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Wang S, Liu T, Ren C, Zhao Y, Qiao S, Zhang Y, Pang S. Heterogeneous graph inference with range constrainted L 2,1-collaborative matrix factorization for small molecule-miRNA association prediction. Comput Biol Chem 2024; 110:108078. [PMID: 38677013 DOI: 10.1016/j.compbiolchem.2024.108078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 04/03/2024] [Accepted: 04/16/2024] [Indexed: 04/29/2024]
Abstract
MicroRNAs (miRNAs) play a vital role in regulating gene expression and various biological processes. As a result, they have been identified as effective targets for small molecule (SM) drugs in disease treatment. Heterogeneous graph inference stands as a classical approach for predicting SM-miRNA associations, showcasing commendable convergence accuracy and speed. However, most existing methods do not adequately address the inherent sparsity in SM-miRNA association networks, and imprecise SM/miRNA similarity metrics reduce the accuracy of predicting SM-miRNA associations. In this research, we proposed a heterogeneous graph inference with range constrained L2,1-collaborative matrix factorization (HGIRCLMF) method to predict potential SM-miRNA associations. First, we computed the multi-source similarities of SM/miRNA and integrated these similarity information into a comprehensive SM/miRNA similarity. This step improved the accuracy of SM and miRNA similarity, ensuring reliability for the subsequent inference of the heterogeneity map. Second, we used a range constrained L2,1-collaborative matrix factorization (RCLMF) model to pre-populate the SM-miRNA association matrix with missing values. In this step, we developed a novel matrix decomposition method that enhances the robustness and formative nature of SM-miRNA edges between SM networks and miRNA networks. Next, we built a well-established SM-miRNA heterogeneous network utilizing the processed biological information. Finally, HGIRCLMF used this network data to infer unknown association pair scores. We implemented four cross-validation experiments on two distinct datasets, and HGIRCLMF acquired the highest areas under the curve, surpassing six state-of-the-art computational approaches. Furthermore, we performed three case studies to validate the predictive power of our method in practical application.
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Affiliation(s)
- Shudong Wang
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China
| | - Tiyao Liu
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China
| | - Chuanru Ren
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China
| | - Yawu Zhao
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China
| | - Sibo Qiao
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China
| | - Yuanyuan Zhang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, China.
| | - Shanchen Pang
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China
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Xia Y, Sun M, Huang H, Jin WL. Drug repurposing for cancer therapy. Signal Transduct Target Ther 2024; 9:92. [PMID: 38637540 PMCID: PMC11026526 DOI: 10.1038/s41392-024-01808-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/05/2024] [Accepted: 03/19/2024] [Indexed: 04/20/2024] Open
Abstract
Cancer, a complex and multifactorial disease, presents a significant challenge to global health. Despite significant advances in surgical, radiotherapeutic and immunological approaches, which have improved cancer treatment outcomes, drug therapy continues to serve as a key therapeutic strategy. However, the clinical efficacy of drug therapy is often constrained by drug resistance and severe toxic side effects, and thus there remains a critical need to develop novel cancer therapeutics. One promising strategy that has received widespread attention in recent years is drug repurposing: the identification of new applications for existing, clinically approved drugs. Drug repurposing possesses several inherent advantages in the context of cancer treatment since repurposed drugs are typically cost-effective, proven to be safe, and can significantly expedite the drug development process due to their already established safety profiles. In light of this, the present review offers a comprehensive overview of the various methods employed in drug repurposing, specifically focusing on the repurposing of drugs to treat cancer. We describe the antitumor properties of candidate drugs, and discuss in detail how they target both the hallmarks of cancer in tumor cells and the surrounding tumor microenvironment. In addition, we examine the innovative strategy of integrating drug repurposing with nanotechnology to enhance topical drug delivery. We also emphasize the critical role that repurposed drugs can play when used as part of a combination therapy regimen. To conclude, we outline the challenges associated with repurposing drugs and consider the future prospects of these repurposed drugs transitioning into clinical application.
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Affiliation(s)
- Ying Xia
- Center for Clinical Laboratories, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, PR China
- The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, 550001, PR China
- School of Clinical Laboratory Science, Guizhou Medical University, Guiyang, 550004, PR China
- Division of Gastroenterology and Hepatology, Department of Medicine and, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Ming Sun
- Center for Clinical Laboratories, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, PR China
- School of Clinical Laboratory Science, Guizhou Medical University, Guiyang, 550004, PR China
| | - Hai Huang
- Center for Clinical Laboratories, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, PR China.
- School of Clinical Laboratory Science, Guizhou Medical University, Guiyang, 550004, PR China.
| | - Wei-Lin Jin
- Institute of Cancer Neuroscience, Medical Frontier Innovation Research Center, The First Hospital of Lanzhou University, The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, PR China.
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Yu Z, Wu Z, Wang Z, Wang Y, Zhou M, Li W, Liu G, Tang Y. Network-Based Methods and Their Applications in Drug Discovery. J Chem Inf Model 2024; 64:57-75. [PMID: 38150548 DOI: 10.1021/acs.jcim.3c01613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.
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Affiliation(s)
- Zhuohang Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Ze Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yimeng Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Moran Zhou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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Wang S, Li Y, Zhang Y, Pang S, Qiao S, Zhang Y, Wang F. Generative Adversarial Matrix Completion Network based on Multi-Source Data Fusion for miRNA-Disease Associations Prediction. Brief Bioinform 2023; 24:bbad270. [PMID: 37482409 DOI: 10.1093/bib/bbad270] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 06/16/2023] [Accepted: 07/04/2023] [Indexed: 07/25/2023] Open
Abstract
Numerous biological studies have shown that considering disease-associated micro RNAs (miRNAs) as potential biomarkers or therapeutic targets offers new avenues for the diagnosis of complex diseases. Computational methods have gradually been introduced to reveal disease-related miRNAs. Considering that previous models have not fused sufficiently diverse similarities, that their inappropriate fusion methods may lead to poor quality of the comprehensive similarity network and that their results are often limited by insufficiently known associations, we propose a computational model called Generative Adversarial Matrix Completion Network based on Multi-source Data Fusion (GAMCNMDF) for miRNA-disease association prediction. We create a diverse network connecting miRNAs and diseases, which is then represented using a matrix. The main task of GAMCNMDF is to complete the matrix and obtain the predicted results. The main innovations of GAMCNMDF are reflected in two aspects: GAMCNMDF integrates diverse data sources and employs a nonlinear fusion approach to update the similarity networks of miRNAs and diseases. Also, some additional information is provided to GAMCNMDF in the form of a 'hint' so that GAMCNMDF can work successfully even when complete data are not available. Compared with other methods, the outcomes of 10-fold cross-validation on two distinct databases validate the superior performance of GAMCNMDF with statistically significant results. It is worth mentioning that we apply GAMCNMDF in the identification of underlying small molecule-related miRNAs, yielding outstanding performance results in this specific domain. In addition, two case studies about two important neoplasms show that GAMCNMDF is a promising prediction method.
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Affiliation(s)
- ShuDong Wang
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum (East China), 66 Changjiang Xi Lu, 266580, Shandong, China
| | - YunYin Li
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum (East China), 66 Changjiang Xi Lu, 266580, Shandong, China
| | - YuanYuan Zhang
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum (East China), 66 Changjiang Xi Lu, 266580, Shandong, China
| | - ShanChen Pang
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum (East China), 66 Changjiang Xi Lu, 266580, Shandong, China
| | - SiBo Qiao
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum (East China), 66 Changjiang Xi Lu, 266580, Shandong, China
| | - Yu Zhang
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum (East China), 66 Changjiang Xi Lu, 266580, Shandong, China
| | - FuYu Wang
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum (East China), 66 Changjiang Xi Lu, 266580, Shandong, China
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Wang S, Ren C, Zhang Y, Li Y, Pang S, Song T. Identifying potential small molecule-miRNA associations via Robust PCA based on γ-norm regularization. Brief Bioinform 2023; 24:bbad312. [PMID: 37670501 DOI: 10.1093/bib/bbad312] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 07/18/2023] [Accepted: 08/10/2023] [Indexed: 09/07/2023] Open
Abstract
Dysregulation of microRNAs (miRNAs) is closely associated with refractory human diseases, and the identification of potential associations between small molecule (SM) drugs and miRNAs can provide valuable insights for clinical treatment. Existing computational techniques for inferring potential associations suffer from limitations in terms of accuracy and efficiency. To address these challenges, we devise a novel predictive model called RPCA$\Gamma $NR, in which we propose a new Robust principal component analysis (PCA) framework based on $\gamma $-norm and $l_{2,1}$-norm regularization and design an Augmented Lagrange Multiplier method to optimize it, thereby deriving the association scores. The Gaussian Interaction Profile Kernel Similarity is calculated to capture the similarity information of SMs and miRNAs in known associations. Through extensive evaluation, including Cross Validation Experiments, Independent Validation Experiment, Efficiency Analysis, Ablation Experiment, Matrix Sparsity Analysis, and Case Studies, RPCA$\Gamma $NR outperforms state-of-the-art models concerning accuracy, efficiency and robustness. In conclusion, RPCA$\Gamma $NR can significantly streamline the process of determining SM-miRNA associations, thus contributing to advancements in drug development and disease treatment.
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Affiliation(s)
- Shudong Wang
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum (East China), 66 Changjiang Xi Lu, 266580 Shandong, China
| | - Chuanru Ren
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum (East China), 66 Changjiang Xi Lu, 266580 Shandong, China
| | - Yulin Zhang
- College of Mathematics and Systems Science, Shandong University of Science and Technology, Xin An Street, 266590 Shandong, China
| | - Yunyin Li
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum (East China), 66 Changjiang Xi Lu, 266580 Shandong, China
| | - Shanchen Pang
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum (East China), 66 Changjiang Xi Lu, 266580 Shandong, China
| | - Tao Song
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum (East China), 66 Changjiang Xi Lu, 266580 Shandong, China
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Qu J, Song Z, Cheng X, Jiang Z, Zhou J. Neighborhood-based inference and restricted Boltzmann machine for small molecule-miRNA associations prediction. PeerJ 2023; 11:e15889. [PMID: 37641598 PMCID: PMC10460564 DOI: 10.7717/peerj.15889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/21/2023] [Indexed: 08/31/2023] Open
Abstract
Background A growing number of experiments have shown that microRNAs (miRNAs) can be used as target of small molecules (SMs) to regulate gene expression for treating diseases. Therefore, identifying SM-related miRNAs is helpful for the treatment of diseases in the domain of medical investigation. Methods This article presents a new computational model, called NIRBMSMMA (neighborhood-based inference (NI) and restricted Boltzmann machine (RBM)), which we developed to identify potential small molecule-miRNA associations (NIRBMSMMA). First, grounded on known SM-miRNAs associations, SM similarity and miRNA similarity, NI was used to predict score of an unknown SM-miRNA pair by reckoning the sum of known associations between neighbors of the SM (miRNA) and the miRNA (SM). Second, utilizing a two-layered generative stochastic artificial neural network, RBM was used to predict SM-miRNA association by learning potential probability distribution from known SM-miRNA associations. At last, an ensemble learning model was conducted to combine NI and RBM for identifying potential SM-miRNA associations. Results Furthermore, we conducted global leave one out cross validation (LOOCV), miRNA-fixed LOOCV, SM-fixed LOOCV and five-fold cross validation to assess performance of NIRBMSMMA based on three datasets. Results showed that NIRBMSMMA obtained areas under the curve (AUC) of 0.9912, 0.9875, 0.8376 and 0.9898 ± 0.0009 under global LOOCV, miRNA-fixed LOOCV, SM-fixed LOOCV and five-fold cross validation based on dataset 1, respectively. For dataset 2, the AUCs are 0.8645, 0.8720, 0.7066 and 0.8547 ± 0.0046 in turn. For dataset 3, the AUCs are 0.9884, 0.9802, 0.8239 and 0.9870 ± 0.0015 in turn. Also, we conducted case studies to further assess the predictive performance of NIRBMSMMA. These results illustrated the proposed model is a useful tool in predicting potential SM-miRNA associations.
