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Current Protein & Peptide Science

Editor-in-Chief

ISSN (Print): 1389-2037
ISSN (Online): 1875-5550

Research Article

ES-MDA: Enhanced Similarity-based MiRNA-Disease Association

Author(s): Li Xu * and Ge-Ning Jiang

Volume 21 , Issue 11 , 2020

Page: [1060 - 1067] Pages: 8

DOI: 10.2174/1389203721666200911151723

Price: $65

Abstract

Accumulating evidence demonstrate that miRNAs can be treated as critical biomarkers in various complex human diseases. Thus, the identifications on potential miRNA-disease associations have become a hotpot for providing better understanding of disease pathology in this field. Recently, with various biological datasets, increasingly computational prediction approaches have been designed to uncover disease-related miRNAs for further experimental validation. To improve the prediction accuracy, several algorithms integrated miRNA similarities of known miRNA-disease associations to enhance the miRNA functional similarity network and disease similarities of known miRNA-disease associations to enhance the disease semantic similarity network. It is anticipated that machine learning methods would become an effective biological resource for clinical experimental guidance.

Keywords: microRNA, disease, lung cancer, network consistency projection, biological resources, pathology.

Graphical Abstract
[1]
Crick, F.H.; Barnett, L.; Brenner, S.; Watts-Tobin, R.J. General nature of the genetic code for proteins. Nature, 1961, 192, 1227-1232.
[http://dx.doi.org/10.1038/1921227a0] [PMID: 13882203]
[2]
Yanofsky, C. Establishing the triplet nature of the genetic code. Cell, 2007, 128(5), 815-818.
[http://dx.doi.org/10.1016/j.cell.2007.02.029] [PMID: 17350564]
[3]
Bertone, P.; Stolc, V.; Royce, T.E.; Rozowsky, J.S.; Urban, A.E.; Zhu, X.; Rinn, J.L.; Tongprasit, W.; Samanta, M.; Weissman, S.; Gerstein, M.; Snyder, M. Global identification of human transcribed sequences with genome tiling arrays. Science, 2004, 306(5705), 2242-2246.
[http://dx.doi.org/10.1126/science.1103388] [PMID: 15539566]
[4]
Deng, S.P.; Zhu, L.; Huang, D.S. Mining the bladder cancer-associated genes by an integrated strategy for the construction and analysis of differential co-expression networks. BMC Genomics, 2015, 16(S3)(Suppl. 3), S4.
[http://dx.doi.org/10.1186/1471-2164-16-S3-S4] [PMID: 25707808]
[5]
Deng, S.P.; Zhu, L.; Huang, D.S. Predicting hub genes associated with cervical cancer through gene co-expression networks; IEEE Computer Society Press, 2016.
[http://dx.doi.org/10.1109/TCBB.2015.2476790]
[6]
Lu, J.; Getz, G.; Miska, E.A.; Alvarez-Saavedra, E.; Lamb, J.; Peck, D.; Sweet-Cordero, A.; Ebert, B.L.; Mak, R.H.; Ferrando, A.A.; Downing, J.R.; Jacks, T.; Horvitz, H.R.; Golub, T.R. MicroRNA expression profiles classify human cancers. Nature, 2005, 435(7043), 834-838.
[http://dx.doi.org/10.1038/nature03702] [PMID: 15944708]
[7]
Gutschner, T.; Diederichs, S. The hallmarks of cancer: a long non- coding RNA point of view. RNA Biol., 2012, 9(6), 703-719.
[http://dx.doi.org/10.4161/rna.20481] [PMID: 22664915]
[8]
Goodrich, J.A.; Kugel, J.F. Non-coding-RNA regulators of RNA polymerase II transcription. Nat. Rev. Mol. Cell Biol., 2006, 7(8), 612-616.
[http://dx.doi.org/10.1038/nrm1946] [PMID: 16723972]
[9]
Bartel, D.P. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell, 2004, 116(2), 281-297.
[http://dx.doi.org/10.1016/S0092-8674(04)00045-5] [PMID: 14744438]
[10]
Ambros, V. The functions of animal microRNAs. Nature, 2004, 431(7006), 350-355.
[http://dx.doi.org/10.1038/nature02871] [PMID: 15372042]
[11]
Jopling, C.L.; Yi, M.; Lancaster, A.M.; Lemon, S.M.; Sarnow, P. Modulation of hepatitis C virus RNA abundance by a liver-specific MicroRNA. Science, 2005, 309(5740), 1577-1581.
