Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (2): 288-298.doi: 10.1007/s11390-021-0798-x

Special Issue: Emerging Areas

• Special Section on AI and Big Data Analytics in Biology and Medicine • Previous Articles     Next Articles

Predicting CircRNA-Disease Associations Based on Improved Weighted Biased Meta-Structure

Xiu-Juan Lei1, Senior Member, CCF, Member, ACM, IEEE, Chen Bian1, and Yi Pan2,3,*, Senior Member, IEEE        

  1. 1 School of Computer Science, Shaanxi Normal University, Xi'an 710119, China;
    2 School of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
    3 Department of Computer Science, Georgia State University, Atlanta, GA 30302, U.S.A
  • Received:2020-07-13 Revised:2021-02-23 Online:2021-03-05 Published:2021-04-01
  • Contact: Yi Pan E-mail:yipan@gsu.edu
  • About author:Xiu-Juan Lei is a professor and Ph.D. supervisor at Shaanxi Normal University, Xi'an. She received her Ph.D. degree in Northwestern Polytechnical University, Xi'an, in 2005. Her research interests include bioinformatics and intelligent computing.
  • Supported by:
    The work was supported by the National Natural Science Foundation of China under Grant Nos. 61972451, 61672334 and 61902230, and the Fundamental Research Funds for the Central Universities of China under Grant No. GK201901010.

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Circular RNAs (circRNAs) are RNAs with a special closed loop structure, which play important roles in tumors and other diseases. Due to the time consumption of biological experiments, computational methods for predicting associations between circRNAs and diseases become a better choice. Taking the limited number of verified circRNA-disease associations into account, we propose a method named CDWBMS, which integrates a small number of verified circRNA-disease associations with a plenty of circRNA information to discover the novel circRNA-disease associations. CDWBMS adopts an improved weighted biased meta-structure search algorithm on a heterogeneous network to predict associations between circRNAs and diseases. In terms of leave-one-out-cross-validation (LOOCV), 10-fold cross-validation and 5-fold cross-validation, CDWBMS yields the area under the receiver operating characteristic curve (AUC) values of 0.921 6, 0.917 2 and 0.900 5, respectively. Furthermore, case studies show that CDWBMS can predict unknow circRNA-disease associations. In conclusion, CDWBMS is an effective method for exploring disease-related circRNAs.

Key words: circular RNA (circRNA); circRNA-disease association; meta-structure; heterogeneous network;

[1] Kristensen L S, Andersen M S, Stagsted L V W, Ebbesen K K, Kjems J. The biogenesis, biology and characterization of circular RNAs. Nat. Rev. Genet., 2019, 20(7):675-691. DOI:10.1038/s41576-019-0158-7.
[2] Wilusz J E. A 360? view of circular RNAs:From biogenesis to functions. Wiley Interdiscip. Rev. RNA, 2018, 9(4):Article No. e1478. DOI:10.1002/wrna.1478.
[3] Sanger H L, Klotz G, Riesner D, Gross H J, Kleinschmidt A K. Viroids are single-stranded covalently closed circular RNA molecules existing as highly base-paired rod-like structures. Proc Natl. Acad. Sci. USA, 1976, 73(11):3852-3856. DOI:10.1073/pnas.73.11.3852.
[4] Capel B, Swain A, Nicolis S, Hacker A, Walter M, Koopman P, Goodfellow P, Lovell-Badge R. Circular transcripts of the testis-determining gene Sry in adult mouse testis. Cell, 1993, 73(5):1019-1030. DOI:10.1016/0092-8674(93)90279-Y.
[5] Cocquerelle C, Daubersies P, Majérus M A, Kerckaert J P, Bailleul B. Splicing with inverted order of exons occurs proximal to large introns. EMBO J., 1992, 11(3):1095-1098. DOI:10.1002/j.1460-2075.1992.tb05148.x.
[6] Cocquerelle C, Mascrez B, Hétuin D, Bailleul B. Missplicing yields circular RNA molecules. FASEB J., 1993, 7(1):155-160. DOI:10.1096/fasebj.7.1.7678559.
[7] Nigro J M, Cho K R, Fearon E R, Kern S E, Ruppert J M, Oliner J D, Kinzler K W, Vogelstein B. Scrambled exons. Cell, 1991, 64(3):607-613. DOI:10.1016/0092-8674(91)90244-S.
[8] Hansen T B, Jensen T I, Clausen B H, Bramsen J B, Finsen B, Damgaard C K, Kjems J. Natural RNA circles function as efficient microRNA sponges. Nature, 2013, 495(7441):384-388. DOI:10.1038/nature11993.
[9] Memczak S, Jens M, Elefsinioti A et al. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature, 2013, 495(7441):333-338. DOI:10.1038/nature11928.
[10] Enuka Y, Lauriola M, Feldman M E, Sas-Chen A, Ulitsky I, Yarden Y. Circular RNAs are long-lived and display only minimal early alterations in response to a growth factor. Nucleic Acids Res., 2016, 44(3):1370-1383. DOI:10.1093/nar/gkv1367.
[11] Pamudurti N R, Bartok O, Jens M et al. Translation of CircRNAs. Mol. Cell, 2017, 66(1):9-21. DOI:10.1016/j.molcel.2017.02.021.
[12] Maass P G, Glažar P, Memczak S et al. A map of human circular RNAs in clinically relevant tissues. J. Mol. Med., 2017, 95(11):1179-1189. DOI:10.1007/s00109-017-1582-9.
[13] Aufiero S, Van Den Hoogenhof M M G, Reckman Y J et al. Cardiac circRNAs arise mainly from constitutive exons rather than alternatively spliced exons. RNA, 2018, 24(6):815-827. DOI:10.1261/rna.064394.117.
[14] Rybak-Wolf A, Stottmeister C, Glažar P et al. Circular RNAs in the mammalian brain are highly abundant, conserved, and dynamically expressed. Mol. Cell, 2015, 58(5):870-885. DOI:10.1016/j.molcel.2015.03.027.
[15] Li Z, Huang C, Bao C et al. Exon-intron circular RNAs regulate transcription in the nucleus. Nat. Struct. Mol. Biol., 2015, 22(3):256-264. DOI:10.1038/nsmb.2959.
[16] Lux S, Bullinger L. Circular RNAs in cancer. Adv. Exp. Med. Biol., 2018, 1087:215-230. DOI:10.1007/978-981-13-1426-117.
[17] Liu J, Li D, Luo H, Zhu X. Circular RNAs:The star molecules in cancer. Mol. Aspects. Med., 2019, 70:141-152. DOI:10.1016/j.mam.2019.10.006.
[18] Smid M, Wilting S M, Uhr K et al. The circular RNome of primary breast cancer. Genome Res., 2019, 29(3):356-366. DOI:10.1101/gr.238121.118.
[19] Liu H, Bi J, Dong W, Yang M, Shi J, Jiang N, Lin T, Huang J. Invasion-related circular RNA circFNDC3B inhibits bladder cancer progression through the miR-1178-3p/G3BP2/SRC/FAK axis. Mol. Cancer, 2018, 17(1):Article No. 161. DOI:10.1186/s12943-018-0908-8.
[20] Xia Q, Ding T, Zhang G, Li Z, Zeng L, Zhu Y, Guo J, Hou J, Zhu T, Zheng J, Wang J. Circular RNA expression profiling identifies prostate cancer-specific circRNAs in prostate cancer. Cell Physiol. Biochem., 2018, 50(5):1903-1915. DOI:10.1159/000494870.
[21] Fan C, Lei X, Fang Z, Jiang Q, Wu F X. CircR2Disease:A manually curated database for experimentally supported circular RNAs associated with various diseases. Database (Oxford), 2018, 2018:Article No. bay044. DOI:10.1093/database/bay044.
[22] Ji P, Wu W, Chen S, Zheng Y, Zhou L, Zhang J, Cheng H, Yan J, Zhang S, Yang P, Zhao F. Expanded expression landscape and prioritization of circular RNAs in mammals. Cell Rep., 2019, 26(12):3444-3460. DOI:10.1016/j.celrep.2019.02.078.
[23] Yao D, Zhang L, Zheng M, Sun X, Lu Y. Circ2Disease:A manually curated database of experimentally validated circRNAs in human disease. 2018, 8(1):Article No. 11018. DOI:10.1038/s41598-018-29360-3.
[24] Zhao Z, Wang K, Wu F, Wang W, Zhang K, Hu H, Liu Y, Jiang T. circRNA disease:A manually curated database of experimentally supported circRNA-disease associations. Cell Death and Disease, 2018, 9(5):Article No. 475. DOI:10.1038/s41419-018-0503-3.
[25] Ghosal S, Das S, Sen R, Basak P, Chakrabarti J. Circ2Traits:A comprehensive database for circular RNA potentially associated with disease and traits. Front. Genet., 2013, 4:Article No. 283. DOI:10.3389/fgene.2013.00283.
[26] Lan W, Wang J, Li M, Liu J, Wu F X, Pan Y. Predicting microRNA-disease associations based on improved microRNA and disease similarities. IEEE/ACM Trans. Comput. Biol. Bioinform., 2018, 15(6):1774-1782. DOI:10.1109/TCBB.2016.2586190.
[27] Lan W, Li M, Zhao K, Liu J, Wu F X, Pan Y, Wang J. LDAP:A web server for lncRNA-disease association prediction. Bioinformatics, 2017, 33(3):458-460. DOI:10.1093/bioinformatics/btw639.
[28] Yan C, Wang J, Ni P, Lan W, Wu F X, Pan Y. DNRLMFMDA:Predicting microRNA-disease associations based on similarities of microRNAs and diseases. IEEE/ACM Trans. Comput. Biol. Bioinform., 2019, 16(1):233-243. DOI:10.1109/TCBB.2017.2776101.
[29] Peng W, Lan W, Yu Z, Wang J, Pan Y. A framework for integrating multiple biological networks to predict microRNA-disease associations. IEEE Trans. Nano Bioscience, 2017, 16(2):100-107. DOI:10.1109/TNB.2016.2633276.
[30] Wu L, Li M, Wang J X, Wu F X. Controllability and its applications to biological networks. Journal of Computer Science and Technology, 2019, 34(1):16-34. DOI:10.1007/s11390-019-1896-x.
[31] Fang Z, Lei X. Prediction of miRNA-circRNA associations based on k-NN multi-label with random walk restart on a heterogeneous network. Big Data Mining and Analytics, 2019, 2(4):261-272. DOI:10.26599/BDMA.2019.9020010.
[32] Fan C, Lei X, Wu F X. Prediction of CircRNA-disease associations using KATZ model based on heterogeneous networks. Int. J. Biol. Sci., 2018, 14(14):1950-1959. DOI:10.7150/ijbs.28260.
[33] Lei X, Fang Z, Chen L, Wu F X. PWCDA:Path weighted method for predicting circRNA-disease associations. Int. J. Mol. Sci., 2018, 19(11):Article No. 3410. DOI:10.3390/ijms19113410.
[34] Yan C, Wang J, Wu F X. DWNN-RLS:Regularized least squares method for predicting circRNA-disease associations. BMC Bioinformatics, 2018, 19(Suppl 19):Article No. 520. DOI:10.1186/s12859-018-2522-6.
[35] Wei H, Liu B. iCircDA-MF:Identification of circRNAdisease associations based on matrix factorization. Brief Bioinform., 2019, 21(4):1356-1367. DOI:10.1093/bib/bbz057.
[36] Zhang W, Yu C, Wang X, Liu F. Predicting circRNAdisease associations through linear neighborhood label propagation method. IEEE Access, 2019, 7:83474-83483. DOI:10.1109/ACCESS.2019.2920942.
[37] Lei X, Zhang W. BRWSP:Predicting circRNA-disease associations based on biased random walk to search paths on a multiple heterogeneous network. Complexity, 2019, 2019:Article No. 5938035. DOI:10.1155/2019/5938035.
[38] Wang Y, Nie C, Zang T, Wang Y. Predicting circRNAdisease associations based on circRNA expression similarity and functional similarity. Frontiers in Genetics, 2019, 10:Article No. 832. DOI:10.3389/fgene.2019.00832.
[39] Li S, Li Y, Chen B, Zhao J, Yu S, Tang Y, Zheng Q, Li Y, Wang P, He X, Huang S. exoRBase:A database of circRNA, lncRNA and mRNA in human blood exosomes. Nucleic Acids Res., 2018, 46(D1):D106-D112. DOI:10.1093/nar/gkx891.
[40] Glažar P, Papavasileiou P, Rajewsky N. circBase:A database for circular RNAs. RNA, 2014, 20(11):1666-1670. DOI:10.1261/rna.043687.113.
[41] Muppirala U K, Honavar V G, Dobbs D. Predicting RNA-protein interactions using only sequence information. BMC Bioinformatics, 2011, 12(1):Article No. 489. DOI:10.1186/1471-2105-12-489.
[42] Van Laarhoven T, Nabuurs S B, Marchiori E. Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics, 2011, 27(21):3036-3043. DOI:10.1093/bioinformatics/btr500.
[43] 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. DOI:10.1093/bioinformatics/btq241.
[44] Huang Z, Zheng Y, Cheng R, Sun Y, Mamoulis N, Li X. Meta structure:Computing relevance in large heterogeneous information networks. In Proc. the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2016, pp.1595-1604. DOI:10.1145/2939672.2939815.
[45] Zhao H, Yao Q, Li J, Song Y, Lee D L. Meta-graph based recommendation fusion over heterogeneous information networks. In Proc. the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2017, pp.635-644. DOI:10.1145/3097983.3098063.
[46] Long Y, Luo J. WMGHMDA:A novel weighted metagraph-based model for predicting human microbe-disease association on heterogeneous information network. BMC Bioinformatics, 2019, 20(1):Article No. 541. DOI:10.1186/s12859-019-3066-0.
[47] Lei X, Tie J. Prediction of disease-related metabolites using bi-random walks. PLoS ONE, 2019, 14(11):Article No. e0225380. DOI:10.1371/journal.pone.0225380.
[48] Jiang Y, Liu B, Yu L, Yan C, Bian H. Predict MiRNAdisease association with collaborative filtering. Neuroinformatics, 2018, 16(3):363-372. DOI:10.1007/s12021-018-9386-9.
[49] Necula L, Matei L, Dragu D, Neagu A I, Mambet C, Nedeianu S, Bleotu C, Diaconu C C, Chivu-Economescu M. Recent advances in gastric cancer early diagnosis. World J. Gastroenterol, 2019, 25(17):2029-2044. DOI:10.3748/wjg.v25.i17.2029.
[50] Weitz J, Koch M, Debus J, Höhler T, Galle P R, Büchler M W. Colorectal cancer. Lancet, 2005, 365(9454):153-165. DOI:10.1016/S0140-6736(05)17706-X.
[51] Sun Y S, Zhao Z, Yang Z N, Xu F, Lu H J, Zhu Z Y, Shi W, Jiang J, Yao P P, Zhu H P. Risk factors and preventions of breast cancer. Int. J. Biol. Sci., 2017, 13(11):1387-1397. DOI:10.7150/ijbs.21635.
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[3] Zhang Cui; Zhao Qinping; Xu Jiafu;. Kernel Language KLND[J]. , 1986, 1(3): 65 -79 .
[4] Wang Jianchao; Wei Daozheng;. An Effective Test Generation Algorithm for Combinational Circuits[J]. , 1986, 1(4): 1 -16 .
[5] Huang Heyan;. A Parallel Implementation Model of HPARLOG[J]. , 1986, 1(4): 27 -38 .
[6] Huang Xuedong; Cai Lianhong; Fang Ditang; Chi Bianjin; Zhou Li; Jiang Li;. A Computer System for Chinese Character Speech Input[J]. , 1986, 1(4): 75 -83 .
[7] Shi Zhongzhi;. Knowledge-Based Decision Support System[J]. , 1987, 2(1): 22 -29 .
[8] Tang Tonggao; Zhao Zhaokeng;. Stack Method in Program Semantics[J]. , 1987, 2(1): 51 -63 .
[9] Xia Peisu; Fang Xinwo; Wang Yuxiang; Yan Kaiming; Zhang Tingjun; Liu Yulan; Zhao Chunying; Sun Jizhong;. Design of Array Processor Systems[J]. , 1987, 2(3): 163 -173 .
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