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Affiliation(s)
- Jia Qu
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu, China
| | - Zihao Song
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu, China
| | - Xiaolong Cheng
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu, China
| | - Zhibin Jiang
- Department of Computer Science and Engineering, Shaoxing University, Shaoxing, Zhejiang, China
| | - Jie Zhou
- Department of Computer Science and Engineering, Shaoxing University, Shaoxing, Zhejiang, China
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Niu Z, Gao X, Xia Z, Zhao S, Sun H, Wang H, Liu M, Kong X, Ma C, Zhu H, Gao H, Liu Q, Yang F, Song X, Lu J, Zhou X. Prediction of small molecule drug-miRNA associations based on GNNs and CNNs. Front Genet 2023; 14:1201934. [PMID: 37323664 PMCID: PMC10268031 DOI: 10.3389/fgene.2023.1201934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 05/17/2023] [Indexed: 06/17/2023] Open
Abstract
MicroRNAs (miRNAs) play a crucial role in various biological processes and human diseases, and are considered as therapeutic targets for small molecules (SMs). Due to the time-consuming and expensive biological experiments required to validate SM-miRNA associations, there is an urgent need to develop new computational models to predict novel SM-miRNA associations. The rapid development of end-to-end deep learning models and the introduction of ensemble learning ideas provide us with new solutions. Based on the idea of ensemble learning, we integrate graph neural networks (GNNs) and convolutional neural networks (CNNs) to propose a miRNA and small molecule association prediction model (GCNNMMA). Firstly, we use GNNs to effectively learn the molecular structure graph data of small molecule drugs, while using CNNs to learn the sequence data of miRNAs. Secondly, since the black-box effect of deep learning models makes them difficult to analyze and interpret, we introduce attention mechanisms to address this issue. Finally, the neural attention mechanism allows the CNNs model to learn the sequence data of miRNAs to determine the weight of sub-sequences in miRNAs, and then predict the association between miRNAs and small molecule drugs. To evaluate the effectiveness of GCNNMMA, we implement two different cross-validation (CV) methods based on two different datasets. Experimental results show that the cross-validation results of GCNNMMA on both datasets are better than those of other comparison models. In a case study, Fluorouracil was found to be associated with five different miRNAs in the top 10 predicted associations, and published experimental literature confirmed that Fluorouracil is a metabolic inhibitor used to treat liver cancer, breast cancer, and other tumors. Therefore, GCNNMMA is an effective tool for mining the relationship between small molecule drugs and miRNAs relevant to diseases.
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Sun J, Xu M, Ru J, James-Bott A, Xiong D, Wang X, Cribbs AP. Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications. Eur J Med Chem 2023; 257:115500. [PMID: 37262996 DOI: 10.1016/j.ejmech.2023.115500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/05/2023] [Accepted: 05/15/2023] [Indexed: 06/03/2023]
Abstract
Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.
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Affiliation(s)
- Jianfeng Sun
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
| | - Miaoer Xu
- Department of Biology, Emory University, Atlanta, GA, 30322, USA
| | - Jinlong Ru
- Chair of Prevention of Microbial Diseases, School of Life Sciences Weihenstephan, Technical University of Munich, Freising, 85354, Germany
| | - Anna James-Bott
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
| | - Dapeng Xiong
- Department of Computational Biology, Cornell University, Ithaca, NY, 14853, USA; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, 14853, USA
| | - Xia Wang
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China.
| | - Adam P Cribbs
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
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Wang S, Ren C, Zhang Y, Pang S, Qiao S, Wu W, Lin B. AMCSMMA: Predicting Small Molecule-miRNA Potential Associations Based on Accurate Matrix Completion. Cells 2023; 12:cells12081123. [PMID: 37190032 DOI: 10.3390/cells12081123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/02/2023] [Accepted: 04/04/2023] [Indexed: 05/17/2023] Open
Abstract
Exploring potential associations between small molecule drugs (SMs) and microRNAs (miRNAs) is significant for drug development and disease treatment. Since biological experiments are expensive and time-consuming, we propose a computational model based on accurate matrix completion for predicting potential SM-miRNA associations (AMCSMMA). Initially, a heterogeneous SM-miRNA network is constructed, and its adjacency matrix is taken as the target matrix. An optimization framework is then proposed to recover the target matrix with the missing values by minimizing its truncated nuclear norm, an accurate, robust, and efficient approximation to the rank function. Finally, we design an effective two-step iterative algorithm to solve the optimization problem and obtain the prediction scores. After determining the optimal parameters, we conduct four kinds of cross-validation experiments based on two datasets, and the results demonstrate that AMCSMMA is superior to the state-of-the-art methods. In addition, we implement another validation experiment, in which more evaluation metrics in addition to the AUC are introduced and finally achieve great results. In two types of case studies, a large number of SM-miRNA pairs with high predictive scores are confirmed by the published experimental literature. In summary, AMCSMMA has superior performance in predicting potential SM-miRNA associations, which can provide guidance for biological experiments and accelerate the discovery of new SM-miRNA associations.
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Affiliation(s)
- Shudong Wang
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China
| | - Chuanru Ren
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China
| | - Yulin Zhang
- College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266580, China
| | - Shanchen Pang
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China
| | - Sibo Qiao
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China
| | - Wenhao Wu
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China
| | - Boyang Lin
- College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China
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Rafiq M, Dandare A, Javed A, Liaquat A, Raja AA, Awan HM, Khan MJ, Naeem A. Competing Endogenous RNA Regulatory Networks of hsa_circ_0126672 in Pathophysiology of Coronary Heart Disease. Genes (Basel) 2023; 14:genes14030550. [PMID: 36980823 PMCID: PMC10047999 DOI: 10.3390/genes14030550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 02/08/2023] [Accepted: 02/20/2023] [Indexed: 02/25/2023] Open
Abstract
Coronary heart disease (CHD) is a global health concern, and its molecular origin is not fully elucidated. Dysregulation of ncRNAs has been linked to many metabolic and infectious diseases. This study aimed to explore the role of circRNAs in the pathogenesis of CHD and predicted a candidate circRNA that could be targeted for therapeutic approaches to the disease. circRNAs associated with CHD were identified and CHD gene expression profiles were obtained, and analyzed with GEO2R. In addition, differentially expressed miRNA target genes (miR-DEGs) were identified and subjected to functional enrichment analysis. Networks of circRNA/miRNA/mRNA and the miRNA/affected pathways were constructed. Furthermore, a miRNA/mRNA homology study was performed. We identified that hsa_circ_0126672 was strongly associated with the CHD pathology by competing for endogenous RNA (ceRNA) mechanisms. hsa_circ_0126672 characteristically sponges miR-145-5p, miR-186-5p, miR-548c-3p, miR-7-5p, miR-495-3p, miR-203a-3p, and miR-21. Up-regulation of has_circ_0126672 affected various CHD-related cellular functions, such as atherosclerosis, JAK/STAT, and Apelin signaling pathways. Our results also revealed a perfect and stable interaction for the hybrid of miR-145-5p with NOS1 and RPS6KB1. Finally, miR-145-5p had the highest degree of interaction with the validated small molecules. Henchashsa_circ_0126672 and target miRNAs, notably miR-145-5p, could be good candidates for the diagnosis and therapeutic approaches to CHD.
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Affiliation(s)
- Muhammad Rafiq
- Department of Biosciences, COMSATS University Islamabad, Islamabad 45550, Pakistan
- Department of Biochemistry, Shifa College of Medicine, Shifa Tameer-e-Millat University, Islamabad 45550, Pakistan
| | - Abdullahi Dandare
- Department of Biosciences, COMSATS University Islamabad, Islamabad 45550, Pakistan
- Department of Biochemistry, Usmanu Danfodiyo University Sokoto, Sokoto P.M.B 2346, Nigeria
| | - Arham Javed
- Department of Biosciences, COMSATS University Islamabad, Islamabad 45550, Pakistan
- Department of Biochemistry, Shifa College of Medicine, Shifa Tameer-e-Millat University, Islamabad 45550, Pakistan
| | - Afrose Liaquat
- Department of Biochemistry, Shifa College of Medicine, Shifa Tameer-e-Millat University, Islamabad 45550, Pakistan
| | - Afraz Ahmad Raja
- Department of Biosciences, COMSATS University Islamabad, Islamabad 45550, Pakistan
| | - Hassaan Mehboob Awan
- Department of Biosciences, COMSATS University Islamabad, Islamabad 45550, Pakistan
| | - Muhammad Jawad Khan
- Department of Biosciences, COMSATS University Islamabad, Islamabad 45550, Pakistan
- Correspondence: (M.J.K.); (A.N.); Tel.: +92-519-049-6140 (M.J.K)
| | - Aisha Naeem
- Health Research Governance Department, Ministry of Public Health, Doha P.O. Box 42, Qatar
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA
- Correspondence: (M.J.K.); (A.N.); Tel.: +92-519-049-6140 (M.J.K)
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11
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Wang MN, Li Y, Lei LL, Ding DW, Xie XJ. Combining non-negative matrix factorization with graph Laplacian regularization for predicting drug-miRNA associations based on multi-source information fusion. Front Pharmacol 2023; 14:1132012. [PMID: 36817132 PMCID: PMC9931722 DOI: 10.3389/fphar.2023.1132012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 01/16/2023] [Indexed: 02/05/2023] Open
Abstract
Increasing evidences suggest that miRNAs play a key role in the occurrence and progression of many complex human diseases. Therefore, targeting dysregulated miRNAs with small molecule drugs in the clinical has become a new treatment. Nevertheless, it is high cost and time-consuming for identifying miRNAs-targeted with drugs by biological experiments. Thus, more reliable computational method for identification associations of drugs with miRNAs urgently need to be developed. In this study, we proposed an efficient method, called GNMFDMA, to predict potential associations of drug with miRNA by combining graph Laplacian regularization with non-negative matrix factorization. We first calculated the overall similarity matrices of drugs and miRNAs according to the collected different biological information. Subsequently, the new drug-miRNA association adjacency matrix was reformulated based on the K nearest neighbor profiles so as to put right the false negative associations. Finally, graph Laplacian regularization collaborative non-negative matrix factorization was used to calculate the association scores of drugs with miRNAs. In the cross validation, GNMFDMA obtains AUC of 0.9193, which outperformed the existing methods. In addition, case studies on three common drugs (i.e., 5-Aza-CdR, 5-FU and Gemcitabine), 30, 31 and 34 of the top-50 associations inferred by GNMFDMA were verified. These results reveal that GNMFDMA is a reliable and efficient computational approach for identifying the potential drug-miRNA associations.
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Affiliation(s)
- Mei-Neng Wang
- School of Mathematics and Computer Science, Yichun University, Yichun, China
| | - Yu Li
- School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China,*Correspondence: Yu Li,
| | - Li-Lan Lei
- School of Mathematics and Computer Science, Yichun University, Yichun, China
| | - De-Wu Ding
- School of Mathematics and Computer Science, Yichun University, Yichun, China
| | - Xue-Jun Xie
- School of Mathematics and Computer Science, Yichun University, Yichun, China
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12
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Mohanan EM, Jhala D, More CB, Patel AK, Joshi C. Bioinformatics analysis of miRNA and its associated genes to identify potential biomarkers of oral submucous fibrosis and oral malignancy. Cancer Rep (Hoboken) 2023; 6:e1787. [PMID: 36708238 PMCID: PMC10075298 DOI: 10.1002/cnr2.1787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/14/2022] [Accepted: 01/06/2023] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND MicroRNAs are a group of non-coding RNA that controls the gene expression. The interaction between miRNA and mRNA is thought to be dynamic. Oral cancer "The cancer of mouth" is quite prevailing in developing countries. miRNA has been found associated with oral cancer targeting tumor growth, cell proliferation, metastasis, invasion. The significant association of miRNA with genes could be used as a remarkable tool for diagnosis as well as prognostic analysis of oral cancer. AIM The aim of the present study is to evaluate common upregulated and downregulated miRNAs in oral submucous fibrosis (OSMF) and oral malignancy (OM) patients that can be used as diagnostic biomarkers, and to find out their interactions with target genes to establish associated networks in cancer pathways. METHODS AND RESULTS Using miRDeep2 and DESeq analysis, the upregulated and downregulated miRNA in OSMF (Oral Submucous Fibrosis) and OM (Oral Malignancies) samples were compared to GEO (Gene Expression Omnibus) control dataset. There were 50 common downregulated miRNAs and 13 common upregulated miRNAs in OSMF and OM samples. miRNet analysis of common upregulated miRNA and common downregulated miRNA identified 1295 and 5954 genes, respectively connected with cancer pathways. From analysis of Hub genes, HRAS, STAT3, TP53, MYC, PTEN, CTNNB1, CCND1, JUN, VEGFA, KRAS were found associated with downregulated miRNA and VEGFA, TP53, MDM2, PTEN, MYC, ERBB2, CDKN1A, HSP90AA1, CCND1, AKTI were found associated with upregulated miRNA. The gene enrichment analysis of these hub genes were associated with cell communication, metabolic process, cell proliferation, and cellular component organization. Hub Genes linked with upregulated miRNA had an enrichment ratio of 11.828, whereas hub genes linked with downregulated miRNA had an enrichment ratio of 45.912. CONCLUSION We identified common deregulated miRNAs between OSMF and OM patients, which were further analyzed to find out associations with the genes correlated to cancer pathways. The hub genes identified in this study were found to have a significant impact on tumor growth and carcinogenesis. Also, the enrichment of these genes has revealed that the genes are associated with cellular communication, metabolic processes and various biological regulation. These deregulated miRNAs can be used to make a panel of biomarkers to diagnose oral cancer from blood even before its onset.