[http://dx.doi.org/10.1126/science.1113329] [PMID: 16141076]
[12]
Vasudevan, S.; Tong, Y.; Steitz, J.A. Switching from repression to activation: microRNAs can up-regulate translation. Science, 2007, 318(5858), 1931-1934.
[http://dx.doi.org/10.1126/science.1149460] [PMID: 18048652]
[13]
Kozomara, A.; Griffiths-Jones, S. miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res., 2011, 39(Database issue), D152-D157.
[http://dx.doi.org/10.1093/nar/gkq1027] [PMID: 21037258]
[14]
Chen, K.; Rajewsky, N. Deep conservation of microRNA-target relationships and 3'UTR motifs in vertebrates, flies, and nematodes. Cold Spring Harb. Symp. Quant. Biol., 2006, 71, 149-156.
[http://dx.doi.org/10.1101/sqb.2006.71.039] [PMID: 17381291]
[15]
Cheng, A.M.; Byrom, M.W.; Shelton, J.; Ford, L.P. Antisense inhibition of human miRNAs and indications for an involvement of miRNA in cell growth and apoptosis. Nucleic Acids Res., 2005, 33(4), 1290-1297.
[http://dx.doi.org/10.1093/nar/gki200] [PMID: 15741182]
[16]
Karp, X.; Ambros, V. Developmental biology. Encountering microRNAs in cell fate signaling. Science, 2005, 310(5752), 1288-1289.
[http://dx.doi.org/10.1126/science.1121566] [PMID: 16311325]
[17]
Miska, E.A. How microRNAs control cell division, differentiation and death. Curr. Opin. Genet. Dev., 2005, 15(5), 563-568.
[http://dx.doi.org/10.1016/j.gde.2005.08.005] [PMID: 16099643]
[18]
Cui, Q.; Yu, Z.; Purisima, E.O.; Wang, E. Principles of microRNA regulation of a human cellular signaling network. Mol. Syst. Biol., 2006, 2, 46.
[http://dx.doi.org/10.1038/msb4100089] [PMID: 16969338]
[19]
Xu, P.; Guo, M.; Hay, B.A. MicroRNAs and the regulation of cell death. Trends Genet., 2004, 20(12), 617-624.
[http://dx.doi.org/10.1016/j.tig.2004.09.010] [PMID: 15522457]
[20]
Bartel, D.P. MicroRNAs: target recognition and regulatory functions. Cell, 2009, 136(2), 215-233.
[http://dx.doi.org/10.1016/j.cell.2009.01.002] [PMID: 19167326]
[21]
Latronico, M.V.; Catalucci, D.; Condorelli, G. Emerging role of microRNAs in cardiovascular biology. Circ. Res., 2007, 101(12), 1225-1236.
[http://dx.doi.org/10.1161/CIRCRESAHA.107.163147] [PMID: 18063818]
[22]
Lu, M.; Zhang, Q.; Deng, M.; Miao, J.; Guo, Y.; Gao, W.; Cui, Q. An analysis of human microRNA and disease associations. PLoS One, 2008, 3(10), e3420.
[http://dx.doi.org/10.1371/journal.pone.0003420] [PMID: 18923704]
[23]
Duttagupta, R.; Jiang, R.; Gollub, J.; Getts, R.C.; Jones, K.W. Impact of cellular miRNAs on circulating miRNA biomarker signatures. PLoS One, 2011, 6(6), e20769.
[http://dx.doi.org/10.1371/journal.pone.0020769] [PMID: 21698099]
[24]
Yanaihara, N.; Caplen, N.; Bowman, E.; Seike, M.; Kumamoto, K.; Yi, M.; Stephens, R.M.; Okamoto, A.; Yokota, J.; Tanaka, T.; Calin, G.A.; Liu, C.G.; Croce, C.M.; Harris, C.C. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell, 2006, 9(3), 189-198.
[http://dx.doi.org/10.1016/j.ccr.2006.01.025] [PMID: 16530703]
[25]
Janssen, H.L.; Reesink, H.W.; Lawitz, E.J.; Zeuzem, S.; Rodriguez-Torres, M.; Patel, K.; van der Meer, A.J.; Patick, A.K.; Chen, A.; Zhou, Y.; Persson, R.; King, B.D.; Kauppinen, S.; Levin, A.A.; Hodges, M.R. Treatment of HCV infection by targeting microRNA. N. Engl. J. Med., 2013, 368(18), 1685-1694.
[http://dx.doi.org/10.1056/NEJMoa1209026] [PMID: 23534542]
[26]
Weinberg, M.S.; Wood, M.J. Short non-coding RNA biology and neurodegenerative disorders: novel disease targets and therapeutics. Hum. Mol. Genet., 2009, 18(R1), R27-R39.
