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学者姓名：熊壮
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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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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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Platinum-based neoadjuvant chemotherapy (NACT) followed by radical hysterectomy has been proposed as an alternative treatment approach for cervical cancer (CC) in stage Ib2-IIb, who had a strong desire to be treated with surgery. Our study aims to develop a model based on multimodal MRI by using radiomics and deep learning to predict the treatment response in CC patients treated with neoadjuvant chemoradiotherapy (NACRT). From August 2009 to June 2013, CC patients in stage Ib2-IIb (FIGO 2008) who received NACRT at Fujian Cancer Hospital were enrolled in our study. Clinical information, contrast-enhanced T1-weighted imaging (CE-T1WI), and T2-weighted imaging (T2WI) data were respectively collected. Radiomic features and deep abstract features were extracted from the images using radiomics and deep learning models, respectively. Then, ElasticNet and SVM-RFE were employed for feature selection to construct four single-sequence feature sets. Early fusion of two multi-sequence feature sets and one hybrid feature set were performed, followed by classification prediction using four machine learning classifiers. Subsequently, the performance of the models in predicting the response to NACRT was evaluated by separating patients into training and validation sets. Additionally, overall survival (OS) and disease-free survival (DFS) were assessed using Kaplan-Meier survival curves. Among the four machine learning models, SVM exhibited the best predictive performance (AUC=0.86). Among the seven feature sets, the hybrid feature set achieved the highest values for AUC (0.86), ACC (0.75), Recall (0.75), Precision (0.81), and F1-score (0.75) in the validation set, outperforming other feature sets. Furthermore, the predicted outcomes of the model were closely associated with patient OS and DFS (p = 0.0044; p = 0.0039). A model based on MRI images with features from multiple sequences and different methods could precisely predict the response to NACRT in CC patients. This model could assist clinicians in devising personalized treatment plans and predicting patient survival outcomes. © The Author(s) 2024.
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| GB/T 7714 | Cai, Z. , Li, S. , Xiong, Z. et al. Multimodal MRI-based deep-radiomics model predicts response in cervical cancer treated with neoadjuvant chemoradiotherapy [J]. | Scientific Reports , 2024 , 14 (1) . |
| MLA | Cai, Z. et al. "Multimodal MRI-based deep-radiomics model predicts response in cervical cancer treated with neoadjuvant chemoradiotherapy" . | Scientific Reports 14 . 1 (2024) . |
| APA | Cai, Z. , Li, S. , Xiong, Z. , Lin, J. , Sun, Y. . Multimodal MRI-based deep-radiomics model predicts response in cervical cancer treated with neoadjuvant chemoradiotherapy . | Scientific Reports , 2024 , 14 (1) . |
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Diverse individuals age at different rates and display variable susceptibilities to tissue aging, functional decline and aging-related diseases. Centenarians, exemplifying extreme longevity, serve as models for healthy aging. The field of human aging and longevity research is rapidly advancing, garnering significant attention and accumulating substantial data in recent years. Omics technologies, encompassing phenomics, genomics, transcriptomics, proteomics, metabolomics and microbiomics, have provided multidimensional insights and revolutionized cohort-based investigations into human aging and longevity. Accumulated data, covering diverse cells, tissues and cohorts across the lifespan necessitates the establishment of an open and integrated database. Addressing this, we established the Human Aging and Longevity Landscape (HALL), a comprehensive multi-omics repository encompassing a diverse spectrum of human cohorts, spanning from young adults to centenarians. The core objective of HALL is to foster healthy aging by offering an extensive repository of information on biomarkers that gauge the trajectory of human aging. Moreover, the database facilitates the development of diagnostic tools for aging-related conditions and empowers targeted interventions to enhance longevity. HALL is publicly available at https://ngdc.cncb.ac.cn/hall/index. Graphical Abstract
