ORIGINAL RESEARCH article
Front. Genet.
Sec. Computational Genomics
Volume 16 - 2025 | doi: 10.3389/fgene.2025.1633391
This article is part of the Research TopicAdvances in circRNA Research: Disease Associations and Diagnostic InnovationsView all articles
Geometry-Enhanced Graph Neural Networks Accelerate circRNA Therapeutic Target Discovery
Provisionally accepted
- 1Shenzhen Institute of Information Technology, Shenzhen, China
- 2Wenzhou University of Technology, Wenzhou, China
- 3Zhongnan Hospital of Wuhan University, Wuhan, China
- 4Changsha Medical University, Changsha, China
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Circular RNAs (circRNAs) play pivotal roles in various biological processes and disease progression, particularly in modulating drug responses and resistance mechanisms. Accurate prediction of circRNA-drug associations (CDAs) is essential for biomarker discovery and the advancement of therapeutic strategies. Although several computational approaches have been proposed for identifying novel circRNA therapeutic targets, their performance is often limited by inadequate modeling of higher-order geometric information within circRNA-drug interaction networks. To overcome these challenges, we propose G2CDA, a geometric graph representation learning framework specifically designed to enhance the identification of CDAs and facilitate therapeutic target discovery. G2CDA introduces torsion-based geometric encoding into the message propagation process of the circRNAdrug network. For each potential association, we construct local simplicial complexes, extract their geometric features, and integrate these features as adaptive weights during message propagation and aggregation. This design promotes a richer understanding of local topological structures, thereby improving the robustness and expressiveness of learned circRNA and drug representations. Extensive benchmark evaluations on public datasets demonstrate that G2CDA outperforms state-of-the-art CDA prediction models, particularly in identifying novel associations. Case studies further confirm its effectiveness by uncovering potential drug interactions with the ALDH3A2 and ANXA2 biomarkers.Collectively, G2CDA provides a robust and interpretable framework for accelerating circRNA-based therapeutic target discovery and streamlining drug development pipelines. Our code are archived in: https://github.com/lizhen5000/G2CDA.
Keywords: Drug Development, circRNA therapeutic targets, Geometric graph representation, Biomarker Discovery, circRNA-drug network
Received: 22 May 2025; Accepted: 16 Jun 2025.
Copyright: © 2025 Li, Qi, Huang, Zhang, Tan and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zhen Li, Shenzhen Institute of Information Technology, Shenzhen, China
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