[http://dx.doi.org/10.1093/hmg/ddp070] [PMID: 19297399]
[27]
Huang, D.S.; Zheng, C.H. Independent component analysis-based penalized discriminant method for tumor classification using gene expression data. Bioinformatics, 2006, 22(15), 1855-1862.
[http://dx.doi.org/10.1093/bioinformatics/btl190] [PMID: 16709589]
[28]
Yu, H-J; Huang, D-S Normalized feature vectors: a novel alignment- free sequence comparison method based on the numbers of adjacent amino acids. IEEE/ACM Trans. Comput. Biol. Bioinform., 2013, 10(2), 457-467.
[29]
Liu, S.G.; Qin, X.G.; Zhao, B.S.; Qi, B.; Yao, W.J.; Wang, T.Y.; Li, H.C.; Wu, X.N. Differential expression of miRNAs in esophageal cancer tissue. Oncol. Lett., 2013, 5(5), 1639-1642.
[http://dx.doi.org/10.3892/ol.2013.1251] [PMID: 23761828]
[30]
Bonci, D.; Coppola, V.; Musumeci, M.; Addario, A.; Giuffrida, R.; Memeo, L.; D’Urso, L.; Pagliuca, A.; Biffoni, M.; Labbaye, C.; Bartucci, M.; Muto, G.; Peschle, C.; De Maria, R. The miR-15a-miR-16-1 cluster controls prostate cancer by targeting multiple oncogenic activities. Nat. Med., 2008, 14(11), 1271-1277.
[http://dx.doi.org/10.1038/nm.1880] [PMID: 18931683]
[31]
Foss, KM; Sima, C; Ugolini, D; Neri, M; Allen, KE; Weiss, GJ miR-1254 and miR-574-5p: serum-based microRNA biomarkers for early-stage non-small cell lung cancer Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer, 2011, 6(3), 482-488.
[32]
Ge, T.T.; Liang, Y.; Fu, R.; Wang, G.J.; Ruan, E.B.; Qu, W.; Wang, X.M.; Liu, H.; Wu, Y.H.; Song, J.; Wang, H.Q.; Xing, L.M.; Guan, J.; Li, L.J.; Shao, Z.H. [Expressions of miR-21, miR-155 and miR-210 in plasma of patients with lymphoma and its clinical significance]. Zhongguo Shi Yan Xue Ye Xue Za Zhi, 2012, 20(2), 305-309.
[PMID: 22541087]
[33]
Li, Y.; Qiu, C.; Tu, J.; Geng, B.; Yang, J.; Jiang, T.; Cui, Q. HMDD v2.0: a database for experimentally supported human microRNA and disease associations. Nucleic Acids Res., 2014, 42(Database issue), D1070-D1074.
[http://dx.doi.org/10.1093/nar/gkt1023] [PMID: 24194601]
[34]
Cui, H.L.; Zhang, Y.D.; Ren, F.; Amp, N.H. dbDEMC2.0: a database of differentially expressed miRNAs in human cancers v2.0. Chna J. Modern Med., 2014, 24(3), 77-79.
[35]
Jiang, Q.; Wang, Y.; Hao, Y.; Juan, L.; Teng, M.; Zhang, X.; Li, M.; Wang, G.; Liu, Y. miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res., 2009, 37(Database issue), D98-D104.
[http://dx.doi.org/10.1093/nar/gkn714] [PMID: 18927107]
[36]
Jiang, Q.; Hao, Y.; Wang, G.; Juan, L.; Zhang, T.; Teng, M.; Liu, Y.; Wang, Y. Prioritization of disease microRNAs through a human phenome-microRNAome network. BMC Syst. Biol., 2010, 4(Suppl. 1), S2.
[http://dx.doi.org/10.1186/1752-0509-4-S1-S2] [PMID: 20522252]
[37]
Mørk, S.; Pletscher-Frankild, S.; Palleja Caro, A.; Gorodkin, J.; Jensen, L.J. Protein-driven inference of miRNA-disease associations. Bioinformatics, 2014, 30(3), 392-397.
[http://dx.doi.org/10.1093/bioinformatics/btt677] [PMID: 24273243]
[38]
Zhu, L.; Deng, S-P.; Huang, D-S. A Two-Stage Geometric Method for Pruning Unreliable Links in Protein-Protein Networks. IEEE Trans. Nanobioscience, 2015, 14(5), 528-534.
[http://dx.doi.org/10.1109/TNB.2015.2420754] [PMID: 25861086]
[39]
Zhu, L.; You, Z.H.; Huang, D.S.; Wang, B. t-LSE: a novel robust geometric approach for modeling protein-protein interaction networks. PLoS One, 2013, 8(4), e58368.
