Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, No. 2699, Qianjin Street, Changchun 130012, China.
Brief Bioinform. 2024 Sep 23;25(6). doi: 10.1093/bib/bbae573.
Research shows that competing endogenous RNA is widely involved in gene regulation in cells, and identifying the association between circular RNA (circRNA), microRNA (miRNA), and cancer can provide new hope for disease diagnosis, treatment, and prognosis. However, affected by reductionism, previous studies regarded the prediction of circRNA-miRNA interaction, circRNA-cancer association, and miRNA-cancer association as separate studies. Currently, few models are capable of simultaneously predicting these three associations.
Inspired by holism, we propose a multi-task prediction method based on neighborhood structure embedding and signed graph representation learning, CMCSG, to infer the relationship between circRNA, miRNA, and cancer. Our method aims to extract feature descriptors of all molecules from the circRNA-miRNA-cancer regulatory network using known types of association information to predict unknown types of molecular associations. Specifically, we first constructed the circRNA-miRNA-cancer association network (CMCN), which is constructed based on the experimentally verified biomedical entity regulatory network; next, we combine topological structure embedding methods to extract feature representations in CMCN from local and global perspectives, and use denoising autoencoder for enhancement; then, combined with balance theory and state theory, molecular features are extracted from the point of social relations through the propagation and aggregation of signed graph attention network; finally, the GBDT classifier is used to predict the association of molecules. The results show that CMCSG can effectively predict the relationship between circRNA, miRNA, and cancer. Additionally, the case studies also demonstrate that CMCSG is capable of accurately identifying biomarkers across various types of cancer. The data and source code can be found at https://github.com/1axin/CMCSG.
研究表明，竞争内源性 RNA 广泛参与细胞中的基因调控，鉴定环状 RNA（circRNA）、微小 RNA（miRNA）与癌症之间的关联可为疾病诊断、治疗和预后提供新的希望。然而，受还原论的影响，先前的研究将 circRNA-miRNA 相互作用、circRNA-癌症关联和 miRNA-癌症关联的预测视为独立的研究。目前，很少有模型能够同时预测这三种关联。
受整体论的启发，我们提出了一种基于邻域结构嵌入和有符号图表示学习的多任务预测方法 CMCSG，以推断 circRNA、miRNA 和癌症之间的关系。我们的方法旨在使用已知类型的关联信息从 circRNA-miRNA-癌症调控网络中提取所有分子的特征描述符，以预测未知类型的分子关联。具体来说，我们首先构建了 circRNA-miRNA-癌症关联网络（CMCN），该网络是基于实验验证的生物医学实体调控网络构建的；接下来，我们结合拓扑结构嵌入方法从局部和全局角度提取 CMCN 中的特征表示，并使用去噪自编码器进行增强；然后，结合平衡理论和状态理论，通过有符号图注意力网络的传播和聚合，从社会关系的角度提取分子特征；最后，使用 GBDT 分类器预测分子的关联。结果表明，CMCSG 可以有效地预测 circRNA、miRNA 和癌症之间的关系。此外，案例研究还表明，CMCSG 能够准确识别各种癌症类型的生物标志物。数据和源代码可在 https://github.com/1axin/CMCSG 找到。
