癌症相关microRNA与靶基因的生物信息学分析

doi:10.16288/j.yczz.14-439

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HEREDITAS(Beijing) ›› 2015, Vol. 37 ›› Issue (9): 855-864.doi: 10.16288/j.yczz.14-439

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Bioinformatic analysis of cancer-related microRNAs and their target genes

Xue Zhou¹, Yilan Du², Ping Jin³, Fei Ma³

1. School of Chemistry and Biological Engineering, Nanjing Normal University Taizhou College, Taizhou 225300, China;
2. Health Management Department of Daping Hospital, Third Military Medical University, Chongqing 400042,China;
3. College of Life Science, Nanjing Normal University, Nanjing 210046, China

Received:2015-03-22 Revised:2015-07-06 Online:2015-09-20 Published:2015-09-20

Abstract

Abstract: MicroRNAs (miRNAs) are a class of ～22 nucleotide endogenous noncoding RNAs which regulate gene expression by targeting complementary transcripts. Recent studies have found that miRNAs are closely related to tumorigenesis and can act as oncogenes or tumor suppressor genes to influence the occurrence and development of tumor. To further reveal characteristics of cancer-related miRNAs and the functions of miRNA targets, we obtained 475 miRNAs involved in cancer through database searching and information retrieval. We systematically analyzed and compared the features including conservation, SNP distribution, cancer spectrum width, and transcriptional regulation between cancer and non-cancer related miRNAs as well as between intergenic and intragenic miRNAs. Our results showed that cancer-related miRNAs have higher conservation and lower SNP frequency compared to non-cancer-related miRNAs, and the cancer spectrum of one miRNA is positively correlated with its conservation. Genome analysis showed that cancer-related miRNAs tend to present as clusters compared with non-cancer-related miRNAs. Further association analysis between cancer progression and host genes, cancer-related miRNAs or target genes found that the host genes of some non-cancer related miRNAs tend to be targeted by cancer-related miRNAs. This study provides theoretical basis for further understanding the relationship between miRNA and cancer progression as well as the miRNA-based cancer diagnosis.

Key words: microRNAs (miRNAs), cancer, target gene, host gene, bioinformatics

Xue Zhou, Yilan Du, Ping Jin, Fei Ma. Bioinformatic analysis of cancer-related microRNAs and their target genes[J]. HEREDITAS(Beijing), 2015, 37(9): 855-864.

References

[1] Lee Y, Kim M, Han JJ, Yeom KH, Lee S, Baek SH, Kim VN. MicroRNA genes are transcribed by RNA polymerase II. EMBO J , 2004, 23(20): 4051-4060.
[2] Lee RC, Feinbaum RL, Ambros V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14 . Cell , 1993, 75(5): 843-854.
[3] Wightman B, Ha I, Ruvkun G. Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans . Cell , 1993, 75(5): 855-862.
[4] Ambros V, Bartel B, Bartel DP, Burge CB, Carrington JC, Chen XM, Dreyfuss G, Eddy SR, Griffiths-Jones S, Marshall M, Matzke M, Ruvkun G, Tuschl T. A uniform system for microRNA annotation. RNA , 2003, 9(3): 277-279.
[5] Bushati N, Cohen SM. microRNA functions. Annu Rev Cell Dev Biol , 2007, 23: 175-205.
[6] Sun BK, Tsao H. Small RNAs in development and disease. J Am Acad Dermatol , 2008, 59(5): 725-737.
[7] Brase JC, Wuttig D, Kuner R, Sültmann H. Serum microRNAs as non-invasive biomarkers for cancer. Mol Cancer , 2010, 9(1): 306.
[8] Cortez MA, Calin GA. MicroRNA identification in plasma and serum: a new tool to diagnose and monitor diseases. Expert Opin Biol Ther , 2009, 9(6): 703-711.
[9] Kozomara A, Griffiths-Jones S. miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res , 2011, 39(Database issue): D152-D157.
[10] Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ. miRBase: tools for microRNA genomics. Nucleic Acids Res , 2008, 36(Database issue): D154-D158.
[11] Monteys AM, Spengler RM, Wan J, Tecedor L, Lennox KA, Xing Y, Davidson BL. Structure and activity of putative intronic miRNA promoters. RNA , 2010, 16(3): 495-505.
[12] Altuvia Y, Landgraf P, Lithwick G, Elefant N, Pfeffer S, Aravin A, Brownstein MJ, Tuschl T, Margalit H. Clustering and conservation patterns of human microRNAs. Nucleic Acids Res , 2005, 33(8): 2697-2706.
[13] Yu J, Wang F, Yang GH, Wang FL, Ma YN, Du ZW, Zhang JW. Human microRNA clusters: genomic organization and expression profile in leukemia cell lines. Biochem Biophys Res Commun , 2006, 349(1): 59-68.
[14] Kasinski AL, Slack FJ. Epigenetics and genetics. MicroRNAs en route to the clinic: progress in validating and targeting microRNAs for cancer therapy. Nat Rev Cancer , 2011, 11(12): 849-864.
[15] Calin GA, Sevignani C, Dumitru CD, Hyslop T, Noch E, Yendamuri S, Shimizu M, Rattan S, Bullrich F, Negrini M, Croce CM. Human microRNA genes are frequently located at fragile sites and genomic regions involved in cancers. Proc Natl Acad Sci USA , 2004, 101(9): 2999-3004.
[16] Zhang BH, Pan XP, Cobb GP, Anderson TA. microRNAs as oncogenes and tumor suppressors. Dev Biol , 2007, 302(1): 1-12.
[17] 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(7): 2257-2261.
[18] Lee YS, Kim HK, Chung S, Kim KS, Dutta A. Depletion of human micro-RNA miR-125b reveals that it is critical for the proliferation of differentiated cells but not for the down-regulation of putative targets during differentiation. J Biol Chem , 2005, 280(17): 16635-16641.
[19] Roldo C, Missiaglia E, Hagan JP, Falconi M, Capelli P, Bersani S, Calin GA, Volinia S, Liu CG, Scarpa A, Croce CM. MicroRNA expression abnormalities in pancreatic endocrine and acinar tumors are associated with distinctive pathologic features and clinical behavior. J Clin Oncol , 2006, 24(29): 4677-4684.
[20] Chang TC, Wentzel EA, Kent OA, Ramachandran K, Mullendore M, Lee KH, Feldmann G, Yamakuchi M, Ferlito M, Lowenstein CJ, Arking DE, Beer MA, Maitra A, Mendell JT. Transactivation of miR-34a by p53 broadly influences gene expression and promotes apoptosis. Mol Cell , 2007, 26(5): 745-752.
[21] Lodyg

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Bioinformatic analysis of cancer-related microRNAs and their target genes

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