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| GB/T 7714 | Li, Hao , Wu, Song , Li, Jiaming et al. HALL: a comprehensive database for human aging and longevity studies [J]. | NUCLEIC ACIDS RESEARCH , 2023 . |
| MLA | Li, Hao et al. "HALL: a comprehensive database for human aging and longevity studies" . | NUCLEIC ACIDS RESEARCH (2023) . |
| APA | Li, Hao , Wu, Song , Li, Jiaming , Xiong, Zhuang , Yang, Kuan , Ye, Weidong et al. HALL: a comprehensive database for human aging and longevity studies . | NUCLEIC ACIDS RESEARCH , 2023 . |
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The development of spatial transcriptome sequencing technology has revolutionized our comprehension of complex tissues and propelled life and health sciences into an era of spatial omics. However, the current availability of databases for accessing and analyzing spatial transcriptomic data is limited. In response, we have established CROST (https://ngdc.cncb.ac.cn/crost), a comprehensive repository of spatial transcriptomics. CROST encompasses high-quality samples and houses 182 spatial transcriptomic datasets from diverse species, organs, and diseases, comprising 1033 sub-datasets and 48 043 tumor-related spatially variable genes (SVGs). Additionally, it encompasses a standardized spatial transcriptome data processing pipeline, integrates single-cell RNA sequencing deconvolution spatial transcriptomics data, and evaluates correlation, colocalization, intercellular communication, and biological function annotation analyses. Moreover, CROST integrates the transcriptome, epigenome, and genome to explore tumor-associated SVGs and provides a comprehensive understanding of their roles in cancer progression and prognosis. Furthermore, CROST provides two online tools, single-sample gene set enrichment analysis and SpatialAP, for users to annotate and analyze the uploaded spatial transcriptomics data. The user-friendly interface of CROST facilitates browsing, searching, analyzing, visualizing, and downloading desired information. Collectively, CROST offers fresh and comprehensive insights into tissue structure and a foundation for understanding multiple biological mechanisms in diseases, particularly in tumor tissues. [Graphical Abstract]
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| GB/T 7714 | Wang, Guoliang , Wu, Song , Xiong, Zhuang et al. CROST: a comprehensive repository of spatial transcriptomics [J]. | NUCLEIC ACIDS RESEARCH , 2023 , 52 (D1) : D882-D890 . |
| MLA | Wang, Guoliang et al. "CROST: a comprehensive repository of spatial transcriptomics" . | NUCLEIC ACIDS RESEARCH 52 . D1 (2023) : D882-D890 . |
| APA | Wang, Guoliang , Wu, Song , Xiong, Zhuang , Qu, Hongzhu , Fang, Xiangdong , Bao, Yiming . CROST: a comprehensive repository of spatial transcriptomics . | NUCLEIC ACIDS RESEARCH , 2023 , 52 (D1) , D882-D890 . |
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The National Genomics Data Center (NGDC), which is a part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support the global academic and industrial communities. With the rapid accumulation of multi-omics data at an unprecedented pace, CNCB-NGDC continuously expands and updates core database resources through big data archiving, integrative analysis and value-added curation. Importantly, NGDC collaborates closely with major international databases and initiatives to ensure seamless data exchange and interoperability. Over the past year, significant efforts have been dedicated to integrating diverse omics data, synthesizing expanding knowledge, developing new resources, and upgrading major existing resources. Particularly, several database resources are newly developed for the biodiversity of protists (P10K), bacteria (NTM-DB, MPA) as well as plant (PPGR, SoyOmics, PlantPan) and disease/trait association (CROST, HervD Atlas, HALL, MACdb, BioKA, BioKA, RePoS, PGG.SV, NAFLDkb). All the resources and services are publicly accessible at https://ngdc.cncb.ac.cn. Graphical Abstract
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| GB/T 7714 | Bao, Yiming , Zhang, Zhang , Zhao, Wenming et al. Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2024 [J]. | NUCLEIC ACIDS RESEARCH , 2023 . |
| MLA | Bao, Yiming et al. "Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2024" . | NUCLEIC ACIDS RESEARCH (2023) . |
| APA | Bao, Yiming , Zhang, Zhang , Zhao, Wenming , Xiao, Jingfa , He, Shunmin , Zhang, Guoqing et al. Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2024 . | NUCLEIC ACIDS RESEARCH , 2023 . |
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