MVGAE：一种基于合成致死性的非小细胞肺癌药物预测的多视图图自动编码器模型
MVGAE: A Multi-View Graph Auto-Encoder Model for Drug Prediction of Non-Small Cell Lung Cancer Based on Synthetic Lethality.
摘要(中文译文)
识别治疗靶基因及其相应的靶向药物对非小细胞肺癌(non-small cell lung cancer, NSCLC)的治疗具有重要意义。本研究提出了一种多视图图自编码器模型(multi-view graph auto-encoder, MVGAE),该模型与网络信息引导的自适应正-未标记学习(network-informed adaptive positive-unlabeled, NIAPU)模型及合成致死多视图图自编码器(synthetic lethality multi-view graph auto-encoder, SLMGAE)模型共同构成了一个集成的计算框架。该框架整合了多源生物网络数据,包括蛋白质-蛋白质相互作用网络、疾病-基因关联信息以及基因-药物二分图,用于数据挖掘。通过系统分析和计算筛选,我们最终利用NIAPU模型预测了七个与非小细胞肺癌相关的潜在驱动基因。SLMGAE模型预测了九个与这些驱动基因具有合成致死(synthetic lethality, SL)相互作用的基因作为候选治疗靶点。基于这些合成致死靶点,MVGAE模型进一步预测了相应的靶向药物。值得注意的是,在优先排序的靶点中,现有研究表明 和 在功能受损背景下表现出条件性合成致死效应。此外,部分预测的候选药物(如PAZOPANIB)此前已被报道在非小细胞肺癌治疗中发挥积极作用。本研究凸显了MVGAE作为一种新型药物重定位计算框架的价值,并展示了其与互补模型整合如何有效优先排序潜在治疗靶点和候选药物,为精准治疗策略提供了坚实的计算基础。
Abstract
Identifying therapeutic target genes and their corresponding targeted drugs is of significant importance for the treatment of non-small cell lung cancer (NSCLC). This study proposes a multi-view graph auto-encoder model (MVGAE), which, together with the network-informed adaptive positive-unlabeled (NIAPU) and synthetic lethality multi-view graph auto-encoder (SLMGAE) model, constitutes an integrated computational framework. The framework integrates multi-source biological network data, including protein-protein interaction networks, disease-gene association information, and gene-drug bipartite graphs, for data mining. Through systematic analysis and computational screening, we ultimately predicted seven potential driver genes associated with NSCLC using the NIAPU model. The SLMGAE model predicted nine genes with synthetic lethality (SL) interactions to these driver genes as candidate therapeutic targets. Based on these SL targets, the MVGAE model further predicted corresponding targeted drugs. Notably, among the prioritized targets, existing studies indicate that and exhibit conditional SL effects in the context of functional impairment. Furthermore, several of the predicted candidate drugs (such as PAZOPANIB) have been previously reported to play a positive role in NSCLC treatment. This study highlights MVGAE as a novel computational framework for drug repurposing and demonstrates how its integration with complementary models can effectively prioritize potential therapeutic targets and candidate drugs, providing a robust computational basis for precision treatment strategies.
如何引用
AMA
Shaobo Hu, Runsheng Jiang, Ning Zhao. MVGAE: A Multi-View Graph Auto-Encoder Model for Drug Prediction of Non-Small Cell Lung Cancer Based on Synthetic Lethality.. Current issues in molecular biology. 2026; doi:10.3390/cimb48030269.
APA
Shaobo Hu, Runsheng Jiang, Ning Zhao (2026). MVGAE: A Multi-View Graph Auto-Encoder Model for Drug Prediction of Non-Small Cell Lung Cancer Based on Synthetic Lethality.. Current issues in molecular biology. https://doi.org/10.3390/cimb48030269
GB/T 7714
Shaobo Hu, Runsheng Jiang, Ning Zhao. MVGAE: A Multi-View Graph Auto-Encoder Model for Drug Prediction of Non-Small Cell Lung Cancer Based on Synthetic Lethality.[J]. Current issues in molecular biology, 2026 doi:10.3390/cimb48030269.
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