Login Register Cart 0
Generic placeholder image

Current Gene Therapy

Editor-in-Chief

ISSN (Print): 1566-5232
ISSN (Online): 1875-5631

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

MDAlmc: A Novel Low-rank Matrix Completion Model for MiRNADisease Association Prediction by Integrating Similarities among MiRNAs and Diseases

Author(s): Kun Wang, Junlin Xu, Geng Tian, Yang Li, Xueying Zeng* and Jialiang Yang*

Volume 23, Issue 4, 2023

Published on: 24 May, 2023

Page: [316 - 327] Pages: 12

DOI: 10.2174/1566523223666230419101405

Price: $65

conference banner
Abstract

Introduction: The importance of microRNAs (miRNAs) has been emphasized by an increasing number of studies, and it is well-known that miRNA dysregulation is associated with a variety of complex diseases. Revealing the associations between miRNAs and diseases are essential to disease prevention, diagnosis, and treatment.

Methods: However, traditional experimental methods in validating the roles of miRNAs in diseases could be very expensive, labor-intensive and time-consuming. Thus, there is a growing interest in predicting miRNA-disease associations by computational methods. Though many computational methods are in this category, their prediction accuracy needs further improvement for downstream experimental validation. In this study, we proposed a novel model to predict miRNA-disease associations by low-rank matrix completion (MDAlmc) integrating miRNA functional similarity, disease semantic similarity, and known miRNA-disease associations. In the 5-fold cross-validation, MDAlmc achieved an average AUROC of 0.8709 and AUPRC of 0.4172, better than those of previous models.

Results: Among the case studies of three important human diseases, the top 50 predicted miRNAs of 96% (breast tumors), 98% (lung tumors), and 90% (ovarian tumors) have been confirmed by previous literatures. And the unconfirmed miRNAs were also validated to be potential disease-associated miRNAs.

Conclusion: MDAlmc is a valuable computational resource for miRNA–disease association prediction.

