiently transfected for 5 hours. Subsequently, cells were trypsinized and fixed in 70% ethanol for 20 minutes at −20°C, washed in PBS, and incubated with a solution containing 3.5 μM Tris- HCl pH 7.6, 10 mM NaCl, 50 μg/ml propidium iodide (PI), 20 μg/ml RNase, and 0.1% igepal CA-630 for 20 minutes on ice to label DNA. 10,000 events were acquired on the FL-2 channel for the PI signal. Sample acquisition and analyses were performed with CellQuest or FACSuite software (BD Biosciences).
MDA-MB-231 and BT-549 cells were seeded at a density of 150,000 cells per 35-mm cell culture dish, and MDA-MB-468 cells were seeded at 200,000 cells per 35-mm cell culture dish and transfected for 5 hours. After 96 hour incubation in complete medium, floating cells, pooled with trypsinized adherent cells, were stained with Annexin V-allophycocyanin (APC; BD Pharmingen) according to the supplier’s protocol, and the amount of bound Annexin V-APC was quantified with a FACSCalibur cytometer (BD Biosciences). 10,000 events were acquired on the FL-4 channel for the Annexin V-APC signal.
Primers for qPCR
Sequence 5’ to 3a
For analysis of miR-34b/c expression levels, total RNA was extracted from MDA-MB-231, MDA-MB-468 and T47D cells with Trizol according to manufacturer’s instructions (Invitrogen). Small RNAs were reversely transcribed with miRNA specific primers, quantified by the TaqMan MicroRNA assays (Applied Biosystems) and normalized to two reference genes (RNU44 and U47).
Cells were lysed in radioimmune precipitation assay buffer (10 Mm Tris–HCl (pH 7.2), 160 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS, 1 mM EDTA, and 1 mM EGTA) containing 40 μl/ml Complete protease inhibitor (Roche Applied Science) and incubated on ice for 30 min. Lysates were cleared by centrifugation at 14,000 × g for 10 min at 4°C, diluted in sample buffer containing β-mercaptoethanol, and boiled for 5 min. Protein concentration was determined by Bradford assay, equal amount of proteins were electrophoretically separated on either 10% or 12% NuPAGE Novex BisTris gels (Invitrogen) and transferred to polyvinylidene difluoride membranes (Millipore). Membranes were blocked with phosphate-buffered saline containing 5% nonfat milk and probed with antibodies to Cyclin D1 and PKCα (1:500; Santa Cruz Biotechnology), CDK4 (1:1000; Millipore), CDK6 (1:1000; Cell Signaling Technology), CDC23 (1:1000, Abcam) and actin (1:1000; MP Biomedicals). Proteins were visualized with horseradish peroxidase-labeled secondary antibody (Amersham Biosciences) using the SuperSignal system (Pierce) as substrate. Chemiluminescence was detected using a CCD camera (Fujifilm).
HiSeq miRNA expression data of 658 breast tumors and 86 normal breast tissue samples and mRNA data from corresponding samples were downloaded from the TCGA database (http://cancergenome.nih.gov/). The data used were downloaded in December 2013. The tumors were clustered based on mRNA expression data using the hclust function in R. Survival analyses were performed on the 310 breast tumors that had follow up data using the Survival package. The TCGA “New tumor event” variable (recurrence) defined as new tumor event after initial treatment was used as end point for survival analyses. Pairwise comparisons were evaluated with a t-test.
The levels of miR-34b and miR-34c were analyzed in two ER-negative (MDA-MB-231 and MDA-MB-468) and one ER-positive (T47D) cell line. In the ER-negative MDA-MB-468 cell line the levels were barely detected whereas the magnitude of expression was similar in the other cell lines (Additional file 1).
To obtain some insight into putative mediators of the miR-34c effect, we next analyzed mRNA and protein levels of the cell cycle regulators cyclin D1, CDK4 and CDK6, which have been identified as targets of miR-34c and its relatives [17, 18, 32]. In line with this, we found that miR-34c overexpression resulted in decreased protein levels of cyclin D1, CDK4 and CDK6 in all cell lines (Figure 5A). A significant decrease in their mRNA levels was also detected (Figure 5C-E).