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Affiliation(s)
- Ezhuthachan Mithu Mohanan
- Gujarat Biotechnology Research Centre, Department of Science and Technology, Government of Gujarat, Gandhinagar, Gujarat, India
| | - Dhwani Jhala
- Gujarat Biotechnology Research Centre, Department of Science and Technology, Government of Gujarat, Gandhinagar, Gujarat, India
| | - Chandramani B More
- Department of Oral Medicine & Radiology, K.M. Shah Dental College and Hospital, Vadodara, Gujarat, India
| | - Amrutlal K Patel
- Gujarat Biotechnology Research Centre, Department of Science and Technology, Government of Gujarat, Gandhinagar, Gujarat, India
| | - Chaitanya Joshi
- Gujarat Biotechnology Research Centre, Department of Science and Technology, Government of Gujarat, Gandhinagar, Gujarat, India
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13
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Li J, Lin H, Wang Y, Li Z, Wu B. Prediction of potential small molecule-miRNA associations based on heterogeneous network representation learning. Front Genet 2022; 13:1079053. [PMID: 36531225 PMCID: PMC9755196 DOI: 10.3389/fgene.2022.1079053] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 11/21/2022] [Indexed: 11/25/2023] Open
Abstract
MicroRNAs (miRNAs) are closely associated with the occurrences and developments of many complex human diseases. Increasing studies have shown that miRNAs emerge as new therapeutic targets of small molecule (SM) drugs. Since traditional experiment methods are expensive and time consuming, it is particularly crucial to find efficient computational approaches to predict potential small molecule-miRNA (SM-miRNA) associations. Considering that integrating multi-source heterogeneous information related with SM-miRNA association prediction would provide a comprehensive insight into the features of both SMs and miRNAs, we proposed a novel model of Small Molecule-MiRNA Association prediction based on Heterogeneous Network Representation Learning (SMMA-HNRL) for more precisely predicting the potential SM-miRNA associations. In SMMA-HNRL, a novel heterogeneous information network was constructed with SM nodes, miRNA nodes and disease nodes. To access and utilize of the topological information of the heterogeneous information network, feature vectors of SM and miRNA nodes were obtained by two different heterogeneous network representation learning algorithms (HeGAN and HIN2Vec) respectively and merged with connect operation. Finally, LightGBM was chosen as the classifier of SMMA-HNRL for predicting potential SM-miRNA associations. The 10-fold cross validations were conducted to evaluate the prediction performance of SMMA-HNRL, it achieved an area under of ROC curve of 0.9875, which was superior to other three state-of-the-art models. With two independent validation datasets, the test experiment results revealed the robustness of our model. Moreover, three case studies were performed. As a result, 35, 37, and 22 miRNAs among the top 50 predicting miRNAs associated with 5-FU, cisplatin, and imatinib were validated by experimental literature works respectively, which confirmed the effectiveness of SMMA-HNRL. The source code and experimental data of SMMA-HNRL are available at https://github.com/SMMA-HNRL/SMMA-HNRL.
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Affiliation(s)
- Jianwei Li
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
- Hebei Province Key Laboratory of Big Data Calculation, Hebei University of Technology, Tianjin, China
| | - Hongxin Lin
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
| | - Yinfei Wang
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
| | - Zhiguang Li
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
| | - Baoqin Wu
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
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14
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Peng L, Tu Y, Huang L, Li Y, Fu X, Chen X. DAESTB: inferring associations of small molecule-miRNA via a scalable tree boosting model based on deep autoencoder. Brief Bioinform 2022; 23:6827720. [PMID: 36377749 DOI: 10.1093/bib/bbac478] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 09/28/2022] [Accepted: 10/08/2022] [Indexed: 11/16/2022] Open
Abstract
MicroRNAs (miRNAs) are closely related to a variety of human diseases, not only regulating gene expression, but also having an important role in human life activities and being viable targets of small molecule drugs for disease treatment. Current computational techniques to predict the potential associations between small molecule and miRNA are not that accurate. Here, we proposed a new computational method based on a deep autoencoder and a scalable tree boosting model (DAESTB), to predict associations between small molecule and miRNA. First, we constructed a high-dimensional feature matrix by integrating small molecule-small molecule similarity, miRNA-miRNA similarity and known small molecule-miRNA associations. Second, we reduced feature dimensionality on the integrated matrix using a deep autoencoder to obtain the potential feature representation of each small molecule-miRNA pair. Finally, a scalable tree boosting model is used to predict small molecule and miRNA potential associations. The experiments on two datasets demonstrated the superiority of DAESTB over various state-of-the-art methods. DAESTB achieved the best AUC value. Furthermore, in three case studies, a large number of predicted associations by DAESTB are confirmed with the public accessed literature. We envision that DAESTB could serve as a useful biological model for predicting potential small molecule-miRNA associations.
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Affiliation(s)
- Li Peng
- College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, Hunan, China.,Hunan Key Laboratory for Service computing and Novel Software Technology
| | - Yuan Tu
- College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, Hunan, China
| | - Li Huang
- Academy of Arts and Design, Tsinghua University, Beijing, 10084, China.,The Future Laboratory, Tsinghua University, Beijing, 10084, China
| | - Yang Li
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, 411105, China
| | - Xiangzheng Fu
- College of Information Science and Engineering, Hunan University, Changsha, 410082, Hunan, China
| | - Xiang Chen
- College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, Hunan, China
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15
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A message passing framework with multiple data integration for miRNA-disease association prediction. Sci Rep 2022; 12:16259. [PMID: 36171337 PMCID: PMC9519928 DOI: 10.1038/s41598-022-20529-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 09/14/2022] [Indexed: 11/08/2022] Open
Abstract
Micro RNA or miRNA is a highly conserved class of non-coding RNA that plays an important role in many diseases. Identifying miRNA-disease associations can pave the way for better clinical diagnosis and finding potential drug targets. We propose a biologically-motivated data-driven approach for the miRNA-disease association prediction, which overcomes the data scarcity problem by exploiting information from multiple data sources. The key idea is to enrich the existing miRNA/disease-protein-coding gene (PCG) associations via a message passing framework, followed by the use of disease ontology information for further feature filtering. The enriched and filtered PCG associations are then used to construct the inter-connected miRNA-PCG-disease network to train a structural deep network embedding (SDNE) model. Finally, the pre-trained embeddings and the biologically relevant features from the miRNA family and disease semantic similarity are concatenated to form the pair input representations to a Random Forest classifier whose task is to predict the miRNA-disease association probabilities. We present large-scale comparative experiments, ablation, and case studies to showcase our approach's superiority. Besides, we make the model prediction results for 1618 miRNAs and 3679 diseases, along with all related information, publicly available at http://software.mpm.leibniz-ai-lab.de/ to foster assessments and future adoption.
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16
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Qin L, Wang J, Wu Z, Li W, Liu G, Tang Y. Drug Repurposing for Newly Emerged Diseases via Network-Based Inference on A Gene-Disease-Drug Network. Mol Inform 2022; 41:e2200001. [PMID: 35338586 DOI: 10.1002/minf.202200001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 03/25/2022] [Indexed: 11/06/2022]
Abstract
Identification of disease-drug associations is an effective strategy for drug repurposing, especially in searching old drugs for newly emerged diseases like COVID-19. In this study, we put forward a network-based method named NEDNBI to predict disease-drug associations based on a gene-disease-drug tripartite network, which could be applied in drug repurposing. The novelty of our method lies in the fact that no negative data are required, and new disease could be added into the disease-drug network with gene as the bridge. The comprehensive evaluation results showed that the proposed method had good performance, with AUC value 0.948 ± 0.009 for 10-fold cross validation. In a case study, 8 of the 20 predicted old drugs have been tested clinically for the treatment of COVID-19, which illustrated the usefulness of our method in drug repurposing. The source code and data of the method are available at https://github.com/Qli97/NEDNBI.
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Affiliation(s)
- Li Qin
- East China University of Science and Technology School of Pharmacy, CHINA
| | - Jiye Wang
- East China University of Science and Technology School of Pharmacy, CHINA
| | - Zengrui Wu
- East China University of Science and Technology, CHINA
| | | | - Guixia Liu
- East China University of Science and Technology, CHINA
| | - Yun Tang
- East China University of Science and Technology, CHINA
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17
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Yu F, Li B, Sun J, Qi J, De Wilde RL, Torres-de la Roche LA, Li C, Ahmad S, Shi W, Li X, Chen Z. PSRR: A Web Server for Predicting the Regulation of miRNAs Expression by Small Molecules. Front Mol Biosci 2022; 9:817294. [PMID: 35386297 PMCID: PMC8979021 DOI: 10.3389/fmolb.2022.817294] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 01/10/2022] [Indexed: 12/13/2022] Open
Abstract
Background: MicroRNAs (miRNAs) play key roles in a variety of pathological processes by interacting with their specific target mRNAs for translation repression and may function as oncogenes (oncomiRs) or tumor suppressors (TSmiRs). Therefore, a web server that could predict the regulation relations between miRNAs and small molecules is expected to achieve implications for identifying potential therapeutic targets for anti-tumor drug development. Methods: Upon obtaining positive/known small molecule-miRNA regulation pairs from SM2miR, we generated a multitude of high-quality negative/unknown pairs by leveraging similarities between the small molecule structures. Using the pool of the positive and negative pairs, we created the Dataset1 and Dataset2 datasets specific to up-regulation and down-regulation pairs, respectively. Manifold machine learning algorithms were then employed to construct models of predicting up-regulation and down-regulation pairs on the training portion of pairs in Dataset1 and Dataset2, respectively. Prediction abilities of the resulting models were further examined by discovering potential small molecules to regulate oncogenic miRNAs identified from miRNA sequencing data of endometrial carcinoma samples. Results: The random forest algorithm outperformed four machine-learning algorithms by achieving the highest AUC values of 0.911 for the up-regulation model and 0.896 for the down-regulation model on the testing datasets. Moreover, the down-regulation and up-regulation models yielded the accuracy values of 0.91 and 0.90 on independent validation pairs, respectively. In a case study, our model showed highly-reliable results by confirming all top 10 predicted regulation pairs as experimentally validated pairs. Finally, our predicted binding affinities of oncogenic miRNAs and small molecules bore a close resemblance to the lowest binding energy profiles using molecular docking. Predictions of the final model are freely accessible through the PSRR web server at https://rnadrug.shinyapps.io/PSRR/. Conclusion: Our study provides a novel web server that could effectively predict the regulation of miRNAs expression by small molecules.
