结直肠癌（CRC）是全球第三大确诊癌症和第二大致命癌症，是一个重大的公共卫生问题。近年来，越来越多的证据表明，microRNA (miRNA) 可以通过减少人类信使 RNA (mRNA) 的丰度或翻译来控制其表达，在包括结直肠癌在内的各种癌症中充当癌基因或肿瘤抑制因子。由于 CRC 中致癌 miRNA 的显着上调，阐明其潜在机制并识别失调的 miRNA 靶点可能为改善当前治疗干预措施提供基础。在本文中，我们提出了 Gra-CRC-miRTar，一种预训练的核苷酸到图神经网络框架，用于识别 CRC 中潜在的 miRNA 靶点。与之前的研究不同，我们构建了两个预训练的模型来编码RNA序列并将其转化为de Bruijn图。我们采用不同的图神经网络来学习潜在表示。然后，从 de Bruijn 图生成的嵌入被输入到多层感知器 (MLP) 中以执行预测任务。我们的大量实验表明，Gra-CRC-miRTar 比其他深度学习算法和现有预测器实现了更好的性能。此外，我们的分析还通过实验验证的 CRC 中 miRNA-mRNA 对成功揭示了 201 个功能相互作用中的 172 个。总的来说，我们的努力提供了一个准确有效的框架来识别 CRC 中潜在的 miRNA 靶点，该框架也可用于揭示其他恶性肿瘤中 miRNA 靶点的相互作用，从而促进新型疗法的开发。 Gra-CRC-miRTar Web 服务器可在以下位置找到：http://gra-crc-mirtar.com/。© 2024 作者。

Colorectal cancer (CRC) is the third most diagnosed cancer and the second deadliest cancer worldwide representing a major public health problem. In recent years, increasing evidence has shown that microRNA (miRNA) can control the expression of targeted human messenger RNA (mRNA) by reducing their abundance or translation, acting as oncogenes or tumor suppressors in various cancers, including CRC. Due to the significant up-regulation of oncogenic miRNAs in CRC, elucidating the underlying mechanism and identifying dysregulated miRNA targets may provide a basis for improving current therapeutic interventions. In this paper, we proposed Gra-CRC-miRTar, a pre-trained nucleotide-to-graph neural network framework, for identifying potential miRNA targets in CRC. Different from previous studies, we constructed two pre-trained models to encode RNA sequences and transformed them into de Bruijn graphs. We employed different graph neural networks to learn the latent representations. The embeddings generated from de Bruijn graphs were then fed into a Multilayer Perceptron (MLP) to perform the prediction tasks. Our extensive experiments show that Gra-CRC-miRTar achieves better performance than other deep learning algorithms and existing predictors. In addition, our analyses also successfully revealed 172 out of 201 functional interactions through experimentally validated miRNA-mRNA pairs in CRC. Collectively, our effort provides an accurate and efficient framework to identify potential miRNA targets in CRC, which can also be used to reveal miRNA target interactions in other malignancies, facilitating the development of novel therapeutics. The Gra-CRC-miRTar web server can be found at: http://gra-crc-mirtar.com/.© 2024 The Authors.