[http://dx.doi.org/10.1371/journal.pone.0058368] [PMID: 23560036]
[40]
Shi, H.; Xu, J.; Zhang, G.; Xu, L.; Li, C.; Wang, L.; Zhao, Z.; Jiang, W.; Guo, Z.; Li, X. Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes. BMC Syst. Biol., 2013, 7, 101.
[http://dx.doi.org/10.1186/1752-0509-7-101] [PMID: 24103777]
[41]
Xuan, P.; Han, K.; Guo, M.; Guo, Y.; Li, J.; Ding, J.; Liu, Y.; Dai, Q.; Li, J.; Teng, Z.; Huang, Y. Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors. PLoS One, 2013, 8(8), e70204.
[http://dx.doi.org/10.1371/journal.pone.0070204] [PMID: 23950912]
[42]
Chen, X.; Liu, M.X.; Yan, G.Y. RWRMDA: predicting novel human microRNA-disease associations. Mol. Biosyst., 2012, 8(10), 2792-2798.
[http://dx.doi.org/10.1039/c2mb25180a] [PMID: 22875290]
[43]
Xuan, P.; Han, K.; Guo, Y.; Li, J.; Li, X.; Zhong, Y.; Zhang, Z.; Ding, J. Prediction of potential disease-associated microRNAs based on random walk. Bioinformatics, 2015, 31(11), 1805-1815.
[http://dx.doi.org/10.1093/bioinformatics/btv039] [PMID: 25618864]
[44]
Huang, D-S. Systematic theory of neural networks for pattern recognition; Publishing House of Electronic Industry of China: Beijing, 1996, p. 201.
[45]
HUANG D-S. Radial basis probabilistic neural networks: model and application. Int. J. Pattern Recognit. Artif. Intell., 1999, 13(07), 1083-1101.
[http://dx.doi.org/10.1142/S0218001499000604]
[46]
Huang, D.S.; Du, J.X. A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks. IEEE Trans. Neural Netw., 2008, 19(12), 2099-2115.
[http://dx.doi.org/10.1109/TNN.2008.2004370] [PMID: 19054734]
[47]
Zheng, C.H.; Huang, D.S.; Zhang, L.; Kong, X.Z. Tumor clustering using nonnegative matrix factorization with gene selection. IEEE Transactions on Information Technology in Biomedicine A Publication of the IEEE Engineering in Medicine &. Biol. Soc., 2009, 13(4), 599-607.
[48]
Xu, J.; Li, C.X.; Lv, J.Y.; Li, Y.S.; Xiao, Y.; Shao, T.T.; Huo, X.; Li, X.; Zou, Y.; Han, Q.L.; Li, X.; Wang, L.H.; Ren, H. Prioritizing candidate disease miRNAs by topological features in the miRNA target-dysregulated network: case study of prostate cancer. Mol. Cancer Ther., 2011, 10(10), 1857-1866.
[http://dx.doi.org/10.1158/1535-7163.MCT-11-0055] [PMID: 21768329]
[49]
Chen, X.; Yan, G.Y. Semi-supervised learning for potential human microRNA-disease associations inference. Sci. Rep., 2014, 4, 5501.
[http://dx.doi.org/10.1038/srep05501] [PMID: 24975600]
[50]
Chen, X.; Yan, C.C.; Zhang, X.; Li, Z.; Deng, L.; Zhang, Y.; Dai, Q. RBMMMDA: predicting multiple types of disease-microRNA associations. Sci. Rep., 2015, 5, 13877.
[http://dx.doi.org/10.1038/srep13877] [PMID: 26347258]
[51]
Wang, D.; Wang, J.; Lu, M.; Song, F.; Cui, Q. Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases. Bioinformatics, 2010, 26(13), 1644-1650.
[http://dx.doi.org/10.1093/bioinformatics/btq241] [PMID: 20439255]
[52]
Gu, C.; Liao, B.; Li, X.; Li, K. Network Consistency Projection for Human miRNA-Disease Associations Inference. Sci. Rep., 2016, 6, 36054.
[http://dx.doi.org/10.1038/srep36054] [PMID: 27779232]
[53]
Rifkin, R.; Klautau, A. In Defense of One-Vs-All Classification. J. Mach. Learn. Res., 2004, 5(1), 101-141.
[54]
van Laarhoven, T.; Nabuurs, S.B.; Marchiori, E. Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics, 2011, 27(21), 3036-3043.
