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Current Gene Therapy

Author(s): Tianyi Zhao, Donghua Wang, Yang Hu, Ningyi Zhang, Tianyi Zang* and Yadong Wang

DOI: 10.2174/1566523219666190924113737

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Identifying Alzheimer’s Disease-related miRNA Based on Semi-clustering

Page: [216 - 223] Pages: 8

  • * (Excluding Mailing and Handling)

Abstract

Background: More and more scholars are trying to use it as a specific biomarker for Alzheimer’s Disease (AD) and mild cognitive impairment (MCI). Multiple studies have indicated that miRNAs are associated with poor axonal growth and loss of synaptic structures, both of which are early events in AD. The overall loss of miRNA may be associated with aging, increasing the incidence of AD, and may also be involved in the disease through some specific molecular mechanisms.

Objective: Identifying Alzheimer’s disease-related miRNA can help us find new drug targets, early diagnosis.

Materials and Methods: We used genes as a bridge to connect AD and miRNAs. Firstly, proteinprotein interaction network is used to find more AD-related genes by known AD-related genes. Then, each miRNA’s correlation with these genes is obtained by miRNA-gene interaction. Finally, each miRNA could get a feature vector representing its correlation with AD. Unlike other studies, we do not generate negative samples randomly with using classification method to identify AD-related miRNAs. Here we use a semi-clustering method ‘one-class SVM’. AD-related miRNAs are considered as outliers and our aim is to identify the miRNAs that are similar to known AD-related miRNAs (outliers).

Results and Conclusion: We identified 257 novel AD-related miRNAs and compare our method with SVM which is applied by generating negative samples. The AUC of our method is much higher than SVM and we did case studies to prove that our results are reliable.

Keywords: Alzheimer's disease, gene, miRNA, semi-cluster, one-class SVM, MMSE.

