Expression profiling of cancerous and normal breast tissues identifies microRNAs that are differentially expressed in serum from patients with (metastatic) breast cancer and healthy volunteers

Eleni van Schooneveld, Maartje Ca Wouters, Ilse Van der Auwera, Dieter J Peeters, Hans Wildiers, Peter A Van Dam, Ignace Vergote, Peter B Vermeulen, Luc Y Dirix, Steven J Van Laere, Eleni van Schooneveld, Maartje Ca Wouters, Ilse Van der Auwera, Dieter J Peeters, Hans Wildiers, Peter A Van Dam, Ignace Vergote, Peter B Vermeulen, Luc Y Dirix, Steven J Van Laere

Abstract

Introduction: MicroRNAs (miRNAs) are a group of small noncoding RNAs involved in the regulation of gene expression. As such, they regulate a large number of cellular pathways, and deregulation or altered expression of miRNAs is associated with tumorigenesis. In the current study, we evaluated the feasibility and clinical utility of circulating miRNAs as biomarkers for the detection and staging of breast cancer.

Methods: miRNAs were extracted from a set of 84 tissue samples from patients with breast cancer and eight normal tissue samples obtained after breast-reductive surgery. After reverse transcription and preamplification, 768 miRNAs were profiled by using the TaqMan low-density arrays. After data normalization, unsupervised hierarchical cluster analysis (UHCA) was used to investigate global differences in miRNA expression between cancerous and normal samples. With fold-change analysis, the most discriminating miRNAs between both tissue types were selected, and their expression was analyzed on serum samples from 20 healthy volunteers and 75 patients with breast cancer, including 16 patients with untreated metastatic breast cancer. miRNAs were extracted from 200 μl of serum, reverse transcribed, and analyzed in duplicate by using polymerase chain reaction (qRT-PCR).

Results: UHCA showed major differences in miRNA expression between tissue samples from patients with breast cancer and tissue samples from breast-reductive surgery (P < 0.0001). Generally, miRNA expression in cancerous samples tends to be repressed when compared with miRNA expression in healthy controls (P = 0.0685). The four most discriminating miRNAs by fold-change (miR-215, miR-299-5p, miR-411, and miR-452) were selected for further analysis on serum samples. All miRNAs at least tended to be differentially expressed between serum samples from patients with cancer and serum samples from healthy controls (miR-215, P = 0.094; miR-299-5P, P = 0.019; miR-411, P = 0.002; and miR-452, P = 0.092). For all these miRNAs, except for miR-452, the greatest difference in expression was observed between serum samples from healthy volunteers and serum samples from untreated patients with metastatic breast cancer.

Conclusions: Our study provides a basis for the establishment of miRNAs as biomarkers for the detection and eventually staging of breast cancer through blood-borne testing. We identified and tested a set of putative biomarkers of breast cancer and demonstrated that altered levels of these miRNAs in serum from patients with breast cancer are particularly associated with the presence of metastatic disease.

