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iCDA-CMG: identifying circRNA-disease associations by federating multi-similarity fusion and collective matrix completion

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Abstract

Circular RNAs (circRNAs) are a special class of non-coding RNAs with covalently closed-loop structures. Studies prove that circRNAs perform critical roles in various biological processes, and the aberrant expression of circRNAs is closely related to tumorigenesis. Therefore, identifying potential circRNA-disease associations is beneficial to understand the pathogenesis of complex diseases at the circRNA level and helps biomedical researchers and practitioners to discover diagnostic biomarkers accurately. However, it is tremendously laborious and time-consuming to discover disease-related circRNAs with conventional biological experiments. In this study, we develop an integrative framework, called iCDA-CMG, to predict potential associations between circRNAs and diseases. By incorporating multi-source prior knowledge, including known circRNA-disease associations, disease similarities and circRNA similarities, we adopt a collective matrix completion-based graph learning model to prioritize the most promising disease-related circRNAs for guiding laborious clinical trials. The results show that iCDA-CMG outperforms other state-of-the-art models in terms of cross-validation and independent prediction. Moreover, the case studies for several representative cancers suggest the effectiveness of iCDA-CMG in screening circRNA candidates for human diseases, which will contribute to elucidating the pathogenesis mechanisms and unveiling new opportunities for disease diagnosis and targeted therapy.

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Acknowledgements

The authors are grateful to the members of Yide Yang’s group (School of Medicine, Hunan Normal University) for helpful discussions and valuable comments.

Funding

This work was supported in part by the National Natural Science Foundation of China (Grant No. 62002116), the Hunan Provincial Natural Science Foundation of China (2020JJ5373), the Scientific Research Fund of Hunan Provincial Education Department (No.20B348), and the Hunan Provincial Science & Technology Project Foundation (2018TP1018, 2018RS3065).

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Authors and Affiliations

  1. Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China

    Qiu Xiao & Jiancheng Zhong

  2. Hunan Xiangjiang Artificial Intelligence Academy, Changsha, 410000, China

    Qiu Xiao

  3. School of Information Science and Engineering, Hunan First Normal University, Changsha, 410205, China

    Xiwei Tang

  4. College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China

    Jiawei Luo

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  1. Qiu Xiao
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  2. Jiancheng Zhong
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  3. Xiwei Tang
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  4. Jiawei Luo
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Contributions

QX and JWL conceived the model, prepared the data sets, performed and analyzed experiments, and wrote the manuscript. JCZ and XWT analyzed the prediction results. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jiancheng Zhong or Jiawei Luo.

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The authors declare no conflict of interest.

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

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Communicated by Stefan Hohmann.

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Xiao, Q., Zhong, J., Tang, X. et al. iCDA-CMG: identifying circRNA-disease associations by federating multi-similarity fusion and collective matrix completion. Mol Genet Genomics 296, 223–233 (2021). https://doi.org/10.1007/s00438-020-01741-2

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