A Novel Approach Based on Point Cut Set to Predict Associations of Diseases and LncRNAs

Author(s): Linai Kuang, Haochen Zhao, Lei Wang*, Zhanwei Xuan, Tingrui Pei.

Journal Name: Current Bioinformatics

Volume 14 , Issue 4 , 2019

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Abstract:

Background: In recent years, more evidence have progressively indicated that Long non-coding RNAs (lncRNAs) play vital roles in wide-ranging human diseases, which can serve as potential biomarkers and drug targets. Comparing with vast lncRNAs being found, the relationships between lncRNAs and diseases remain largely unknown.

Objective: The prediction of novel and potential associations between lncRNAs and diseases would contribute to dissect the complex mechanisms of disease pathogenesis.

Method: In this paper, a new computational method based on Point Cut Set is proposed to predict LncRNA-Disease Associations (PCSLDA) based on known lncRNA-disease associations. Compared with the existing state-of-the-art methods, the major novelty of PCSLDA lies in the incorporation of distance difference matrix and point cut set to set the distance correlation coefficient of nodes in the lncRNA-disease interaction network. Hence, PCSLDA can be applied to forecast potential lncRNAdisease associations while known disease-lncRNA associations are required only.

Results: Simulation results show that PCSLDA can significantly outperform previous state-of-the-art methods with reliable AUC of 0.8902 in the leave-one-out cross-validation and AUCs of 0.7634 and 0.8317 in 5-fold cross-validation and 10-fold cross-validation respectively. And additionally, 70% of top 10 predicted cancer-lncRNA associations can be confirmed.

Conclusion: It is anticipated that our proposed model can be a great addition to the biomedical research field.

Keywords: Point set cut, interactive network, LncRNA-disease associations, prediction, lncRNA similarity, disease similarity.

[1]
Leu K, Obermayer B, Rajamani S, Gerland U, Chen IA. The prebiotic evolutionary advantage of transferring genetic information from RNA to DNA. Nucleic Acids Res 2011; 39(18): 8135-47.
[2]
Wang L, Peng H, Zheng J. Similarities/Dissimilarities Analysis of Protein Sequences Based on Recurrence Quantification Analysis Current Bioinformatics 2015; 10(1): 112-9.
[3]
Lander ES, Linton LM, Birren B, et al. Initial sequencing and analysis of the human genome 2001; 409(6822): 860-921.
[4]
Batista PJ, Chang HY. Long noncoding RNAs: cellular address codes in development and disease. Cell 2013; 152(6): 1298-307.
[5]
Gibb EA, Brown CJ, Lam WL. The functional role of long non-coding RNA in human carcinomas. Mol Cancer 2011; 10(1): 38.
[6]
Geisler S, Coller J. RNA in unexpected places: long non-coding RNA functions in diverse cellular contexts. Nat Rev Mol Cell Biol 2013; 14(11): 699-712.
[7]
He X, Tan X, Wang X, et al. C-Myc-activated long noncoding RNA CCAT1 promotes colon cancer cell proliferation and invasion. Tumour Biol 2014; 35(12): 12181-8.
[8]
Gupta RA, Shah N, Wang KC, et al. Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature 2010; 464(7291): 1071-6.
[9]
Chung S, Nakagawa H, Uemura M, et al. Association of a novel long non-coding RNA in 8q24 with prostate cancer susceptibility. Cancer Sci 2011; 102(1): 245-52.
[10]
Uchida S, Dimmeler S. Long noncoding RNAs in cardiovascular diseases. Circ Res 2015; 116(4): 737-50.
[11]
Congrains A, Kamide K, Oguro R, et al. Genetic variants at the 9p21 locus contribute to atherosclerosis through modulation of ANRIL and CDKN2A/B. Atherosclerosis 2012; 220(2): 449-55.
[12]
Huang WT, Guo XQ, Dai JP, et al. MicroRNA and lncRNA in Neurodegenerative Diseases. Prog Biochem Biophys 2010; 37(8): 826-33.
[13]
Johnson R. Long non-coding RNAs in Huntington’s disease neurodegeneration. Neurobiol Dis 2012; 46(2): 245-54.
[14]
Liu JY, Yao J, Li XM, et al. Pathogenic role of lncRNA-MALAT1 in endothelial cell dysfunction in diabetes mellitus. Cell Death Dis 2014; 5(10): e1506.
[15]
Wahlestedt C. Targeting long non-coding RNA to therapeutically upregulate gene expression. Nat Rev Drug Discov 2013; 12(6): 433-46.
[16]
Fenoglio C, Ridolfi E, Galimberti D, Scarpini E. An emerging role for long non-coding RNA dysregulation in neurological disorders. Int J Mol Sci 2013; 14(10): 20427-42.
[17]
Bu D, Yu K, Sun S, et al. NONCODE v3.0: integrative annotation of long noncoding RNAs. Nucleic Acids Res 2012; 40: D210-5.
[18]
Amaral PP, Clark MB, Gascoigne DK, Dinger ME, Mattick JS. lncRNAdb: a reference database for long noncoding RNAs. Nucleic Acids Res 2011; 39: D146-51.
[19]
Chen G, Wang Z, Wang D, et al. LncRNADisease: a database for long-non-coding RNA-associated diseases. Nucleic Acids Res 2013; 41: D983-6.
[20]
Peng H, Lan C, Zheng Y, Hutvagner G, Tao D, Li J. Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite. BMC Bioinformatics 2017; 18(1): 193.
[21]
Lan W, Li M, Zhao K, et al. LDAP: a web server for lncRNA-disease association prediction. Bioinformatics 2017; 33(3): 458-60.
[22]
Ping P, Zhu X, Wang L. Similarities/dissimilarities analysis of protein sequences based on pca-fft. J Biol Syst 2017; 25: 1-17.
[23]
Chen X, Yan GY. Novel human lncRNA-disease association inference based on lncRNA expression profiles. Bioinformatics 2013; 29(20): 2617-24.
[24]
Yang X, Gao L, Guo X, et al. A network based method for analysis of lncRNA-disease associations and prediction of lncRNAs implicated in diseases. PLoS One 2014; 9(1): e87797.
[25]
Sun J, Shi H, Wang Z, et al. Inferring novel lncRNA-disease associations based on a random walk model of a lncRNA functional similarity network. Mol Biosyst 2014; 10(8): 2074-81.
[26]
Lu C, Yang M, Luo F, et al. Prediction of lncRNA-disease associations based on inductive matrix completion. Bioinformatics 2018; 34(19): 3357-64.
[27]
Zhang L, Deng Q, Su Y, et al. A Box-Covering-Based Routing Algorithm for Large-Scale SDNs. IEEE Access 2017; 5(99): 4048-56.
[28]
Gunia S, Kakies C, Erbersdobler A, Hakenberg OW, Koch S, May M. Expression of p53, p21 and cyclin D1 in penile cancer: p53 predicts poor prognosis. J Clin Pathol 2012; 65(3): 232-6.
[29]
Ruprecht B, Zaal EA, Zecha J, et al. Lapatinib Resistance in Breast Cancer Cells Is Accompanied by Phosphorylation-Mediated Reprogramming of Glycolysis. Cancer Res 2017; 77(8): 1842-53.
[30]
Barton MK. Local consolidative therapy may be beneficial in patients with oligometastatic non-small cell lung cancer. CA Cancer J Clin 2017; 67(2): 89-90.
[31]
Aarnio M, Sankila R, Pukkala E, et al. Cancer risk in mutation carriers of DNA-mismatch-repair genes. Int J Cancer 1999; 81(2): 214-8.
[32]
Xu S, Sui S, Zhang J, et al. Downregulation of long noncoding RNA MALAT1 induces epithelial-to-mesenchymal transition via the PI3K-AKT pathway in breast cancer. Int J Clin Exp Pathol 2015; 8(5): 4881-91.
[33]
Hu T, Lu YR. BCYRN1, a c-MYC-activated long non-coding RNA, regulates cell metastasis of non-small-cell lung cancer. Cancer Cell Int 2015; 15(1): 36.
[34]
Zhu M, Chen Q, Liu X, et al. lncRNA H19/miR-675 axis represses prostate cancer metastasis by targeting TGFBI. FEBS J 2014; 281(16): 3766-75.
[35]
Cremers RG, Eeles RA, Bancroft EK, et al. The role of the prostate cancer gene 3 urine test in addition to serum prostate-specific antigen level in prostate cancer screening among breast cancer, early-onset gene mutation carriers. Urol Oncol 2015; 33(5): 202.e19-28.
[36]
Enciso-Mora V, Hosking FJ, Houlston RS. Risk of breast and prostate cancer is not associated with increased homozygosity in outbred populations. Eur J Hum Genet 2010; 18(8): 909-14.
[37]
Nanchari SR, Cingeetham A, Meka P, et al. Rrp1B gene polymorphism (1307T>C) in metastatic progression of breast cancer. Tumour Biol 2015; 36(2): 615-21.
[38]
Chen X. Predicting lncRNA-disease associations and constructing lncRNA functional similarity network based on the information of miRNA. Sci Rep 2015; 5: 13186.
[39]
Wang Y, Chen L, Chen B, et al. Mammalian ncRNA-disease repository: a global view of ncRNA-mediated disease network. Cell Death Dis 2013; 4(8): e765.


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Article Details

VOLUME: 14
ISSUE: 4
Year: 2019
Page: [333 - 343]
Pages: 11
DOI: 10.2174/1574893613666181026122045
Price: $58

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