Figures

Figure 1
Figure 1
The distribution of polymerase chain reaction (PCR) efficiencies, calculated as the differences between the Ct values of undiluted sample and the Ct values of a 10-fold diluted sample. (A) Theoretically, this difference should equal 3.32 or 2log(10). All miRNA assays with a difference in Ct value between 3.32% and 25% were included for further analysis. A blue dashed line indicates the boundaries of the interval; a blue solid line indicates the theoretical expected value of 3.32. To account for differences in preamplification, we compared the Ct values of a sample before and after preamplification. The median difference in Ct value was 8, and all miRNA assays with a difference of 8% ± 25% were included for further analysis. The scatterplot in (B) demonstrates an almost perfect linear relation for those selected miRNAs before and after preamplification. The blue line represents the regression line, for which the equation is given on top of the scatterplot. To evaluate assay reproducibility, we tested four samples in duplicate. The scatterplot in (C) demonstrates the result for one of these samples. The blue line represents the regression line, and the correlation coefficient resulting from the comparison of both profiles is given on top of the scatterplot. Further technical validation of our miRNA-expression data was performed for 12 samples by analyzing their miRNA-expression profile with the nCounter Analysis System and comparing this result with the qRT-PCR-based miRNA-expression profile. The scatterplot in (D) demonstrates the result for one of these samples. The blue line represents the regression line, and the correlation coefficient resulting from the comparison of both profiles is given on top of the scatterplot.
Figure 2
Figure 2
Heatmap showing the result of an UHCA (Manhattan distance, Ward linkage) for all 373 miRNAs in all 92 samples. The miRNA-expression data are represented in matrix format, with rows indicating miRNAs and columns indicating samples. Overexpressed miRNAs are color-coded red, and repressed miRNAs are color-coded green. Color saturation indicates the level of overexpression. Six samples clusters could be discerned based on miRNA-expression differences, indicated by alternating blue and grey colors in the dendrogram. Underneath the sample dendrogram, the molecular-subtype classification is indicated (red, Basal-like; orange, ErbB2+; green, Luminal A; blue, Luminal B; gray, Normal-like). The true-normal breast samples are indicated by a darker shade of gray. The colored bar to the side of the heatmap indicates the array card to which the corresponding assay is allocated (red, A; blue, B).
Figure 3
Figure 3
Comparison of the mRNA-based molecular subtype classification by using the SSP method with the miRNA-based classification by using the expression centroids reported by Blenkiron and colleagues. This analysis was performed only for those samples for which Affymetrix mRNA-expression profiles are available (N = 66). The SSP-classification is provided in the X-axis (B, Basal; E, ErbB2+; LA, Luminal A; LB, Luminal B; N, Normal-like; and R, Rest). The Spearman correlation coefficients resulting from the miRNA-based molecular subtype classification are indicated in the Y-axis. For each miRNA-based molecular subtype-specific centroid and each sample in our data set, the Spearman correlation coefficients were determined. The molecular subtype-specific correlation coefficients were statistically compared between samples belonging to and not belonging to the SSP-defined molecular subtype of interest. P values are indicated under the corresponding boxplots.
Figure 4
Figure 4
Comparison of normal breast samples with tumor samples. We identified 59 differentially expressed miRNAs between tumor samples and normal breast samples. The median expression of these miRNAs is significantly elevated in normal breast samples, as illustrated by the boxplot (A). The top four miRNAs (miR-215, miR-299-5p, miR-411, and miR-452) with the greatest difference between normal breast samples and breast tumor samples by fold change are depicted in panels B through E. The corresponding false discovery rate is provided in top of each boxplot. All miRNAs are significantly overexpressed in normal breast samples.
Figure 5
Figure 5
Comparison of the expression profiles of miR-215, miR-299-5p, miR-411, and miR-452 between serum samples from patients with breast cancer and serum samples from healthy volunteers (A through D). The boxplots on panels E through H represent the comparison of the expression profiles of the same miRNAs between serum samples from healthy volunteers and from patients with metastatic breast cancer receiving and not receiving treatment. The P values indicating the significance of the difference are indicated on top of the boxplots.

