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Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal
Rui Yin1, Hongru Zhao1, Lu Li2
1Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA.
Abstract:
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/.
Insights
We developed Gra-CRC-miRTar, a novel deep learning framework, to accurately identify microRNA (miRNA) targets in colorectal cancer (CRC). This tool aids in understanding cancer mechanisms and developing new therapeutics.
Area of Science:
- Bioinformatics
- Computational Biology
- Oncology
Background:
- Colorectal cancer (CRC) is a leading cause of cancer death globally.
- MicroRNAs (miRNAs) play crucial roles as oncogenes or tumor suppressors in CRC development.
- Identifying miRNA-mRNA interactions is vital for therapeutic advancements in CRC.
Purpose of the Study:
- To develop a novel computational framework, Gra-CRC-miRTar, for identifying potential miRNA targets in colorectal cancer.
- To leverage deep learning and graph neural networks for enhanced prediction accuracy.
- To provide a tool for uncovering miRNA-mediated regulatory mechanisms in CRC.
Main Methods:
- Constructed pre-trained nucleotide-to-graph models encoding RNA sequences as de Bruijn graphs.
- Utilized graph neural networks to learn latent representations of RNA sequences.
- Employed a Multilayer Perceptron (MLP) for predicting miRNA-target interactions.
Main Results:
- Gra-CRC-miRTar demonstrated superior performance compared to existing deep learning algorithms and predictors.
- The framework successfully identified 172 experimentally validated functional miRNA-mRNA interactions in CRC.
- Achieved high accuracy in predicting potential miRNA targets crucial for CRC.
Conclusions:
- Gra-CRC-miRTar offers an accurate and efficient method for identifying miRNA targets in colorectal cancer.
- The framework can be extended to investigate miRNA targets in other cancer types.
- This work facilitates the development of novel miRNA-based therapeutic strategies for malignancies.
