Regulation of microRNA Expression

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Regulation of microRNA Expression The Hypoxic Component Ritu Kulshreshtha1 Manuela Ferracin2 Massimo Negrini2 George A. Calin3 Ramana V. Davuluri4,* Mircea Ivan1,*

Abstract

Oncology Research Institute; Tufts-New England Medical Center; Boston, Massachusetts USA

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microRNAs are involved in a wide variety of normal and pathological cellular processes, including tumorigenic transformation. Despite significant progress made towards understanding their mechanisms of action, much less is known about the regula‑ tion of expression of specific microRNAs. Recent reports have established a link between hypoxia, a key feature of the tumor microenvironment, and a group of microRNAs. Select members of this group seem to affect apoptotic signaling in a hypoxic environment and are also predicted to target genes of critical importance for tumor biology. Interestingly, most hypoxia‑induced microRNAs are also overexpressed in human cancers, suggesting a role in tumorigenesis. We hereby discuss the known and predicted regulators of microRNA expression and approaches for expanding this fledgling research area.

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2Department of Experimental and Diagnostic Medicine; Interdepartmental Center

for Cancer Research; University of Ferrara; Ferrara, Italy

Cancer Center; Tzagournis Medical Research Facility; Ohio State University; Columbus, Ohio USA

Original manuscript submitted: 05/04/07 Manuscript accepted: 05/07/07

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Previously published online as a Cell Cycle E-publication: http://www.landesbioscience.com/journals/cc/abstract.php?id=4410

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Acknowledgements

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microRNA, hypoxia, cancer, regulation, expression profiles, target genes

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This work was supported by the NIH grant P30 DK‑34928 and AACR/PanCan career development award to M.I.; Kimmel Scholar award to G.A.C.; grants from Italian Ministry of Public Health, Italian Association for Cancer Research (AIRC) and by Comitato Sostenitori Progetto CAN‑2006 to M.N.; M.F. is a recipient of a fellowship from Fondazione Italiana per la Ricerca sul Cancro (FIRC). R.D. is supported by grants from National Cancer Institute (Project 3 of U54CA113001), National Human Genome Research Institute (R01HG003362) and American Cancer Society (RSG‑06‑268‑01).

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Since the discovery of C. elegans lin‑4, we have witnessed a ‘small RNA revolution’, a large variety of short transcripts being identified in virtually every multicellular organism. MicroRNAs exert their actions largely by silencing target genes, via either translational repression or mRNA degradation.1,2 The balance can shift towards the former or the latter mechanism, depending on the degree of microRNA::target pairing. The “microtranscriptome” is estimated to represent approximately 1–2% of all the mammalian genes, and likely impacts the regulation of the majority of translated genes. As a result, microRNAs are thought to play critical roles in the coordination of a wide variety of cellular processes, including differentiation, proliferation, death and metabolism.3,4,5 Changes in microRNAs expression have been associated with tumorigenesis and could play an important role in this complex process.6‑11 For example, a significant percentage of microRNAs are encoded by genes located at fragile sites, regions of amplification or loss of heterozygosity, or common break‑point regions associated with cancers. Moreover, specific microRNA expression changes have been described in tumors, and in some cases shown to correlate with their clinico-pathological features.6,9,12 However, the mechanisms for such alterations remain incompletely understood.

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*Correspondence to: Mircea Ivan; Molecular Oncology Research Institute; TuftsNew England Medical Center; 750 Washington Street, Box 5609, Boston, Massachusetts 02111 USA; Tel.: 617.636.7514; Fax: 617.636.6127; Email: [email protected] / Ramana Davuluri; Tzagournis Medical Research Facility; 420 West 12th Avenue, Room 524, Columbus, Ohio 43210 USA; Tel.: 614.688.3088; Fax: 614.688.4006; Email: [email protected]

microRNAs: Tiny Regulators with Wide Impact

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of Texas MD Anderson Cancer Center; Experimental Therapeutics Department; Houston, Texas USA