References

    1. Ambros V. MicroRNA pathways in flies and worms: growth, death, fat, stress, and timing. Cell. 2003;113:673–676. doi: 10.1016/S0092-8674(03)00428-8.
    1. Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116:281–297. doi: 10.1016/S0092-8674(04)00045-5.
    1. He L, Hannon GJ. MicroRNAs: small RNAs with a big role in gene regulation. Nat Rev Genet. 2004;5:522–531. doi: 10.1038/nrg1379.
    1. Lim LP, Lau NC, Garrett-Engele P, Grimson A, Schelter JM, Castle J, Bartel DP, Linsley PS, Johnson JM. Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature. 2005;433:769–773. doi: 10.1038/nature03315.
    1. Sevignani C, Calin GA, Siracusa LD, Croce CM. Mammalian microRNAs: a small world for fine-tuning gene expression. Mamm Genome. 2006;17:189–202. doi: 10.1007/s00335-005-0066-3.
    1. Zamore PD, Haley B. Ribo-gnome: the big world of small RNAs. Science. 2005;309:1519–1524. doi: 10.1126/science.1111444.
    1. 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. doi: 10.1016/j.cell.2004.12.035.
    1. Erson AE, Petty EM. MicroRNAs in development and disease. Clin Genet. 2008;74:296–306. doi: 10.1111/j.1399-0004.2008.01076.x.
    1. Cheng AM, Byrom MW, Shelton J, Ford LP. Antisense inhibition of human miRNAs and indications for an involvement of miRNA in cell growth and apoptosis. Nucleic Acids Res. 2005;33:1290–1297. doi: 10.1093/nar/gki200.
    1. Xu P, Guo M, Hay BA. MicroRNAs and the regulation of cell death. Trends Genet. 2004;20:617–624. doi: 10.1016/j.tig.2004.09.010.
    1. Karp X, Ambros V. Developmental biology: encountering microRNAs in cell fate signaling. Science. 2005;310:1288–1289. doi: 10.1126/science.1121566.
    1. Pillai RS. MicroRNA function: multiple mechanisms for a tiny RNA? RNA. 2005;11:1753–1761. doi: 10.1261/rna.2248605.
    1. 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. doi: 10.1038/nature03702.
    1. Blenkiron C, Goldstein LD, Thorne NP, Spiteri I, Chin S-F, Dunning MJ, Barbosa-Morais NL, Teschendorff AE, Green AR, Ellis IO, Tavaré S, Caldas C, Miska EA. MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype. Genome Biol. 2007;8:R214. doi: 10.1186/gb-2007-8-10-r214.
    1. 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. doi: 10.1002/path.2586.
    1. Volinia S, Calin GA, Liu CG, Ambs S, Cimmino A, Petrocca F, Visone R, Iorio M, Roldo C, Ferracin M, Prueitt RL, Yanaihara N, Lanza G, Scarpa A, Vecchione A, Negrini M, Harris CC, Croce CM. A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci USA. 2006;103:2257–2261. doi: 10.1073/pnas.0510565103.
    1. Martello G, Rosato A, Ferrari F, Manfrin A, Cordenonsi M, Dupont S, Enzo E, Guzzardo V, Rondina M, Spruce T, Parenti AR, Daidone MG, Bicciato S, Piccolo S. A microRNA targeting dicer for metastasis control. Cell. 2010;141:1195–1207. doi: 10.1016/j.cell.2010.05.017.
    1. Ma L, Teruya-Feldstein J, Weinberg RA. Tumour invasion and metastasis initiated by microRNA-10b in breast cancer. Nature. 2007;449:682–688. doi: 10.1038/nature06174.
    1. Johnson SM, Grosshans H, Shingara J, Byrom M, Jarvis R, Cheng A, Labourier E, Reinert KL, Brown D, Slack FJ. RAS is regulated by the let-7 microRNA family. Cell. 2005;120:635–647. doi: 10.1016/j.cell.2005.01.014.
    1. Fabbri M, Garzon R, Cimmino A, Liu Z, Zanesi N, Callegari E, Liu S, Alder H, Costinean S, Fernandez-Cymering C, Volinia S, Guler G, Morrison CD, Chan KK, Marcucci G, Calin GA, Huebner K, Croce CM. MicroRNA-29 family reverts aberrant methylation in lung cancer by targeting DNA methyltransferases 3A and 3B. Proc Natl Acad Sci USA. 2007;104:15805–15810. doi: 10.1073/pnas.0707628104.
    1. Bombonati A, Sgroi DC. The molecular pathology of breast cancer progression. J Pathol. 2011;223:307–317.
    1. Perou CM, Sørlie T, Eisen MB, Van De Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, Fluge O, Pergamenschikov A, Williams C, Zhu SX, Lønning PE, Børresen-Dale AL, Brown PO, Botstein D. Molecular portraits of human breast tumours. Nature. 2000;406:747–752. doi: 10.1038/35021093.
    1. Prat A, Parker JS, Karginova O, Fan C, Livasy C, Herschkowitz JI, He X, Perou CM. Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast Cancer Res. 2010;12:R68. doi: 10.1186/bcr2635.
    1. Gatza ML, Lucas JE, Barry WT, Kim JW, Wang Q, Crawford MD, Datto MB, Kelley M, Mathey-Prevot B, Potti A, Nevins JR. A pathway-based classification of human breast cancer. Proc Natl Acad Sci USA. 2010;107:6994–6999. doi: 10.1073/pnas.0912708107.
    1. Iorio MV, Ferracin M, Liu CG, Veronese A, Spizzo R, Sabbioni S, Magri E, Pedriali M, Fabbri M, Campiglio M, Ménard S, Palazzo JP, Rosenberg A, Musiani P, Volinia S, Nenci I, Calin GA, Querzoli P, Negrini M, Croce C. MicroRNA gene expression deregulation in human breast cancer. Cancer Res. 2005;65:7065–7070. doi: 10.1158/0008-5472.CAN-05-1783.
    1. Xi Y, Nakajima G, Gavin E, Morris CG, Kudo K, Hayashi K, Ju J. Systematic analysis of microRNA expression of RNA extracted from fresh frozen and formalin-fixed paraffin-embedded samples. RNA. 2007;13:1668–1674. doi: 10.1261/rna.642907.
    1. Li J, Smyth P, Flavin R, Cahill S, Denning K, Aherne S, Guenther SM, O'Leary JJ, Sheils O. Comparison of miRNA expression patterns using total RNA extracted from matched samples of formalin-fixed paraffin-embedded (FFPE) cells and snap frozen cells. BMC Biotechnol. 2007;7:1–6. doi: 10.1186/1472-6750-7-1.
    1. Gilad S, Meiri E, Yogev Y, Benjamin S, Lebanony D, Yerushalmi N, Benjamin H, Kushnir M, Cholakh H, Melamed N, Bentwich Z, Hod M, Goren Y, Chajut A. Serum microRNAs are promising novel biomarkers. PLoS ONE. 2008;3:e3148. doi: 10.1371/journal.pone.0003148.
    1. Mitchell PS, Parkin RK, Kroh EM, Frits BR, Wyman SK, Pogosova-Agadjanyan EL, Peterson A, Noteboom J, O'Briant KCO, Allen A, Lin DW, Urban N, Drescher CW, Knudsen BS, Stirewalt DL, Gentleman R, Vessella RL, Nelson PS, Martin DB, Tewari M. Circulating microRNAs as stable blood-based markers for cancer detection. Proc Natl Acad Sci USA. 2008;105:10513–10518. doi: 10.1073/pnas.0804549105.
    1. Heneghan HM, Miller N, Lowery AJ, Sweeney KJ, Newell J, Kerin MJ. Circulating microRNAs as novel minimally invasive biomarkers for breast cancer. Ann Surg. 2010;251:499–505. doi: 10.1097/SLA.0b013e3181cc939f.
    1. Van der Auwera I, Elst HJ, Van Laere SJ, Maes H, Huget P, van Dam P, Van Marck EA, Vermeulen PB, Dirix LY. The presence of circulating total DNA and methylated genes is associated with circulating tumour cells in blood from breast cancer patients. Br J Cancer. 2009;100:1277–1286. doi: 10.1038/sj.bjc.6605013.
    1. Van der Auwera I, Peeters D, Benoy IH, Elst HJ, Van Laere SJ, Prové A, Maes H, Huget P, van Dam P, Vermeulen PB, Dirix LY. Circulating tumour cell detection: a direct comparison between the CellSearch System, the AdnaTest and CK-19/mammaglobin RT-PCR in patients with metastatic breast cancer. Br J Cancer. 2010;102:276–284. doi: 10.1038/sj.bjc.6605472.
    1. Therasse P, Arbuck SG, Eisenhauer EA, Wanders J, Kaplan RS, Rubinstein L, Verweij J, Van Glabbeke M, van Oosterom AT, Christian MC, Gwyther SG. New guidelines to evaluate the response to treatment in solid tumors: European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J Natl Cancer Inst. 2000;92:205–216. doi: 10.1093/jnci/92.3.205.
    1. Sieuwerts AM, Mostert B, Bolt-de Vries J, Peeters D, de Jongh FE, Stouthard JM, Dirix LY, van Dam PA, Van Galen A, de Weerd V, Kraan J, van der Spoel P, Ramírez-Moreno R, van Deurzen CH, Smid M, Yu JX, Jiang J, Wang Y, Gratama JW, Sleijfer S, Foekens JA, Martens JW. mRNA and microRNA expression profiles in circulating tumor cells and primary tumors of metastatic breast cancer patients. Clin Cancer Res. 2011;17:3600–3618. doi: 10.1158/1078-0432.CCR-11-0255.
    1. Mestdagh P, Van Vlierberghe P, De Weer A, Muth D, Westermann F, Speleman F, Vandesompele J. A novel and universal method for microRNA RT-qPCR data normalization. Genome Biol. 2009;10:R64. doi: 10.1186/gb-2009-10-6-r64.
    1. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001;25:402–408. doi: 10.1006/meth.2001.1262.
    1. Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, Davies S, Fauron C, He X, Hu Z, Quackenbush JF, Stijleman IJ, Palazzo J, Marron JS, Nobel AB, Mardis E, Nielsen TO, Ellis MJ, Perou CM, Bernard PS. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009;27:1160–1167. doi: 10.1200/JCO.2008.18.1370.
    1. Van Laere S, Van der Auwera I, Van den Eynden G, Van Hummelen P, van Dam P, Van Marck E, Vermeulen PB, Dirix L. Distinct molecular phenotype of inflammatory breast cancer compared to non-inflammatory breast cancer using Affymetrix-based genome-wide gene-expression analysis. Br J Cancer. 2007;97:1165–1174. doi: 10.1038/sj.bjc.6603967.
    1. Van der Auwera I, Yu W, Suo L, Van Neste L, van Dam P, Van Marck EA, Pauwels P, Vermeulen PB, Dirix LY, Van Laere SJ. Array-based DNA methylation profiling for breast cancer subtype discrimination. PLoS ONE. 2010;5:e12616. doi: 10.1371/journal.pone.0012616.
    1. Van der Auwera I, Limame R, van Dam P, Vermeulen PB, Dirix LY, Van Laere SJ. Integrated miRNA and mRNA expression profiling of the inflammatory breast cancer subtype. Br J Cancer. 2010;103:532–541. doi: 10.1038/sj.bjc.6605787.
    1. Cheng C, Fu X, Alves P, Gerstein M. mRNA expression profiles show differential regulatory effects of microRNAs between estrogen receptor-positive and estrogen receptor-negative breast cancer. Genome Biol. 2009;10:R90. doi: 10.1186/gb-2009-10-9-r90.
    1. Dedes KJ, Natrajan R, Lambros MB, Geyer FC, Lopez-Garcia MA, Savage K, Jones RL, Reis-Filho JS. Down-regulation of the miRNA master regulators Drosha and Dicer is associated with specific subgroups of breast cancer. Eur J Cancer. 2011;47:138–150. doi: 10.1016/j.ejca.2010.08.007.
    1. Janssen EAM, Slewa A, Gudlaugsson E, Jonsdottir K, Skaland I, Søiland H, Baak JP. Biologic profiling of lymph node negative breast cancers by means of microRNA expression. Mod Pathol. 2010;23:1567–1576. doi: 10.1038/modpathol.2010.177.
    1. Bockmeyer CL, Christgen M, Müller M, Fischer S, Ahrens P, Länger F, Kreipe H, Lehmann U. MicroRNA profiles of healthy basal and luminal mammary epithelial cells are distinct and reflected in different breast cancer subtypes. Breast Cancer Res Treat. 2011. in press .
    1. Lowery AJ, Miller N, Devaney A, McNeill RE, Davoren PA, Lemetre C, Benes V, Schmidt S, Blake J, Ball G, Kerin MJ. MicroRNA signatures predict oestrogen receptor, progesterone receptor and HER2/neu receptor status in breast cancer. Breast Cancer Res. 2009;11:R27. doi: 10.1186/bcr2257.
    1. Castellano L, Giamas G, Jacob J, Coombes RC, Lucchesi W, Thiruchelvam P, Barton G, Jiao LR, Wait R, Waxman J, Hannon GJ, Stebbing J. The estrogen receptor-alpha-induced microRNA signature regulates itself and its transcriptional response. Proc Natl Acad Sci USA. 2009;106:15732–15737. doi: 10.1073/pnas.0906947106.
    1. Liu S, Goldstein RH, Scepansky EM, Rosenblatt M. Inhibition of rho-associated kinase signaling prevents breast cancer metastasis to human bone. Cancer Res. 2009;69:8742–8751. doi: 10.1158/0008-5472.CAN-09-1541.
    1. Li H, Bian C, Liao L, Li J, Zhao RC. miR-17-5p promotes human breast cancer cell migration and invasion through suppression of HBP1. Breast Cancer Res Treat. 2011;126:565–575. doi: 10.1007/s10549-010-0954-4.
    1. Ma X, Becker Buscaglia LE, Barker JR, Li Y. MicroRNAs in NF-kappaB signaling. J Mol Cell Biol. 2011;3:159–166. doi: 10.1093/jmcb/mjr007.
    1. Herschkowitz JI, Zhao W, Zhang M, Usary J, Murrow G, Edwards D, Knezevic J, Greene SB, Darr D, Troester MA, Hilsenbeck SG, Medina D, Perou CM, Rosen JM. Breast Cancer Special Feature: Comparative oncogenomics identifies breast tumors enriched in functional tumor-initiating cells. Proc Natl Acad Sci USA. 2011. in press .
    1. Gregory PA, Bracken CP, Smith E, Bert AG, Wright JA, Roslan S, Morris M, Wyatt L, Farshid G, Lim YY, Lindeman GJ, Shannon MF, Drew PA, Khew-Goodall Y, Goodall GJ. An autocrine TGF-beta/ZEB/miR-200 signaling network regulates establishment and maintenance of epithelial-mesenchymal transition. Mol Biol Cell. 2011;22:1686–1698. doi: 10.1091/mbc.E11-02-0103.
    1. Gregory PA, Bert AG, Paterson EL, Barry SC, Tsykin A, Farshid G, Vadas MA, Khew-Goodall Y, Goodall GJ. The miR-200 family and miR-205 regulate epithelial to mesenchymal transition by targeting ZEB1 and SIP1. Nature. 2008;10:593–601.
    1. Yu F, Yao H, Zhu P, Zhang X, Pan Q, Gong C, Huang Y, Hu X, Su F, Lieberman J, Song E. let-7 regulates self renewal and tumorigenicity of breast cancer cells. Cell. 2007;131:1109–1123. doi: 10.1016/j.cell.2007.10.054.
    1. Song B, Wang Y, Titmus MA, Botchkina G, Formentini A, Kornmann M, Ju J. Molecular mechanism of chemoresistance by miR-215 in osteosarcoma and colon cancer cells. Mol Cancer. 2010;9:96. doi: 10.1186/1476-4598-9-96.
    1. Duan Z, Person RE, Lee H-H, Huang S, Donadieu J, Badolato R, Grimes HL, Papayannopoulou T, Horwitz MS. Epigenetic regulation of protein-coding and microRNA genes by the Gfi1-interacting tumor suppressor PRDM5. Mol Cell Biol. 2007;27:6889–6902. doi: 10.1128/MCB.00762-07.
    1. Fang X, Yoon J-G, Li L, Yu W, Shao J, Hua D, Zheng S, Hood L, Goodlett DR, Foltz G, Lin B. The SOX2 response program in glioblastoma multiforme: an integrated ChIP-seq, expression microarray, and microRNA analysis. BMC Genomics. 2011;12:11. doi: 10.1186/1471-2164-12-11.
    1. Lavon I, Zrihan D, Granit A, Einstein O, Fainstein N, Cohen MA, Cohen MA, Zelikovitch B, Shoshan Y, Spektor S, Reubinoff BE, Felig Y, Gerlitz O, Ben-Hur T, Smith Y, Siegal T. Gliomas display a microRNA expression profile reminiscent of neural precursor cells. Neuro Oncol. 2010;12:422–433.
    1. Hunter MP, Ismail N, Zhang X, Aguda BD, Lee EJ, Yu L, Xiao T, Schafer J, Lee ML, Schmittgen TD, Nana-Sinkam SP, Jarjoura D, Marsh CB. Detection of microRNA expression in human peripheral blood microvesicles. PLoS ONE. 2008;3:e3694. doi: 10.1371/journal.pone.0003694.
    1. Arroyo JD, Chevillet JR, Kroh EM, Ruf IK, Pritchard CC, Gibson DF, Mitchell PS, Bennett CF, Pogosova-Agadjanyan EL, Stirewalt DL, Tait JF, Tewari M. Argonaute2 complexes carry a population of circulating microRNAs independent of vesicles in human plasma. Proc Natl Acad Sci USA. 2011;108:5003–5008. doi: 10.1073/pnas.1019055108.
    1. Vickers KC, Palmisano BT, Shoucri BM, Shamburek RD, Remaley AT. MicroRNAs are transported in plasma and delivered to recipient cells by high-density lipoproteins. Nat Cell Biol. 2011;13:423–433. doi: 10.1038/ncb2210.
    1. Valadi H, Ekström K, Bossios A, Sjöstrand M, Lee JJ, Lötvall JO. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat Cell Biol. 2007;9:654–659. doi: 10.1038/ncb1596.
    1. Muralidharan-Chari V, Clancy JW, Sedgwick A, D'Souza-Schorey C. Microvesicles: mediators of extracellular communication during cancer progression. J Cell Sci. 2010;123:1603–1611. doi: 10.1242/jcs.064386.
    1. Pigati L, Yaddanapudi SC, Iyengar R, Kim DJ, Hearn SA, Danforth D, Hastings ML, Duelli DM. Selective release of microRNA species from normal and malignant mammary epithelial cells. PLoS ONE. 2010;5:e13515. doi: 10.1371/journal.pone.0013515.

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