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Updated: Jul 4, 2025
In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
Published on: October 21, 2022
A computational model of circRNA-associated diseases based on a graph neural network: prediction and case studies for
Mengting Niu1,2, Chunyu Wang3, Zhanguo Zhang4
1School of Electronic and Communication Engineering, Shenzhen Polytechnic University, Shenzhen, 518055, China.
Insights
This study introduces CircDA, a novel computational tool that predicts associations between circular RNAs (circRNAs) and diseases using multiomics data. CircDA accurately identifies novel circRNA-disease links, aiding disease mechanism research and therapeutic development.
Area of Science:
- Biochemistry
- Genomics
- Computational Biology
Background:
- Circular RNAs (circRNAs) are crucial in disease development.
- Understanding circRNA-disease links is vital for disease etiology and treatment.
- Previous work developed the GMNN2CD algorithm for circRNA-disease association prediction.
Purpose of the Study:
- To develop an updated web server, CircDA, for predicting circRNA-disease associations.
- To incorporate multisource biological data into circRNA-disease association prediction.
- To validate CircDA's predictions using human hepatocellular carcinoma (HCC) data.
Main Methods:
- CircDA utilizes a Tumarkov-based deep learning framework.
- It models multiomics data as a heterogeneous biomolecular association network, with molecules as nodes and interactions as edges.
- The approach abstracts and integrates diverse biological data to represent complex molecular relationships.
Main Results:
- CircDA successfully identified missing associations between known circRNAs and diseases in HCC, cervical, and gastric cancers.
- Experimental validation using RT-qPCR in HCC tissues confirmed significant differential expression of five circRNAs.
- These findings demonstrate CircDA's capability to predict novel circRNA-disease associations.
Conclusions:
- CircDA provides an efficient computational method for identifying circRNA-associated diseases and disease-associated circRNAs.
- The tool offers valuable guidance for exploring circRNA roles in diseases.
- An accessible online server and open-sourced code are available for broader use and further algorithm development.
Background:
Circular RNAs (circRNAs) have been confirmed to play a vital role in the occurrence and development of diseases. Exploring the relationship between circRNAs and diseases is of far-reaching significance for studying etiopathogenesis and treating diseases. To this end, based on the graph Markov neural network algorithm (GMNN) constructed in our previous work GMNN2CD, we further considered the multisource biological data that affects the association between circRNA and disease and developed an updated web server CircDA and based on the human hepatocellular carcinoma (HCC) tissue data to verify the prediction results of CircDA.
Results:
CircDA is built on a Tumarkov-based deep learning framework. The algorithm regards biomolecules as nodes and the interactions between molecules as edges, reasonably abstracts multiomics data, and models them as a heterogeneous biomolecular association network, which can reflect the complex relationship between different biomolecules. Case studies using literature data from HCC, cervical, and gastric cancers demonstrate that the CircDA predictor can identify missing associations between known circRNAs and diseases, and using the quantitative real-time PCR (RT-qPCR) experiment of HCC in human tissue samples, it was found that five circRNAs were significantly differentially expressed, which proved that CircDA can predict diseases related to new circRNAs.
Conclusions:
This efficient computational prediction and case analysis with sufficient feedback allows us to identify circRNA-associated diseases and disease-associated circRNAs. Our work provides a method to predict circRNA-associated diseases and can provide guidance for the association of diseases with certain circRNAs. For ease of use, an online prediction server ( http://server.malab.cn/CircDA ) is provided, and the code is open-sourced ( https://github.com/nmt315320/CircDA.git ) for the convenience of algorithm improvement.
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