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学者姓名：罗上益
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Abstract ：
Many traditional drug prediction models mostly focus on analyzing single omics data, while ignoring the rich multi-omics data in bioinformatics. Moreover, they failed to make full use of the drug complementary information of sequence features and graphical features when considering the SMILES(Simplified molecular input line entry system) features. In view of this, we propose a deep learning model GSDRP that effectively integrates omics data and drug attribute information. SA-BiLSTM is used to extract one-dimensional sequence features of drugs, and Graph Transformer and GAT_GCN capture two-dimensional structural features, which are then fused by the graph sequence attention module. At the same time, the omics data features are processed by convolutional neural network. Finally, the cross-attention module in GSDRP facilitates the fusion of omics and drug features to enhance interactions for better prediction. Experiments on the Cancer Drug Sensitivity Database (GDSC) show that GSDRP can effectively combine multi-omics information such as genome aberration (MUT_CNA) and gene expression (GE) with the 1-D and 2-D features of SMILES to significantly improve the accuracy of drug response prediction. The comparison results with four other state-of-the-art methods further demonstrate the superior performance of GSDRP in drug response prediction. In addtion,we identify important omics markers and important characteristics of drugs that affect HCC celllines response prediction. This will not only help to understand the therapeutic mechanism of hepatocellular carcinoma, but also provide strong support for future individualized treatment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Keyword ：
Deep Learning Deep Learning Drug Response Prediction Drug Response Prediction Graph Sequence Fusion Graph Sequence Fusion Multi-omics Multi-omics
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| GB/T 7714 | Peng, X. , Dang, Y. , Huang, J. et al. GSDRP: Fusing Drug Sequence Features with Graph Features to Predict Drug Response [未知]. |
| MLA | Peng, X. et al. "GSDRP: Fusing Drug Sequence Features with Graph Features to Predict Drug Response" [未知]. |
| APA | Peng, X. , Dang, Y. , Huang, J. , Luo, S. , Xiong, Z. . GSDRP: Fusing Drug Sequence Features with Graph Features to Predict Drug Response [未知]. |
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Abstract ：
Liver cancer manifests as a profoundly heterogeneous malignancy, posing significant challenges in terms of both therapeutic intervention and prognostic evaluation. Given that the liver is the largest metabolic organ, a prognostic risk model grounded in single-cell transcriptome analysis and a metabolic perspective can facilitate precise prevention and treatment strategies for liver cancer. Hence, we identified 11 cell types in a scRNA-seq profile comprising 105,829 cells and found that the metabolic activity of malignant cells increased significantly. Subsequently, a prognostic risk model incorporating tumor heterogeneity, cell interactions, tumor cell metabolism, and differentially expressed genes was established based on eight genes; this model can accurately distinguish the survival outcomes of liver cancer patients and predict the response to immunotherapy. Analyzing the immune status and drug sensitivity of the high- and low-risk groups identified by the model revealed that the high-risk group had more active immune cell status and greater expression of immune checkpoints, indicating potential risks associated with liver cancer-targeted drugs. In summary, this study provides direct evidence for the stratification and precise treatment of liver cancer patients, and is an important step in establishing reliable predictors of treatment efficacy in liver cancer patients. © 2024 by the authors.
Keyword ：
liver cancer liver cancer metabolic reprogramming metabolic reprogramming prognostic risk model prognostic risk model single-cell RNA-seq single-cell RNA-seq
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| GB/T 7714 | Xiong, Z. , Li, L. , Wang, G. et al. Integrated Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Metabolic Reprogramming of Liver Cancer and Establishes a Prognostic Risk Model [J]. | Genes , 2024 , 15 (6) . |
| MLA | Xiong, Z. et al. "Integrated Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Metabolic Reprogramming of Liver Cancer and Establishes a Prognostic Risk Model" . | Genes 15 . 6 (2024) . |
| APA | Xiong, Z. , Li, L. , Wang, G. , Guo, L. , Luo, S. , Liao, X. et al. Integrated Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Metabolic Reprogramming of Liver Cancer and Establishes a Prognostic Risk Model . | Genes , 2024 , 15 (6) . |
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