Cyclin D1, CDK4, and CDK6 are mainly considered to be important in the G1/S transition but the main effect observed following miR-34c treatment was actually an arrest in G2/M. We thus analyzed the protein and mRNA levels of CDC23 which is an important regulator of mitotic progression. CDC23 mRNA has been shown to be pulled-down as well as downregulated by miR-34a in colorectal cancer cells  and downregulated by miR-34c in prostate cancer cells . In addition, CDC23 is predicted to contain a putative miR-34c binding site in the 3’UTR by five target prediction tools (MiRanda , DIANAmT , miRWALK , PICTAR5  and Targetscan , indicating that CDC23 might be a direct target of miR-34c. A decrease both in protein and mRNA levels of CDC23 was indeed observed in all cell lines following miR-34c expression (Figure 5F-G) suggesting that suppression of CDC23 may mediate some miR-34c effects, either as a direct target of miR-34c or via an indirect mechanism.
In cancers, dysregulation of miRNA is a common feature that can affect downstream targets and further influence tumorigenic events such as proliferation, metastasis and apoptosis . Family members of miR-34 have been reported to be downregulated in several different cancers, including prostate , neuroblastoma , colon , lung  and breast [14, 15]. In addition, epigenetic silencing through CpG methylation [35, 36] and homozygous deletions affecting the miR-34a and miR-34b/c loci (1p36 and 11q23, respectively) has been identified in neuroblastoma and other tumors [5, 7, 37–39].
Our analyses of TCGA data indicate that low levels of miR-34b and/or miR-34c may predict a worse outcome of breast cancer. However, the data are not in line with previous reports indicating that miR-34a and miR-34b are downregulated in breast cancer [40–42]. It was only for miR-34c in basal-like breast cancers that lower expression levels could be seen. This indicates that miR-34c may be the most relevant miR-34 family member to overexpress in basal-like breast cancer cells.
In this study, we have identified an anti-proliferative and pro-apoptotic effect by miR-34c in basal-like breast cancer cells, in concordance with reports from studies in other cancers [16, 21]. Previous studies have pointed out a role for miR-34a [13, 18, 35, 43–45], and in some cases for miR-34c [18, 31], in suppression of the cell cycle, mainly by induction of G1 cell cycle arrest. Our data rather indicate that miR-34c induced a G2/M arrest in breast cancer cells. This is more in line with the miR-34a-promoted mitotic catastrophe and G2/M arrest in irradiated glioblastoma cells . One member of the anaphase-promoting complex (APC), CDC23, has been reported to be a target of miR-34a  and show a decreased mRNA expression in response to miR-34c in prostate cancer cells . In our analysis we detect a significant decrease of CDC23 both at mRNA and protein levels in response to miR-34c expression. CDC23 may be a mediator of miR-34c effects, but more specific experiments are needed to settle CDC23 as a direct miR-34c target. The decrease in G1 and increase in G2/M could be replicated by down regulation of CDC23 supporting the hypothesis that downregulation of CDC23 may mediate some of the observed miR-34c effects. However, there was no effect on cells in the sub-G1 phase suggesting that miR-34c-induced cell death may be mediated by other mechanisms.
PKCα protein levels were not influenced by miR-34c and a downregulation of PKCα is therefore conceivably not involved in the observed effects. However, the PRKCA mRNA levels were affected, albeit in different directions depending on cell line. The diverging effects on PRKCA mRNA levels suggest that it is less likely a direct target of miR-34c.
We also observed that miR-34c induces death in breast cancer cells. This could be a consequence of a G2/M arrest or involve other mechanisms, such as suppression of the pro-survival factors BCL2 [13, 32] or SIRT1 . The fact that siCDC23 induces a G2/M arrest, but no increasing in sub-G1 phase, indicates that the effects may be separate. Induction of cell death actually seems to be a more general miR-34 effect since they have been shown to lead to increased cell death in several cell types [5, 10, 48]. Along with the growth-suppressing and cell death-inducing effects shown in this study, miR-34c has been shown to reduce the migratory and self-renewing capacity of breast tumor-initiating cells  and to inhibit metastatic invasion in vivo . Our study further indicates that miR-34c has tumor-suppressive effects in breast cancer and, together with other reports, this implies miR-34c to be a potential mediator for novel miRNA replacement therapies .
In conclusion, we have detected a suppressive role for miR-34c in breast cancer cell growth and a G2/M cell cycle arrest in response to miR-34c induction. We also identified CDC23 as a miR-34c-regulated target that could be responsible for the miR-34c-induced cell cycle arrest.
The Cancer Genome Atlas
Cyclin-dependent kinase 4
Cyclin-dependent kinase 6
Cell division cycle 23
Protein kinase Cα.
This work was supported by grants from the Swedish Research Council, the Swedish Cancer Society, the Gunnar Nilsson, Ollie and Elof Ericsson, and Kock foundations, and Malmö University Hospital research funds. The results are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/.
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