MicroRNA miR-30 family regulates non-attachment growth of breast cancer cells
© Ouzounova et al.; licensee BioMed Central Ltd. 2013
Received: 8 September 2012
Accepted: 23 February 2013
Published: 28 February 2013
A subset of breast cancer cells displays increased ability to self-renew and reproduce breast cancer heterogeneity. The characterization of these so-called putative breast tumor-initiating cells (BT-ICs) may open the road for novel therapeutic strategies. As microRNAs (miRNAs) control developmental programs in stem cells, BT-ICs may also rely on specific miRNA profiles for their sustained activity. To explore the notion that miRNAs may have a role in sustaining BT-ICs, we performed a comprehensive profiling of miRNA expression in a model of putative BT-ICs enriched by non-attachment growth conditions.
We found breast cancer cells grown under non-attachment conditions display a unique pattern of miRNA expression, highlighted by a marked low expression of miR-30 family members relative to parental cells. We further show that miR-30a regulates non-attachment growth. A target screening revealed that miR-30 family redundantly modulates the expression of apoptosis and proliferation-related genes. At least one of these targets, the anti-apoptotic protein AVEN, was able to partially revert the effect of miR-30a overexpression. Finally, overexpression of miR-30a in vivo was associated with reduced breast tumor progression.
miR30-family regulates the growth of breast cancer cells in non-attachment conditions. This is the first analysis of target prediction in a whole family of microRNAs potentially involved in survival of putative BT-ICs.
KeywordsBreast cancer BT-ICs Mammospheres microRNAs miR-30 family AVEN
Breast tumor initiating cells (BT-ICs) are functionally defined by their unlimited renewal potential and ability to reproduce tumor heterogeneity, attracting attention as therapeutic targets [1, 2]. There is growing evidence that molecular pathways required for normal stem cell functions are deregulated in BT-ICs . As occurs with normal organogenesis and cell differentiation, the selective activation and repression of these pathways may be mediated by microRNAs (miRNAs). These short non-coding RNAs inhibit gene expression by mRNA degradation or translational inhibition [4, 5]. Their importance in establishing developmental programs of expression is illustrated by the requirement of miRNA processing proteins like Dicer during embryogenesis , and the presence of specific miRNAs in pluripotent cells . Since miRNAs drive terminal differentiation, downregulation of specific miRNAs may play an important role in the development and progression of cancer , including breast cancer . Therefore, the aberrant expression of specific miRNAs could lead to a pathologic expansion of immature cells. To gain insight into this untested hypothesis, we performed a miRNA profiling in putative BT-ICs enriched from breast cancer cell lines. Our studies revealed a family of miRNAs that play a key role in defining features of these cells. We further identified and validated the targets of this family of miRNAs and studied its role in survival of BT-ICs in vitro and in vivo.
MicroRNA profiling in putative BT-ICs
miRNAs differentially expressed in mammospheres
No miRNAs were significantly overexpressed in mam-mospheres, and therefore we focused our attention in those miRNAs significantly downregulated. Results were validated using an independent expression array platform, together with specific Taqman qRT-PCR assays. Results obtained with the Illumina Human v2 bead array, were consistent with the oligonucleotide array data, showing no significantly overexpressed miRNAs in mammospheres (Additional file 3: Figure S2A-C). miR-30a was the most significantly down regulated miRNA in mammospheres compared to parental MCF7 cells, while miR-26a and miR-345 were also found to be significantly downregulated (Additional file 3: Figure S2D). The differential expression of several miRNAs including miR-30a and miR-26a were further confirmed using TaqMan probes (Figure 1E). Absolute copy number quantification was performed by using a standard miR30a probe at different dilutions (Additional file 4: Figure S3A and Figure S3B). Extrapolating to these standards, we defined an average of approximately 20 copies of miR-30a per MCF7 cell. This is significantly higher than the 1 copy per cell obtained in mammospheres (Additional file 3: Figure S2B). In addition, a significant down-regulation of miR-30a expression was found in mammospheres derived from the non-related mammary cancer cell line, 4T1, relative to parental 4T1 cells (Figure 1F). These results have revealed a panel of differentially expressed miRNAs, and demonstrated that miR-30 family downregulation is not cell line specific, and may indeed play an important role in mammosphere formation and maintenance of cell growth under non-attachment conditions.