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What Regulates the Regulators? In contrast to the wealth of information about microRNA biogenesis and their biological effects, the knowledge about microRNA regulation has comparatively lagged behind. Their basic transcription machinery is fundamentally similar to the “classic” (protein‑ encoding) genes, depending on RNA polymerase II for at least part of the miR‑nome (see below for additional comments).13 Furthermore, primary transcripts (pri‑microRNAs) contain cap structures, undergo splicing, and poly‑adenylation. Such resemblances hinted to the possibility that microRNA induction/repression could also be controlled by transcription factors involved in the regulation of “classic genes” expression in response to various endogenous and exogenous stimuli. Experimental evidence supports this hypothesis. For example, miR‑1 genes are direct transcriptional targets of muscle differentiation regulators including Serum Response Factor, MyoD and Mef2.14-16 This “fine‑tuning” molecular mechanism seems to play an important role in the titration of critical regulatory genes during cardiogenesis. Another oncogene product and transcription factor, c‑MYC, is an activator of the 17–92 microRNA cluster, and this mechanism plays an important role in tumor formation.17 Similarly, E2F transcription factor family was also found to regulate this cluster.18 Thus, it is Cell Cycle

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Regulation of microRNA Expression: The Hypoxic Component

conceivable that engaging a microRNA component in addition to translated genes is a more frequent feature of transcription factors action. Based on recently published data, we proposed that such a mechanism also applies to hypoxia‑inducible factors (HIF), well‑ documented master regulators of the response to low oxygen. More recent data have increased the complexity of microRNA expression, beyond pol II control. Genomic analysis of microRNAs in the human chromosome 19 microRNA cluster by Borchert et al.19 has revealed that transcription of this cluster (and perhaps other miRs) occurs via RNA Pol III, rather than by Pol II. However, additional mechanisms (perhaps involving transcription factors and coactivators) that fine‑tune the expression of Pol‑III driven microRNAs have not been elucidated.

Molecular Mechanisms of Hypoxia Sensing Hypoxia is an essential feature of the neoplastic microenvironment. Tumors with extensive low oxygen tension tend to exhibit poor prognosis and resistance to conventional therapy. The molecular mechanisms of response to hypoxia are extremely complex, a key role being played by a transcriptional regulator, hypoxia‑inducible factor (HIF), which orchestrates the expression of a wide variety of genes thought to be critical for adaptation to low oxygen.20‑24 Hypoxia‑inducible factors (HIFs) are members of the basic helix‑loop‑helix ‑ PAS family of transcription factors containing an oxygen‑regulated alpha subunit and a constitutively expressed beta subunit (also known as aryl hydrocarbon receptor nuclear translocator—ARNT). The alpha subunits are rapidly degraded under normoxic conditions via the proteasomal pathway following enzymatic hydroxylation on conserved prolyl residues.25,26 Hypoxia leads to inhibition of prolyl hydroxylation and, as a consequence, to HIF stabilization. HIF then activates a highly complex transcription program, comprising in excess of one hundred genes that regulate processes such as angiogenesis, glucose metabolism, migration, survival and death. The activation/ overexpression of the alpha subunit(s) of HIF in cancer is a very common occurrence and is suspected to account at least in part for the well‑established tumor‑associated properties of deregulated glycolysis and angiogenesis.27

Hypoxia‑Regulated microRNAs (HRMs) Our work has recently identified a group of hypoxia‑regulated microRNAs (HRMs), providing an additional link between a tumor‑specific stress factor and gene expression control. This group included: miR‑21, 23a, 23b, 24, 26a, 26b, 27a, 30b, 93, 103, 103, 106a, 107, 125b, 181a, 181b, 181c, 192, 195, 210 and 213 which were consistently induced in response to hypoxia in the breast‑ and colon‑cancer cells tested.28 In addition to our study, three recent articles reported microRNAs that respond to low oxygen with some notable similarities, including miR‑210, miR‑30b, 93 and 181b.29-31 However, a significant number of microRNAs differed between the studies, which is not necessarily surprising, given the different cellular backgrounds and microarrays employed, which is a recognized source of variability. Additionally, our study concentrated only on microRNAs that exhibited consistent upregulation in at least two cell lines and at several time points in hypoxia, potentially increasing the stringency of the screen. Some of the microRNAs identified by Hebert et al.30 as hypoxia‑ induced were not present on our arrays (miR‑429, 498, 572, 563, 637, 628). Moreover, there was a potentially significant difference www.landesbioscience.com