[http://dx.doi.org/10.1093/bioinformatics/btr500] [PMID: 21893517]
[55]
Chen, X.; Yan, C.C.; Zhang, X.; You, Z.H.; Deng, L.; Liu, Y.; Zhang, Y.; Dai, Q. WBSMDA: Within and Between Score for MiRNA-Disease Association prediction. Sci. Rep., 2016, 6, 21106.
[http://dx.doi.org/10.1038/srep21106] [PMID: 26880032]
[56]
Doghman, M.; El Wakil, A.; Cardinaud, B.; Thomas, E.; Wang, J.; Zhao, W.; Peralta-Del Valle, M.H.; Figueiredo, B.C.; Zambetti, G.P.; Lalli, E. Regulation of insulin-like growth factor-mammalian target of rapamycin signaling by microRNA in childhood adrenocortical tumors. Cancer Res., 2010, 70(11), 4666-4675.
[http://dx.doi.org/10.1158/0008-5472.CAN-09-3970] [PMID: 20484036]
[57]
Birks, D.K.; Barton, V.N.; Donson, A.M.; Handler, M.H.; Vibhakar, R.; Foreman, N.K. Survey of MicroRNA expression in pediatric brain tumors. Pediatr. Blood Cancer, 2011, 56(2), 211-216.
[http://dx.doi.org/10.1002/pbc.22723] [PMID: 21157891]
[58]
Rahman, M.M.; Qian, Z.R.; Wang, E.L.; Sultana, R.; Kudo, E.; Nakasono, M.; Hayashi, T.; Kakiuchi, S.; Sano, T. Frequent overexpression of HMGA1 and 2 in gastroenteropancreatic neuroendocrine tumours and its relationship to let-7 downregulation. Br. J. Cancer, 2009, 100(3), 501-510.
[http://dx.doi.org/10.1038/sj.bjc.6604883] [PMID: 19156147]
[59]
Zhang, R.; He, Y.; Zhang, X.; Xing, B.; Sheng, Y.; Lu, H.; Wei, Z. Estrogen receptor-regulated microRNAs contribute to the BCL2/BAX imbalance in endometrial adenocarcinoma and precancerous lesions. Cancer Lett., 2012, 314(2), 155-165.
[http://dx.doi.org/10.1016/j.canlet.2011.09.027] [PMID: 22014978]
[60]
Alder, H.; Taccioli, C.; Chen, H.; Jiang, Y.; Smalley, K.J.; Fadda, P.; Ozer, H.G.; Huebner, K.; Farber, J.L.; Croce, C.M.; Fong, L.Y. Dysregulation of miR-31 and miR-21 induced by zinc deficiency promotes esophageal cancer. Carcinogenesis, 2012, 33(9), 1736-1744.
[http://dx.doi.org/10.1093/carcin/bgs204] [PMID: 22689922]
[61]
Huang, D.S.; Jiang, W. A general CPL-AdS methodology for fixing dynamic parameters in dual environments. IEEE Trans. Syst. Man Cybern. B Cybern., 2012, 42(5), 1489-1500.
[http://dx.doi.org/10.1109/TSMCB.2012.2192475] [PMID: 22562768]
[62]
Zheng, C-H; Zhang, L; Ng, VT-Y; Shiu, SC-K; Huang, D-S Molecular pattern discovery based on penalized matrix decomposition Computational Biology and Bioinformatics, IEEE/ACM Transactions, 2011, 8(6), 1592-1603.
[63]
Zhang, Y. Epidemiology of esophageal cancer. World J. Gastroenterol., 2013, 19(34), 5598-5606.
[http://dx.doi.org/10.3748/wjg.v19.i34.5598] [PMID: 24039351]
[64]
Liu, R.; Liao, J.; Yang, M.; Shi, Y.; Peng, Y.; Wang, Y.; Pan, E.; Guo, W.; Pu, Y.; Yin, L. Circulating miR-155 expression in plasma: a potential biomarker for early diagnosis of esophageal cancer in humans. J. Toxicol. Environ. Health A, 2012, 75(18), 1154-1162.
[http://dx.doi.org/10.1080/15287394.2012.699856] [PMID: 22891887]
[65]
Zhang, H.F.; Alshareef, A.; Wu, C.; Jiao, J.W.; Sorensen, P.H.; Lai, R.; Xu, L.Y.; Li, E.M. miR-200b induces cell cycle arrest and represses cell growth in esophageal squamous cell carcinoma. Carcinogenesis, 2016, 37(9), 858-869.
[http://dx.doi.org/10.1093/carcin/bgw079] [PMID: 27496804]
[66]
Libin, L.I.; Fang, T.; Zhuang, Z.; Wenji, X.U.; Gastroenterology, D.O. Expression of microRNA-17-92 in esophageal squamous carcinoma and its clinical significance. Fujian Med. J., 2016.