miR-30a regulates non-attachment growth in putative BT-ICs
To further test the generality of the impact of miR-30a regulation in mammosphere formation, we transfected an independent breast cancer cell line (4T1) with miR-30a KD and pre-miR-30a precursor oligos and examined its ability to grow in non-attachment conditions. In general, 4T1 cells displayed an enhanced ability to produced mammospheres compared to MCF7 cells, consistent with the higher invasiveness and metastatic potential of these cells. Importantly, also in 4T1 cells, transfection with pre-miR-30a resulted in a striking reduction in 4T1-derived mammosphere formation (mean 152 spheres/well compared to almost 400 spheres/well in control miR-159-KD, p<0.01) (Figure 2D-2F), consistent with the results obtained in MCF7 cells. However, in contrast to MCF7 cells, we observed a slight (although statistically significant) reduction in the number of mammospheres after downregulation of miR-30a (mean 341, p=0.03) (Figure 2E). Of note, transfections did not have any effect in cell growth and viability of parental 4T1 and MCF7 cells. Together, these results revealed a functional role of miR-30a in sustaining the growth of breast cancer cells in non-attachment conditions and suggest that miR-30a may regulate essential pathways for the self-renewal of putative BT-ICs.
Identification of miR-30a target genes in putative BT-ICs
Putative targets of miR-30a after whole genome expression (WGE) analysis of miR-30a overexpression
Among the significantly downregulated genes, we selected FOXD1 and AVEN for further validation using luciferase assays. AVEN was amongst the most significantly downregulated genes after miR-30a over-expression, while FOXD1 was a predicted miR-30a target by 6 different algorithms (miRanda, PicTar, PITA, TargetScan, RNAhybrid, and MiRTarget2). We cloned the 3′UTR sequences of FOXD1 and AVEN, containing the seed sequence of miR-30a, in pGL3 control vector expressing constitutively the luciferase gene. A construct containing the 3′UTR sequence lacking the complete miR-30a seed sequence was used as a control. These constructs were transfected into MCF7 cells, which express low, but detectable, levels of endogenous miR-30a (Figure 2 and Additional file 4: Figure S3). A significant reduction in luciferase expression was observed after transfection with both 3′UTR constructs, compared to pGL3 control (Figure 3D). Moreover, a further reduction in luciferase activity was observed after overexpressing miR-30a in co-transfected cells (Figure 3D). These results suggest that miR-30a is specifically targeting the 3′UTR regions of FOXD1 and AVEN. Together, these findings support the differentially expressed genes as direct targets of miR-30a and thus they may play a major role in mammosphere growth. However, a potential compensatory effect by other members of the miR-30 family may explain the lack of effect after efficient downregulation of miR-30a.
miR-30 family displays gene target redundancy in breast cancer cells
To define functional enrichment of miR30 targets, we used the Database for Annotation, Visualization and Integrated Discovery (DAVID ) v6.7. First, we used the final list of 118 putative targets of miR30 family of miRNAs. However, no significant enrichment was found for any functional category or pathway when using the whole human transcriptome as a reference. Next, we performed enrichment analyses in the extended 330 genes list, that represents all transcripts significantly regulated (in any direction) after miR30 family modulation, independently of the presence of a putative seed sequences in their 3′UTR. We analyzed their enrichment in specific Gene Ontology (GO) categories, and biological pathways (BioCarta and KEGG). Only those categories with p value < 0.005 for three different statistical tests (LS and KS permutation, and Efron-Tibshirani’s GSA test), were considered as statistically significant. Top significant GO category was cytokinesis, while both GO and BioCarta pathway analyses were significant for a number of metabolic processes (e.g. cellular aldehyde, acetyl-CoA, reductive carboxylate cycle, and galactose metabolism) (Additional file 6). Interestingly, KEGG pathway analysis produced two significant categories: Polyadenylation of mRNA, and EGF Signaling Pathway. Polyadenylation is known to be involved in mRNA stability, while EGF signalling has been shown to promote cancer cell proliferation and to enhance mammosphere formation .
miR-30 overexpression impairs breast cancer tumor formation
AVEN overexpression can rescue mammosphere growth in presence of miR30a
The negative effect of AVEN silencing in sphere formation suggests an independent role of this protein in survival under non-attachment conditions. Moreover, the ability of AVEN to rescue miR30a effect suggests that the role of miR30a expression in non-attachment growth can be partially mediated through targeting of the transcript for this anti-apoptotic protein.