in the experimental conditions employed by the different groups: hypoxia mimetics,29 versus 1% oxygen for 1 hr,30 versus 5% oxygen for 8 hr,30 versus 0.2% for various periods of time from 8–48hr.28 In addition to the microRNAs that respond to hypoxia by upregulation, the following microRNAs were identified as downregulated in hypoxic cells: miR‑122a, 565, 195, 30e‑5p, 374, 19a, 101, 424, 29b, 186, 141, 320, 422b, and 197 in SCC cells, miR‑15b, 16, 20a, 20b, 30b and 224 in CNE cells, and miR‑424 in trophoblasts.29-31 In our study, we have also detected microRNAs that exhibited downregulation at the level of microarrays (miR‑126, 128, 138, 323, 326), however the changes were generally restricted to one cell line and were not pursued at this stage (Kulshreshtha et al., unpublished). Moreover, other microRNAs, such the let‑7 family (with the members let‑7g, let‑7e and let‑7i) were found by Hebert et al.30 as hypoxia‑inducible in squamous cell carcinoma (SCC) in contrast to another study by Hua et al.29 which shows let‑7 ‑ a, c, d, e, f and g as downregulated by hypoxia in CNE cells (from nasopharyngeal carcinoma). In our hands, several let‑7 forms (f, g, i) exhibited contrasting changes in different colon and breast cancer cell lines (Kulshreshtha et al., unpublished), suggesting that the let‑7 family could contain bona fide hypoxia‑responsive microRNAs, though in a more cell‑specific manner. Whether such microRNAs are downregulated directly by hypoxia is unclear based on the available data. Hypoxia is also known to slow down or arrest the cell cycle; therefore microRNAs that are selectively expressed in one phase of the cell cycle are expected to exhibit decreased levels. A general dysfunction of microRNA biogenesis machinery in response to hypoxia is unlikely according to a recent study in human trophoblasts, which showed unaltered expression of key microRNA processing proteins Ago2, Drosha, Exp5, Dicer and DP103.31

Mechanisms of HRM Regulation in Hypoxia Our study experimentally confirmed an important regulatory role of HIF, at least for a subset of hypoxia‑induced microRNAs. The strategy employed a combination of chromatin immunoprecipitation and luciferase‑based reporters driven by fragments of select HRM promoters, thus more directly confirming the recruitment of HIF on select microRNA promoters during hypoxia.28 As regards the microRNAs that are suppressed by hypoxia, HIF could still be a realistic candidate mediator, despite the fact that most studies have dissected its role as transcriptional activator. Indeed, recent data argue that HIF can also function as transcriptional repressor, for example in the case of the CAD gene.32 Additionally, or alternatively, the mediator could be the inhibitory PAS (Per/Arnt/ Sim) domain protein, IPAS, which is structurally related to HIF, but devoid of an endogenous transactivation function.33

Development of Computational Tools for Analysis of microRNA Promoters and Regulatory Transcription Factors, Such as HIF Genome‑wide bioinformatics analyses, using sophisticated mathematical tools, have increased our understanding of the cis‑regulatory architecture of mammalian organisms. Delineating the promoter regions of microRNAs, where relevant transcription factors such as HIF bind is a necessary first step for an expanded understanding of microRNA expression control. Nevertheless, only a few microRNA promoters have been identified experimentally.19,34,35

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Figure 1. A screen shot of genomic location that contains the microRNA cluster: mir‑24‑2, mir‑27a and mir‑23a, predicted pol‑II promoter, CAGEtag clusters and conserved HIF binding sites. “Regulatory potential” and “Sequence conservation across 17 species” tracks show that the microRNAs, the predicted promoter and HIF binding sites fall within highly conserved genome region. “HRM Conserved Transcription Factor Binding Sites” track shows the predicted binding sites (conserved across human, mouse and rat) of AP4, AHR, SRF, SEF1, CREL, HMX and PAX transcription factors, suggesting a possible role in regulating the microRNA cluster.