[67]
Mei, L.L.; Wang, W.J.; Qiu, Y.T.; Xie, X.F.; Bai, J.; Shi, Z.Z. miR-125b-5p functions as a tumor suppressor gene partially by regulating HMGA2 in esophageal squamous cell carcinoma. PLoS One, 2017, 12(10), e0185636.
[http://dx.doi.org/10.1371/journal.pone.0185636] [PMID: 28968424]
[68]
Jemal, A.; Bray, F.; Center, M.M.; Ferlay, J.; Ward, E.; Forman, D. Global cancer statistics. CA Cancer J. Clin., 2011, 61(2), 69-90.
[http://dx.doi.org/10.3322/caac.20107] [PMID: 21296855]
[69]
Ogata-Kawata, H.; Izumiya, M.; Kurioka, D.; Honma, Y.; Yamada, Y.; Furuta, K.; Gunji, T.; Ohta, H.; Okamoto, H.; Sonoda, H.; Watanabe, M.; Nakagama, H.; Yokota, J.; Kohno, T.; Tsuchiya, N. Circulating exosomal microRNAs as biomarkers of colon cancer. PLoS One, 2014, 9(4), e92921.
[http://dx.doi.org/10.1371/journal.pone.0092921] [PMID: 24705249]
[70]
Drusco, A.; Nuovo, G.J.; Zanesi, N.; Di Leva, G.; Pichiorri, F.; Volinia, S.; Fernandez, C.; Antenucci, A.; Costinean, S.; Bottoni, A.; Rosito, I.A.; Liu, C.G.; Burch, A.; Acunzo, M.; Pekarsky, Y.; Alder, H.; Ciardi, A.; Croce, C.M. MicroRNA profiles discriminate among colon cancer metastasis. PLoS One, 2014, 9(6), e96670.
[http://dx.doi.org/10.1371/journal.pone.0096670] [PMID: 24921248]
[71]
Hu, M.; Xia, M.; Chen, X.; Lin, Z.; Xu, Y.; Ma, Y.; Su, L. MicroRNA-141 regulates Smad interacting protein 1 (SIP1) and inhibits migration and invasion of colorectal cancer cells. Dig. Dis. Sci., 2010, 55(8), 2365-2372.
[http://dx.doi.org/10.1007/s10620-009-1008-9] [PMID: 19830559]
[72]
Slaby, O.; Svoboda, M.; Fabian, P.; Smerdova, T.; Knoflickova, D.; Bednarikova, M.; Nenutil, R.; Vyzula, R. Altered expression of miR-21, miR-31, miR-143 and miR-145 is related to clinicopathologic features of colorectal cancer. Oncology, 2007, 72(5-6), 397-402.
[http://dx.doi.org/10.1159/000113489] [PMID: 18196926]
[73]
Zhang, G.J.; Li, Y.; Zhou, H.; Xiao, H.X.; Zhou, T. miR‑20a is an independent prognostic factor in colorectal cancer and is involved in cell metastasis. Mol. Med. Rep., 2014, 10(1), 283-291.
[http://dx.doi.org/10.3892/mmr.2014.2144] [PMID: 24737193]
[74]
Qu, Y.L.; Wang, H.F.; Sun, Z.Q.; Tang, Y.; Han, X.N.; Yu, X.B.; Liu, K. Up-regulated miR-155-5p promotes cell proliferation, invasion and metastasis in colorectal carcinoma. Int. J. Clin. Exp. Pathol., 2015, 8(6), 6988-6994.
[PMID: 26261588]
[75]
Nishida, N.; Yokobori, T.; Mimori, K.; Sudo, T.; Tanaka, F.; Shibata, K.; Ishii, H.; Doki, Y.; Kuwano, H.; Mori, M. MicroRNA miR-125b is a prognostic marker in human colorectal cancer. Int. J. Oncol., 2011, 38(5), 1437-1443.
[PMID: 21399871]
[76]
Stewart, B.; Wild, C. World cancer report 2014; International Agency for Research on Cancer, 2014.
[77]
Lozano, R.; Naghavi, M.; Foreman, K.; Lim, S.; Shibuya, K.; Aboyans, V.; Abraham, J.; Adair, T.; Aggarwal, R.; Ahn, S.Y.; Alvarado, M.; Anderson, H.R.; Anderson, L.M.; Andrews, K.G.; Atkinson, C.; Baddour, L.M.; Barker-Collo, S.; Bartels, D.H.; Bell, M.L.; Benjamin, E.J.; Bennett, D.; Bhalla, K.; Bikbov, B.; Bin Abdulhak, A.; Birbeck, G.; Blyth, F.; Bolliger, I.; Boufous, S.; Bucello, C.; Burch, M.; Burney, P.; Carapetis, J.; Chen, H.; Chou, D.; Chugh, S.S.; Coffeng, L.E.; Colan, S.D.; Colquhoun, S.; Colson, K.E.; Condon, J.; Connor, M.D.; Cooper, L.T.; Corriere, M.; Cortinovis, M.; de Vaccaro, K.C.; Couser, W.; Cowie, B.C.; Criqui, M.H.; Cross, M.; Dabhadkar, K.C.; Dahodwala, N.; De Leo, D.; Degenhardt, L.; Delossantos, A.; Denenberg, J.; Des Jarlais, D.C.; Dharmaratne, S.D.; Dorsey, E.R.; Driscoll, T.; Duber, H.; Ebel, B.; Erwin, P.J.; Espindola, P.; Ezzati, M.; Feigin, V.; Flaxman, A.D.; Forouzanfar, M.H.; Fowkes, F.G.; Franklin, R.; Fransen, M.; Freeman, M.K.; Gabriel, S.E.; Gakidou, E.; Gaspari, F.; Gillum, R.F.; Gonzalez-Medina, D.; Halasa, Y.A.; Haring, D.; Harrison, J.E.; Havmoeller, R.; Hay, R.J.; Hoen, B.; Hotez, P.J.; Hoy, D.; Jacobsen, K.H.; James, S.L.; Jasrasaria, R.; Jayaraman, S.; Johns, N.; Karthikeyan, G.; Kassebaum, N.; Keren, A.; Khoo, J.P.; Knowlton, L.M.; Kobusingye, O.; Koranteng, A.; Krishnamurthi, R.; Lipnick, M.; Lipshultz, S.E.; Ohno, S.L.; Mabweijano, J.; MacIntyre, M.F.; Mallinger, L.; March, L.; Marks, G.B.; Marks, R.; Matsumori, A.; Matzopoulos, R.; Mayosi, B.M.; McAnulty, J.H.; McDermott, M.M.; McGrath, J.; Mensah, G.A.; Merriman, T.R.; Michaud, C.; Miller, M.; Miller, T.R.; Mock, C.; Mocumbi, A.O.; Mokdad, A.A.; Moran, A.; Mulholland, K.; Nair, M.N.; Naldi, L.; Narayan, K.M.; Nasseri, K.; Norman, P.; O’Donnell, M.; Omer, S.B.; Ortblad, K.; Osborne, R.; Ozgediz, D.; Pahari, B.; Pandian, J.D.; Rivero, A.P.; Padilla, R.P.; Perez-Ruiz, F.; Perico, N.; Phillips, D.; Pierce, K.; Pope, C.A., III; Porrini, E.; Pourmalek, F.; Raju, M.; Ranganathan, D.; Rehm, J.T.; Rein, D.B.; Remuzzi, G.; Rivara, F.P.; Roberts, T.; De León, F.R.; Rosenfeld, L.C.; Rushton, L.; Sacco, R.L.; Salomon, J.A.; Sampson, U.; Sanman, E.; Schwebel, D.C.; Segui-Gomez, M.; Shepard, D.S.; Singh, D.; Singleton, J.; Sliwa, K.; Smith, E.; Steer, A.; Taylor, J.A.; Thomas, B.; Tleyjeh, I.M.; Towbin, J.A.; Truelsen, T.; Undurraga, E.A.; Venketasubramanian, N.; Vijayakumar, L.; Vos, T.; Wagner, G.R.; Wang, M.; Wang, W.; Watt, K.; Weinstock, M.A.; Weintraub, R.; Wilkinson, J.D.; Woolf, A.D.; Wulf, S.; Yeh, P.H.; Yip, P.; Zabetian, A.; Zheng, Z.J.; Lopez, A.D.; Murray, C.J.; AlMazroa, M.A.; Memish, Z.A. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet, 2012, 380(9859), 2095-2128.
[http://dx.doi.org/10.1016/S0140-6736(12)61728-0] [PMID: 23245604]
[78]
Bostwick, D. Urological Surgical Pathology; Mosby Inc, 2012.
[79]
Nguyen, H.C.; Xie, W.; Yang, M.; Hsieh, C.L.; Drouin, S.; Lee,G.S.; Kantoff, P.W. Expression differences of circulating microRNAs in metastatic castration resistant prostate cancer and low-risk, localized prostate cancer. Prostate, 2013, 73(4), 346-354.
[http://dx.doi.org/10.1002/pros.22572] [PMID: 22887127]
[80]
Szczyrba, J.; Löprich, E.; Wach, S.; Jung, V.; Unteregger, G.; Barth, S.; Grobholz, R.; Wieland, W.; Stöhr, R.; Hartmann, A.; Wullich, B.; Grässer, F. The microRNA profile of prostate carcinoma obtained by deep sequencing. Mol. Cancer Res., 2010, 8(4), 529-538.
[http://dx.doi.org/10.1158/1541-7786.MCR-09-0443] [PMID: 20353999]
[81]
Xu, B.; Niu, X.; Zhang, X.; Tao, J.; Wu, D.; Wang, Z.; Li, P.; Zhang, W.; Wu, H.; Feng, N.; Wang, Z.; Hua, L.; Wang, X. miR-143 decreases prostate cancer cells proliferation and migration and enhances their sensitivity to docetaxel through suppression of KRAS. Mol. Cell. Biochem., 2011, 350(1-2), 207-213.
[http://dx.doi.org/10.1007/s11010-010-0700-6] [PMID: 21197560]
[82]
Macfarlane, R.J.; Watahiki, A.; Wang, Y.Z.; Chi, K.N. 1103 POSTER Overexpression of MiR141 and MiR126 Distinguishes Metastatic Castration Resistant Prostate Cancer (mCRPC) From Localized Prostate Cancer (PCa) and Controls in Human Plasma. Eur. J. Cancer, 2011, 47(11), S125-S125.
[http://dx.doi.org/10.1016/S0959-8049(11)70746-8]
[83]
Leite, K.R.; Sousa-Canavez, J.M.; Reis, S.T.; Tomiyama, A.H.; Camara-Lopes, L.H.; Sañudo, A.; Antunes, A.A.; Srougi, M. Change in expression of miR-let7c, miR-100, and miR-218 from high grade localized prostate cancer to metastasis. Urol. Oncol., 2011, 29(3), 265-269.
[http://dx.doi.org/10.1016/j.urolonc.2009.02.002] [PMID: 19372056]
[84]
Chen, X.; Liu, M.X.; Cui, Q.H.; Yan, G.Y. Prediction of disease-related interactions between microRNAs and environmental factors based on a semi-supervised classifier. PLoS One, 2012, 7(8), e43425.
[http://dx.doi.org/10.1371/journal.pone.0043425] [PMID: 22937049]
[85]
Chen, X. miREFRWR: a novel disease-related microRNA-environmental factor interactions prediction method. Mol. Biosyst., 2016, 12(2), 624-633.
[http://dx.doi.org/10.1039/C5MB00697J] [PMID: 26689259]
[86]
Zhang, W.; Zhang, C.; Chen, H.; Li, L.; Tu, Y.; Liu, C.; Shi, S.; Zen, K.; Liu, Z. Evaluation of microRNAs miR-196a, miR-30a-5P, and miR-490 as biomarkers of disease activity among patients with FSGS. Clin. J. Am. Soc. Nephrol., 2014, 9(9), 1545-1552.
[http://dx.doi.org/10.2215/CJN.11561113] [PMID: 25107948]
[87]
Krek, A.; Grün, D.; Poy, M.N.; Wolf, R.; Rosenberg, L.; Epstein, E.J.; MacMenamin, P.; da Piedade, I.; Gunsalus, K.C.; Stoffel, M.; Rajewsky, N. Combinatorial microRNA target predictions. Nat. Genet., 2005, 37(5), 495-500.
[http://dx.doi.org/10.1038/ng1536] [PMID: 15806104]
[88]
Deng, S.P.; Huang, D.S. SFAPS: an R package for structure/function analysis of protein sequences based on informational spectrum method. IEEE International Conference on Bioinformatics and Biomedicine, 2014, pp. 29-34.
[http://dx.doi.org/10.1016/j.ymeth.2014.08.004]
[89]
Huang, D.S.; Zhang, L.; Han, K.; Deng, S.; Yang, K.; Zhang, H. Prediction of protein-protein interactions based on protein-protein correlation using least squares regression. Curr. Protein Pept. Sci., 2014, 15(6), 553-560.
[http://dx.doi.org/10.2174/1389203715666140724084019] [PMID: 25059329]
[90]
Zhu, L.; Guo, W.L.; Deng, S.P.; Huang, D.S. ChIP-PIT: Enhancing the Analysis of ChIP-Seq Data Using Convex-Relaxed Pair- Wise Interaction Tensor Decomposition. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2016, 13(1), 55-63.
[http://dx.doi.org/10.1109/TCBB.2015.2465893] [PMID: 26886732]

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