Due to their ability to simultaneously target multiple transcripts, miRNAs are able to participate in most cellular processes. In the same way, their deregulation has been frequently observed in complex human diseases, including cancer. In this report we studied the potential role of miRNAs in sustaining the subpopulation of breast cancer cells with the highest tumor-initiating ability. We identified miR-30 as a family of miRNAs strongly regulated under non-attachment conditions of cell growth, a standard method for selecting BT-ICs. By modulating the expression of miR-30 family we were able to regulate the growth in non-attachment conditions, as shown by sphere formation assays performed in vitro, and ex vivo. In addition, upregulation of miR-30 family expression impaired tumor growth in a mouse xenograft model. We were able to further identify potential common targets of miR-30 family with a role in survival and proliferation. These findings explain why a common downregulation of multiple members of the same miRNA family may be necessary for sustaining the growth in non-attachment conditions.
An important consideration is the absolute copy number of miRNAs necessary to target a specific mRNA. This has been recently addressed using high throughput assays (Sensor-seq) [25, 26]. Interestingly, only a fraction of detected miRNAs (41%) displayed suppressive activity, and this activity correlated with a miRNA expression above 100 reads per million, or 100 copies per cell. Our absolute quantification of miR30a shows approximately 20 copies per MCF7 cell. Although this is significantly higher than the number of copies observed in mammospheres, the absolute copy number lies below those reported as biologically relevant [25, 26]. This borderline number of copies may explain a relatively “mild” effect of miR30a knockdown. However, based on our own data, many miR30a targets were common targets to other members of the miR30 family. Therefore, the cumulative concentration of miR30 family may reach a level beyond biological suppressive activity. However, we were not able to see an increased effect on mammosphere formation after silencing the whole miR30 family (data not shown). Other considerations include the ratio between miRNA and target mRNA abundance, and the longer half life of miRNAs compared to mRNAs.
We performed the first comprehensive analyses of miR-30 family targets. Importantly, two recent studies combined proteomics and microarrays to reveal that changes in protein expression mediated by a miRNA are usually associated with altered mRNA expression, suggesting that mRNA degradation may be the major component of mammalian miRNA repression [4, 5]. These recent findings give strong support to our strategy for target identification by using mRNA screening.
The importance of different miR-30 family members has been highlighted in several contexts. miR-30e was shown to regulate self-renewal and inhibit apoptosis in BT-ICs . Overexpression of miR-30e in these cells inhibits their self-renewal capacity by reducing Ubc9, and induces apoptosis through silencing ITGB3. Although Ubc9 and ITGB3 were not included in our final list of miR-30a targets, we studied in more detail other targets also potentially involved in apoptosis and proliferation. FOXD1 (forkhead box protein 1) has a role in tumor formation , while AVEN (apoptosis, caspase activation inhibitor) has an established role in apoptosis regulation [21, 24]. Although AVEN is unlikely to be the only miR-30a target involved in non-attachment growth, our results suggest an important role in this process that should be followed in further studies. For example, it would be interesting to understand the mechanisms by which AVEN rescues cell death, and whether they differ from the truncated deltaN-AVEN form . In addition, because of the transient transfections used in our experiments, our results may be an underestimation of the role of miR30 and AVEN, and studies on stable/inducible systems may provide more drastic effects. In line with our findings, ectopic expression of miR-30 in BT-ICs xenografts reduced tumorigenesis and lung metastasis in non obese diabetic/severe combined immunodeficient mice, whereas blocking miR-30e expression enhanced tumorigenesis and metastasis . In this sense, miR-30 downregulation may correlate with an in vitro expansion of putative BT-ICs. In addition, recent studies suggested a role of miR-30 family in epithelial-mesenchymal transition [29, 30] and replicative senescence , processes closely linked to stem cell biology and tumor suppression, respectively.
A potential link between miR-30 expression and clinical parameters has also been shown. miR-30 was recently found to be part of a metastatic signature in a series of breast, bladder, colon and lung cancers . Indeed miR-30c expression has been suggested as a predictor of endocrine therapy in ER+ breast cancer . Interestingly, it was shown that mir-30 family members are all down-regulated in both estrogen receptor– and progesterone receptor–negative tumors, suggesting that expression of these miRNAs is regulated by these hormones . Indeed, two members of the miR30 family have been recently shown to be downregulated by progestins . In addition, miR-30a-5p, as well as miR-26a and miR-26b, were shown to be downregulated in tumors with high proliferation index .
Our study indicates that putative BT-ICs enriched in a mammosphere assay have a distinct miRNA profile, essential for their proliferation balance. In vitro, this distinct profile is necessary to acquire the capacity to grow in non-attachment conditions. In vivo, this profile may be involved in a higher ability to induce tumors. We highlight the specific role of miR-30 family in these two contexts, and performed the first comprehensive analyses of miR-30 family targets.
The animal studies have been approved by the Animal Care Committee of University of Ottawa. All mice received normal diet and were monitored daily by the Animal Care and Veterinary Service-(ACVS) staff. Mice did not receive any invasive treatment except one time subcutaneous (s.c.) injection of 4T1 cancer cells. The experimental endpoint was a total sacrifice 3 weeks after cancer cells inoculation. It was chosen to prevent physiological changes (walking and moving) of mice due to tumor size and to avoid tumor necrosis. Method of euthanasia: Mice received injectable Ketamine/Xylazine before cervical dislocation (0.1 ml of a mix of Ketamine [200 mg/kg] and Xylazine [100 mg/kg] via IP). The standards for animal care and use conform with or exceed those defined in the Canadian Council on Animal Care’s Guide to the Care and Use of Experimental Animals, Vol. 1, 2nd edn., 1993 and the Animals for Research Act, R.S.O. 1990, c. A.22, s. 17. Study protocol number ME-259.
Cell culture and mammosphere production
Breast cancer cell lines (American Type Culture Collection) were grown in standard (10% fetal calf serum, 1% penicillin/streptomycin, 1% sodium pyruvate and 1% glutamine) medium. Slightly attached cells from semi-confluent culture dishes were centrifuged and plated in mammosphere conditions, as previously described . Alternatively, cells were counted and tested for viability with Trypan blue after trypsinization, and plated under non-attachment conditions with mammosphere medium.
Transfections and luciferase assays
Pre-miR mature microRNA (Pre-miR-30a) sequence (Applied Biosystems) and knock-down (miR-30a-KD) locked nucleic acid (LNA) (Exiqon) were used for overexpression and inhibition of miR-30a, respectively. miR-159 LNA was used as negative control. Inhibition of the five members of the miR-30 family (a,b,c,d,e) was obtained using the equimolar mix of two oligos, oligo 1 targeting the miR-30a, miR-30d and miR-30e, and oligo 2 targeting miR-30b and miR-30c (Exiqon, custom miRNA family knock-down design).
For luciferase assays, MCF7 cells plated in 24-well plates were transfected with 0.8 μg of the empty pGL3-Basic, pGL3-Control, pGL3-FOXD1-3′UTR, or pGL3-FOXD1/mut-3′UTR. Assays were performed 48h after transfection using the Dual Luciferasse Reporter Assay system (Promega), and normalized with Renilla luciferase activity. All transfections were performed using Lipofectamine 2000 (Invitrogen) according to the manufacturer’s instructions.
Plasmid for AVEN expression assays (full length and control) ligated into a pcDNA3.1 vector, were a kind gift from I. Melzer (Frankfurt, Germany), and have been previously described . For silencing of AVEN we used commercially available endoribonuclease-prepared siRNA pools (esiRNA) targeting AVEN coding sequence (Sigma).
Transcriptome and MicroRNA expression arrays
Total RNA was extracted using TRIzol (Sigma) according to the manufacturer’s instructions. For miRNA expression, RNA labeling and hybridization on Ohio State University miRNA microarray chips (OSU CCC rel. 4.0) were done as described elsewhere . For transcriptome analyses, total RNA from cells transfected with Pre-miR-30, KD-miR-30a, KD-miR-30 family or control KD-miR-159 was reverse-transcribed (Ambion Illumina Total Prep) and hybridized on HT12 Human bead chips (Illumina). Validation of miRNA microarray was performed on Illumina Human v2 microarray, containing 1146 miRNA probes (covering 95% of miRBase1 v12.0 known human miRNAs). Both, miRNA and mRNA expression were also validated using TaqMan (Applied Biosystems) and SyBR green (Eurogentec) quantitative RT-PCR, respectively. Microarray raw data has been deposited in the Gene Expression Omnibus repository under the accession number GSE36565 (submitter: Hernandez-Vargas H).