By analyzing the promoters of intergenic microRNA genes in Caenorhabditis elegans, Homo sapiens, Arabidopsis thaliana and Oryza sativa, Zhou et al.36 showed that most known microRNA genes in these four species have the same type of Pol‑II promoters as protein‑coding genes, as identified by promoter prediction programs, such as FirstEF.37 In order to increase the reliability of such predictions, an integrative approach is necessary. Thus, we are currently developing an annotation system (Sun et al., manuscript in preparation) that combines the genomic alignments of mRNAs and microRNAs with ab initio Pol‑II promoter predictions of FirstEF and CAGE tags.37,38 Preliminarily, we found that approximately 60% of the 474 human and 366 mouse microRNAs are intergenic, while the rest fall within a known protein‑coding gene (intragenic). The majority of intragenic microRNAs are located in intronic regions. The computational approach predicted most (84% in human and 89% in mouse) of the intragenic microRNA promoters, some of which overlap with the promoters of corresponding protein‑coding gene. It is known that intragenic microRNAs, which overlap with protein‑coding genes, are encoded from the sense strand, and tend to utilize the same promoter as the host gene. In support of this, the ‘host’ transcript and microRNAs usually exhibit similar expression profiles indicating that they are transcribed as part of a common transcription units.39,40 For intergenic microRNAs, the computational pipeline predicted promoters of approximately 70% of human and 72% of mouse microRNAs. We speculate that the missed predictions for the rest of the microRNAs was due to our search criteria within only 1428

10 kb upstream region, lack of CAGE tags and FirstEF predictions or pol‑III rather than pol‑II dependence. By analysis of the promoters of all known and predicted microRNAs, we predicted HIF binding sites by position weight matrix (PWM) approach.28 While our methodology analyzed the 5 kb promoter region of all the microRNAs, the promoter regions can span much longer regions. However, within the 5 kb analyzed promoter regions, we found that nearly 40% of human and 50% of mouse microRNA promoters have one or more predicted HIF binding sites. We then asked the following question: “is the HRM group associated with significantly more HIF consensuses than the average random group of microRNAs of the same size?”. The analysis, performed separately for individual types of HIF binding consensuses (Q3 and Q5) revealed that the HRMs (as a group) contains significantly more microRNAs with at least one HIF site than the average random 23 microRNAs (for HIF1_Q3, p= 0.00294; and for HIF_Q5, p= 0.011).28 We then searched for HIF sites that fall in evolutionarily conserved regions as determined using the “Vertebrate Multiz Alignment & Conservation (17 Species)” at UCSC genome browser.41 We found that approximately 6% of the human microRNAs have one or more HIF sites that are significantly conserved across seventeen species. An example for a cluster that contains three hypoxia regulated microRNAs is shown in Figure 1. The high degree of conservation is likely to reflect functional importance and could help us predict additional bona fide HRMs, that were not identified based on the original array‑based screen.

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death, microRNA‑induced changes in the baseline level of such components could conceivably change the response to stresses, such as hypoxia. It is conceivable that some HRMs influence survival/death in hypoxia by targeting genes that are not directly involved in apoptosis. This seems to be the case for miR‑210, which does not target any apoptotic gene in silico, but decreases the activity of Caspase 3/7. Our results do not imply, however, that HRMs exhibit a general anti‑apoptotic effect in all cell types in hypoxia. Indeed, genes such as the anti‑apoptotic Bcl2 are also predicted targets of some HRMs, such as miR‑103, 107, 21 and 30. The effect could be very complex, especially given the number of microRNAs that are coordinately induced, and the outcome could vary depending on the cellular context. Another process known to be affected by hypoxia is proliferation, since many cell types undergo cell cycle slowdown or arrest during oxygen deprivation. A plethora of cell cycle genes are identified as putative HRMs targets, such as: cdc25A (miR‑21, miR‑103/107), cyclin D2 (miR‑26, miR‑103/107), Figure 2. A selection of in silico targets of hypoxia regulated microRNAs predicts a potentially cyclin E1 (miR‑26), cyclin H (miR‑23), cdk6 significant impact in tumor biology (targets are indicated using the standard gene symbols). (miR‑26, miR‑103/107). One could speculate that persistent and coordinated induction of HRMs in Such tools will be extended in the future to the study of other hypoxia, could exhibit an independent regulatory impact on cell transcription factors that could play important roles in microRNA cycle. regulation in response to exogenous or endogenous stimuli An additional gene of relevance for this subject is VEGF for which or stresses. a group of regulatory microRNAs have been identified, including miR‑16, miR‑20a, miR‑20b, let‑7b, miR‑17‑5p, miR‑27a, miR‑106a, miR‑106b, miR‑107, miR‑193a, miR‑210, miR‑320 and miR‑361.29 Identification of Cellular HRMs Targets Interestingly, most of these microRNAs have been identified by at Several programs for target gene prediction are currently available, least one of the recent studies as responsive to hypoxia, either by the most widely used being: PicTar (pictar.bio.nyu.edu), TargetScan induction or by repression, which could lead to an extra layer of (www.targetscan.com) and miRBase (microrna.sanger.ac.uk/cgi‑bin/ complexity in the angiogenic response. targets/v1/search.pl).42-44 They employ different algorithms and A microRNA target of potential clinical impact is HMGA2 ranking criteria and are known to produce only a partially overlap- (high‑mobility group A2), a gene shown to alter the response ping set of candidates. Identification of targets with biological impact to chemotherapy. Hypoxia downregulates HMGA2, and several remains arguably one of the most complex efforts in the study of microRNAs, including miR‑98, let‑7g, 7e and 7i seem to be involved microRNAs. in this mechanism.30 In the case of hypoxia‑regulated microRNAs, in silico searches reveal a highly complex spectrum of candidate targets, including Implications for Tumor Biology and Therapy genes involved in proliferation, apoptosis, DNA repair, chromatin remodeling, metabolism, and migration. Each HRM is predicted Recent investigations have dissected a large number of cancers to downregulate in excess of ten genes, sometimes as many as 200, (breast, lung, colon, stomach, prostate carcinomas and pancreatic which could confound the effort to identify biologically‑relevant endocrine tumors) for microRNA expression and identified specific targets. A selection of gene families with potential impact in cancer alterations compared to normal cells.46 We analyzed the pattern that are affected by HRMs is shown in Figure 2. of microRNAs changes during hypoxia using the same technology One set of targets worth pursuing are cell death regulators, given and analytical tools. Interestingly, the overwhelming majority of the importance of this process in a stressful environment, such as HRMs are also overexpressed in at least some types of tumor types, hypoxia. Using PicTar, TargetScan and MirBase prediction programs, suggesting that hypoxia may represent a contributing element for a number of core component genes of the apoptotic machinery were microRNA alterations in cancer. An microRNA (and HRM) that has received attention in the found to be potentially targeted by HRMs: BID (miR‑23), BIM (miR‑24); CASP3 (miR‑30), CASP 7 (miR‑23), APAF1 (miR‑27), context of tumor formation is miR‑21, which is constantly overexBAK1 (miR‑26), Bnip3L (miR‑23). Additionally, Bcl2 is also an pressed in glioblastoma.47 Knock‑down of this microRNA in tissue experimentally confirmed target of miR15 and 16, which were found culture induces apoptosis of glioblastoma cells, suggesting the role in to respond to hypoxia by downregulation, at least in CNE cells.29,45 survival. Glioblastomas are known to exhibit extensive areas of hypoxia While it is thought that post‑translational processing of proapop- and necrosis, therefore overexpression of an antiapoptotic microRNA totic proteins is at the center of the activation of programmed cell could be very important for the biology of this tumor type. www.landesbioscience.com