Graphical Abstract

[1]
Liao ZJ, 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]
[2]
Jiang L, Xiao Y, Ding Y, Tang J, Guo F. Discovering cancer subtypes via an accurate fusion strategy on multiple profile data. Front Genet 2019; 10: 20.
[http://dx.doi.org/10.3389/fgene.2019.00020] [PMID: 30804977]
[3]
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]
[4]
Peng Y, Croce CM. The role of MicroRNAs in human cancer. Signal Transduct Target Ther 2016; 1: 15004.
[http://dx.doi.org/10.1038/sigtrans.2015.4. eCollection 2016] [PMID: 29263891]
[5]
Jiang Q, Wang G, Jin S, Li Y, Wang Y. Predicting human microRNA-disease associations based on support vector machine. Int J Data Min Bioinform 2013; 8(3): 282-93.
[http://dx.doi.org/10.1504/IJDMB.2013.056078] [PMID: 24417022]
[6]
Zou Q, Qu K, Luo Y, Yin D, Ju Y, Tang H. Predicting diabetes mellitus with machine learning techniques. Front Genet 2018; 9: 515.
[http://dx.doi.org/10.3389/fgene.2018.00515] [PMID: 30459809]
[7]
Wang L, Ping P, Kuang L, Ye S. lqbal FMB, Pei T. A novel approach based on bipartite network to predict human microbe-disease associations. Curr Bioinform 2018; 13(2): 141-8.
[http://dx.doi.org/10.2174/1574893612666170911143601]
[8]
Cheng L, Wang P, Tian R, et al. LncRNA2Target v2.0: A comprehensive database for target genes of lncRNAs in human and mouse. Nucleic Acids Res 2019; 47(D1): D140-4.
[http://dx.doi.org/10.1093/nar/gky1051] [PMID: 30380072]
[9]
Smith-Vikos T, Slack FJ. MicroRNAs circulate around Alzheimer’s disease. Genome Biol 2013; 14(7): 125-5.
[http://dx.doi.org/10.1186/gb4116] [PMID: 23889814]
[10]
Zhao T, Zhang N, Zhang Y, et al. A novel method to identify pre-microRNA in various species knowledge base on various species. J Biomed Semantics 2017; 8(1): 30.
[http://dx.doi.org/10.1186/s13326-017-0143-z] [PMID: 29297389]
[11]
Jiang L, Ding Y, Tang J, Guo F. MDA-SKF: Similarity kernel fusion for accurately discovering miRNA-Disease Association. Front Genet 2018; 9(618): 618.
[http://dx.doi.org/10.3389/fgene.2018.00618] [PMID: 30619454]
[12]
Jiang L, Xiao Y, Ding Y, Tang J, Guo F. FKL-Spa-LapRLS: An accurate method for identifying human microRNA-disease association. BMC Genomics 2018; 19(911): 911.
[http://dx.doi.org/10.1186/s12864-018-5273-x] [PMID: 30598109]
[13]
Ardekani BA, Bermudez E, Mubeen AM, Bachman AH. Alzheimer’s disease neuroimaging initiative. Prediction of incipient Alzheimer’s disease dementia in patients with mild cognitive impairment. J Alzheimers Dis 2017; 55(1): 269-81.
[http://dx.doi.org/10.3233/JAD-160594] [PMID: 27662309]
[14]
Li C, Zheng X, Yang Z, Kuang L. Predicting short-term electricity demand by combining the advantages of ARMA and XGBoost in fog computing environment. Wirel Commun and Mob Comput 2018; 2018: 1-18.
[http://dx.doi.org/10.1155/2018/5018053]
[15]
Jiang Q, Jin S, Jiang Y, et al. Alzheimer’s disease variants with the Genome-Wide significance are significantly enriched in immune pathways and active in immune cells. Mol Neurobiol 2017; 54(1): 594-600.
[http://dx.doi.org/10.1007/s12035-015-9670-8] [PMID: 26746668]
[16]
Liu G, Jin S, Hu Y, Jiang Q. Disease status affects the association between rs4813620 and the expression of Alzheimer’s disease susceptibility gene TRIB3. Proc Natl Acad Sci USA 2018; 115(45): E10519-20.
[http://dx.doi.org/10.1073/pnas.1812975115] [PMID: 30355771]
[17]
Jutten RJ, Harrison J, de Jong FJ, et al. A composite measure of cognitive and functional progression in Alzheimer’s disease: Design of the capturing changes in cognition study. Alzheimers Dement (N Y) 2017; 3(1): 130-8.
[http://dx.doi.org/10.1016/j.trci.2017.01.004] [PMID: 29067324]
[18]
Ewers M, Sperling RA, Klunk WE, Weiner MW, Hampel H. Neuroimaging markers for the prediction and early diagnosis of Alzheimer’s disease dementia. Trends Neurosci 2011; 34(8): 430-42.
[http://dx.doi.org/10.1016/j.tins.2011.05.005] [PMID: 21696834]
[19]
Peng J, Guan J, Shang X. Predicting parkinson’s disease genes based on node2vec and autoencoder. Front Genet 2019; 10: 226.
[http://dx.doi.org/10.3389/fgene.2019.00226] [PMID: 31001311]
[20]
Gaugler JE, Ascher-Svanum H, Roth DL, Fafowora T, Siderowf A, Beach TG. Characteristics of patients misdiagnosed with Alzheimer’s disease and their medication use: An analysis of the NACC-UDS database. BMC Geriatr 2013; 13(1): 137-7.
[http://dx.doi.org/10.1186/1471-2318-13-137] [PMID: 24354549]
[21]
Cheng L, Zhuang H, Ju H, et al. Exposing the causal effect of body mass index on the risk of type 2 diabetes mellitus: A mendelian randomization study. Front Genet 2019; 10(94): 94.
[http://dx.doi.org/10.3389/fgene.2019.00094] [PMID: 30891058]
[22]
Cheng L, Zhuang H, Yang S, Jiang H, Wang S, Zhang J. Exposing the causal effect of C-Reactive Protein on the risk of type 2 diabetes mellitus: A mendelian randomization study. Front Genet 2018; 9: 657.
[http://dx.doi.org/10.3389/fgene.2018.00657]
[23]
Wang T, Xiao S, Liu Y, et al. The efficacy of plasma biomarkers in early diagnosis of Alzheimer’s disease. Int J Geriatr Psychiatry 2014; 29(7): 713-9.
[http://dx.doi.org/10.1002/gps.4053] [PMID: 24318929]
[24]
Tan L, Yu JT, Liu QY, et al. Circulating miR-125b as a biomarker of Alzheimer’s disease. J Neurol Sci 2014; 336(1-2): 52-6.
[http://dx.doi.org/10.1016/j.jns.2013.10.002] [PMID: 24139697]
[25]
Tan L, Yu JT, Tan MS, et al. Genome-wide serum microRNA expression profiling identifies serum biomarkers for Alzheimer’s disease. J Alzheimers Dis 2014; 40(4): 1017-27.
[http://dx.doi.org/10.3233/JAD-132144] [PMID: 24577456]
[26]
Leidinger P, Backes C, Deutscher S, et al. A blood based 12-miRNA signature of Alzheimer disease patients. Genome Biol 2013; 14(7): R78.
[http://dx.doi.org/10.1186/gb-2013-14-7-r78] [PMID: 23895045]
[27]
Cheng L, Sun J, Xu W, Dong L, Hu Y, Zhou M. OAHG: An integrated resource for annotating human genes with multi-level ontologies. Sci Rep 2016; 6(1): 34820.
[http://dx.doi.org/10.1038/srep34820] [PMID: 27703231]
[28]
Liu G, Zhao Y, Jin S, et al. Circulating vitamin E levels and Alzheimer's disease: A Mendelian randomization study Neurobiol Aging 2018; 72: 189. e9.
[http://dx.doi.org/10.1016/j.neurobiolaging.2018.08.008]
[29]
Liu G, Hu Y, Han Z, Jin S, Jiang Q. Genetic variant rs17185536 regulates SIM1 gene expression in human brain hypothalamus. Proc Natl Acad Sci USA 2019; 116(9): 3347-8.
[http://dx.doi.org/10.1073/pnas.1821550116] [PMID: 30755538]
[30]
Peng J, Hui W, Li Q, et al. A learning-based framework for miRNA-disease association identification using neural networks. Bioinformatics 2019.pii: btz254
[http://dx.doi.org/10.1093/bioinformatics/btz254] [PMID: 30977780]
[31]
Peng J, Zhu L, Wang Y, Chen J. Mining relationships among Multiple entities in biological networks. IEEE/ACM Trans Comput Biol Bioinformatics 2019.
[http://dx.doi.org/10.1109/TCBB.2019.2904965] [PMID: 30872239]
[32]
Cheng L, Hu Y, Sun J, Zhou M, Jiang Q. DincRNA: A comprehensive web-based bioinformatics toolkit for exploring disease associations and ncRNA function. Bioinformatics 2018; 34(11): 1953-6.
[http://dx.doi.org/10.1093/bioinformatics/bty002] [PMID: 29365045]
[33]
Cheng L, Yang H, Zhao H, et al. MetSigDis: A manually curated resource for the metabolic signatures of diseases. Brief Bioinform 2019; 20(1): 203-9.
[PMID: 28968812]
[34]
Cheng L, Jiang Y, Ju H, et al. InfAcrOnt: Calculating cross-ontology term similarities using information flow by a random walk. BMC Genomics 2018; 19(Suppl. 1): 919.
[http://dx.doi.org/10.1186/s12864-017-4338-6] [PMID: 29363423]
[35]
Deng L, Wang J, Zhang J. Predicting gene ontology function of human MicroRNAs by integrating multiple networks. Front Genet 2019; 10: 3.
[http://dx.doi.org/10.3389/fgene.2019.00003] [PMID: 30761178]
[36]
Zhang J, Zhang Z, Chen Z, Deng L. Integrating multiple heterogeneous networks for novel LncRNA-Disease association inference. IEEE/ACM Trans Comput Biol Bioinformatics 2019; 16(2): 396-406.
[http://dx.doi.org/10.1109/TCBB.2017.2701379] [PMID: 28489543]
[37]
Zhang X, Zou Q, Rodriguez-Paton A, Zeng X. Meta-Path methods for prioritizing candidate disease miRNAs. IEEE/ACM Trans Comput Biol Bioinformatics 2019; 16(1): 283-91.
[http://dx.doi.org/10.1109/TCBB.2017.2776280] [PMID: 29990255]
[38]
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]
[39]
Cheng L, Hu Y. Human disease system biology. Curr Gene Ther 2018; 18(5): 255-6.
[http://dx.doi.org/10.2174/1566523218666181010101114] [PMID: 30306867]
[40]
Xuan P, Han K, Guo M, et al. Correction: Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors. PLoS One 2013; 8(9)e70204
[http://dx.doi.org/10.1371/journal.pone.0070204] [PMID: 24116246]
[41]
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]
[42]
Zhang J, Zou S, Deng L. Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk. BMC Med Genomics 2018; 11(5): 99.
[http://dx.doi.org/10.1186/s12920-018-0414-2] [PMID: 30453964]
[43]
Deng L, Wu H, Liu C, Zhan W, Zhang J. Probing the functions of long non-coding RNAs by exploiting the topology of global association and interaction network. Comput Biol Chem 2018; 74: 360-7.
[http://dx.doi.org/10.1016/j.compbiolchem.2018.03.017] [PMID: 29573966]
[44]
Deng L, Wang J, Xiao Y, Wang Z, Liu H. Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network. BMC Bioinformatics 2018; 19(1): 370.
[http://dx.doi.org/10.1186/s12859-018-2390-0] [PMID: 30309340]
[45]
Niu YW, Liu H, Wang GH, et al. Maximal entropy random walk on heterogenous network for MiRNA-disease association prediction. Math Biosci 2018; 306: 1-9.
[46]
Shi H, Xu J, Zhang G, et al. 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]
[47]
Prabahar A, Natarajan J, Immunemi R. A database of prioritized immune miRNA disease associations and its interactome. MicroRNA 2017; 6(1): 71-8.
[http://dx.doi.org/10.2174/2211536606666170117112322] [PMID: 28124611]
[48]
Liu Y, Zeng X, He Z, Zou Q. Inferring microRNA-disease associations by random walk on a heterogeneous network with multiple data sources. IEEE/ACM Trans Comput Biol Bioinformatics 2017; 14(4): 905-15.
[http://dx.doi.org/10.1109/TCBB.2016.2550432] [PMID: 27076459]
[49]
You Z-H, Wang LP, Chen X, et al. PRMDA: Personalized recommendation-based MiRNA-disease association prediction. Oncotarget 2017; 8(49): 85568-83.
[http://dx.doi.org/10.18632/oncotarget.20996] [PMID: 29156742]
[50]
Piñero J, Bravo A, Queralt-Rosinach N, et al. DisGeNET: A comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res 2017; 45(D1): D833-9.
[PMID: 27924018]
[51]
Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2019; 47(D1): D607-13.
[http://dx.doi.org/10.1093/nar/gky1131] [PMID: 30476243]
[52]
Dweep H, Gretz N. miRWalk2.0: A comprehensive atlas of microRNA-target interactions. Nat Methods 2015; 12(8): 697.
[http://dx.doi.org/10.1038/nmeth.3485] [PMID: 26226356]
[53]
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: D1070-4.
[http://dx.doi.org/10.1093/nar/gkt1023] [PMID: 24194601]
[54]
Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: Archive for functional genomics data sets--update. Nucleic Acids Res 2013; 41: D991-5.
[PMID: 23193258]
[55]
Li Y, Niu M, Zou Q. ELM-MHC: An improved MHC identification method with extreme learning machine algorithm. J Proteome Res 2019; 18(3): 1392-401.
[http://dx.doi.org/10.1021/acs.jproteome.9b00012] [PMID: 30698979]
[56]
Yu L, Sun X, Tian S, et al. Drug and nondrug classification based on deep learning with various feature selection strategies. Curr Bioinform 2018; 13(3): 253-9.
[http://dx.doi.org/10.2174/1574893612666170125124538]
[57]
Jia C, Zuo Y, Zou Q. O-GlcNAcPRED-II: An integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K-means PCA oversampling technique. Bioinformatics 2018; 34(12): 2029-36.
[http://dx.doi.org/10.1093/bioinformatics/bty039] [PMID: 29420699]
[58]
Zeng X, Liao Y, Liu Y, Zou Q. Prediction and validation of disease genes using HeteSim scores. IEEE/ACM Trans Comput Biol Bioinformatics 2017; 14(3): 687-95.
[http://dx.doi.org/10.1109/TCBB.2016.2520947] [PMID: 26890920]
[59]
Tan JX, Li SH, Zhang ZM, et al. Identification of hormone binding proteins based on machine learning methods. Math Biosci Eng 2019; 16(4): 2466-80.
[http://dx.doi.org/10.3934/mbe.2019123] [PMID: 31137222]
[60]
Lv H, Zhang ZM, Li SH, Tan JX, Chen W, Lin H. Evaluation of different computational methods on 5-methylcytosine sites identification. Brief Bioinform 2019.bbz048
[PMID: 31157855]
[61]
Yang W, Xu X-J, Huang J, Ding H, Lin H. A brief survey of machine learning methods in protein sub-Golgi localization. Curr Bioinform 2019; 14: 234-40.
[http://dx.doi.org/10.2174/1574893613666181113131415]
[62]
Feng CQ, Zhang ZY, Zhu XJ, et al. iTerm-PseKNC: A sequencebased tool for predicting bacterial transcriptional terminators. Bioinformatics 201; 35(9): 1469-77.
[PMID: 30247625]
[63]
Dao FY. LV H, Wang F, et al. Identify origin of replication in Saccharomyces cerevisiae using two-step feature selection technique. Bioinformatics 2019; 35(12): 2075-83.
[PMID: 30428009]
[64]
Guo R, Fan G, Zhang J, et al. A 9-microRNA signature in serum serves as a noninvasive biomarker in early diagnosis of Alzheimer’s disease. J Alzheimers Dis 2017; 60(4): 1365-77.
[http://dx.doi.org/10.3233/JAD-170343] [PMID: 29036818]
[65]
Cheng L, Doecke JD, Sharples RA, et al. Prognostic serum miRNA biomarkers associated with Alzheimer’s disease shows concordance with neuropsychological and neuroimaging assessment. Mol Psychiatry 2015; 20(10): 1188.
[66]
Li Y, Song D, Jiang Y, et al. CR1 rs3818361 Polymorphism Contributes to Alzheimer’s Disease susceptibility in chinese population. Mol Neurobiol 2016; 53(6): 4054-9.
[http://dx.doi.org/10.1007/s12035-015-9343-7] [PMID: 26189835]
[67]
Liu G, Jiang Q. Alzheimer’s disease CD33 rs3865444 variant does not contribute to cognitive performance. Proc Natl Acad Sci USA 2016; 113(12): E1589-90.
[http://dx.doi.org/10.1073/pnas.1600852113] [PMID: 26933222]
[68]
Liu G, Xu Y, Jiang Y, Zhang L, Feng R, Jiang Q. PICALM rs3851179 variant confers susceptibility to alzheimer’s disease in chinese population. Mol Neurobiol 2017; 54(5): 3131-6.
[PMID: 27048444]
[69]
Liu G, Wang T, Tian R, et al. Alzheimer’s disease risk variant rs2373115 Regulates GAB2 and NARS2 expression in human brain tissues. J Mol Neurosci 2018; 66(1): 37-43.
[http://dx.doi.org/10.1007/s12031-018-1144-9] [PMID: 30088171]
[70]
Zou Q, Xing P, Wei L, Liu B. Gene2vec: Gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA. RNA 2019; 25(2): 205-18.
[http://dx.doi.org/10.1261/rna.069112.118] [PMID: 30425123]