« Previous
Graphical Abstract

[1]
Filipowicz W, Bhattacharyya SN, Sonenberg N. Mechanisms of post-transcriptional regulation by microRNAs: Are the answers in sight? Nat Rev Genet 2008; 9(2): 102-14.
[http://dx.doi.org/10.1038/nrg2290] [PMID: 18197166]
[2]
Bartel DP. MicroRNAs: Target recognition and regulatory functions. Cell 2009; 136(2): 215-33.
[http://dx.doi.org/10.1016/j.cell.2009.01.002] [PMID: 19167326]
[3]
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): 843-54.
[http://dx.doi.org/10.1016/0092-8674(93)90529-Y] [PMID: 8252621]
[4]
Ambros V. MicroRNA pathways in flies and worms: Growth, death, fat, stress, and timing. Cell 2003; 113(6): 673-6.
[http://dx.doi.org/10.1016/S0092-8674(03)00428-8] [PMID: 12809598]
[5]
Ru X, Cao P, Li L, Zou Q. Selecting essential microRNAs using a novel voting method. Mol Ther Nucleic Acids 2019; 18: 16-23.
[http://dx.doi.org/10.1016/j.omtn.2019.07.019] [PMID: 31479921]
[6]
Taganov KD, Boldin MP, Chang KJ, Baltimore D. NF-κB-dependent induction of microRNA miR-146, an inhibitor targeted to signaling proteins of innate immune responses. Proc Natl Acad Sci USA 2006; 103(33): 12481-6.
[http://dx.doi.org/10.1073/pnas.0605298103] [PMID: 16885212]
[7]
Chen JF, Mandel EM, Thomson JM, et al. The role of microRNA-1 and microRNA-133 in skeletal muscle proliferation and differentia-tion. Nat Genet 2006; 38(2): 228-33.
[http://dx.doi.org/10.1038/ng1725] [PMID: 16380711]
[8]
Chen CZ, Li L, Lodish HF, Bartel DP. MicroRNAs modulate hematopoietic lineage differentiation. Science 2004; 303(5654): 83-6.
[http://dx.doi.org/10.1126/science.1091903] [PMID: 14657504]
[9]
Carleton M, Cleary MA, Linsley PS. MicroRNAs and cell cycle regulation. Cell Cycle 2007; 6(17): 2127-32.
[http://dx.doi.org/10.4161/cc.6.17.4641] [PMID: 17786041]
[10]
Urbich C, Kuehbacher A, Dimmeler S. Role of microRNAs in vascular diseases, inflammation, and angiogenesis. Cardiovasc Res 2008; 79(4): 581-8.
[http://dx.doi.org/10.1093/cvr/cvn156] [PMID: 18550634]
[11]
Petrocca F, Visone R, Onelli MR, et al. E2F1-regulated microRNAs impair TGFbeta-dependent cell-cycle arrest and apoptosis in gastric cancer. Cancer Cell 2008; 13(3): 272-86.
[http://dx.doi.org/10.1016/j.ccr.2008.02.013] [PMID: 18328430]
[12]
Leung AKL, Sharp PA. MicroRNA functions in stress responses. Mol Cell 2010; 40(2): 205-15.
[http://dx.doi.org/10.1016/j.molcel.2010.09.027] [PMID: 20965416]
[13]
Zhu Q, Fan Y, Pan X. Fusing multiple biological networks to effectively predict miRNA-disease associations. Curr Bioinform 2021; 16(3): 371-84.
[http://dx.doi.org/10.2174/1574893615999200715165335]
[14]
Dai Q, Chu Y, Li Z, et al. MDA-CF: Predicting MiRNA-Disease associations based on a cascade forest model by fusing multi-source information. Comput Biol Med 2021; 136: 104706.
[http://dx.doi.org/10.1016/j.compbiomed.2021.104706] [PMID: 34371319]
[15]
Tang W, Wan S, Yang Z, Teschendorff AE, Zou Q. Tumor origin detection with tissue-specific miRNA and DNA methylation markers. Bioinformatics 2018; 34(3): 398-406.
[http://dx.doi.org/10.1093/bioinformatics/btx622] [PMID: 29028927]
[16]
Catto JWF, Alcaraz A, Bjartell AS, et al. MicroRNA in prostate, bladder, and kidney cancer: A systematic review. Eur Urol 2011; 59(5): 671-81.
[http://dx.doi.org/10.1016/j.eururo.2011.01.044] [PMID: 21296484]
[17]
Yang M, Yang H, Ji L, et al. A multi-omics machine learning framework in predicting the survival of colorectal cancer patients. Comput Biol Med 2022; 146: 105516.
[http://dx.doi.org/10.1016/j.compbiomed.2022.105516] [PMID: 35468406]
[18]
Chuang KH, Whitney-Miller CL, Chu CY, et al. MicroRNA‐494 is a master epigenetic regulator of multiple invasion‐suppressor mi-croRNAs by targeting ten eleven translocation 1 in invasive human hepatocellular carcinoma tumors. Hepatology 2015; 62(2): 466-80.
[http://dx.doi.org/10.1002/hep.27816] [PMID: 25820676]
[19]
Zou Q, Li J, Wang C, Zeng X. Approaches for recognizing disease genes based on network. BioMed Res Int 2014; 2014: 416323.
[http://dx.doi.org/10.1155/2014/416323] [PMID: 24707485]