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Interestingly, several HRMs were also found induced in hypertrophic or failing hearts (miR‑21, 23, 24, 125b, 195 and 210) and could play important roles in the cardiovascular pathology, where hypoxia is a documented critical factor.48 From an experimental standpoint, the hypoxic response involves a multitude of microRNAs, and therefore it is likely that manipulation of any individual HRM will fail to fully capture the phenotypic impact of this mechanism in low oxygen. Obviously, the simultaneous expression of all HRMs is hardly feasible, but combinations of up to 4 microRNAs of this group is currently being tested in multi‑well format. This will allow us to test the possible synergism in HRMs action, from the standpoint of effects on cell death, or ability to downregulate select targets. The involvement of microRNA in tumorigenesis points towards the possibility of future applications for cancer therapy. The availability of microRNA derivatives with increased half life and binding efficiency, such as AMOs (anti‑microRNA oligonucleotides), LNAs (locked nucleic acids) and antagomirs represent potentially important developments for such purpose.49,50,51 Moreover, expression of select microRNAs could improve the outcome of conventional therapies. As proof of concept, miR‑21 overexpression enhances the effect of gemcitabine on cholangiocarcinoma cells.52 The patterns of microRNA alterations reported in cancer versus normal tissues is very likely the sum of a large variety of highly complex molecular signals, including activation of oncogenic pathways. In the light of recent reports, hypoxia emerges as a potential contributor to such expression changes. However, other well‑documented microenvironmental factors, such as pH alterations, local decrease in glucose levels, paracrine growth factors and tumor‑stromal interactions could add to the complexity of microRNA deregulation in cancer. References 1. Bartel DP. MicroRNAs: Genomics, biogenesis, mechanism, and function. Cell 2004; 116:281‑297. 2. Rana TM. Illuminating the silence: Understanding the structure and function of small RNAs. Nat Rev Mol Cell Biol 2007; 8:23‑36. 3. Karp X, Ambros V. Developmental biology: Encountering microRNAs in cell fate signaling. Science 2005; 310:1288‑1289. 4. Miska EA. How microRNAs control cell division, differentiation and death. Curr Opin Genet Dev 2005; 15:563‑568. 5. Kloosterman WP, Plasterk RH. The diverse functions of microRNAs in animal development and disease. Dev Cell 2006; 11:441‑50. 6. Calin GA, Ferracin M, Cimmino A, Di Leva G, Shimizu M, Wojcik SE, Iorio MV, Visone R, Sever NI, Fabbri M, Iuliano R, Palumbo T, Pichiorri F, Roldo C, Garzon R, Sevignani C, Rassenti L, Alder H, Volinia S, Liu CG, Kipps TJ, Negrini M, Croce CM. A microRNA signature associated with prognosis and progression in chronic lymphocytic leukemia. N Engl J Med 2005; 353:1793‑1801. 7. Croce CM, Calin GA. miRNAs, cancer, and stem cell division. Cell 2005; 122:6‑7. 8. He L, Thomson JM, Hemann MT, Hernando‑ Monge E, Mu D, Goodson S, Powers S, Cordon‑Cardo C, Lowe SW, Hannon GJ, Hammond SM. A microRNA polycistron as a potential human oncogene. Nature 2005; 435:828‑833. 9. Iorio MV, Ferracin M, Liu CG, 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. Liu CG, Calin GA, Meloon B, Gamliel N, Sevignani C, Ferracin M, Dumitru CD, Shimizu M, Zupo S, Dono M, Alder H, Bullrich F, Negrini M, Croce CM. An oligonucleotide microchip for genome‑wide microRNA profiling in human and mouse tissues. Proc Natl Acad Sci USA 2004; 101:9740‑9744. 11. Lu J, Getz G, Miska EA, Alvarez‑Saavedra E, Lamb J, Peck D, Sweet‑Cordero A, Ebert BL, Mak RH, Ferrando AA, Downing JR, Jacks T, Horvitz HR, Golub TR. MicroRNA expression profiles classify human cancers. Nature 2005; 435:834‑838. 12. Yanaihara N, Caplen N, Bowman E, Seike M, Kumamoto K, Yi M, Stephens RM, Okamoto A, Yokota J, Tanaka T, Calin GA, Liu CG, Croce CM, Harris CC. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 2006; 9:189‑198. 13. Lee Y, Kim M, Han J, Yeom KH, Lee S, Baek SH, Kim VN. MicroRNA genes are transcribed by RNA polymerase II. EMBO J 2004; 23:4051‑4060.

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