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Current Bioinformatics

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ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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Research Article

Identification of Key Prognosis-related microRNAs in Early- and Late- Stage Gynecological Cancers Based on TCGA Data

Author(s): Venugopala Reddy Mekala, Chiang Hui-Shan, Chang Jan-Gowth* and Ka-Lok Ng*

Volume 17, Issue 9, 2022

Published on: 09 September, 2022

Page: [860 - 872] Pages: 13

DOI: 10.2174/1574893617666220802154148

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Abstract

Background: Gynecological cancers (GCs), mainly diagnosed in the late stages of the disease, remain the leading cause of global mortality in women. microRNAs (miRNAs) have been explored as diagnostic and prognostic biomarkers of cancer. Evaluating miRNA signatures to develop prognostic models could be useful in predicting high-risk patients with GC. Specifically, the identification of miRNAs associated with different stages of cancer can be beneficial in patients diagnosed with cancer.

Objective: This study aimed to identify potential miRNA signatures for constructing optimal prognostic models in three major GCs using The Cancer Genome Atlas (TCGA) database.

Methods: Stage-specific Differentially Expressed microRNAs (DEmiRs) were identified and validated in public and in-house expression datasets. Moreover, various bioinformatics investigations were used to identify potential DEmiRs associated with the disease. All DEmiRs were analyzed using three penalized Cox regression models: lasso, adaptive lasso, and elastic net algorithms. The combined outcomes were evaluated using Best Subset Regression (BSR). Prognostic DEmiR models were evaluated using Kaplan–Meier plots to predict risk scores in patients. The biological pathways of the potential DEmiRs were identified using functional enrichment analysis.

Results: A total of 65 DEmiRs were identified in the three cancer types; among them, 17 demonstrated dysregulated expression in public datasets of cervical cancer, and the expression profiles of 9 DEmiRs were changed in CCLE-OV cells, whereas those of 10 are dysregulated in CCLE-UCEC cells. Additionally, ten miRNA expression profiles were observed to be the same as DEmiRs in three OV cancer cell lines. Approximately 30 DEmiRs were experimentally validated in particular cancers. Furthermore, 23 DEmiRs were correlated with the overall survival of the patients. The combined analysis of the three penalized Cox models and BSR analysis predicted eight potential DEmiRs. A total of five models based on five DEmiRs (hsa-mir-526b, hsa-mir-508, and hsa-mir-204 in CESC and hsa-mir-137 and hsa-mir- 1251 in UESC samples) successfully differentiated high-risk and low-risk patients. Functional enrichment analysis revealed that these DEmiRs play crucial roles in GCs.

Conclusion: We report potential DEmiR-based prognostic models to predict the high-risk patients with GC and demonstrate the roles of miRNA signatures in the early- and late-stage of GCs.

Keywords: Gynecological cancers, differential expression, microRNA, biomarkers, prognosis, survival analysis, risk models.

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[1]
Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021; 71(3): 209-49.
[http://dx.doi.org/10.3322/caac.21660] [PMID: 33538338]
[2]
Kanekura K, Nishi H, Isaka K, Kuroda M. MicroRNA and gynecologic cancers. J Obstet Gynaecol Res 2016; 42(6): 612-7.
[http://dx.doi.org/10.1111/jog.12995] [PMID: 27098274]
[3]
Medhin LB, Tekle LA, Achila OO, Said S. Incidence of cervical, ovarian and uterine cancer in eritrea: Data from the national health laboratory, 2011-2017. Sci Rep 2020; 10(1): 9099.
[http://dx.doi.org/10.1038/s41598-020-66096-5] [PMID: 32499531]
[4]
Alshamrani AA. Roles of microRNAs in ovarian cancer tumorigenesis: Two decades later, what have we learned? Front Oncol 2020; 10: 1084.
[http://dx.doi.org/10.3389/fonc.2020.01084] [PMID: 32850313]
[5]
Condrat CE, Thompson DC, Barbu MG, et al. miRNAs as biomarkers in disease: Latest findings regarding their role in diagnosis and prognosis. Cells 2020; 9(2): 276.
[http://dx.doi.org/10.3390/cells9020276] [PMID: 31979244]
[6]
Lawrie CH, Gal S, Dunlop HM, et al. Detection of elevated levels of tumour-associated microRNAs in serum of patients with diffuse large B-cell lymphoma. Br J Haematol 2008; 141(5): 672-5.
[http://dx.doi.org/10.1111/j.1365-2141.2008.07077.x] [PMID: 18318758]
[7]
Krasniqi E, Sacconi A, Marinelli D, et al. MicroRNA-based signatures impacting clinical course and biology of ovarian cancer: A miRNOmics study. Biomark Res 2021; 9(1): 57.
[http://dx.doi.org/10.1186/s40364-021-00289-6] [PMID: 34256855]
[8]
Srivastava SK, Ahmad A, Zubair H, et al. MicroRNAs in gynecological cancers: Small molecules with big implications. Cancer Lett 2017; 407: 123-38.
[http://dx.doi.org/10.1016/j.canlet.2017.05.011] [PMID: 28549791]
[9]
Dong P, Xu D, Xiong Y, et al. The expression, functions and mechanisms of circular RNAs in gynecological cancers. Cancers (Basel) 2020; 12(6): 1472.
[http://dx.doi.org/10.3390/cancers12061472] [PMID: 32512912]
[10]
Yokoi A, Matsuzaki J, Yamamoto Y, et al. Integrated extracellular microRNA profiling for ovarian cancer screening. Nat Commun 2018; 9(1): 4319.
[http://dx.doi.org/10.1038/s41467-018-06434-4] [PMID: 30333487]
[11]
Di Fiore R, Suleiman S, Pentimalli F, et al. Could MicroRNAs be useful tools to improve the diagnosis and treatment of rare gynecological cancers? A brief overview. Int J Mol Sci 2021; 22(8): 3822.
[http://dx.doi.org/10.3390/ijms22083822] [PMID: 33917022]
[12]
Tehrani SS, Zaboli E, Sadeghi F, et al. MicroRNA-26a-5p as a potential predictive factor for determining the effectiveness of trastuzumab therapy in HER-2 positive breast cancer patients. Biomedicine (Taipei) 2021; 11(2): 30-9.
[PMID: 35223402]
[13]
Rupaimoole R, Slack FJ. MicroRNA therapeutics: Towards a new era for the management of cancer and other diseases. Nat Rev Drug Discov 2017; 16(3): 203-22.
[http://dx.doi.org/10.1038/nrd.2016.246] [PMID: 28209991]
[14]
Shah V, Shah J. Recent trends in targeting miRNAs for cancer therapy. J Pharm Pharmacol 2020; 72(12): 1732-49.
[http://dx.doi.org/10.1111/jphp.13351] [PMID: 32783235]
[15]
Subramanian S, Trogdon J, Ekwueme DU, Gardner JG, Whitmire JT, Rao C. Cost of cervical cancer treatment: Implications for providing coverage to low-income women under the Medicaid expansion for cancer care. Womens Health Issues 2010; 20(6): 400-5.
[http://dx.doi.org/10.1016/j.whi.2010.07.002] [PMID: 21050999]