[20]
Niu YW, Wang GH, Yan GY, Chen X. Integrating random walk and binary regression to identify novel miRNA-disease association. BMC Bioinformatics 2019; 20(1): 59.
[http://dx.doi.org/10.1186/s12859-019-2640-9] [PMID: 30691413]
[21]
Cho WCS. MicroRNAs: Potential biomarkers for cancer diagnosis, prognosis and targets for therapy. Int J Biochem Cell Biol 2010; 42(8): 1273-81.
[http://dx.doi.org/10.1016/j.biocel.2009.12.014] [PMID: 20026422]
[22]
Liao Z, Li D, Wang X, Li L, Zou Q. Cancer diagnosis through IsomiR expression with machine learning method. Curr Bioinform 2018; 13(1): 57-63.
[http://dx.doi.org/10.2174/1574893611666160609081155]
[23]
Zeng X, Liu L, Lü L, Zou Q. Prediction of potential disease-associated microRNAs using structural perturbation method. Bioinformatics 2018; 34(14): 2425-32.
[http://dx.doi.org/10.1093/bioinformatics/bty112] [PMID: 29490018]
[24]
Zou Q, Li J, Song L, Zeng X, Wang G. Similarity computation strategies in the microRNA-disease network: A survey. Brief Funct Genomics 2016; 15(1): 55-64.
[PMID: 26134276]
[25]
Xu J, Zhu W, Cai L, et al. LRMCMDA: Predicting miRNAdisease association by integrating low-rank matrix completion with miRNA and disease similarity information. IEEE Access 2020; 8: 80728-38.
[http://dx.doi.org/10.1109/ACCESS.2020.2990533]
[26]
Li X, Lin Y, Gu C, Yang J. FCMDAP: Using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs. BMC Syst Biol 2019; 13(S2): 26.
[http://dx.doi.org/10.1186/s12918-019-0696-9] [PMID: 30953512]
[27]
Xu J, Cai L, Liao B, et al. Identifying potential miRNAs–disease associations with probability matrix factorization. Front Genet 2019; 10: 1234.
[http://dx.doi.org/10.3389/fgene.2019.01234] [PMID: 31921290]
[28]
Li Y, Qiu C, Tu J, et al. HMDD v2.0: A database for experimentally supported human microRNA and disease associations. Nucleic Acids Res 2014; 42(D1): D1070-4.
[http://dx.doi.org/10.1093/nar/gkt1023] [PMID: 24194601]
[29]
Jiang Q, Hao Y, Wang G, et al. 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]
[30]
Jiang Q, Wang Y, Hao Y, et al. miR2Disease: A manually curated database for microRNA deregulation in human disease. Nucleic Acids Res 2009; 37: D98-D104.
[http://dx.doi.org/10.1093/nar/gkn714] [PMID: 18927107]
[31]
Chen Z, Wang X, Gao P, Liu H, Song B. Predicting disease related microRNA based on similarity and topology. Cells 2019; 8(11): 1405.
[http://dx.doi.org/10.3390/cells8111405] [PMID: 31703479]
[32]
Sun D, Li A, Feng H, Wang M. NTSMDA: prediction of miRNA–disease associations by integrating network topological similarity. Mol Biosyst 2016; 12(7): 2224-32.
[http://dx.doi.org/10.1039/C6MB00049E] [PMID: 27153230]
[33]
Yao Y, Ji B, Shi S, et al. IMDAILM: Inferring miRNA-Disease Association by Integrating lncRNA and miRNA Data. IEEE Access 2020; 8: 16517-27.
[http://dx.doi.org/10.1109/ACCESS.2019.2958055]
[34]
Xiao X, Zhu W, Liao B, et al. BPLLDA: Predicting lncRNA-disease associations based on simple paths with limited lengths in a hetero-geneous network. Front Genet 2018; 9: 411.
[http://dx.doi.org/10.3389/fgene.2018.00411] [PMID: 30459803]
[35]
Chen X, Wang CC, Yin J, You ZH. Novel Human miRNA-disease association inference based on random forest. Mol Ther Nucleic Acids 2018; 13: 568-79.
[http://dx.doi.org/10.1016/j.omtn.2018.10.005] [PMID: 30439645]
[36]
Wang CC, Chen X, Yin J, Qu J. An integrated framework for the identification of potential miRNA-disease association based on novel negative samples extraction strategy. RNA Biol 2019; 16(3): 257-69.
[http://dx.doi.org/10.1080/15476286.2019.1568820] [PMID: 30646823]
[37]
Wang D, Wang J, Lu M, Song F, Cui Q. Inferring the human microRNA functional similarity and functional network based on mi-croRNA-associated diseases. Bioinformatics 2010; 26(13): 1644-50.
[http://dx.doi.org/10.1093/bioinformatics/btq241] [PMID: 20439255]
[38]
Zeng X, Zhang X, Zou Q. Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks. Brief Bioinform 2016; 17(2): 193-203.
[http://dx.doi.org/10.1093/bib/bbv033] [PMID: 26059461]
[39]