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Affiliation(s)
- Fanrong Yu
- Department of Obstetrics and Gynecology, Fengxian District Central Hospital, Shanghai Jiao Tong University Affiliated to Sixth People’s Hospital South Campus, Shanghai, China
| | - Bihui Li
- Department of Oncology, The Second Affiliated Hospital of Guilin Medical University, Guilin, China
| | - Jianfeng Sun
- Department of Bioinformatics, Wissenschaftzentrum Weihenstephan, Technical University of Munich, Freising, Germany
| | - Jing Qi
- Institute for Transplantation Diagnostics and Cell Therapeutics, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstraße, Düsseldorf, Germany
| | - Rudy Leon De Wilde
- University Hospital for Gynecology, Pius-Hospital, University Medicine Oldenburg, Oldenburg, Germany
| | | | - Cheng Li
- Department of Orthopaedic Surgery, Beijing Jishuitan Hospital, Fourth Clinical College of Peking University, Beijing, China
| | - Sajjad Ahmad
- Department of Health and Biological Sciences, Abasyn University, Peshawar, Pakistan
| | - Wenjie Shi
- University Hospital for Gynecology, Pius-Hospital, University Medicine Oldenburg, Oldenburg, Germany
| | - Xiqing Li
- Oncology Department, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Zhengzhou, China
- *Correspondence: Zihao Chen, ; Xiqing Li,
| | - Zihao Chen
- University Hospital for Gynecology, Pius-Hospital, University Medicine Oldenburg, Oldenburg, Germany
- *Correspondence: Zihao Chen, ; Xiqing Li,
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18
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Yu Z, Wu Z, Li W, Liu G, Tang Y. ADENet: a novel network-based inference method for prediction of drug adverse events. Brief Bioinform 2022; 23:6510157. [PMID: 35039845 DOI: 10.1093/bib/bbab580] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 12/02/2021] [Accepted: 12/19/2021] [Indexed: 11/13/2022] Open
Abstract
Identification of adverse drug events (ADEs) is crucial to reduce human health risks and improve drug safety assessment. With an increasing number of biological and medical data, computational methods such as network-based methods were proposed for ADE prediction with high efficiency and low cost. However, previous network-based methods rely on the topological information of known drug-ADE networks, and hence cannot make predictions for novel compounds without any known ADE. In this study, we introduced chemical substructures to bridge the gap between the drug-ADE network and novel compounds, and developed a novel network-based method named ADENet, which can predict potential ADEs for not only drugs within the drug-ADE network, but also novel compounds outside the network. To show the performance of ADENet, we collected drug-ADE associations from a comprehensive database named MetaADEDB and constructed a series of network-based prediction models. These models obtained high area under the receiver operating characteristic curve values ranging from 0.871 to 0.947 in 10-fold cross-validation. The best model further showed high performance in external validation, which outperformed a previous network-based and a recent deep learning-based method. Using several approved drugs as case studies, we found that 32-54% of the predicted ADEs can be validated by the literature, indicating the practical value of ADENet. Moreover, ADENet is freely available at our web server named NetInfer (http://lmmd.ecust.edu.cn/netinfer). In summary, our method would provide a promising tool for ADE prediction and drug safety assessment in drug discovery and development.
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Affiliation(s)
- Zhuohang Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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19
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Shen L, Liu F, Huang L, Liu G, Zhou L, Peng L. VDA-RWLRLS: An anti-SARS-CoV-2 drug prioritizing framework combining an unbalanced bi-random walk and Laplacian regularized least squares. Comput Biol Med 2022; 140:105119. [PMID: 34902608 PMCID: PMC8664497 DOI: 10.1016/j.compbiomed.2021.105119] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 11/08/2021] [Accepted: 12/02/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND A new coronavirus disease named COVID-19, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is rapidly spreading worldwide. However, there is currently no effective drug to fight COVID-19. METHODS In this study, we developed a Virus-Drug Association (VDA) identification framework (VDA-RWLRLS) combining unbalanced bi-Random Walk, Laplacian Regularized Least Squares, molecular docking, and molecular dynamics simulation to find clues for the treatment of COVID-19. First, virus similarity and drug similarity are computed based on genomic sequences, chemical structures, and Gaussian association profiles. Second, an unbalanced bi-random walk is implemented on the virus network and the drug network, respectively. Third, the results of the random walks are taken as the input of Laplacian regularized least squares to compute the association score for each virus-drug pair. Fourth, the final associations are characterized by integrating the predictions from the virus network and the drug network. Finally, molecular docking and molecular dynamics simulation are implemented to measure the potential of screened anti-COVID-19 drugs and further validate the predicted results. RESULTS In comparison with six state-of-the-art association prediction models (NGRHMDA, SMiR-NBI, LRLSHMDA, VDA-KATZ, VDA-RWR, and VDA-BiRW), VDA-RWLRLS demonstrates superior VDA prediction performance. It obtains the best AUCs of 0.885 8, 0.835 5, and 0.862 5 on the three VDA datasets. Molecular docking and dynamics simulations demonstrated that remdesivir and ribavirin may be potential anti-COVID-19 drugs. CONCLUSIONS Integrating unbalanced bi-random walks, Laplacian regularized least squares, molecular docking, and molecular dynamics simulation, this work initially screened a few anti-SARS-CoV-2 drugs and may contribute to preventing COVID-19 transmission.
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Affiliation(s)
- Ling Shen
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412 007, Hunan, China
| | - Fuxing Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412 007, Hunan, China
| | - Li Huang
- Academy of Arts and Design, Tsinghua University, Beijing, 10 084, Beijing, China; The Future Laboratory, Tsinghua University, Beijing, 10 084, Beijing, China
| | - Guangyi Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412 007, Hunan, China
| | - Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412 007, Hunan, China.
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412 007, Hunan, China; College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, 412 007, Hunan, China.
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20
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Abdelbaky I, Tayara H, Chong KT. Identification of miRNA-Small Molecule Associations by Continuous Feature Representation Using Auto-Encoders. Pharmaceutics 2021; 14:pharmaceutics14010003. [PMID: 35056899 PMCID: PMC8780428 DOI: 10.3390/pharmaceutics14010003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 12/17/2021] [Accepted: 12/18/2021] [Indexed: 12/17/2022] Open
Abstract
MicroRNAs (miRNAs) are short non-coding RNAs that play important roles in the body and affect various diseases, including cancers. Controlling miRNAs with small molecules is studied herein to provide new drug repurposing perspectives for miRNA-related diseases. Experimental methods are time- and effort-consuming, so computational techniques have been applied, relying mostly on biological feature similarities and a network-based scheme to infer new miRNA–small molecule associations. Collecting such features is time-consuming and may be impractical. Here we suggest an alternative method of similarity calculation, representing miRNAs and small molecules through continuous feature representation. This representation is learned by the proposed deep learning auto-encoder architecture. Our suggested representation was compared to previous works and achieved comparable results using 5-fold cross validation (92% identified within top 25% predictions), and better predictions for most of the case studies (avg. of 31% vs. 25% identified within the top 25% of predictions). The results proved the effectiveness of our proposed method to replace previous time- and effort-consuming methods.
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Affiliation(s)
- Ibrahim Abdelbaky
- Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt;
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Korea
- Correspondence: (H.T.); (K.T.C.); Tel.: +82-63-270-2478 (K.T.C.)
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea
- Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Korea
- Correspondence: (H.T.); (K.T.C.); Tel.: +82-63-270-2478 (K.T.C.)
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21
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Wang SH, Wang CC, Huang L, Miao LY, Chen X. Dual-Network Collaborative Matrix Factorization for predicting small molecule-miRNA associations. Brief Bioinform 2021; 23:6447431. [PMID: 34864865 DOI: 10.1093/bib/bbab500] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 10/25/2021] [Accepted: 11/02/2021] [Indexed: 01/01/2023] Open
Abstract
MicroRNAs (miRNAs) play crucial roles in multiple biological processes and human diseases and can be considered as therapeutic targets of small molecules (SMs). Because biological experiments used to verify SM-miRNA associations are time-consuming and expensive, it is urgent to propose new computational models to predict new SM-miRNA associations. Here, we proposed a novel method called Dual-network Collaborative Matrix Factorization (DCMF) for predicting the potential SM-miRNA associations. Firstly, we utilized the Weighted K Nearest Known Neighbors (WKNKN) method to preprocess SM-miRNA association matrix. Then, we constructed matrix factorization model to obtain two feature matrices containing latent features of SM and miRNA, respectively. Finally, the predicted SM-miRNA association score matrix was obtained by calculating the inner product of two feature matrices. The main innovations of this method were that the use of WKNKN method can preprocess the missing values of association matrix and the introduction of dual network can integrate more diverse similarity information into DCMF. For evaluating the validity of DCMF, we implemented four different cross validations (CVs) based on two distinct datasets and two different case studies. Finally, based on dataset 1 (dataset 2), DCMF achieved Area Under receiver operating characteristic Curves (AUC) of 0.9868 (0.8770), 0.9833 (0.8836), 0.8377 (0.7591) and 0.9836 ± 0.0030 (0.8632 ± 0.0042) in global Leave-One-Out Cross Validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV and 5-fold CV, respectively. For case studies, plenty of predicted associations have been confirmed by published experimental literature. Therefore, DCMF is an effective tool to predict potential SM-miRNA associations.
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Affiliation(s)
- Shu-Hao Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.,Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China
| | - Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.,Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China
| | - Li Huang
- Academy of Arts and Design, Tsinghua University, Beijing, 10084, China.,The Future Laboratory, Tsinghua University, Beijing, 10084, China
| | - Lian-Ying Miao
- School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.,Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China
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22
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Luo J, Shen C, Lai Z, Cai J, Ding P. Incorporating Clinical, Chemical and Biological Information for Predicting Small Molecule-microRNA Associations Based on Non-Negative Matrix Factorization. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2535-2545. [PMID: 32092012 DOI: 10.1109/tcbb.2020.2975780] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Small molecule(SM) drugs can affect the expression of miRNAs, which plays crucial roles in many important biological processes. The chemical structure and clinical information of small molecule can simultaneously incorporate information such as anatomical distribution, therapeutic effects and structural characteristics. It is necessary to develop a novel model that incorporates small molecule chemical structure and clinical information to reveal the unknown small molecule-miRNA associations. In this study, we developed a new framework based on non-negative matrix factorization, called SMANMF, to discover the potential small molecules-miRNAs associations. First, the functional similarity of two miRNAs can be obtained by computing the overlap of the target gene sets in which the miRNAs interact together, and we integrated two types of small molecule similarities, including chemical similarity and clinical similarity. Then, we utilized a non-negative matrix factorization model to discover the unknown relationship between small molecules and miRNAs. The evaluation results indicate that our model can achieve superior prediction performance compared with previous approaches in 5-fold cross-validation. At the same time, the results of case studies also reveal that the SMANMF model has good predictive performance for predicting the potential association between small molecules and miRNAs.
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23
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Wang J, Wang C, Shen L, Zhou L, Peng L. Screening Potential Drugs for COVID-19 Based on Bound Nuclear Norm Regularization. Front Genet 2021; 12:749256. [PMID: 34691157 PMCID: PMC8529063 DOI: 10.3389/fgene.2021.749256] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 08/23/2021] [Indexed: 01/04/2023] Open
Abstract
The novel coronavirus pneumonia COVID-19 infected by SARS-CoV-2 has attracted worldwide attention. It is urgent to find effective therapeutic strategies for stopping COVID-19. In this study, a Bounded Nuclear Norm Regularization (BNNR) method is developed to predict anti-SARS-CoV-2 drug candidates. First, three virus-drug association datasets are compiled. Second, a heterogeneous virus-drug network is constructed. Third, complete genomic sequences and Gaussian association profiles are integrated to compute virus similarities; chemical structures and Gaussian association profiles are integrated to calculate drug similarities. Fourth, a BNNR model based on kernel similarity (VDA-GBNNR) is proposed to predict possible anti-SARS-CoV-2 drugs. VDA-GBNNR is compared with four existing advanced methods under fivefold cross-validation. The results show that VDA-GBNNR computes better AUCs of 0.8965, 0.8562, and 0.8803 on the three datasets, respectively. There are 6 anti-SARS-CoV-2 drugs overlapping in any two datasets, that is, remdesivir, favipiravir, ribavirin, mycophenolic acid, niclosamide, and mizoribine. Molecular dockings are conducted for the 6 small molecules and the junction of SARS-CoV-2 spike protein and human angiotensin-converting enzyme 2. In particular, niclosamide and mizoribine show higher binding energy of −8.06 and −7.06 kcal/mol with the junction, respectively. G496 and K353 may be potential key residues between anti-SARS-CoV-2 drugs and the interface junction. We hope that the predicted results can contribute to the treatment of COVID-19.
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Affiliation(s)
- Juanjuan Wang
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Chang Wang
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Ling Shen
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China.,College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, China
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24
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Wang CC, Zhu CC, Chen X. Ensemble of kernel ridge regression-based small molecule-miRNA association prediction in human disease. Brief Bioinform 2021; 23:6407727. [PMID: 34676393 DOI: 10.1093/bib/bbab431] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/06/2021] [Accepted: 09/18/2021] [Indexed: 12/12/2022] Open
Abstract
MicroRNAs (miRNAs) play crucial roles in human disease and can be targeted by small molecule (SM) drugs according to numerous studies, which shows that identifying SM-miRNA associations in human disease is important for drug development and disease treatment. We proposed the method of Ensemble of Kernel Ridge Regression-based Small Molecule-MiRNA Association prediction (EKRRSMMA) to uncover potential SM-miRNA associations by combing feature dimensionality reduction and ensemble learning. First, we constructed different feature subsets for both SMs and miRNAs. Then, we trained homogeneous base learners based on distinct feature subsets and took the average of scores obtained from these base learners as SM-miRNA association score. In EKRRSMMA, feature dimensionality reduction technology was employed in the process of construction of feature subsets to reduce the influence of noisy data. Besides, the base learner, namely KRR_avg, was the combination of two classifiers constructed under SM space and miRNA space, which could make full use of the information of SM and miRNA. To assess the prediction performance of EKRRSMMA, we conducted Leave-One-Out Cross-Validation (LOOCV), SM-fixed local LOOCV, miRNA-fixed local LOOCV and 5-fold CV based on two datasets. For Dataset 1 (Dataset 2), EKRRSMMA got the Area Under receiver operating characteristic Curves (AUCs) of 0.9793 (0.8871), 0.8071 (0.7705), 0.9732 (0.8586) and 0.9767 ± 0.0014 (0.8560 ± 0.0027). Besides, we conducted four case studies. As a result, 32 (5-Fluorouracil), 19 (17β-Estradiol), 26 (5-Aza-2'-deoxycytidine) and 11 (cyclophosphamide) out of top 50 predicted potentially associated miRNAs were confirmed by database or experimental literature. Above evaluation results demonstrated that EKRRSMMA is reliable for predicting SM-miRNA associations.
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Affiliation(s)
- Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Chi-Chi Zhu
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Xing Chen
- Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China
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25
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Cervena K, Novosadova V, Pardini B, Naccarati A, Opattova A, Horak J, Vodenkova S, Buchler T, Skrobanek P, Levy M, Vodicka P, Vymetalkova V. Analysis of MicroRNA Expression Changes During the Course of Therapy In Rectal Cancer Patients. Front Oncol 2021; 11:702258. [PMID: 34540669 PMCID: PMC8444897 DOI: 10.3389/fonc.2021.702258] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 08/16/2021] [Indexed: 12/28/2022] Open
Abstract
MicroRNAs (miRNAs) regulate gene expression in a tissue-specific manner. However, little is known about the miRNA expression changes induced by the therapy in rectal cancer (RC) patients. We evaluated miRNA expression levels before and after therapy and identified specific miRNA signatures reflecting disease course and treatment responses of RC patients. First, miRNA expression levels were assessed by next-generation sequencing in two plasma samplings (at the time of diagnosis and a year after) from 20 RC patients. MiR-122-5p and miR-142-5p were classified for subsequent validation in plasma and plasma extracellular vesicles (EVs) on an independent group of RC patients (n=107). Due to the intrinsic high differences in miRNA expression levels between samplings, cancer-free individuals (n=51) were included in the validation phase to determine the baseline expression levels of the selected miRNAs. Expression levels of these miRNAs were significantly different between RC patients and controls (for all p <0.001). A year after diagnosis, miRNA expression profiles were significantly modified in patients responding to treatment and were no longer different from those measured in cancer-free individuals. On the other hand, patients not responding to therapy maintained low expression levels in their second sampling (miR-122-5p: plasma: p=0.05, EVs: p=0.007; miR-142-5p: plasma: p=0.008). Besides, overexpression of miR-122-5p and miR-142-5p in RC cell lines inhibited cell growth and survival. This study provides novel evidence that circulating miR-122-5p and miR-142-5p have a high potential for RC screening and early detection as well as for the assessment of patients' outcomes and the effectiveness of treatment schedule.
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Affiliation(s)
- Klara Cervena
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia.,Institute of Biology and Medical Genetics, 1stMedical Faculty, Charles University, Prague, Czechia
| | - Vendula Novosadova
- Czech Centre for Phenogenomics, Institute of Molecular Genetics of the Czech Academy of Sciences, Vestec, Prague, Czechia
| | - Barbara Pardini
- Molecular Genetics Epidemiology Unit, Italian Institute for Genomic Medicine, c/o IRCCS Candiolo,, Turin, Italy.,Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - Alessio Naccarati
- Molecular Genetics Epidemiology Unit, Italian Institute for Genomic Medicine, c/o IRCCS Candiolo,, Turin, Italy.,Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - Alena Opattova
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia.,Institute of Biology and Medical Genetics, 1stMedical Faculty, Charles University, Prague, Czechia.,Biomedical Centre, Faculty of Medicine in Pilsen, Charles University, Pilsen, Czechia
| | - Josef Horak
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia.,Third Faculty of Medicine, Charles University, Prague, Czechia
| | - Sona Vodenkova
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia.,Biomedical Centre, Faculty of Medicine in Pilsen, Charles University, Pilsen, Czechia
| | - Tomas Buchler
- Department of Oncology, First Faculty of Medicine, Charles University and Thomayer Hospital, Prague, Czechia
| | - Pavel Skrobanek
- Department of Oncology, First Faculty of Medicine, Charles University and Thomayer Hospital, Prague, Czechia
| | - Miroslav Levy
- Department of Surgery, First Faculty of Medicine, Charles University and Thomayer Hospital, Prague, Czechia
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia.,Institute of Biology and Medical Genetics, 1stMedical Faculty, Charles University, Prague, Czechia.,Biomedical Centre, Faculty of Medicine in Pilsen, Charles University, Pilsen, Czechia
| | - Veronika Vymetalkova
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czechia.,Institute of Biology and Medical Genetics, 1stMedical Faculty, Charles University, Prague, Czechia.,Biomedical Centre, Faculty of Medicine in Pilsen, Charles University, Pilsen, Czechia
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26
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Chen X, Zhou C, Wang CC, Zhao Y. Predicting potential small molecule-miRNA associations based on bounded nuclear norm regularization. Brief Bioinform 2021; 22:6353837. [PMID: 34404088 DOI: 10.1093/bib/bbab328] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 07/24/2021] [Accepted: 07/26/2021] [Indexed: 12/14/2022] Open
Abstract
Mounting evidence has demonstrated the significance of taking microRNAs (miRNAs) as the target of small molecule (SM) drugs for disease treatment. Given the fact that exploring new SM-miRNA associations through biological experiments is extremely expensive, several computing models have been constructed to reveal the possible SM-miRNA associations. Here, we built a computing model of Bounded Nuclear Norm Regularization for SM-miRNA Associations prediction (BNNRSMMA). Specifically, we first constructed a heterogeneous SM-miRNA network utilizing miRNA similarity, SM similarity, confirmed SM-miRNA associations and defined a matrix to represent the heterogeneous network. Then, we constructed a model to complete this matrix by minimizing its nuclear norm. The Alternating Direction Method of Multipliers was adopted to minimize the nuclear norm and obtain predicted scores. The main innovation lies in two aspects. During completion, we limited all elements of the matrix within the interval of (0,1) to make sure they have practical significance. Besides, instead of strictly fitting all known elements, a regularization term was incorporated to tolerate the noise in integrated similarities. Furthermore, four kinds of cross-validations on two datasets and two types of case studies were performed to evaluate the predictive performance of BNNRSMMA. Finally, BNNRSMMA attained areas under the curve of 0.9822 (0.8433), 0.9793 (0.8852), 0.8253 (0.7350) and 0.9758 ± 0.0029 (0.8759 ± 0.0041) under global leave-one-out cross-validation (LOOCV), miRNA-fixed LOOCV, SM-fixed LOOCV and 5-fold cross-validation based on Dataset 1(Dataset 2), respectively. With regard to case studies, plenty of predicted associations have been verified by experimental literatures. All these results confirmed that BNNRSMMA is a reliable tool for inferring associations.
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Affiliation(s)
- Xing Chen
- Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China
| | - Chi Zhou
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Yan Zhao
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
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27
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Winkle M, El-Daly SM, Fabbri M, Calin GA. Noncoding RNA therapeutics - challenges and potential solutions. Nat Rev Drug Discov 2021; 20:629-651. [PMID: 34145432 PMCID: PMC8212082 DOI: 10.1038/s41573-021-00219-z] [Citation(s) in RCA: 726] [Impact Index Per Article: 242.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/27/2021] [Indexed: 02/07/2023]
Abstract
Therapeutic targeting of noncoding RNAs (ncRNAs), such as microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), represents an attractive approach for the treatment of cancers, as well as many other diseases. Over the past decade, substantial effort has been made towards the clinical application of RNA-based therapeutics, employing mostly antisense oligonucleotides and small interfering RNAs, with several gaining FDA approval. However, trial results have so far been ambivalent, with some studies reporting potent effects whereas others demonstrated limited efficacy or toxicity. Alternative entities such as antimiRNAs are undergoing clinical testing, and lncRNA-based therapeutics are gaining interest. In this Perspective, we discuss key challenges facing ncRNA therapeutics - including issues associated with specificity, delivery and tolerability - and focus on promising emerging approaches that aim to boost their success.
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Affiliation(s)
- Melanie Winkle
- Translational Molecular Pathology, MD Anderson Cancer Center, Texas State University, Houston, TX, USA
| | - Sherien M El-Daly
- Medical Biochemistry Department, Medical Research Division - Cancer Biology and Genetics Laboratory, Centre of Excellence for Advanced Sciences - National Research Centre, Cairo, Egypt
| | - Muller Fabbri
- Cancer Biology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - George A Calin
- Translational Molecular Pathology, MD Anderson Cancer Center, Texas State University, Houston, TX, USA.
- The RNA Interference and Non-codingRNA Center, MD Anderson Cancer Center, Texas State University, Houston, TX, USA.
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28
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Shen D, Hu W, He Q, Yang H, Cui X, Zhao S. A highly sensitive electrochemical biosensor for microRNA122 detection based on a target-induced DNA nanostructure. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2021; 13:2823-2829. [PMID: 34075941 DOI: 10.1039/d1ay00390a] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Specific and sensitive biomarker detection is significant for the early diagnosis of cancers. Herein, a highly sensitive electrochemical biosensor employing a tetrahedral DNA nanostructure (TDN) probe and multiple signal amplification strategies has been constructed, and successfully applied to microRNA-122 (miR-122) detection. The platform consisted of a TDN probe anchoring on a gold nanoparticle-coated gold electrode and multiple signal amplification procedures combining the electrodeposition of gold nanoparticles, hybridization chain reaction (HCR), and horseradish peroxidase enzymatic catalysis (HPEC). In the presence of the target, the hairpin structure of the helper probe could be opened and trigger the HCR through the hybridization of H1 and H2 probes, and then avidin-HRP was attached on the surface of the gold electrode that can produce an electro-catalytic signal. We used TDN probe as the scaffold to increase the reactivity and multiple signal amplification greatly improve the sensitivity of this biosensor. This biosensor offers an excellent sensitivity (a limit of detection of 0.74 aM) and differentiation ability for single and multiple mismatches. This multiplexing biosensor for trace microRNA detection shows promising applications in the early diagnosis of cancer.
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Affiliation(s)
- Ding Shen
- Department of Pharmaceutical Engineering, School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou 510006, China.
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29
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Bendova P, Pardini B, Susova S, Rosendorf J, Levy M, Skrobanek P, Buchler T, Kral J, Liska V, Vodickova L, Landi S, Soucek P, Naccarati A, Vodicka P, Vymetalkova V. Genetic variations in microRNA-binding sites of solute carrier transporter genes as predictors of clinical outcome in colorectal cancer. Carcinogenesis 2021; 42:378-394. [PMID: 33319241 DOI: 10.1093/carcin/bgaa136] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 12/01/2020] [Accepted: 12/10/2020] [Indexed: 02/06/2023] Open
Abstract
One of the principal mechanisms of chemotherapy resistance in highly frequent solid tumors, such as colorectal cancer (CRC), is the decreased activity of drug transport into tumor cells due to low expression of important membrane proteins, such as solute carrier (SLC) transporters. Sequence complementarity is a major determinant for target gene recognition by microRNAs (miRNAs). Single-nucleotide polymorphisms (SNPs) in target sequences transcribed into messenger RNA may therefore alter miRNA binding to these regions by either creating a new site or destroying an existing one. miRSNPs may explain the modulation of expression levels in association with increased/decreased susceptibility to common diseases as well as in chemoresistance and the consequent inter-individual variability in drug response. In the present study, we investigated whether miRSNPs in SLC transporter genes may modulate CRC susceptibility and patient's survival. Using an in silico approach for functional predictions, we analyzed 26 miRSNPs in 9 SLC genes in a cohort of 1368 CRC cases and 698 controls from the Czech Republic. After correcting for multiple tests, we found several miRSNPs significantly associated with patient's survival. SNPs in SLCO3A1, SLC22A2 and SLC22A3 genes were defined as prognostic factors in the classification and regression tree analysis. In contrast, we did not observe any significant association between miRSNPs and CRC risk. To the best of our knowledge, this is the first study investigating miRSNPs potentially affecting miRNA binding to SLC transporter genes and their impact on CRC susceptibility or patient's prognosis.
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Affiliation(s)
- Petra Bendova
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine, Videnska, Prague, Czech Republic.,Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Albertov, Prague, Czech Republic.,Biomedical Centre and Department of Surgery, Faculty of Medicine in Pilsen, Charles University, Alej Svobody, Pilsen, Czech Republic
| | - Barbara Pardini
- IIGM Italian Institute for Genomic Medicine, Candiolo, Italy.,Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - Simona Susova
- Biomedical Centre and Department of Surgery, Faculty of Medicine in Pilsen, Charles University, Alej Svobody, Pilsen, Czech Republic.,Toxicogenomics Unit, National Institute of Public Health, Srobarova, Prague, Czech Republic
| | - Jachym Rosendorf
- Biomedical Centre and Department of Surgery, Faculty of Medicine in Pilsen, Charles University, Alej Svobody, Pilsen, Czech Republic
| | - Miloslav Levy
- Department of Surgery, Thomayer University Hospital, Videnska, Prague, Czech Republic
| | - Pavel Skrobanek
- Department of Oncology, Thomayer Hospital, Videnska, Prague, Czech Republic
| | - Tomas Buchler
- Department of Oncology, Thomayer Hospital, Videnska, Prague, Czech Republic
| | - Jan Kral
- Institute for Clinical and Experimental Medicine, IKEM, Prague, Czech Republic
| | - Vaclav Liska
- Biomedical Centre and Department of Surgery, Faculty of Medicine in Pilsen, Charles University, Alej Svobody, Pilsen, Czech Republic
| | - Ludmila Vodickova
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine, Videnska, Prague, Czech Republic.,Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Albertov, Prague, Czech Republic.,Biomedical Centre and Department of Surgery, Faculty of Medicine in Pilsen, Charles University, Alej Svobody, Pilsen, Czech Republic
| | - Stefano Landi
- Department of Biology, University of Pisa, Via Derna, Pisa, Italy
| | - Pavel Soucek
- Biomedical Centre and Department of Surgery, Faculty of Medicine in Pilsen, Charles University, Alej Svobody, Pilsen, Czech Republic.,Toxicogenomics Unit, National Institute of Public Health, Srobarova, Prague, Czech Republic
| | - Alessio Naccarati
- IIGM Italian Institute for Genomic Medicine, Candiolo, Italy.,Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine, Videnska, Prague, Czech Republic.,Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Albertov, Prague, Czech Republic.,Biomedical Centre and Department of Surgery, Faculty of Medicine in Pilsen, Charles University, Alej Svobody, Pilsen, Czech Republic
| | - Veronika Vymetalkova
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine, Videnska, Prague, Czech Republic.,Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Albertov, Prague, Czech Republic.,Biomedical Centre and Department of Surgery, Faculty of Medicine in Pilsen, Charles University, Alej Svobody, Pilsen, Czech Republic
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30
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Wang J, Wu Z, Peng Y, Li W, Liu G, Tang Y. Pathway-Based Drug Repurposing with DPNetinfer: A Method to Predict Drug-Pathway Associations via Network-Based Approaches. J Chem Inf Model 2021; 61:2475-2485. [PMID: 33900090 DOI: 10.1021/acs.jcim.1c00009] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Identification of drug-pathway associations plays an important role in pathway-based drug repurposing. However, it is time-consuming and costly to uncover new drug-pathway associations experimentally. The drug-induced transcriptomics data provide a global view of cellular pathways and tell how these pathways change under different treatments. These data enable computational approaches for large-scale prediction of drug-pathway associations. Here we introduced DPNetinfer, a novel computational method to predict potential drug-pathway associations based on substructure-drug-pathway networks via network-based approaches. The results demonstrated that DPNetinfer performed well in a pan-cancer network with an AUC (area under curve) = 0.9358. Meanwhile, DPNetinfer was shown to have a good capability of generalization on two external validation sets (AUC = 0.8519 and 0.7494, respectively). As a case study, DPNetinfer was used in pathway-based drug repurposing for cancer therapy. Unexpected anticancer activities of some nononcology drugs were then identified on the PI3K-Akt pathway. Considering tumor heterogeneity, seven primary site-based models were constructed by DPNetinfer in different drug-pathway networks. In a word, DPNetinfer provides a powerful tool for large-scale prediction of drug-pathway associations in pathway-based drug repurposing. A web tool for DPNetinfer is freely available at http://lmmd.ecust.edu.cn/netinfer/.
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Affiliation(s)
- Jiye Wang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Yayuan Peng
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
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31
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Prioritizing antiviral drugs against SARS-CoV-2 by integrating viral complete genome sequences and drug chemical structures. Sci Rep 2021; 11:6248. [PMID: 33737523 PMCID: PMC7973547 DOI: 10.1038/s41598-021-83737-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 12/18/2020] [Indexed: 12/21/2022] Open
Abstract
The outbreak of a novel febrile respiratory disease called COVID-19, caused by a newfound coronavirus SARS-CoV-2, has brought a worldwide attention. Prioritizing approved drugs is critical for quick clinical trials against COVID-19. In this study, we first manually curated three Virus-Drug Association (VDA) datasets. By incorporating VDAs with the similarity between drugs and that between viruses, we constructed a heterogeneous Virus-Drug network. A novel Random Walk with Restart method (VDA-RWR) was then developed to identify possible VDAs related to SARS-CoV-2. We compared VDA-RWR with three state-of-the-art association prediction models based on fivefold cross-validations (CVs) on viruses, drugs and virus-drug associations on three datasets. VDA-RWR obtained the best AUCs for the three fivefold CVs, significantly outperforming other methods. We found two small molecules coming together on the three datasets, that is, remdesivir and ribavirin. These two chemical agents have higher molecular binding energies of − 7.0 kcal/mol and − 6.59 kcal/mol with the domain bound structure of the human receptor angiotensin converting enzyme 2 (ACE2) and the SARS-CoV-2 spike protein, respectively. Interestingly, for the first time, experimental results suggested that navitoclax could be potentially applied to stop SARS-CoV-2 and remains to further validation.
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32
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Chen J, Teng D, Wu Z, Li W, Feng Y, Tang Y, Liu G. Insights into the Molecular Mechanisms of Liuwei Dihuang Decoction via Network Pharmacology. Chem Res Toxicol 2020; 34:91-102. [PMID: 33332098 DOI: 10.1021/acs.chemrestox.0c00359] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
The traditional Chinese medicines (TCMs) have been used to treat diseases over a long history, but it is still a great challenge to uncover the underlying mechanisms for their therapeutic effects due to the complexity of their ingredients. Based on a novel network pharmacology-based approach, we explored in this study the potential therapeutic targets of Liuwei Dihuang (LWDH) decoction in its neuroendocrine immunomodulation (NIM) function. We not only collected the known targets of the compounds in LWDH but also predicted the targets for these compounds using the balanced substructure-drug-target network-based inference (bSDTNBI), which is a target prediction method based on network inferring developed by our laboratory. A "target-(pathway)-target" (TPT) network, in which targets of LWDH were connected by relevant pathways, was constructed and divided into several separate modules with strong internal connections. Then the target module that contributes the most to NIM function was determined through a contribution scoring algorithm. Finally, the targets with the highest contribution score to NIM-related diseases in this target module were recommended as potential therapeutic targets of LWDH.
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Affiliation(s)
- Jianhui Chen
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Dan Teng
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Yuqian Feng
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
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33
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Shen C, Luo J, Ouyang W, Ding P, Wu H. Identification of Small Molecule–miRNA Associations with Graph Regularization Techniques in Heterogeneous Networks. J Chem Inf Model 2020; 60:6709-6721. [DOI: 10.1021/acs.jcim.0c00975] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Cong Shen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410083, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410083, China
| | - Wenjue Ouyang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410083, China
| | - Pingjian Ding
- School of Computer Science, University of South China, Hengyang 421001, China
| | - Hao Wu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410083, China
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Zhou L, Wang J, Liu G, Lu Q, Dong R, Tian G, Yang J, Peng L. Probing antiviral drugs against SARS-CoV-2 through virus-drug association prediction based on the KATZ method. Genomics 2020; 112:4427-4434. [PMID: 32745502 PMCID: PMC7832256 DOI: 10.1016/j.ygeno.2020.07.044] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 07/04/2020] [Accepted: 07/27/2020] [Indexed: 12/12/2022]
Abstract
It is urgent to find an effective antiviral drug against SARS-CoV-2. In this study, 96 virus-drug associations (VDAs) from 12 viruses including SARS-CoV-2 and similar viruses and 78 small molecules are selected. Complete genomic sequence similarity of viruses and chemical structure similarity of drugs are then computed. A KATZ-based VDA prediction method (VDA-KATZ) is developed to infer possible drugs associated with SARS-CoV-2. VDA-KATZ obtained the best AUCs of 0.8803 when the walking length is 2. The predicted top 3 antiviral drugs against SARS-CoV-2 are remdesivir, oseltamivir, and zanamivir. Molecular docking is conducted between the predicted top 10 drugs and the virus spike protein/human ACE2. The results showed that the above 3 chemical agents have higher molecular binding energies with ACE2. For the first time, we found that zidovudine may be effective clues of treatment of COVID-19. We hope that our predicted drugs could help to prevent the spreading of COVID.
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Affiliation(s)
- Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou 412007, China
| | - Juanjuan Wang
- School of Computer Science, Hunan University of Technology, Zhuzhou 412007, China
| | - Guangyi Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou 412007, China
| | - Qingqing Lu
- Geneis (Beijing) Co. Ltd., Beijing 100102, China
| | - Ruyi Dong
- Geneis (Beijing) Co. Ltd., Beijing 100102, China
| | - Geng Tian
- Geneis (Beijing) Co. Ltd., Beijing 100102, China
| | | | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou 412007, China.
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Cansaran-Duman D, Tanman Ü, Yangın S, Atakol O. The comparison of miRNAs that respond to anti-breast cancer drugs and usnic acid for the treatment of breast cancer. Cytotechnology 2020; 72:10.1007/s10616-020-00430-7. [PMID: 33128199 PMCID: PMC7695759 DOI: 10.1007/s10616-020-00430-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 10/14/2020] [Indexed: 02/01/2023] Open
Abstract
This study was designed to compare usnic acid with anti-breast cancer drug molecules (A-BCDM) routinely used in the treatment of breast cancer. The miRNA information of 17 anti-breast cancer drug used in breast cancer treatment was obtained from the Small Molecule-miRNA Network-Based Inferance (SMIR-NBI) tool. We had been determined common and different expressed miRNAs between 17 A-BCDM & usnic acid and were classified according to the common miRNAs to reveal molecular similarity. As a result of the bioinformatic analyzes, 20 common miRNAs were determined between 17 A-BCDM and usnic acid. The common miRNAs were analyzed with bioinformatic tolls for determining pathways and targets. The most common miRNAs for 6 of 17 A-BCDM and usnic acid were determined as miR-374a-5p and miR-26a-5p. We compared the anti-proliferative effect of usnic acid and one of the 17 A-BCDM that tamoxifen on MDA-MB-231 triple negative breast cancer cell with real-time cell analysis system. The real time PCR assay was carried out with miR-26a-5p for evaluate to expression level of MDA-MB-231 breast cancer cell and MCF-12A non-cancerous epithelial breast cell. As a result of study, usnic acid as novel candidate drug molecule showed high similarity ratio with 5-Fluorouracil, Sulindac Sulfide, Curcumin and Cisplatin A-BCDM used in treatment of breast cancer. miR-26a-5p as common response miRNA of usnic acid and tamoxifen was showed a decreased level of expression by validated qRT-PCR assay. The obtained from study, in addition to 17 A-BCDM, usnic acid has also the potential to be used as a candidate molecule in the treatment of breast cancer. Moreover, miR-26a-5p might be used as a biomarker in the treatment of breast cancer but further analysis is required.
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Affiliation(s)
| | - Ümmügülsüm Tanman
- Ankara University, Biotechnology Institute, Keçiören, Ankara, Turkey
| | - Sevcan Yangın
- Ankara University, Biotechnology Institute, Keçiören, Ankara, Turkey
| | - Orhan Atakol
- Faculty of Science, Department of Chemistry, Ankara University, Tandoğan, Ankara, Turkey
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Peng L, Tian X, Shen L, Kuang M, Li T, Tian G, Yang J, Zhou L. Identifying Effective Antiviral Drugs Against SARS-CoV-2 by Drug Repositioning Through Virus-Drug Association Prediction. Front Genet 2020; 11:577387. [PMID: 33193695 PMCID: PMC7525008 DOI: 10.3389/fgene.2020.577387] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 08/18/2020] [Indexed: 12/12/2022] Open
Abstract
A new coronavirus called SARS-CoV-2 is rapidly spreading around the world. Over 16,558,289 infected cases with 656,093 deaths have been reported by July 29th, 2020, and it is urgent to identify effective antiviral treatment. In this study, potential antiviral drugs against SARS-CoV-2 were identified by drug repositioning through Virus-Drug Association (VDA) prediction. 96 VDAs between 11 types of viruses similar to SARS-CoV-2 and 78 small molecular drugs were extracted and a novel VDA identification model (VDA-RLSBN) was developed to find potential VDAs related to SARS-CoV-2. The model integrated the complete genome sequences of the viruses, the chemical structures of drugs, a regularized least squared classifier (RLS), a bipartite local model, and the neighbor association information. Compared with five state-of-the-art association prediction methods, VDA-RLSBN obtained the best AUC of 0.9085 and AUPR of 0.6630. Ribavirin was predicted to be the best small molecular drug, with a higher molecular binding energy of -6.39 kcal/mol with human angiotensin-converting enzyme 2 (ACE2), followed by remdesivir (-7.4 kcal/mol), mycophenolic acid (-5.35 kcal/mol), and chloroquine (-6.29 kcal/mol). Ribavirin, remdesivir, and chloroquine have been under clinical trials or supported by recent works. In addition, for the first time, our results suggested several antiviral drugs, such as FK506, with molecular binding energies of -11.06 and -10.1 kcal/mol with ACE2 and the spike protein, respectively, could be potentially used to prevent SARS-CoV-2 and remains to further validation. Drug repositioning through virus-drug association prediction can effectively find potential antiviral drugs against SARS-CoV-2.
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Affiliation(s)
- Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Xiongfei Tian
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Ling Shen
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Ming Kuang
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Tianbao Li
- Geneis (Beijing) Co., Ltd., Beijing, China
| | - Geng Tian
- Geneis (Beijing) Co., Ltd., Beijing, China
| | | | - Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
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37
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Wu Z, Peng Y, Yu Z, Li W, Liu G, Tang Y. NetInfer: A Web Server for Prediction of Targets and Therapeutic and Adverse Effects via Network-Based Inference Methods. J Chem Inf Model 2020; 60:3687-3691. [PMID: 32687354 DOI: 10.1021/acs.jcim.0c00291] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In this study, we developed a web server named NetInfer for prediction of targets and therapeutic and adverse effects via network-based inference methods. Compared with our previously developed standalone version of NetInfer, this web server provides a user-friendly interface. With the web server, users can easily predict potential target proteins, microRNAs, Anatomical Therapeutic Chemical (ATC) classification codes, or adverse drug events for small molecules of their interests in a few steps. Most of the prediction models were constructed on the basis of our previous studies, where those models have been evaluated systematically and demonstrated high performance. The high-quality models can generate accurate predictions. As a case study, we predicted ATC codes and target proteins for several drugs. The predicted therapeutic effects of these drugs on cardiovascular diseases and their potential molecular mechanisms were validated by the literature. This successful case study demonstrated that our web server would be a powerful tool in drug repositioning and systems pharmacology. The web server of NetInfer is freely available at http://lmmd.ecust.edu.cn/netinfer/.
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Affiliation(s)
- Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yayuan Peng
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zhuohang Yu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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38
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Shen C, Luo J, Lai Z, Ding P. Multiview Joint Learning-Based Method for Identifying Small-Molecule-Associated MiRNAs by Integrating Pharmacological, Genomics, and Network Knowledge. J Chem Inf Model 2020; 60:4085-4097. [DOI: 10.1021/acs.jcim.0c00244] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Cong Shen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410083, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410083, China
| | - Zihan Lai
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410083, China
| | - Pingjian Ding
- School of Computer Science, University of South China, Hengyang 421001, China
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39
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Xu H, Lin Y, Sun L, Fang X, Jia L. An integrated target recognition and polymerase primer probe for microRNA detection. Talanta 2020; 219:121302. [PMID: 32887044 DOI: 10.1016/j.talanta.2020.121302] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 06/09/2020] [Accepted: 06/13/2020] [Indexed: 11/29/2022]
Abstract
Extremely sensitive and visual measurements of microRNA (miRNA) in situ for early detection and monitoring of diseases remains a major challenge. To address this issue, this work reports a rapid, highly sensitive and selective microRNA (miRNA) biosensing strategy based on isothermal circular strand-displacement polymerization (ICSDP), and miRNA imaging was performed inside cells. In this work, a double hairpin DNA probe (HP1/HP2 complex) embedded with a sensing region and polymerase primer region was designed. Briefly, after the specific binding of target miRNA with the HP1/HP2 probe, HP1/HP2 itself can function as a primer to initiate the ICSDP with the help of Klenow Fragment (KF), yielding target miRNA for new rounds of ICSDP. In this process, one target can produce multiple signal outputs (1: n), achieving low abundance of miRNA detection. Under optimized conditions, the proposed strategy showed high sensitivity with a detection limit of 5 pM within 15 min and can also easily distinguish the control miRNA from the target miRNA. This method can be further applied to image the intracellular miRNA of interest in situ inside the cancer cells.
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Affiliation(s)
- Huo Xu
- Institute of Oceanography, Minjiang University, Fuzhou, Fujian, 350108, China.
| | - Yongju Lin
- Institute of Oceanography, Minjiang University, Fuzhou, Fujian, 350108, China
| | - Lijun Sun
- Institute of Oceanography, Minjiang University, Fuzhou, Fujian, 350108, China
| | - Xiaojun Fang
- Cancer Metastasis Alert and Prevention Center, Pharmaceutical Photocatalysis of State Key Laboratory of Photocatalysis on Energy and Environment, and Fujian Provincial Key Laboratory of Cancer Metastasis Chemoprevention and Chemotherapy, College of Chemistry, Fuzhou University, Fuzhou, 350002, China
| | - Lee Jia
- Institute of Oceanography, Minjiang University, Fuzhou, Fujian, 350108, China; Cancer Metastasis Alert and Prevention Center, Pharmaceutical Photocatalysis of State Key Laboratory of Photocatalysis on Energy and Environment, and Fujian Provincial Key Laboratory of Cancer Metastasis Chemoprevention and Chemotherapy, College of Chemistry, Fuzhou University, Fuzhou, 350002, China.
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40
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Ben W, Zhang G, Huang Y, Sun Y. MiR-27a-3p Regulated the Aggressive Phenotypes of Cervical Cancer by Targeting FBXW7. Cancer Manag Res 2020; 12:2925-2935. [PMID: 32431539 PMCID: PMC7198449 DOI: 10.2147/cmar.s234897] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 01/08/2020] [Indexed: 12/11/2022] Open
Abstract
Background Abnormally expressed microRNAs (miRNAs) contribute greatly to the initiation and development of human cancers, including cervical cancer, by regulating the target mRNAs. MiR-27a-3p was up-regulated and acted as an oncogene in multiple cancers. However, the function of miR-27a-3p in cervical cancer has not been fully understood. Methods The expression of miR-27a-3p in cervical cancer tissues and cell lines was detected by RT-pPCR. MTT assay, colony formation assay and flow cytometry analysis were performed to determine the effects of miR-27a-3p on the growth of cervical cancer cells. The targets of miR-27a-3p were predicted using the miRDB database. Luciferase reporter assay was utilized to confirm the binding between miR-27a-3p and the 3ʹ-untranslated region (UTR) of targets. The expression of target proteins was determined by RT-qPCR and Western blot. Results Our results found that miR-27a-3p was overexpressed in cervical cancer tissues and cell lines. Down-regulation of miR-27a-3p significantly inhibited the proliferation, colony formation and promoted apoptosis of cervical cancer cells. Overexpression of miR-27a-3p enhanced the cell proliferation. miR-27a-3p was found to bind the 3ʹ-UTR of F-box and WD repeat domain containing 7 (FBXW7) and resulted in the down-regulation of FBXW7. The up-regulated level of miR-27a-3p was inversely correlated with that of FBXW7 in cervical cancer tissues. Additionally, reintroducing of FBXW7 significantly attenuated the promoting effect of miR-27a-3p on the proliferation of cervical cancer cells. Conclusion These results indicated the growth-promoting function of miR-27a-3p in cervical cancer via targeting FBXW7. Our finding suggested the potential application of miR-27a-3p/FBXW7 axis in the diagnosis and treatment of cervical cancer.
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Affiliation(s)
- Wei Ben
- Obstetrics and Gynecology Department, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, People's Republic of China
| | - Guangmei Zhang
- Obstetrics and Gynecology Department, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, People's Republic of China
| | - Yangang Huang
- Obstetrics and Gynecology Department, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, People's Republic of China
| | - Yuhui Sun
- Obstetrics and Gynecology Department, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, People's Republic of China
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41
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Liu F, Peng L, Tian G, Yang J, Chen H, Hu Q, Liu X, Zhou L. Identifying Small Molecule-miRNA Associations Based on Credible Negative Sample Selection and Random Walk. Front Bioeng Biotechnol 2020; 8:131. [PMID: 32258003 PMCID: PMC7090022 DOI: 10.3389/fbioe.2020.00131] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 02/10/2020] [Indexed: 12/05/2022] Open
Abstract
Recently, many studies have demonstrated that microRNAs (miRNAs) are new small molecule drug targets. Identifying small molecule-miRNA associations (SMiRs) plays an important role in finding new clues for various human disease therapy. Wet experiments can discover credible SMiR associations; however, this is a costly and time-consuming process. Computational models have therefore been developed to uncover possible SMiR associations. In this study, we designed a new SMiR association prediction model, RWNS. RWNS integrates various biological information, credible negative sample selections, and random walk on a triple-layer heterogeneous network into a unified framework. It includes three procedures: similarity computation, negative sample selection, and SMiR association prediction based on random walk on the constructed small molecule-disease-miRNA association network. To evaluate the performance of RWNS, we used leave-one-out cross-validation (LOOCV) and 5-fold cross validation to compare RWNS with two state-of-the-art SMiR association methods, namely, TLHNSMMA and SMiR-NBI. Experimental results showed that RWNS obtained an AUC value of 0.9829 under LOOCV and 0.9916 under 5-fold cross validation on the SM2miR1 dataset, and it obtained an AUC value of 0.8938 under LOOCV and 0.9899 under 5-fold cross validation on the SM2miR2 dataset. More importantly, RWNS successfully captured 9, 17, and 37 SMiR associations validated by experiments among the predicted top 10, 20, and 50 SMiR candidates with the highest scores, respectively. We inferred that enoxacin and decitabine are associated with mir-21 and mir-155, respectively. Therefore, RWNS can be a powerful tool for SMiR association prediction.
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Affiliation(s)
- Fuxing Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Geng Tian
- Geneis (Beijing) Co. Ltd., Beijing, China
| | | | - Hui Chen
- College of Chemical Engineering, Xiangtan University, Xiangtan, China
| | - Qi Hu
- Xiangya Second Hospital, Central South University, Changsha, Hunan, China
| | - Xiaojun Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
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42
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Zhao Y, Chen X, Yin J, Qu J. SNMFSMMA: using symmetric nonnegative matrix factorization and Kronecker regularized least squares to predict potential small molecule-microRNA association. RNA Biol 2019; 17:281-291. [PMID: 31739716 DOI: 10.1080/15476286.2019.1694732] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Accumulating studies have shown that microRNAs (miRNAs) could be used as targets of small-molecule (SM) drugs to treat diseases. In recent years, researchers have proposed many computational models to reveal miRNA-SM associations due to the huge cost of experimental methods. Considering the shortcomings of the previous models, such as the prediction accuracy of some models is low or some cannot be applied for new SMs (miRNAs), we developed a novel model named Symmetric Nonnegative Matrix Factorization for Small Molecule-MiRNA Association prediction (SNMFSMMA). Different from some models directly applying the integrated similarities, SNMFSMMA first performed matrix decomposition on the integrated similarity matrixes, and calculated the Kronecker product of the new integrated similarity matrixes to obtain the SM-miRNA pair similarity. Further, we applied regularized least square to obtain the mapping function of the SM-miRNA pairs to the associated probabilities by minimizing the objective function. On the basis of Dataset 1 and 2 extracted from SM2miR v1.0 database, we implemented global leave-one-out cross validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV and 5-fold cross-validation to evaluate the prediction performance. Finally, the AUC values obtained by SNMFSMMA in these validation reached 0.9711 (0.8895), 0.9698 (0.8884), 0.8329 (0.7651) and 0.9644 ± 0.0035 (0.8814 ± 0.0033) based on Dataset 1 (Dataset 2), respectively. In the first case study, 5 of the top 10 associations predicted were confirmed. In the second, 7 and 8 of the top 10 predicted miRNAs related with 5-FU and 5-Aza-2'-deoxycytidine were confirmed. These results demonstrated the reliable predictive power of SNMFSMMA.
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Affiliation(s)
- Yan Zhao
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Jun Yin
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Jia Qu
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
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43
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Wang CC, Chen X. A Unified Framework for the Prediction of Small Molecule–MicroRNA Association Based on Cross-Layer Dependency Inference on Multilayered Networks. J Chem Inf Model 2019; 59:5281-5293. [DOI: 10.1021/acs.jcim.9b00667] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
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44
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Jin S, Zeng X, Fang J, Lin J, Chan SY, Erzurum SC, Cheng F. A network-based approach to uncover microRNA-mediated disease comorbidities and potential pathobiological implications. NPJ Syst Biol Appl 2019; 5:41. [PMID: 31754458 PMCID: PMC6853960 DOI: 10.1038/s41540-019-0115-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 09/10/2019] [Indexed: 12/20/2022] Open
Abstract
Disease-disease relationships (e.g., disease comorbidities) play crucial roles in pathobiological manifestations of diseases and personalized approaches to managing those conditions. In this study, we develop a network-based methodology, termed meta-path-based Disease Network (mpDisNet) capturing algorithm, to infer disease-disease relationships by assembling four biological networks: disease-miRNA, miRNA-gene, disease-gene, and the human protein-protein interactome. mpDisNet is a meta-path-based random walk to reconstruct the heterogeneous neighbors of a given node. mpDisNet uses a heterogeneous skip-gram model to solve the network representation of the nodes. We find that mpDisNet reveals high performance in inferring clinically reported disease-disease relationships, outperforming that of traditional gene/miRNA-overlap approaches. In addition, mpDisNet identifies network-based comorbidities for pulmonary diseases driven by underlying miRNA-mediated pathobiological pathways (i.e., hsa-let-7a- or hsa-let-7b-mediated airway epithelial apoptosis and pro-inflammatory cytokine pathways) as derived from the human interactome network analysis. The mpDisNet offers a powerful tool for network-based identification of disease-disease relationships with miRNA-mediated pathobiological pathways.
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Affiliation(s)
- Shuting Jin
- Department of Computer Science, Xiamen University, Xiamen, 361005 China
| | - Xiangxiang Zeng
- School of Information Science and Engineering, Hunan University, Changsha, 410082 China
| | - Jiansong Fang
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195 USA
| | - Jiawei Lin
- Department of Computer Science, Xiamen University, Xiamen, 361005 China
| | - Stephen Y. Chan
- Pittsburgh Heart, Lung, Blood, and Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh Medical Center (UPMC) and University of Pittsburgh School of Medicine, Pittsburgh, PA 15213 USA
| | - Serpil C. Erzurum
- Department of Pathobiology, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195 USA
- Respiratory Institute, Cleveland Clinic, Cleveland, OH 44195 USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195 USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195 USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106 USA
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45
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Turanli B, Altay O, Borén J, Turkez H, Nielsen J, Uhlen M, Arga KY, Mardinoglu A. Systems biology based drug repositioning for development of cancer therapy. Semin Cancer Biol 2019; 68:47-58. [PMID: 31568815 DOI: 10.1016/j.semcancer.2019.09.020] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 09/23/2019] [Accepted: 09/24/2019] [Indexed: 01/20/2023]
Abstract
Drug repositioning is a powerful method that can assists the conventional drug discovery process by using existing drugs for treatment of a disease rather than its original indication. The first examples of repurposed drugs were discovered serendipitously, however data accumulated by high-throughput screenings and advancements in computational biology methods have paved the way for rational drug repositioning methods. As chemotherapeutic agents have notorious side effects that significantly reduce quality of life, drug repositioning promises repurposed noncancer drugs with little or tolerable adverse effects for cancer patients. Here, we review current drug-related data types and databases including some examples of web-based drug repositioning tools. Next, we describe systems biology approaches to be used in drug repositioning for effective cancer therapy. Finally, we highlight examples of mostly repurposed drugs for cancer treatment and provide an overview of future expectations in the field for development of effective treatment strategies.
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Affiliation(s)
- Beste Turanli
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden; Department of Bioengineering, Marmara University, Istanbul, Turkey; Department of Bioengineering, Istanbul Medeniyet University, Istanbul, Turkey
| | - Ozlem Altay
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden
| | - Jan Borén
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital Gothenburg, Sweden
| | - Hasan Turkez
- Department of Molecular Biology and Genetics, Erzurum Technical University, Erzurum 25240, Turkey
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden
| | | | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm SE-17121, Sweden; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London SE1 9RT, United Kingdom.
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Yin J, Chen X, Wang CC, Zhao Y, Sun YZ. Prediction of Small Molecule–MicroRNA Associations by Sparse Learning and Heterogeneous Graph Inference. Mol Pharm 2019; 16:3157-3166. [DOI: 10.1021/acs.molpharmaceut.9b00384] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Jun Yin
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Yan Zhao
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Ya-Zhou Sun
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
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Su L, Liu G, Wang J, Xu D. A rectified factor network based biclustering method for detecting cancer-related coding genes and miRNAs, and their interactions. Methods 2019; 166:22-30. [PMID: 31121299 DOI: 10.1016/j.ymeth.2019.05.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 04/14/2019] [Accepted: 05/13/2019] [Indexed: 12/12/2022] Open
Abstract
Detecting cancer-related genes and their interactions is a crucial task in cancer research. For this purpose, we proposed an efficient method, to detect coding genes, microRNAs (miRNAs), and their interactions related to a particular cancer or a cancer subtype using their expression data from the same set of samples. Firstly, biclusters specific to a particular type of cancer are detected based on rectified factor networks and ranked according to their associations with general cancers. Secondly, coding genes and miRNAs in each bicluster are prioritized by considering their differential expression and differential correlation values, protein-protein interaction data, and potential cancer markers. Finally, a rank fusion process is used to obtain the final comprehensive rank by combining multiple ranking results. We applied our proposed method on breast cancer datasets. Results show that our method outperforms other methods in detecting breast cancer-related coding genes and miRNAs. Furthermore, our method is very efficient in computing time, which can handle tens of thousands genes/miRNAs and hundreds of patients in hours on a desktop. This work may aid researchers in studying the genetic architecture of complex diseases, and improving the accuracy of diagnosis.
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Affiliation(s)
- Lingtao Su
- Department of Computer Science and Technology, Jilin University, Changchun 130012, China; Department of Electrical Engineering & Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Guixia Liu
- Department of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Juexin Wang
- Department of Electrical Engineering & Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Dong Xu
- Department of Electrical Engineering & Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.
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48
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Zhao Y, Chen X, Yin J. Adaptive boosting-based computational model for predicting potential miRNA-disease associations. Bioinformatics 2019; 35:4730-4738. [DOI: 10.1093/bioinformatics/btz297] [Citation(s) in RCA: 87] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Revised: 03/19/2019] [Accepted: 04/18/2019] [Indexed: 12/24/2022] Open
Abstract
AbstractMotivationRecent studies have shown that microRNAs (miRNAs) play a critical part in several biological processes and dysregulation of miRNAs is related with numerous complex human diseases. Thus, in-depth research of miRNAs and their association with human diseases can help us to solve many problems.ResultsDue to the high cost of traditional experimental methods, revealing disease-related miRNAs through computational models is a more economical and efficient way. Considering the disadvantages of previous models, in this paper, we developed adaptive boosting for miRNA-disease association prediction (ABMDA) to predict potential associations between diseases and miRNAs. We balanced the positive and negative samples by performing random sampling based on k-means clustering on negative samples, whose process was quick and easy, and our model had higher efficiency and scalability for large datasets than previous methods. As a boosting technology, ABMDA was able to improve the accuracy of given learning algorithm by integrating weak classifiers that could score samples to form a strong classifier based on corresponding weights. Here, we used decision tree as our weak classifier. As a result, the area under the curve (AUC) of global and local leave-one-out cross validation reached 0.9170 and 0.8220, respectively. What is more, the mean and the standard deviation of AUCs achieved 0.9023 and 0.0016, respectively in 5-fold cross validation. Besides, in the case studies of three important human cancers, 49, 50 and 50 out of the top 50 predicted miRNAs for colon neoplasms, hepatocellular carcinoma and breast neoplasms were confirmed by the databases and experimental literatures.Availability and implementationThe code and dataset of ABMDA are freely available at https://github.com/githubcode007/ABMDA.Supplementary informationSupplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yan Zhao
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Jun Yin
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
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Wang CC, Chen X, Qu J, Sun YZ, Li JQ. RFSMMA: A New Computational Model to Identify and Prioritize Potential Small Molecule-MiRNA Associations. J Chem Inf Model 2019; 59:1668-1679. [PMID: 30840454 DOI: 10.1021/acs.jcim.9b00129] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
More and more studies found that many complex human diseases occur accompanied by aberrant expression of microRNAs (miRNAs). Small molecule (SM) drugs have been utilized to treat complex human diseases by affecting the expression of miRNAs. Several computational methods were proposed to infer underlying associations between SMs and miRNAs. In our study, we proposed a new calculation model of random forest based small molecule-miRNA association prediction (RFSMMA) which was based on the known SM-miRNA associations in the SM2miR database. RFSMMA utilized the similarity of SMs and miRNAs as features to represent SM-miRNA pairs and further implemented the machine learning algorithm of random forest to train training samples and obtain a prediction model. In RFSMMA, integrating multiple kinds of similarity can avoid the bias of single similarity and choosing more reliable features from original features can represent SM-miRNA pairs more accurately. We carried out cross validations to assess predictive accuracy of RFSMMA. As a result, RFSMMA acquired AUCs of 0.9854, 0.9839, 0.7052, and 0.9917 ± 0.0008 under global leave-one-out cross validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV, and 5-fold cross validation, respectively, under data set 1. Based on data set 2, RFSMMA obtained AUCs of 0.8456, 0.8463, 0.6653, and 0.8389 ± 0.0033 under four cross validations according to the order mentioned above. In addition, we implemented a case study on three common SMs, namely, 5-fluorouracil, 17β-estradiol, and 5-aza-2'-deoxycytidine. Among the top 50 associated miRNAs of these three SMs predicted by RFSMMA, 31, 32, and 28 miRNAs were verified, respectively. Therefore, RFSMMA is shown to be an effective and reliable tool for identifying underlying SM-miRNA associations.
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Affiliation(s)
- Chun-Chun Wang
- School of Information and Control Engineering , China University of Mining and Technology , Xuzhou 221116 , China
| | - Xing Chen
- School of Information and Control Engineering , China University of Mining and Technology , Xuzhou 221116 , China
| | - Jia Qu
- School of Information and Control Engineering , China University of Mining and Technology , Xuzhou 221116 , China
| | - Ya-Zhou Sun
- College of Computer Science and Software Engineering , Shenzhen University , Shenzhen 518060 , China
| | - Jian-Qiang Li
- College of Computer Science and Software Engineering , Shenzhen University , Shenzhen 518060 , China
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Abstract
One of the most important resources for researchers of noncoding RNAs is the information available in public databases spread over the internet. However, the effective exploration of this data can represent a daunting task, given the large amount of databases available and the variety of stored data. This chapter describes a classification of databases based on information source, type of RNA, source organisms, data formats, and the mechanisms for information retrieval, detailing the relevance of each of these classifications and its usability by researchers. This classification is used to update a 2012 review, indexing now more than 229 public databases. This review will include an assessment of the new trends for ncRNA research based on the information that is being offered by the databases. Additionally, we will expand the previous analysis focusing on the usability and application of these databases in pathogen and disease research. Finally, this chapter will analyze how currently available database schemas can help the development of new and improved web resources.
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