Mouse model experiments
4T1 cells were cultured in RPMI-1640 media (ATCC) containing 10% FBS, penicillin/streptomycin (0.05 mg/mL) at 37°C in a humidified atmosphere with 5% CO2. Six- to 8-week-old BALB/c female mice weighting 18–20 g (Charles River, Montreal, QC) were randomly distributed into experimental groups: control, miRNA-30a-KD, miRNA-159-KD, miRNA-30-family KD, and pre-miR-30a (5–6 mice per group). Mice were housed in a controlled atmosphere (temperature 22 ± 2°C; humidity 55 ± 2%) with a 12 h light/dark cycle. Mice were injected s.c. with transfected 4T1 cells (1400 cells/0.1 ml/ mice) into the second left mammary gland fat pad. After three weeks, tumors were collected and weighted. Approximately 0.05 g of each tumor was minced and dissociated in RPMI-1640 media containing 300 U/ml collagenase (Sigma), and 100 U/ml hyaluronidase (Sigma) at 37°C for 2 h. Cells were sieved sequentially through a 100 μm and a 40 μm cell strainer (BD Biosciences) to obtain a single cell suspension, and counted in a haemocytometer. Single cells were plated in ultralow attachment 96-well plates (Costar) at 104 cells/0.2 ml/well in DMEM-F12 (#12660, Invitrogen), supplemented with 10 ng/ml EGF, 20 ng/ml bFGF, 5 μg/ml insulin, 1 mM sodium pyruvate, 0.5 μg/ml hydrocortisone, and penicillin/streptomycin (0.05 mg/mL). Cells grown in these conditions as nonadherent spherical clusters of cells (mammospheres) were counted after 7 days.
4T1 cells were also used to study metastatic behavior. Because 4T1cells are resistant to 6-thioguanine, micro-metastatic cells (as few as 1) can be detected in many distant site organs with better accuracy that most tumour models. Lungs obtained after the different conditions were minced and dissociated in RPMI-1640 media containing 300 U/ml collagenase (#C7657, Sigma), at 4°C for 75 min. After the filtration through a 40 μm cell strainer (BD Biosciences), cells were collected and resuspended in RPMI-1640 containing 10% FBS (ATCC), penicillin/streptomycin (0.05 mg/mL) and 60 μM 6-thioguanine 60 (Sigma). Cells were plated in 10-cm culture dishes (Corning) at 37°C in a humidified atmosphere with 5% CO2. After 14 days, cells were fixed by methanol and stained with 0.03% methylene blue solution. All blue colonies were counted, one colony representing one clonogenic metastatic cell.
BRBArrayTools v3.8.1 was used for bead array analysis, as described above. For other comparisons, means and differences of the means with 95% confidence intervals were obtained using GraphPad Prism (GraphPad Software Inc.). Two-tailed student t test was used for unpaired analysis comparing average expression between classes. Pearson’s correlation was used to study the association between mammosphere formation and metastatic behavior. P values < 0.05 were considered statistically significant.
Raw miRNA and transcriptome data were background subtracted, quantile normalized, and further analyzed by BRB-Array Tools Version 3.8.1 (developed by Dr. Richard Simon and the BRB-ArrayTools Development Team). For quantile normalization we used the median array as the reference array. The normalization is performed by computing a gene-by-gene difference between each array and the reference array, and subtracting the median difference from the log-intensities on that array, so that the gene-by-gene difference between the normalized array and the reference array is 0.
Gene ontology analyses were performed with the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.7 using the whole human genome as reference. In addition, geneset enrichment was done in BRB-Array Tools for Gene Ontology categories, and biological pathways (BioCarta and KEGG).
For class comparison analyses p value < 0.001 and false discovery rate (FDR) < 0.15 were used as cut-offs. Genes significantly distinguishing the classes were further analyzed with the miRecords resource (http://mirecords.biolead.org/prediction_query.php) to identify predicted targets for miR-30. Only genes predicted by at least 6 out of 10 miRNA predicting tools were taken into account. PITA algorithm (http://genie.weizmann.ac.il/pubs/mir07/mir07_dyn_data.html) was used to identify the seed sequences for each gene.