Tian L, Wang SL. Exploring miRNA sponge networks of breast cancer by combining miRNA-disease-lncRNA and miRNA-target net-works. Curr Bioinform 2021; 16(3): 385-94.
[http://dx.doi.org/10.2174/1574893615999200711171530]
[40]
Cui F, Zhou M, Zou Q. Computational biology and chemistry Special section editorial: Computational analyses for miRNA. Comput Biol Chem 2021; 91: 107448.
[http://dx.doi.org/10.1016/j.compbiolchem.2021.107448] [PMID: 33579616]
[41]
Liu Y, Li X, Feng X, Wang L. A novel neighborhood-based computational model for potential MiRNA-disease association prediction. Comput Math Methods Med 2019; 2019: 1-10.
[http://dx.doi.org/10.1155/2019/5145646] [PMID: 30800172]
[42]
Qu Y, Zhang H, Liang C, Dong X. KATZMDA: Prediction of miRNA-disease associations based on KATZ model. IEEE Access 2018; 6: 3943-50.
[http://dx.doi.org/10.1109/ACCESS.2017.2754409]
[43]
Xuan P, Han K, Guo M, et al. 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]
[44]
Chen X, Liu MX, Yan GY. RWRMDA: predicting novel human microRNA–disease associations. Mol Biosyst 2012; 8(10): 2792-8.
[http://dx.doi.org/10.1039/c2mb25180a] [PMID: 22875290]
[45]
Chen X, Yan CC, Zhang X, You ZH, Huang YA, Yan GY. HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction. Oncotarget 2016; 7(40): 65257-69.
[http://dx.doi.org/10.18632/oncotarget.11251] [PMID: 27533456]
[46]
Ezzat A, Wu M, Li XL, Kwoh CK. Drug-target interaction prediction using ensemble learning and dimensionality reduction. Methods 2017; 129: 81-8.
[http://dx.doi.org/10.1016/j.ymeth.2017.05.016] [PMID: 28549952]
[47]
Gu W, Xie X, He Y, Zhang Z. [Drug-target protein interaction prediction based on AdaBoost algorithm]. Sheng Wu I Hsueh Kung Cheng Hsueh Tsa Chih 2018; 35(6): 935-42.
[PMID: 30583320]
[48]
Meng Y, Lu C, Jin M, Xu J, Zeng X, Yang J. A weighted bilinear neural collaborative filtering approach for drug repositioning. Brief Bioinform 2022; 23(2): bbab581.
[http://dx.doi.org/10.1093/bib/bbab581] [PMID: 35039838]
[49]
Yang J, Peng S, Zhang B, et al. Human geroprotector discovery by targeting the converging subnetworks of aging and age-related diseas-es. Geroscience 2020; 42(1): 353-72.
[http://dx.doi.org/10.1007/s11357-019-00106-x] [PMID: 31637571]
[50]
Liu H, Qiu C, Wang B, et al. Evaluating DNA methylation, gene expression, somatic mutation, and their combinations in inferring tumor tissue-of-origin. Front Cell Dev Biol 2021; 9: 619330.
[http://dx.doi.org/10.3389/fcell.2021.619330] [PMID: 34012960]
[51]
Xu J, Cai L, Liao B, Zhu W, Yang J. CMF-Impute: an accurate imputation tool for single-cell RNA-seq data. Bioinformatics 2020; 36(10): 3139-47.
[http://dx.doi.org/10.1093/bioinformatics/btaa109] [PMID: 32073612]
[52]
Xu J, Li CX, Lv JY, et al. Prioritizing candidate disease miRNAs by topological features in the miRNA target-dysregulated network: case study of prostate cancer. Mol Cancer Ther 2011; 10(10): 1857-66.
[http://dx.doi.org/10.1158/1535-7163.MCT-11-0055] [PMID: 21768329]
[53]
Chen X, Yan GY. Semi-supervised learning for potential human microRNA-disease associations inference. Sci Rep 2014; 4(1): 5501.
[http://dx.doi.org/10.1038/srep05501] [PMID: 24975600]
[54]
Li JQ, Rong ZH, Chen X, Yan GY, You ZH. MCMDA: Matrix completion for MiRNA-disease association prediction. Oncotarget 2017; 8(13): 21187-99.
[http://dx.doi.org/10.18632/oncotarget.15061] [PMID: 28177900]
[55]
Chen X, Jiang ZC, Xie D, et al. A novel computational model based on super-disease and miRNA for potential miRNA–disease associa-tion prediction. Mol Biosyst 2017; 13(6): 1202-12.
[http://dx.doi.org/10.1039/C6MB00853D] [PMID: 28470244]
[56]
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]
[57]
Chen X, Yan CC, Zhang X, et al. WBSMDA: Within and between score for MiRNA-disease association prediction. Sci Rep 2016; 6(1): 21106.
[http://dx.doi.org/10.1038/srep21106] [PMID: 26880032]
[58]
Yu H, Chen X, Lu L. Large-scale prediction of microRNA-disease associations by combinatorial prioritization algorithm. Sci Rep 2017; 7(1): 43792.
