School of Electronic and Communication Engineering, Shenzhen Polytechnic University, Shenzhen, 518055, China.
School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
BMC Biol. 2024 Jan 29;22(1):24. doi: 10.1186/s12915-024-01826-z.
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.
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.
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.
环状 RNA（circRNAs）已被证实在疾病的发生和发展中发挥着重要作用。探索 circRNAs 与疾病之间的关系，对于研究发病机制和治疗疾病具有深远的意义。为此，我们在前作 GMNN2CD 所构建的图马尔可夫神经网络算法（GMNN）的基础上，进一步考虑了影响 circRNA 与疾病关联的多源生物数据，并开发了一个更新的基于人类肝细胞癌（HCC）组织数据的网络服务器 CircDA，用于验证 CircDA 的预测结果。
CircDA 构建在基于图马尔可夫的深度学习框架上。该算法将生物分子视为节点，分子间的相互作用视为边，合理地抽象出多组学数据，并将其建模为一个异质的生物分子关联网络，能够反映不同生物分子之间的复杂关系。使用来自 HCC、宫颈癌和胃癌的文献数据进行的案例研究表明，CircDA 预测器可以识别已知 circRNAs 和疾病之间缺失的关联，并且使用人类组织样本中的 HCC 定量实时 PCR（RT-qPCR）实验，发现五个 circRNAs 表达水平有显著差异，这证明了 CircDA 可以预测与新 circRNAs 相关的疾病。
这种高效的计算预测和充分反馈的案例分析使我们能够识别与 circRNA 相关的疾病和与疾病相关的 circRNAs。我们的工作提供了一种预测 circRNA 相关疾病的方法，可以为某些 circRNAs 与疾病的关联提供指导。为了便于使用，我们提供了一个在线预测服务器（http://server.malab.cn/CircDA），并将代码开源（https://github.com/nmt315320/CircDA.git），以便于算法改进。
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