[16]
Chen X, Yin J, Qu J, Huang L. MDHGI: Matrix decomposition and heterogeneous graph inference for miRNA-disease association prediction. PLOS Comput Biol 2018; 14(8): e1006418.
[http://dx.doi.org/10.1371/journal.pcbi.1006418] [PMID: 30142158]
[17]
Chen X, Wang L, Qu J, Guan NN, Li JQ. Predicting miRNA-disease association based on inductive matrix completion. Bioinformatics 2018; 34(24): 4256-65.
[http://dx.doi.org/10.1093/bioinformatics/bty503] [PMID: 29939227]
[18]
Chen X, Zhu C-C, Yin J. Ensemble of decision tree reveals potential miRNA-disease associations. PLOS Comput Biol 2019; 15(7): e1007209.
[http://dx.doi.org/10.1371/journal.pcbi.1007209] [PMID: 31329575]
[19]
Prahm KP, Høgdall C, Karlsen MA, Christensen IJ, Novotny GW, Høgdall E. Identification and validation of potential prognostic and predictive miRNAs of epithelial ovarian cancer. PLoS One 2018; 13(11): e0207319.
[http://dx.doi.org/10.1371/journal.pone.0207319] [PMID: 30475821]
[20]
Chen Q, Zeng X, Huang D, Qiu X. Identification of differentially expressed miRNAs in early-stage cervical cancer with lymph node metastasis across The Cancer Genome Atlas datasets. Cancer Manag Res 2018; 10: 6489-504.
[http://dx.doi.org/10.2147/CMAR.S183488] [PMID: 30568508]
[21]
Liang B, Li Y, Wang T. A three miRNAs signature predicts survival in cervical cancer using bioinformatics analysis. Sci Rep 2017; 7(1): 5624.
[http://dx.doi.org/10.1038/s41598-017-06032-2] [PMID: 28717180]
[22]
Galvão-Lima LJ, Morais AHF, Valentim RAM, Barreto EJSS. miRNAs as biomarkers for early cancer detection and their application in the development of new diagnostic tools. Biomed Eng Online 2021; 20(1): 21.
[http://dx.doi.org/10.1186/s12938-021-00857-9] [PMID: 33593374]
[23]
Ramesh P, Karuppasamy R, Veerappapillai S. A review on recent advancements in diagnosis and classification of cancers using artificial intelligence. Biomedicine (Taipei) 2020; 10(3): 5-17.
[http://dx.doi.org/10.37796/2211-8039.1012] [PMID: 33854922]
[24]
Jiang H-Y, Gang N, Yen-Sheng W, Wei-Biao L. 14-CpG-based signature improves the prognosis prediction of hepatocellular carcinoma patients. Biomed Res Inter. 2020. Available from:
[http://dx.doi.org/10.1155/2020/9762067]
[25]
How C, Pintilie M, Bruce JP, et al. Developing a prognostic micro-RNA signature for human cervical carcinoma. PLoS One 2015; 10(4): e0123946.
[http://dx.doi.org/10.1371/journal.pone.0123946] [PMID: 25880806]
[26]
Wang H, Li SW, Li W, Cai HB. Elastic net-based identification of a multigene combination predicting the survival of patients with cervical cancer. Med Sci Monit 2019; 25: 10105-13.
[http://dx.doi.org/10.12659/MSM.918393] [PMID: 31884508]
[27]
Mokhtaridoost M, Gönen M. An efficient framework to identify key miRNA-mRNA regulatory modules in cancer. Bioinformatics 2020; 36 (Suppl. 2): i592-600.
[http://dx.doi.org/10.1093/bioinformatics/btaa798] [PMID: 33381822]
[28]
Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc B 2005; 67(2): 301-20.
[http://dx.doi.org/10.1111/j.1467-9868.2005.00503.x]
[29]
Lee S, Lim H. Review of statistical methods for survival analysis using genomic data. Genomics Inform 2019; 17(4): e41.
[http://dx.doi.org/10.5808/GI.2019.17.4.e41] [PMID: 31896241]
[30]
Staicu CE, Predescu DV, Rusu CM, et al. Role of microRNAs as clinical cancer biomarkers for ovarian cancer: A short overview. Cells 2020; 9(1): 169.
[http://dx.doi.org/10.3390/cells9010169] [PMID: 31936634]
[31]
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014; 15(12): 550.
[http://dx.doi.org/10.1186/s13059-014-0550-8] [PMID: 25516281]
[32]
Wilting SM, Snijders PJ, Verlaat W, et al. Altered microRNA expression associated with chromosomal changes contributes to cervical carcinogenesis. Oncogene 2013; 32(1): 106-16.
[http://dx.doi.org/10.1038/onc.2012.20] [PMID: 22330141]
[33]
Ghandi M, Huang FW, Jané-Valbuena J, et al. Next-generation characterization of the cancer cell line encyclopedia. Nature 2019; 569(7757): 503-8.
[http://dx.doi.org/10.1038/s41586-019-1186-3] [PMID: 31068700]
[34]
Tsherniak A, et al. Defining a cancer dependency map. Cell 2017; 170(3): 564-76.
[http://dx.doi.org/10.1016/j.cell.2017.06.010]
[35]
Wickham H. ggplot2: Elegant graphics for data analysis. springer 2016.
[36]
Xie B, Ding Q, Han H, Wu D. miRCancer: A microRNA-cancer association database constructed by text mining on literature. Bioinformatics 2013; 29(5): 638-44.
[http://dx.doi.org/10.1093/bioinformatics/btt014] [PMID: 23325619]
[37]
Huang Z, Shi J, Gao Y, et al. HMDD v3.0: A database for experimentally supported human microRNA-disease associations. Nucleic Acids Res 2019; 47(D1): D1013-7.
[http://dx.doi.org/10.1093/nar/gky1010] [PMID: 30364956]
[38]
Yang Z, Wu L, Wang A, et al. dbDEMC 2.0: Updated database of differentially expressed miRNAs in human cancers. Nucleic Acids Res 2017; 45(D1): D812-8.
[http://dx.doi.org/10.1093/nar/gkw1079] [PMID: 27899556]
[39]
Kozomara A, Birgaoanu M, Griffiths-Jones S. miRBase: From microRNA sequences to function. Nucleic Acids Res 2019; 47(D1): D155-62.
[http://dx.doi.org/10.1093/nar/gky1141] [PMID: 30423142]
[40]
Lánczky A, Nagy Á, Bottai G, et al. miRpower: A web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients. Breast Cancer Res Treat 2016; 160(3): 439-46.
[http://dx.doi.org/10.1007/s10549-016-4013-7] [PMID: 27744485]
[41]
Nagy Á, Munkácsy G, Győrffy B. Pancancer survival analysis of cancer hallmark genes. Sci Rep 2021; 11(1): 6047.
[http://dx.doi.org/10.1038/s41598-021-84787-5] [PMID: 33723286]
[42]
Dessie EY, Tu SJ, Chiang HS, et al. Construction and validation of a prognostic gene-based model for overall survival prediction in hepatocellular carcinoma using an integrated statistical and bioinformatic approach. Int J Mol Sci 2021; 22(4): 1632.
[http://dx.doi.org/10.3390/ijms22041632] [PMID: 33562824]
[43]
Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw 2010; 33(1): 1-22.
[http://dx.doi.org/10.18637/jss.v033.i01] [PMID: 20808728]
[44]
Simon RM, Subramanian J, Li MC, Menezes S. Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data. Brief Bioinform 2011; 12(3): 203-14.
[http://dx.doi.org/10.1093/bib/bbr001] [PMID: 21324971]
[45]
Dessie EY, Tsai JJP, Chang JG, Ng KL. A novel miRNA-based classification model of risks and stages for clear cell renal cell carcinoma patients. BMC Bioinformatics 2021; 22(10) (Suppl. 10): 270.
[http://dx.doi.org/10.1186/s12859-021-04189-2] [PMID: 34058987]
[46]
Jin S, Goh G. Bayesian selection of best subsets via hybrid search. Comput Stat 2021; 36(3): 1991-2007.
[http://dx.doi.org/10.1007/s00180-020-00996-y]
[47]
Dong G, Mao L, Huang B, et al. The inverse-probability-of-censoring weighting (IPCW) adjusted win ratio statistic: An unbiased estimator in the presence of independent censoring. J Biopharm Stat 2020; 30(5): 882-99.
[http://dx.doi.org/10.1080/10543406.2020.1757692] [PMID: 32552451]
[48]
McGeary SE, Lin KS, Shi CY, et al. The biochemical basis of microRNA targeting efficacy. Science 2019; 366(6472): eaav1741.
[http://dx.doi.org/10.1126/science.aav1741]
[49]
Pan Y, Lu L, Chen J, Zhong Y, Dai Z. Identification of potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma. Hereditas 2018; 155(1): 21.
[http://dx.doi.org/10.1186/s41065-018-0061-9] [PMID: 29760609]
[50]
Lu T-P, Chien-Yueh L, Mong-Hsun T, et al. miRSystem: An integrated system for characterizing enriched functions and pathways of microRNA targets. PLoS One 2012; 7(8): e42390.
[51]
Agarwal V, Bell GW, Nam JW, et al. Predicting effective microRNA target sites in mammalian mRNAs. elife 2015; 4: e05005.
[http://dx.doi.org/10.7554/eLife.05005]
[52]
Yang Z-M, Long-Hui C, Min H, et al. Serum MicroRNA profiling and bioinformatics of patients with spleen-deficiency syndrome. Evidence-Based Compl Alter Med 2016 2016.
[http://dx.doi.org/10.1155/2016/8726720]
[53]
Chandrashekar DS, Bashel B, Balasubramanya SAH, et al. UALCAN: A portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia 2017; 19(8): 649-58.
[http://dx.doi.org/10.1016/j.neo.2017.05.002] [PMID: 28732212]
[54]
Tao P, Wen H, Yang B, Zhang A, Wu X, Li Q. miR-144 inhibits growth and metastasis of cervical cancer cells by targeting VEGFA and VEGFC. Exp Ther Med 2018; 15(1): 562-8.
[PMID: 29387205]
[55]
Park S, Eom K, Kim J, et al. MiR-9, miR-21, and miR-155 as potential biomarkers for HPV positive and negative cervical cancer. BMC Cancer 2017; 17(1): 658.
[http://dx.doi.org/10.1186/s12885-017-3642-5] [PMID: 28934937]
[56]
Azizmohammadi S, Safari A, Azizmohammadi S, et al. Molecular identification of miR-145 and miR-9 expression level as prognostic biomarkers for early-stage cervical cancer detection. QJM 2017; 110(1): 11-5.
[http://dx.doi.org/10.1093/qjmed/hcw101] [PMID: 27345415]
[57]
Zhang J, Jia J, Zhao L, et al. Down-regulation of microRNA-9 leads to activation of IL-6/Jak/STAT3 pathway through directly targeting IL-6 in HeLa cell. Mol Carcinog 2016; 55(5): 732-42.
[http://dx.doi.org/10.1002/mc.22317] [PMID: 25809226]
[58]
Zhao H-M, Wei W, Sun YH, Gao JH, Wang Q, Zheng JH. MicroRNA-9 promotes tumorigenesis and mediates sensitivity to cisplatin in primary epithelial ovarian cancer cells. Tumour Biol 2015; 36(9): 6867-73.
[http://dx.doi.org/10.1007/s13277-015-3399-x] [PMID: 25846738]
[59]
Zhou B, Xu H, Xia M, et al. Overexpressed miR-9 promotes tumor metastasis via targeting E-cadherin in serous ovarian cancer. Front Med 2017; 11(2): 214-22.
[http://dx.doi.org/10.1007/s11684-017-0518-7] [PMID: 28470508]
[60]
Guo LM, Pu Y, Han Z, et al. MicroRNA-9 inhibits ovarian cancer cell growth through regulation of NF-kappaB1. FEBS J 2009; 276(19): 5537-46.
[http://dx.doi.org/10.1111/j.1742-4658.2009.07237.x] [PMID: 19702828]
[61]
Chen Z, Zhu Y, Fan X, Liu Y, Feng Q. Decreased expression of miR-184 restrains the growth and invasion of endometrial carcinoma cells through CDC25A-dependent Notch signaling pathway. Am J Transl Res 2019; 11(2): 755-64.
[PMID: 30899377]
[62]
Zhang W, Chen JH, Shan T, et al. miR-137 is a tumor suppressor in endometrial cancer and is repressed by DNA hypermethylation. Lab Invest 2018; 98(11): 1397-407.
[http://dx.doi.org/10.1038/s41374-018-0092-x] [PMID: 29955087]
[63]
Aftab M, Poojary SS, Seshan V, et al. Urine miRNA signature as a potential non-invasive diagnostic and prognostic biomarker in cervical cancer. Sci Rep 2021; 11(1): 10323.
[http://dx.doi.org/10.1038/s41598-021-89388-w] [PMID: 33990639]
[64]
Kumar V, Gupta S, Varma K, Sachan M. MicroRNA as biomarker in ovarian cancer management: Advantages and challenges. DNA Cell Biol 2020; 39(12): 2103-24.
[http://dx.doi.org/10.1089/dna.2020.6024] [PMID: 33156705]
[65]
Chen A-H, Qin YE, Tang WF, Tao J, Song HM, Zuo M. MiR-34a and miR-206 act as novel prognostic and therapy biomarkers in cervical cancer. Cancer Cell Int 2017; 17(1): 63.
[http://dx.doi.org/10.1186/s12935-017-0431-9] [PMID: 28615991]
[66]
Li H, Wang J, Xu F, et al. By downregulating PBX3, miR-526b suppresses the epithelial-mesenchymal transition process in cervical cancer cells. Future Oncol 2019; 15(14): 1577-91.
[http://dx.doi.org/10.2217/fon-2018-0575] [PMID: 30859853]
[67]
Liu YH, Li B, Meng FG, Qiu L. MiR-508-5p is a prognostic marker and inhibits cell proliferation and migration in glioma. Eur Rev Med Pharmacol Sci 2017; 21(1): 76-81.
[PMID: 28121353]
[68]
Duan X, Bai J, Wei J, Li Z, Liu X, Xu G. MicroRNA-508-5p suppresses metastasis in human gastric cancer by targeting S-phase kinase associated protein 2. Mol Med Rep 2017; 16(2): 2163-71.
[http://dx.doi.org/10.3892/mmr.2017.6793] [PMID: 28627698]
[69]
Guo SJ, Zeng HX, Huang P, Wang S, Xie CH, Li SJ. MiR-508-3p inhibits cell invasion and epithelial-mesenchymal transition by targeting ZEB1 in triple-negative breast cancer. Eur Rev Med Pharmacol Sci 2018; 22(19): 6379-85.
[PMID: 30338806]
[70]
Hu P, Zhou G, Zhang X, Song G, Zhan L, Cao Y. Long non-coding RNA Linc00483 accelerated tumorigenesis of cervical cancer by regulating miR-508-3p/RGS17 axis. Life Sci 2019; 234: 116789.
[http://dx.doi.org/10.1016/j.lfs.2019.116789] [PMID: 31454494]
[71]
Zhang P, Hou Q, Yue Q. MiR-204-5p/TFAP2A feedback loop positively regulates the proliferation, migration, invasion and EMT process in cervical cancer. Cancer Biomark 2020; 28(3): 381-90.
[http://dx.doi.org/10.3233/CBM-191064] [PMID: 32474464]
[72]
Shu L, Zhang Z, Cai Y. MicroRNA-204 inhibits cell migration and invasion in human cervical cancer by regulating transcription factor 12. Oncol Lett 2018; 15(1): 161-6.
[PMID: 29387215]
[73]
Duan S, Wu A, Chen Z, Yang Y, Liu L, Shu Q. miR-204 regulates cell proliferation and invasion by targeting EphB2 in human cervical cancer. Oncol Res 2018; 26(5): 713-23.
[http://dx.doi.org/10.3727/096504017X15016337254641] [PMID: 28800788]
[74]
Li N, Guo X, Liu L, Wang L, Cheng R. Molecular mechanism of miR-204 regulates proliferation, apoptosis and autophagy of cervical cancer cells by targeting ATF2. Artif Cells Nanomed Biotechnol 2019; 47(1): 2529-35.
[http://dx.doi.org/10.1080/21691401.2019.1628038] [PMID: 31204513]
[75]
Jia Y, Shen M, Zhou Y, Liu H. Development of a 12-biomarkers-based prognostic model for pancreatic cancer using multi-omics integrated analysis. Acta Biochim Pol 2020; 67(4): 501-8.
[http://dx.doi.org/10.18388/abp.2020_5225] [PMID: 33253517]
[76]
Georgieva B, Milev I, Minkov I, Dimitrova I, Bradford AP, Baev V. Characterization of the uterine leiomyoma microRNAome by deep sequencing. Genomics 2012; 99(5): 275-81.
[http://dx.doi.org/10.1016/j.ygeno.2012.03.003] [PMID: 22446413]

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