Apoptosis, caspase activation inhibitor
Breast tumor initiating cells
Forkhead box protein 1
False discovery rate
Quantitative reverse transcription-polymerase chain reaction
This work was supported by la Ligue National (Française) Contre le Cancer, l’Association pour le Recherche Contre le Cancer (l’ARC), l’Institut National du Cancer (INCA), and the Canadian National Institute of Health.
- Magee JA, Piskounova E, Morrison SJ: Cancer stem cells: impact, heterogeneity, and uncertainty. Cancer Cell. 2012, 21: 283-296. 10.1016/j.ccr.2012.03.003.PubMed CentralView ArticlePubMedGoogle Scholar
- Stingl J, Caldas C: Molecular heterogeneity of breast carcinomas and the cancer stem cell hypothesis. Nat Rev Cancer. 2007, 7: 791-799. 10.1038/nrc2212.View ArticlePubMedGoogle Scholar
- Weissman IL: Normal and neoplastic stem cells. Novartis Found Symp. 2005, 265: 35-50. discussion 50–4, 92–7View ArticlePubMedGoogle Scholar
- Baek D, Villen J, Shin C, Camargo FD, Gygi SP, Bartel DP: The impact of microRNAs on protein output. Nature. 2008, 455: 64-71. 10.1038/nature07242.PubMed CentralView ArticlePubMedGoogle Scholar
- Selbach M, Schwanhausser B, Thierfelder N, Fang Z, Khanin R, Rajewsky N: Widespread changes in protein synthesis induced by microRNAs. Nature. 2008, 455: 58-63. 10.1038/nature07228.View ArticlePubMedGoogle Scholar
- Bernstein E, Kim SY, Carmell MA, Murchison EP, Alcorn H, Li MZ, Mills AA, Elledge SJ, Anderson KV, Hannon GJ: Dicer is essential for mouse development. Nat Genet. 2003, 35: 215-217. 10.1038/ng1253.View ArticlePubMedGoogle Scholar
- Houbaviy HB, Murray MF, Sharp PA: Embryonic stem cell-specific MicroRNAs. Dev Cell. 2003, 5: 351-358. 10.1016/S1534-5807(03)00227-2.View ArticlePubMedGoogle Scholar
- Croce CM: Causes and consequences of microRNA dysregulation in cancer. Nat Rev Genet. 2009, 10: 704-714. 10.1038/nrg2634.PubMed CentralView ArticlePubMedGoogle Scholar
- O'Day E, Lal A: MicroRNAs and their target gene networks in breast cancer. Breast Cancer Res. 2010, 12: 201-10.1186/bcr2484.PubMed CentralView ArticlePubMedGoogle Scholar
- Ponti D, Costa A, Zaffaroni N, Pratesi G, Petrangolini G, Coradini D, Pilotti S, Pierotti MA, Daidone MG: Isolation and in vitro propagation of tumorigenic breast cancer cells with stem/progenitor cell properties. Cancer Res. 2005, 65: 5506-5511. 10.1158/0008-5472.CAN-05-0626.View ArticlePubMedGoogle Scholar
- Dontu G, Abdallah WM, Foley JM, Jackson KW, Clarke MF, Kawamura MJ, Wicha MS: In vitro propagation and transcriptional profiling of human mammary stem/progenitor cells. Genes Dev. 2003, 17: 1253-1270. 10.1101/gad.1061803.PubMed CentralView ArticlePubMedGoogle Scholar
- Visvader JE, Lindeman GJ: Cancer stem cells in solid tumours: accumulating evidence and unresolved questions. Nat Rev Cancer. 2008, 8: 755-768. 10.1038/nrc2499.View ArticlePubMedGoogle Scholar
- Correa-Medina M, Bravo-Egana V, Rosero S, Ricordi C, Edlund H, Diez J, Pastori RL: MicroRNA miR-7 is preferentially expressed in endocrine cells of the developing and adult human pancreas. Gene Expr Patterns. 2009, 9: 193-199. 10.1016/j.gep.2008.12.003.View ArticlePubMedGoogle Scholar
- Guo H, Ingolia NT, Weissman JS, Bartel DP: Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature. 2010, 466: 835-840. 10.1038/nature09267.PubMed CentralView ArticlePubMedGoogle Scholar
- Lewis BP, Burge CB, Bartel DP: Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 2005, 120: 15-20. 10.1016/j.cell.2004.12.035.View ArticlePubMedGoogle Scholar
- Elson-Schwab I, Lorentzen A, Marshall CJ: MicroRNA-200 family members differentially regulate morphological plasticity and mode of melanoma cell invasion. PLoS One. 2010, 5: doi:pii: e13176.10.1371/journal.pone.0013176. PubMed PMID: 20957176; PubMed Central PMCID:PMC2949394.Google Scholar
- Miska EA, Alvarez-Saavedra E, Abbott AL, Lau NC, Hellman AB, McGonagle SM, Bartel DP, Ambros VR, Horvitz HR: Most Caenorhabditis elegans microRNAs are individually not essential for development or viability. PLoS Genet. 2007, 3: e215-10.1371/journal.pgen.0030215.PubMed CentralView ArticlePubMedGoogle Scholar
- Yan X, Fu C, Chen L, Qin J, Zeng Q, Yuan H, Nan X, Chen H, Zhou J, Lin Y, Zhang X, Yu C, Yue W, Pei X: Mesenchymal stem cells from primary breast cancer tissue promote cancer proliferation and enhance mammosphere formation partially via EGF/EGFR/Akt pathway. Breast Cancer Res Treat. 2012, 132: 153-164. 10.1007/s10549-011-1577-0.View ArticlePubMedGoogle Scholar
- Pulaski BA, Ostrand-Rosenberg S: Reduction of established spontaneous mammary carcinoma metastases following immunotherapy with major histocompatibility complex class II and B7.1 cell-based tumor vaccines. Cancer Res. 1998, 58: 1486-1493.PubMedGoogle Scholar
- Bartel DP: MicroRNAs: target recognition and regulatory functions. Cell. 2009, 136: 215-233. 10.1016/j.cell.2009.01.002.PubMed CentralView ArticlePubMedGoogle Scholar
- Kutuk O, Temel SG, Tolunay S, Basaga H: Aven blocks DNA damage-induced apoptosis by stabilising Bcl-xL. Eur J Cancer. 2010, 46: 2494-2505. 10.1016/j.ejca.2010.06.011.View ArticlePubMedGoogle Scholar
- Choi J, Hwang YK, Sung KW, Kim DH, Yoo KH, Jung HL, Koo HH: Aven overexpression: association with poor prognosis in childhood acute lymphoblastic leukemia. Leuk Res. 2006, 30: 1019-1025. 10.1016/j.leukres.2005.11.001.View ArticlePubMedGoogle Scholar
- Eissmann M, Melzer IM, Fernandez SBM, Michel G, Hrabe De Angelis M, Hoefler G, Finkenwirth P, Jauch A, Schoell B, Grez M, Schmidt M, Bartholomae CC, Newrzela S, Haetscher N, Rieger MA, Zachskorn C, Mittelbronn M, Zornig M: Overexpression of the anti-apoptotic protein AVEN contributes to increased malignancy in hematopoietic neoplasms. Oncogene. 2012, 10.1038/onc.2012.263. [Epub ahead of print] PubMed PMID: 22751129Google Scholar
- Melzer IM, Fernandez SBM, Bosser S, Lohrig K, Lewandrowski U, Wolters D, Kehrloesser S, Brezniceanu M, Theos AC, Irusta PM, Impens F, Gevaert K, Zornig M: The Apaf-1-binding protein Aven is cleaved by Cathepsin D to unleash its anti-apoptotic potential. Cell Death Differ. 2012, 19: 1435-1445. 10.1038/cdd.2012.17.PubMed CentralView ArticlePubMedGoogle Scholar
- Mullokandov G, Baccarini A, Ruzo A, Jayaprakash AD, Tung N, Israelow B, Evans MJ, Sachidanandam R, Brown BD: High-throughput assessment of microRNA activity and function using microRNA sensor and decoy libraries. Nat Methods. 2012, 9: 840-846. 10.1038/nmeth.2078.PubMed CentralView ArticlePubMedGoogle Scholar
- Brown BD, Gentner B, Cantore A, Colleoni S, Amendola M, Zingale A, Baccarini A, Lazzari G, Galli C, Naldini L: Endogenous microRNA can be broadly exploited to regulate transgene expression according to tissue, lineage and differentiation state. Nat Biotechnol. 2007, 25: 1457-1467. 10.1038/nbt1372.View ArticlePubMedGoogle Scholar
- Yu F, Deng H, Yao H, Liu Q, Su F, Song E: Mir-30 reduction maintains self-renewal and inhibits apoptosis in breast tumor-initiating cells. Oncogene. 2010, 29: 4194-4204. 10.1038/onc.2010.167.View ArticlePubMedGoogle Scholar
- Dahle MK, Gronning LM, Cederberg A, Blomhoff HK, Miura N, Enerback S, Tasken KA, Tasken K: Mechanisms of FOXC2- and FOXD1-mediated regulation of the RI alpha subunit of cAMP-dependent protein kinase include release of transcriptional repression and activation by protein kinase B alpha and cAMP. J Biol Chem. 2002, 277: 22902-22908. 10.1074/jbc.M200131200.View ArticlePubMedGoogle Scholar
- Braun J, Hoang-Vu C, Dralle H, Huttelmaier S: Downregulation of microRNAs directs the EMT and invasive potential of anaplastic thyroid carcinomas. Oncogene. 2010, 29: 4237-4244. 10.1038/onc.2010.169.View ArticlePubMedGoogle Scholar
- Joglekar MV, Patil D, Joglekar VM, Rao GV, Reddy DN, Mitnala S, Shouche Y, Hardikar AA: The miR-30 family microRNAs confer epithelial phenotype to human pancreatic cells. Islets. 2009, 1: 137-147. 10.4161/isl.1.2.9578.View ArticlePubMedGoogle Scholar
- Martinez I, Cazalla D, Almstead LL, Steitz JA, DiMaio D: miR-29 and miR-30 regulate B-Myb expression during cellular senescence. Proc Natl Acad Sci USA. 2011, 108: 522-527. 10.1073/pnas.1017346108.PubMed CentralView ArticlePubMedGoogle Scholar
- Baffa R, Fassan M, Volinia S, O'Hara B, Liu C, Palazzo JP, Gardiman M, Rugge M, Gomella LG, Croce CM, Rosenberg A: MicroRNA expression profiling of human metastatic cancers identifies cancer gene targets. J Pathol. 2009, 219: 214-221. 10.1002/path.2586.View ArticlePubMedGoogle Scholar
- Rodriguez-Gonzalez FG, Sieuwerts AM, Smid M, Look MP, Meijer-van Gelder ME, De Weerd V, Sleijfer S, Martens JWM, Foekens JA: MicroRNA-30c expression level is an independent predictor of clinical benefit of endocrine therapy in advanced estrogen receptor positive breast cancer. Breast Cancer Res Treat. 2011, 127: 43-51. 10.1007/s10549-010-0940-x.View ArticlePubMedGoogle Scholar
- Iorio MV, Ferracin M, Liu C, Veronese A, Spizzo R, Sabbioni S, Magri E, Pedriali M, Fabbri M, Campiglio M, Menard S, Palazzo JP, Rosenberg A, Musiani P, Volinia S, Nenci I, Calin GA, Querzoli P, Negrini M, Croce CM: MicroRNA gene expression deregulation in human breast cancer. Cancer Res. 2005, 65: 7065-7070. 10.1158/0008-5472.CAN-05-1783.View ArticlePubMedGoogle Scholar
- Cochrane DR, Jacobsen BM, Connaghan KD, Howe EN, Bain DL, Richer JK: Progestin regulated miRNAs that mediate progesterone receptor action in breast cancer. Mol Cell Endocrinol. 2012, 355: 15-24. 10.1016/j.mce.2011.12.020.PubMed CentralView ArticlePubMedGoogle Scholar
- Hernandez-Vargas H, Ouzounova M, Le Calvez-Kelm F, Lambert M, McKay-Chopin S, Tavtigian SV, Puisieux A, Matar C, Herceg Z: Methylome analysis reveals Jak-STAT pathway deregulation in putative breast cancer stem cells. Epigenetics. 2011, 6: 428-439. 10.4161/epi.6.4.14515.View ArticlePubMedGoogle Scholar
- Liu C, Calin GA, Volinia S, Croce CM: MicroRNA expression profiling using microarrays. Nat Protoc. 2008, 3: 563-578.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.