[http://dx.doi.org/10.1038/srep43792] [PMID: 28317855]
[59]
You ZH, Huang ZA, Zhu Z, et al. PBMDA: A novel and effective path-based computational model for miRNA-disease association pre-diction. PLOS Comput Biol 2017; 13(3): e1005455.
[http://dx.doi.org/10.1371/journal.pcbi.1005455] [PMID: 28339468]
[60]
Chen X, Xie D, Wang L, Zhao Q, You ZH, Liu H. BNPMDA: Bipartite network projection for MiRNA–disease association prediction. Bioinformatics 2018; 34(18): 3178-86.
[http://dx.doi.org/10.1093/bioinformatics/bty333] [PMID: 29701758]
[61]
Chen X, Clarence Yan C, Zhang X, et al. RBMMMDA: predicting multiple types of disease-microRNA associations. Sci Rep 2015; 5(1): 13877.
[http://dx.doi.org/10.1038/srep13877] [PMID: 26347258]
[62]
Vihinen M. How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis. BMC Genomics 2012; 13 (Suppl. 4): S2.
[http://dx.doi.org/10.1186/1471-2164-13-S4-S2] [PMID: 22759650]
[63]
Yang J, Ju J, Guo L, et al. Prediction of HER2-positive breast cancer recurrence and metastasis risk from histopathological images and clinical information via multimodal deep learning. Comput Struct Biotechnol J 2022; 20: 333-42.
[http://dx.doi.org/10.1016/j.csbj.2021.12.028] [PMID: 35035786]
[64]
Jemal A, Ward EM, Johnson CJ, et al. Annual report to the nation on the status of cancer, 1975–2014, featuring survival. J Natl Cancer Inst 2017; 109(9): djx030.
[http://dx.doi.org/10.1093/jnci/djx030] [PMID: 28376154]
[65]
Parkin DM, Bray F, Ferlay J, Pisani P. Global cancer statistics, 2002. CA Cancer J Clin 2005; 55(2): 74-108.
[http://dx.doi.org/10.3322/canjclin.55.2.74] [PMID: 15761078]
[66]
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA Cancer J Clin 2015; 65(1): 5-29.
[http://dx.doi.org/10.3322/caac.21254] [PMID: 25559415]
[67]
Isobe T, Hisamori S, Hogan DJ, et al. miR-142 regulates the tumorigenicity of human breast cancer stem cells through the canonical WNT signaling pathway. eLife 2014; 3: e01977.
[http://dx.doi.org/10.7554/eLife.01977] [PMID: 25406066]
[68]
You F, Luan H, Sun D, et al. miRNA-106a promotes breast cancer cell proliferation, clonogenicity, migration, and invasion through inhibiting apoptosis and chemosensitivity. DNA Cell Biol 2019; 38(2): 198-207.
[http://dx.doi.org/10.1089/dna.2018.4282] [PMID: 30570350]
[69]
Xu B, Zhang X, Wang S, Shi B. MiR-449a suppresses cell migration and invasion by targeting PLAGL2 in breast cancer. Pathol Res Pract 2018; 214(5): 790-5.
[http://dx.doi.org/10.1016/j.prp.2017.12.012] [PMID: 29653747]
[70]
Wistuba II, Gazdar AF. Lung cancer preneoplasia. Annu Rev Pathol 2006; 1(1): 331-48.
[http://dx.doi.org/10.1146/annurev.pathol.1.110304.100103] [PMID: 18039118]
[71]
Liang H, Fu Z, Jiang X, et al. miR-16 promotes the apoptosis of human cancer cells by targeting FEAT. BMC Cancer 2015; 15(1): 448.
[http://dx.doi.org/10.1186/s12885-015-1458-8] [PMID: 26031775]
[72]
Liu H, Chen Y, Li Y, et al. miR 195 suppresses metastasis and angiogenesis of squamous cell lung cancer by inhibiting the expression of VEGF. Mol Med Rep 2019; 20(3): 2625-32.
[http://dx.doi.org/10.3892/mmr.2019.10496] [PMID: 31322197]
[73]
Liu B, Qu J, Xu F, et al. MiR-195 suppresses non-small cell lung cancer by targeting CHEK1. Oncotarget 2015; 6(11): 9445-56.
[http://dx.doi.org/10.18632/oncotarget.3255] [PMID: 25840419]
[74]
Cannistra SA. Cancer of the Ovary. N Engl J Med 2004; 351(24): 2519-29.
[http://dx.doi.org/10.1056/NEJMra041842] [PMID: 15590954]
[75]
Hennessy BT, Coleman RL, Markman M. Ovarian cancer. Lancet 2009; 374(9698): 1371-82.
[http://dx.doi.org/10.1016/S0140-6736(09)61338-6] [PMID: 19793610]
[76]
Wang L, He J, Xu H, Xu L, Li N. MiR-143 targets CTGF and exerts tumor-suppressing functions in epithelial ovarian cancer. Am J Transl Res 2016; 8(6): 2716-26.
[PMID: 27398154]
[77]
Gong L, Zhang W, Yuan Y, Xing X, Li H, Zhao G. miR-222 promotes invasion and migration of ovarian carcinoma by targeting PTEN. Oncol Lett 2018; 16(1): 984-90.
[http://dx.doi.org/10.3892/ol.2018.8743] [PMID: 29963173]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy