Identifying miRNA-disease association based on integrating miRNA topological similarity and functional similarity

Abstract

Background: MicroRNAs (miRNAs) are a significant type of non-coding RNAs, which usually were encoded by endogenous genes with about ~22 nt nucleotides. Accumulating biological experiments have shown that miRNAs have close associations with various human diseases. Although traditional experimental methods achieve great successes in miRNA-disease interaction identification, these methods also have some limitations. Therefore, it is necessary to develop computational method to predict miRNA-disease interactions.

Methods: Here, we propose a computational framework (MDVSI) to predict interactions between miRNAs and diseases by integrating miRNA topological similarity and functional similarity. Firstly, the CosRA index is utilized to measure miRNA similarity based on network topological feature. Then, in order to enhance the reliability of miRNA similarity, the functional similarity and CosRA similarity are integrated based on linear weight method. Further, the potential miRNA-disease associations are predicted by using recommendation method. In addition, in order to overcome limitation of recommendation method, for new disease, a new strategy is proposed to predict potential interactions between miRNAs and new disease based on disease functional similarity.

Results: To evaluate the performance of different methods, we conduct ten-fold cross validation and de novo test in experiment and compare MDVSI with two the-state-of-art methods. The experimental result shows that MDVSI achieves an AUC of 0.91, which is at least 0.012 higher than other compared methods.

Conclusions: In summary, we propose a computational framework (MDSVI) for miRNA-disease interaction prediction. The experiment results demonstrate that it outperforms other the-state-of-the-art methods. Case study shows that it can effectively identify potential miRNA-disease interactions.

Graphical abstract

Keywords

miRNA-disease association / CosRA index / miRNA functional similarity / recommendation method

References

[1] Ambros, V. (2004) The functions of animal microRNAs. Nature, 431, 350–355

[2] Akhtar, M. M., Micolucci, L., Islam, M. S., Olivieri, F. and Procopio, A. D. (2016) Bioinformatic tools for microRNA dissection. Nucleic Acids Res., 44, 24–44

[3] Iorio, M. V., Ferracin, M., Liu, C. G., Veronese, A., Spizzo, R., Sabbioni, S., Magri, E., Pedriali, M., Fabbri, M., Campiglio, M., (2005) MicroRNA gene expression deregulation in human breast cancer. Cancer Res., 65, 7065–7070

[4] Ashmawy, A. M., Elgeshy, K. M., Abdel Salam, E. T., Ghareeb, M., Kobaisi, M. H., Amin, H. A. A., Sharawy, S. K. and Abdel Wahab, A. H. A. (2017) Crosstalk between liver-related microRNAs and Wnt/b-catenin pathway in hepatocellular carcinoma patients. Arab J. Gastroenterol., 18, 144–150

[5] Kim, Y. K., Yu, J., Han, T. S., Park, S. Y., Namkoong, B., Kim, D. H., Hur, K., Yoo, M. W., Lee, H. J., Yang, H. K., (2009) Functional links between clustered microRNAs: suppression of cell-cycle inhibitors by microRNA clusters in gastric cancer. Nucleic Acids Res., 37, 1672–1681

[6] Moss, E. G., Lee, R. C. and Ambros, V. (1997) The cold shock domain protein LIN-28 controls developmental timing in C. elegans and is regulated by the lin-4 RNA. Cell, 88, 637–646

[7] Lan, W., Chen, Q., Li, T., Yuan, C., Mann, S. and Chen, B. (2014) Identification of important positions within miRNAs by integrating sequential and structural features. Curr. Protein Pept. Sci., 15, 591–597

[8] Chen, Q., Lan, W., Wang, J. X. (2013) Mining featured patterns of miRNA interaction based on sequence and structure similarity. IEEE/ACM Trans. Comput. Biol. Bioinform., 10, 415–422

[9] Luo, H., Lan, W., Chen, Q., Wang, Z., Liu, Z., Yue, X. and Zhu, L. (2018) Inferring microRNA-environmental factor interactions based on multiple biological information fusion. Molecules, 23, 2439

[10] Lan, W., Wang, J., Li, M., Liu, J. and Pan, Y. (2015) Predicting microRNA-disease associations by integrating multiple biological information. In: EEE International Conference on Bioinformatics and Biomedicine (BIBM), pp.183–188

[11] Peng, W., Lan, W., Zhong, J., Wang, J. and Pan, Y. (2017) A novel method of predicting microRNA-disease associations based on microRNA, disease, gene and environment factor networks. Methods, 124, 69–77

[12] Jiang, Q., Hao, Y., Wang, G., Juan, L., Zhang, T., Teng, M., Liu, Y. and Wang, Y. (2010) Prioritization of disease microRNAs through a human phenome-microRNAome network. BMC Syst. Biol., 4, S2

[13] Xuan, P., Han, K., Guo, M., Guo, Y., Li, J., Ding, J., Liu, Y., Dai, Q., Li, J., Teng, Z., (2013) Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors. PLoS One, 8, e70204

[14] Mørk, S., Pletscher-Frankild, S., Palleja Caro, A., Gorodkin, J. and Jensen, L. J. (2014) Protein-driven inference of miRNA-disease associations. Bioinformatics, 30, 392–397

[15] Chen, X. and Yan, G. Y. (2014) Semi-supervised learning for potential human microRNA-disease associations inference. Sci. Rep., 4, 5501

[16] Peng, W., Lan, W., Yu, Z., Wang, J., Pan, Y. (2016) A Framework for integrating multiple biological networks to predict microRNA-disease associations. IEEE Trans. Nanobio., 16, 100–107

[17] Yan, C., Wang, J., Ni, P., Lan, W., Wu, F. X., Pan, Y. (2019) DNRLMF-MDA: predicting microRNA-disease associations based on similarities of microRNAs and diseases. IEEE/ACM Trans. Comput. Biol. Bioinform., 16, 233–243

[18] Luo, J. and Xiao, Q. (2017) A novel approach for predicting microRNA-disease associations by unbalanced bi-random walk on heterogeneous network. J. Biomed. Inform., 66, 194–203

[19] Lan, W., Wang, J., Li, M., Liu, J., Wu, F.X., Pan, Y. (2018) Predicting microRNA-disease associations based on improved microRNA and disease similarities. IEEE/ACM Trans. Comput. Biol. Bioinform., 15, 1774–1782

[20] Liu, Y., Zeng, X., He, Z., Zou, Q. (2016) Inferring microRNA-disease associations by random walk on a heterogeneous network with multiple data sources. IEEE/ACM Trans. Comput. Biol. Bioinform., 14, 905–915

[21] Zou, Q., Li, J., Hong, Q., Lin, Z., Wu, Y., Shi, H. and Ju, Y. (2015) Prediction of microRNA-disease associations based on social network analysis methods. BioMed Res. Int., 2015, 810514

[22] Aushev, V. N., Zborovskaya, I. B., Laktionov, K. K., Girard, N., Cros, M. P., Herceg, Z. and Krutovskikh, V. (2013) Comparisons of microRNA patterns in plasma before and after tumor removal reveal new biomarkers of lung squamous cell carcinoma. PLoS One, 8, e78649

[23] Chang, J. T., Wang, F., Chapin, W. and Huang, R. S. (2016) Identification of microRNAs as breast cancer prognosis markers through the Cancer Genome Atlas. PLoS One, 11, e0168284

[24] Köhler, S., Bauer, S., Horn, D. and Robinson, P. N. (2008) Walking the interactome for prioritization of candidate disease genes. Am. J. Hum. Genet., 82, 949–958

[25] Lan, W., Li, M., Zhao, K., Liu, J., Wu, F. X., Pan, Y. and Wang, J. (2017) LDAP: a web server for lncRNA-disease association prediction. Bioinformatics, 33, 458–460

[26] Lan, W., Huang, L., Lai, D., and Chen, Q. (2018) Identifying Interactions Between Long Noncoding RNAs and Diseases Based on Computational Methods. In: Computational Systems Biology, pp.205–221, Springer Nature

[27] Lan, W., Wang, J. X., Li, M., Peng, W. and Wu, F. (2015) Computational approaches for prioritizing candidate disease genes based on PPI networks. Tsinghua Sci. Technol., 20, 500–512

[28] Li, Y., Qiu, C., Tu, J., Geng, B., Yang, J., Jiang, T. and Cui, Q. (2014) HMDD v2.0: a database for experimentally supported human microRNA and disease associations. Nucleic Acids Res., 42, D1070–D1074

[29] Hwang, S., Kim, C. Y., Yang, S., Kim, E., Hart, T., Marcotte, E. M. and Lee, I. (2019) HumanNet v2: human gene networks for disease research. Nucleic Acids Res., 47, D573–D580

[30] Wang, D., Wang, J., Lu, M., Song, F. and Cui, Q. (2010) Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases. Bioinformatics, 26, 1644–1650

[31] Chen, L. J., Zhang, Z. K., Liu, J. H., Gao, J. and Zhou, T. (2017) A vertex similarity index for better personalized recommendation. Physica A, 466, 607–615

[32] Jiang, H., Wang, J., Li, M., Lan, W., Wu, F., Pan, Y. (2018) miRTRS: a recommendation algorithm for predicting miRNA targets. IEEE/ACM Trans. Comput. Biol. Bioinform., 1

ACKNOWLEDGEMENTS

The work reported in this paper was partially supported by the National Natural Science Foundation of China (Nos. 61702122, 61751314 and 31560317), the Natural Science Foundation of Guangxi (Nos. 2017GXNSFDA198033 and 2018GXNSFBA281193), the Key Research and Development Plan of Guangxi (No. AB17195055), the Bossco Project of Guangxi University (No. 20190240), the Hunan Provincial Science and Technology Program (No. 2018WK4001) and 111 Project (No. B18059).

COMPLIANCE WITH ETHICS GUIDELINES

The authors Qingfeng Chen, Zhao Zhe, Wei Lan, Ruchang Zhang, Zhiqiang Wang, Cheng Luo, and Yi-Ping Pheobe Chen declare that they have no conflict of interests.

This article does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

2019 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature