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Chen Z, Wang Q, Liu J, Wang W, Yuan W, Liu Y, Sun Z, Wang C. Effects of extracellular vesicle-derived noncoding RNAs on pre-metastatic niche and tumor progression. Genes Dis 2024; 11:176-188. [PMID: 37588211 PMCID: PMC10425748 DOI: 10.1016/j.gendis.2022.12.011] [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: 09/07/2022] [Revised: 11/29/2022] [Accepted: 12/08/2022] [Indexed: 01/20/2023] Open
Abstract
A pre-metastatic niche (PMN) is a protective microenvironment that facilitates the colonization of disseminating tumor cells in future metastatic organs. Extracellular vesicles (EVs) play a role in intercellular communication by delivering cargoes, such as noncoding RNAs (ncRNAs). The pivotal role of extracellular vesicle-derived noncoding RNAs (EV-ncRNAs) in the PMN has attracted increasing attention. In this review, we summarized the effects of EV-ncRNAs on the PMN in terms of immunosuppression, vascular permeability and angiogenesis, inflammation, metabolic reprogramming, and fibroblast alterations. In particular, we provided a comprehensive overview of the effects of EV-ncRNAs on the PMN in different cancers. Finally, we discussed the promising clinical applications of EV-ncRNAs, including their potential as diagnostic and prognostic markers and therapeutic targets.
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Affiliation(s)
- Zhuang Chen
- Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
- Academy of Medical Sciences of Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Qiming Wang
- Department of Internal Medicine, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan 450008, China
| | - Jinbo Liu
- Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Wenkang Wang
- Department of Breast Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Weitang Yuan
- Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Yang Liu
- Department of Radiotherapy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan 450008, China
| | - Zhenqiang Sun
- Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Chengzeng Wang
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
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2
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Wang Y, Pan Z, Mou M, Xia W, Zhang H, Zhang H, Liu J, Zheng L, Luo Y, Zheng H, Yu X, Lian X, Zeng Z, Li Z, Zhang B, Zheng M, Li H, Hou T, Zhu F. A task-specific encoding algorithm for RNAs and RNA-associated interactions based on convolutional autoencoder. Nucleic Acids Res 2023; 51:e110. [PMID: 37889083 PMCID: PMC10682500 DOI: 10.1093/nar/gkad929] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/01/2023] [Accepted: 10/10/2023] [Indexed: 10/28/2023] Open
Abstract
RNAs play essential roles in diverse physiological and pathological processes by interacting with other molecules (RNA/protein/compound), and various computational methods are available for identifying these interactions. However, the encoding features provided by existing methods are limited and the existing tools does not offer an effective way to integrate the interacting partners. In this study, a task-specific encoding algorithm for RNAs and RNA-associated interactions was therefore developed. This new algorithm was unique in (a) realizing comprehensive RNA feature encoding by introducing a great many of novel features and (b) enabling task-specific integration of interacting partners using convolutional autoencoder-directed feature embedding. Compared with existing methods/tools, this novel algorithm demonstrated superior performances in diverse benchmark testing studies. This algorithm together with its source code could be readily accessed by all user at: https://idrblab.org/corain/ and https://github.com/idrblab/corain/.
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Affiliation(s)
- Yunxia Wang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Polytechnic Institute, Zhejiang University, Hangzhou 310058, China
| | - Ziqi Pan
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Polytechnic Institute, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Polytechnic Institute, Zhejiang University, Hangzhou 310058, China
| | - Weiqi Xia
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Polytechnic Institute, Zhejiang University, Hangzhou 310058, China
| | - Hongning Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Polytechnic Institute, Zhejiang University, Hangzhou 310058, China
| | - Hanyu Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Polytechnic Institute, Zhejiang University, Hangzhou 310058, China
| | - Jin Liu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Polytechnic Institute, Zhejiang University, Hangzhou 310058, China
| | - Lingyan Zheng
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Polytechnic Institute, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-ZJU Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Polytechnic Institute, Zhejiang University, Hangzhou 310058, China
| | - Hanqi Zheng
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Polytechnic Institute, Zhejiang University, Hangzhou 310058, China
| | - Xinyuan Yu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Polytechnic Institute, Zhejiang University, Hangzhou 310058, China
| | - Xichen Lian
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Polytechnic Institute, Zhejiang University, Hangzhou 310058, China
| | - Zhenyu Zeng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-ZJU Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Zhaorong Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-ZJU Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Bing Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-ZJU Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Mingyue Zheng
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Polytechnic Institute, Zhejiang University, Hangzhou 310058, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Honglin Li
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Polytechnic Institute, Zhejiang University, Hangzhou 310058, China
- School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Polytechnic Institute, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Polytechnic Institute, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-ZJU Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
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3
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Zhang G, Luo Y, Dai X, Dai Z. Benchmarking deep learning methods for predicting CRISPR/Cas9 sgRNA on- and off-target activities. Brief Bioinform 2023; 24:bbad333. [PMID: 37775147 DOI: 10.1093/bib/bbad333] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 08/31/2023] [Accepted: 09/04/2023] [Indexed: 10/01/2023] Open
Abstract
In silico design of single guide RNA (sgRNA) plays a critical role in clustered regularly interspaced, short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) system. Continuous efforts are aimed at improving sgRNA design with efficient on-target activity and reduced off-target mutations. In the last 5 years, an increasing number of deep learning-based methods have achieved breakthrough performance in predicting sgRNA on- and off-target activities. Nevertheless, it is worthwhile to systematically evaluate these methods for their predictive abilities. In this review, we conducted a systematic survey on the progress in prediction of on- and off-target editing. We investigated the performances of 10 mainstream deep learning-based on-target predictors using nine public datasets with different sample sizes. We found that in most scenarios, these methods showed superior predictive power on large- and medium-scale datasets than on small-scale datasets. In addition, we performed unbiased experiments to provide in-depth comparison of eight representative approaches for off-target prediction on 12 publicly available datasets with various imbalanced ratios of positive/negative samples. Most methods showed excellent performance on balanced datasets but have much room for improvement on moderate- and severe-imbalanced datasets. This study provides comprehensive perspectives on CRISPR/Cas9 sgRNA on- and off-target activity prediction and improvement for method development.
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Affiliation(s)
- Guishan Zhang
- College of Engineering, Shantou University, Shantou 515063, China
| | - Ye Luo
- College of Engineering, Shantou University, Shantou 515063, China
| | - Xianhua Dai
- School of Cyber Science and Technology, Sun Yat-sen University, Shenzhen 518107, China
- Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519000, China
| | - Zhiming Dai
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
- Guangdong Province Key Laboratory of Big Data Analysis and Processing, Sun Yat-sen University, Guangzhou 510006, China
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4
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Sheng N, Huang L, Gao L, Cao Y, Xie X, Wang Y. A Survey of Computational Methods and Databases for lncRNA-MiRNA Interaction Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2810-2826. [PMID: 37030713 DOI: 10.1109/tcbb.2023.3264254] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are two prevalent non-coding RNAs in current research. They play critical regulatory roles in the life processes of animals and plants. Studies have shown that lncRNAs can interact with miRNAs to participate in post-transcriptional regulatory processes, mainly involved in regulating cancer development, metastatic progression, and drug resistance. Additionally, these interactions have significant effects on plant growth, development, and responses to biotic and abiotic stresses. Deciphering the potential relationships between lncRNAs and miRNAs may provide new insights into our understanding of the biological functions of lncRNAs and miRNAs, and the pathogenesis of complex diseases. In contrast, gathering information on lncRNA-miRNA interactions (LMIs) through biological experiments is expensive and time-consuming. With the accumulation of multi-omics data, computational models are extremely attractive in systematically exploring potential LMIs. To the best of our knowledge, this is the first comprehensive review of computational methods for identifying LMIs. Specifically, we first summarized the available public databases for predicting animal and plant LMIs. Second, we comprehensively reviewed the computational methods for predicting LMIs and classified them into two categories, including network-based methods and sequence-based methods. Third, we analyzed the standard evaluation methods and metrics used in LMI prediction. Finally, we pointed out some problems in the current study and discuss future research directions. Relevant databases and the latest advances in LMI prediction are summarized in a GitHub repository https://github.com/sheng-n/lncRNA-miRNA-interaction-methods, and we'll keep it updated.
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5
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Kim Y, Lee M. Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments. Int J Mol Sci 2023; 24:10299. [PMID: 37373445 DOI: 10.3390/ijms241210299] [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: 05/24/2023] [Revised: 06/16/2023] [Accepted: 06/17/2023] [Indexed: 06/29/2023] Open
Abstract
This review paper provides an extensive analysis of the rapidly evolving convergence of deep learning and long non-coding RNAs (lncRNAs). Considering the recent advancements in deep learning and the increasing recognition of lncRNAs as crucial components in various biological processes, this review aims to offer a comprehensive examination of these intertwined research areas. The remarkable progress in deep learning necessitates thoroughly exploring its latest applications in the study of lncRNAs. Therefore, this review provides insights into the growing significance of incorporating deep learning methodologies to unravel the intricate roles of lncRNAs. By scrutinizing the most recent research spanning from 2021 to 2023, this paper provides a comprehensive understanding of how deep learning techniques are employed in investigating lncRNAs, thereby contributing valuable insights to this rapidly evolving field. The review is aimed at researchers and practitioners looking to integrate deep learning advancements into their lncRNA studies.
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Affiliation(s)
- Yoojoong Kim
- School of Computer Science and Information Engineering, The Catholic University of Korea, Bucheon 14662, Republic of Korea
| | - Minhyeok Lee
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
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6
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Sabbir Ahmed CM, Canchola A, Paul B, Alam MRN, Lin YH. Altered long non-coding RNAs expression in normal and diseased primary human airway epithelial cells exposed to diesel exhaust particles. Inhal Toxicol 2023; 35:157-168. [PMID: 36877189 PMCID: PMC10424575 DOI: 10.1080/08958378.2023.2185703] [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: 06/24/2022] [Accepted: 02/24/2023] [Indexed: 03/07/2023]
Abstract
BACKGROUND Exposure to diesel exhaust particles (DEP) has been linked to a variety of adverse health effects, including increased morbidity and mortality from cardiovascular diseases, chronic obstructive pulmonary disease (COPD), metabolic syndrome, and lung cancer. The epigenetic changes caused by air pollution have been associated with increased health risks. However, the exact molecular mechanisms underlying the lncRNA-mediated pathogenesis induced by DEP exposure have not been revealed. METHODS Through RNA-sequencing and integrative analysis of both mRNA and lncRNA profiles, this study investigated the role of lncRNAs in altered gene expression in healthy and diseased human primary epithelial cells (NHBE and DHBE-COPD) exposed to DEP at a dose of 30 μg/cm2. RESULTS We identified 503 and 563 differentially expressed (DE) mRNAs and a total of 10 and 14 DE lncRNAs in NHBE and DHBE-COPD cells exposed to DEP, respectively. In both NHBE and DHBE-COPD cells, enriched cancer-related pathways were identified at mRNA level, and 3 common lncRNAs OLMALINC, AC069234.2, and LINC00665 were found to be associated with cancer initiation and progression. In addition, we identified two cis-acting (TMEM51-AS1 and TTN-AS1) and several trans-acting lncRNAs (e.g. LINC01278, SNHG29, AC006064.4, TMEM51-AS1) only differentially expressed in COPD cells, which could potentially play a role in carcinogenesis and determine their susceptibility to DEP exposure. CONCLUSIONS Overall, our work highlights the potential importance of lncRNAs in regulating DEP-induced gene expression changes associated with carcinogenesis, and individuals suffering from COPD are likely to be more vulnerable to these environmental triggers.
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Affiliation(s)
- C. M. Sabbir Ahmed
- Environmental Toxicology Graduate Program, University of California, Riverside, United States
| | - Alexa Canchola
- Environmental Toxicology Graduate Program, University of California, Riverside, United States
| | - Biplab Paul
- Gastrointestinal Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, United States
- Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Md Rubaiat Nurul Alam
- Environmental Toxicology Graduate Program, University of California, Riverside, United States
| | - Ying-Hsuan Lin
- Environmental Toxicology Graduate Program, University of California, Riverside, United States
- Department of Environmental Sciences, University of California, Riverside, United States
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7
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Wu P, Nie Z, Huang Z, Zhang X. CircPCBL: Identification of Plant CircRNAs with a CNN-BiGRU-GLT Model. PLANTS (BASEL, SWITZERLAND) 2023; 12:1652. [PMID: 37111874 PMCID: PMC10143888 DOI: 10.3390/plants12081652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 04/10/2023] [Accepted: 04/13/2023] [Indexed: 06/19/2023]
Abstract
Circular RNAs (circRNAs), which are produced post-splicing of pre-mRNAs, are strongly linked to the emergence of several tumor types. The initial stage in conducting follow-up studies involves identifying circRNAs. Currently, animals are the primary target of most established circRNA recognition technologies. However, the sequence features of plant circRNAs differ from those of animal circRNAs, making it impossible to detect plant circRNAs. For example, there are non-GT/AG splicing signals at circRNA junction sites and few reverse complementary sequences and repetitive elements in the flanking intron sequences of plant circRNAs. In addition, there have been few studies on circRNAs in plants, and thus it is urgent to create a plant-specific method for identifying circRNAs. In this study, we propose CircPCBL, a deep-learning approach that only uses raw sequences to distinguish between circRNAs found in plants and other lncRNAs. CircPCBL comprises two separate detectors: a CNN-BiGRU detector and a GLT detector. The CNN-BiGRU detector takes in the one-hot encoding of the RNA sequence as the input, while the GLT detector uses k-mer (k = 1 - 4) features. The output matrices of the two submodels are then concatenated and ultimately pass through a fully connected layer to produce the final output. To verify the generalization performance of the model, we evaluated CircPCBL using several datasets, and the results revealed that it had an F1 of 85.40% on the validation dataset composed of six different plants species and 85.88%, 75.87%, and 86.83% on the three cross-species independent test sets composed of Cucumis sativus, Populus trichocarpa, and Gossypium raimondii, respectively. With an accuracy of 90.9% and 90%, respectively, CircPCBL successfully predicted ten of the eleven circRNAs of experimentally reported Poncirus trifoliata and nine of the ten lncRNAs of rice on the real set. CircPCBL could potentially contribute to the identification of circRNAs in plants. In addition, it is remarkable that CircPCBL also achieved an average accuracy of 94.08% on the human datasets, which is also an excellent result, implying its potential application in animal datasets. Ultimately, CircPCBL is available as a web server, from which the data and source code can also be downloaded free of charge.
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Affiliation(s)
- Pengpeng Wu
- Anhui Province Key Laboratory of Smart Agricultural Technology and Equipment, Anhui Agricultural University, Hefei 230036, China
- School of Life Science, Anhui Agricultural University, Hefei 230036, China
| | - Zhenjun Nie
- Anhui Province Key Laboratory of Smart Agricultural Technology and Equipment, Anhui Agricultural University, Hefei 230036, China
- School of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China
| | - Zhiqiang Huang
- Anhui Province Key Laboratory of Smart Agricultural Technology and Equipment, Anhui Agricultural University, Hefei 230036, China
- School of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China
| | - Xiaodan Zhang
- Anhui Province Key Laboratory of Smart Agricultural Technology and Equipment, Anhui Agricultural University, Hefei 230036, China
- School of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China
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8
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Recent Deep Learning Methodology Development for RNA–RNA Interaction Prediction. Symmetry (Basel) 2022. [DOI: 10.3390/sym14071302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Genetic regulation of organisms involves complicated RNA–RNA interactions (RRIs) among messenger RNA (mRNA), microRNA (miRNA), and long non-coding RNA (lncRNA). Detecting RRIs is beneficial for discovering biological mechanisms as well as designing new drugs. In recent years, with more and more experimentally verified RNA–RNA interactions being deposited into databases, statistical machine learning, especially recent deep-learning-based automatic algorithms, have been widely applied to RRI prediction with remarkable success. This paper first gives a brief introduction to the traditional machine learning methods applied on RRI prediction and benchmark databases for training the models, and then provides a recent methodology overview of deep learning models in the prediction of microRNA (miRNA)–mRNA interactions and long non-coding RNA (lncRNA)–miRNA interactions.
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Xu D, Yuan W, Fan C, Liu B, Lu MZ, Zhang J. Opportunities and Challenges of Predictive Approaches for the Non-coding RNA in Plants. FRONTIERS IN PLANT SCIENCE 2022; 13:890663. [PMID: 35498708 PMCID: PMC9048598 DOI: 10.3389/fpls.2022.890663] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 03/28/2022] [Indexed: 06/01/2023]
Affiliation(s)
- Dong Xu
- State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou, China
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Wenya Yuan
- State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou, China
| | - Chunjie Fan
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou, China
| | - Bobin Liu
- Jiangsu Key Laboratory for Bioresources of Saline Soils, Jiangsu Synthetic Innovation Center for Coastal Bio-agriculture, School of Wetlands, Yancheng Teachers University, Yancheng, China
| | - Meng-Zhu Lu
- State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou, China
| | - Jin Zhang
- State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou, China
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Peng L, Tan J, Tian X, Zhou L. EnANNDeep: An Ensemble-based lncRNA-protein Interaction Prediction Framework with Adaptive k-Nearest Neighbor Classifier and Deep Models. Interdiscip Sci 2022; 14:209-232. [PMID: 35006529 DOI: 10.1007/s12539-021-00483-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 09/14/2021] [Accepted: 09/15/2021] [Indexed: 01/08/2023]
Abstract
lncRNA-protein interactions (LPIs) prediction can deepen the understanding of many important biological processes. Artificial intelligence methods have reported many possible LPIs. However, most computational techniques were evaluated mainly on one dataset, which may produce prediction bias. More importantly, they were validated only under cross validation on lncRNA-protein pairs, and did not consider the performance under cross validations on lncRNAs and proteins, thus fail to search related proteins/lncRNAs for a new lncRNA/protein. Under an ensemble learning framework (EnANNDeep) composed of adaptive k-nearest neighbor classifier and Deep models, this study focuses on systematically finding underlying linkages between lncRNAs and proteins. First, five LPI-related datasets are arranged. Second, multiple source features are integrated to depict an lncRNA-protein pair. Third, adaptive k-nearest neighbor classifier, deep neural network, and deep forest are designed to score unknown lncRNA-protein pairs, respectively. Finally, interaction probabilities from the three predictors are integrated based on a soft voting technique. In comparing to five classical LPI identification models (SFPEL, PMDKN, CatBoost, PLIPCOM, and LPI-SKF) under fivefold cross validations on lncRNAs, proteins, and LPIs, EnANNDeep computes the best average AUCs of 0.8660, 0.8775, and 0.9166, respectively, and the best average AUPRs of 0.8545, 0.8595, and 0.9054, respectively, indicating its superior LPI prediction ability. Case study analyses indicate that SNHG10 may have dense linkage with Q15717. In the ensemble framework, adaptive k-nearest neighbor classifier can separately pick the most appropriate k for each query lncRNA-protein pair. More importantly, deep models including deep neural network and deep forest can effectively learn the representative features of lncRNAs and proteins.
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Affiliation(s)
- Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China. .,College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, China.
| | - Jingwei Tan
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Xiongfei Tian
- 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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11
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Niu Y, Chen J, Qiao Y. Epigenetic Modifications in Tumor-Associated Macrophages: A New Perspective for an Old Foe. Front Immunol 2022; 13:836223. [PMID: 35140725 PMCID: PMC8818998 DOI: 10.3389/fimmu.2022.836223] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 01/06/2022] [Indexed: 12/11/2022] Open
Abstract
Tumorigenesis is frequently accompanied by chronic inflammation, and the tumor microenvironment (TME) can be considered an ecosystem that consists of tumor cells, endotheliocytes, fibroblasts, immune cells and acellular components such as extracellular matrix. For tumor cells, their survival advantages are dependent on both genetic and epigenetic alterations, while other cells mainly present epigenetic modifications. Macrophages are the most plastic type of immune cells and undergo diverse epigenetic alterations in the TME. Some of these epigenetic modifications mitigate against cancer progression, and others accelerate this process. Due to the complex roles of macrophages in the TME, it is urgent to understand their epigenetic modifications associated with the TME. Here, we mainly summarize recent findings on TME-associated epigenetic alterations of tumor-associated macrophages (TAMs), including DNA methylation, posttranslational modifications of histone proteins, chromatin remodeling, and noncoding RNA-mediated epigenetic regulation. At the end of this review, we also discuss the translational potential of these epigenetic modifications for developing novel cancer therapies targeting TAMs.
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Affiliation(s)
- Yuqin Niu
- The First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China
| | - Jianxiang Chen
- School of Pharmacy, Department of Hepatology, the Affiliated Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou, China
- *Correspondence: Yiting Qiao, ; Jianxiang Chen,
| | - Yiting Qiao
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou, China
- Jinan Microecological Biomedicine Shandong Laboratory, Jinan, China
- *Correspondence: Yiting Qiao, ; Jianxiang Chen,
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12
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Zhang Y, Long Y, Kwoh CK. Class similarity network for coding and long non-coding RNA classification. BMC Bioinformatics 2021; 22:609. [PMID: 34930120 PMCID: PMC8691036 DOI: 10.1186/s12859-021-04517-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 12/06/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Long non-coding RNAs (lncRNAs) play significant roles in varieties of physiological and pathological processes.The premise of the lncRNA functional study is that the lncRNAs are identified correctly. Recently, deep learning method like convolutional neural network (CNN) has been successfully applied to identify the lncRNAs. However, the traditional CNN considers little relationships among samples via an indirect way. RESULTS Inspired by the Siamese Neural Network (SNN), here we propose a novel network named Class Similarity Network in coding RNA and lncRNA classification. Class Similarity Network considers more relationships among input samples in a direct way. It focuses on exploring the potential relationships between input samples and samples from both the same class and the different classes. To achieve this, Class Similarity Network trains the parameters specific to each class to obtain the high-level features and represents the general similarity to each class in a node. The comparison results on the validation dataset under the same conditions illustrate the superiority of our Class Similarity Network to the baseline CNN. Besides, our method performs effectively and achieves state-of-the-art performances on two test datasets. CONCLUSIONS We construct Class Similarity Network in coding RNA and lncRNA classification, which is shown to work effectively on two different datasets by achieving accuracy, precision, and F1-score as 98.43%, 0.9247, 0.9374, and 97.54%, 0.9990, 0.9860, respectively.
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Affiliation(s)
- Yu Zhang
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore.,Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, Cambridge, CB2 0AW, UK
| | - Yahui Long
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410000, China
| | - Chee Keong Kwoh
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore.
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13
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LPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classification. BMC Bioinformatics 2021; 22:568. [PMID: 34836494 PMCID: PMC8620196 DOI: 10.1186/s12859-021-04485-x] [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: 08/24/2021] [Accepted: 11/09/2021] [Indexed: 12/03/2022] Open
Abstract
Background Long noncoding RNAs (lncRNAs) have dense linkages with a plethora of important cellular activities. lncRNAs exert functions by linking with corresponding RNA-binding proteins. Since experimental techniques to detect lncRNA-protein interactions (LPIs) are laborious and time-consuming, a few computational methods have been reported for LPI prediction. However, computation-based LPI identification methods have the following limitations: (1) Most methods were evaluated on a single dataset, and researchers may thus fail to measure their generalization ability. (2) The majority of methods were validated under cross validation on lncRNA-protein pairs, did not investigate the performance under other cross validations, especially for cross validation on independent lncRNAs and independent proteins. (3) lncRNAs and proteins have abundant biological information, how to select informative features need to further investigate. Results Under a hybrid framework (LPI-HyADBS) integrating feature selection based on AdaBoost, and classification models including deep neural network (DNN), extreme gradient Boost (XGBoost), and SVM with a penalty Coefficient of misclassification (C-SVM), this work focuses on finding new LPIs. First, five datasets are arranged. Each dataset contains lncRNA sequences, protein sequences, and an LPI network. Second, biological features of lncRNAs and proteins are acquired based on Pyfeat. Third, the obtained features of lncRNAs and proteins are selected based on AdaBoost and concatenated to depict each LPI sample. Fourth, DNN, XGBoost, and C-SVM are used to classify lncRNA-protein pairs based on the concatenated features. Finally, a hybrid framework is developed to integrate the classification results from the above three classifiers. LPI-HyADBS is compared to six classical LPI prediction approaches (LPI-SKF, LPI-NRLMF, Capsule-LPI, LPI-CNNCP, LPLNP, and LPBNI) on five datasets under 5-fold cross validations on lncRNAs, proteins, lncRNA-protein pairs, and independent lncRNAs and independent proteins. The results show LPI-HyADBS has the best LPI prediction performance under four different cross validations. In particular, LPI-HyADBS obtains better classification ability than other six approaches under the constructed independent dataset. Case analyses suggest that there is relevance between ZNF667-AS1 and Q15717. Conclusions Integrating feature selection approach based on AdaBoost, three classification techniques including DNN, XGBoost, and C-SVM, this work develops a hybrid framework to identify new linkages between lncRNAs and proteins. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04485-x.
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14
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Kang J, Tang Q, He J, Li L, Yang N, Yu S, Wang M, Zhang Y, Lin J, Cui T, Hu Y, Tan P, Cheng J, Zheng H, Wang D, Su X, Chen W, Huang Y. RNAInter v4.0: RNA interactome repository with redefined confidence scoring system and improved accessibility. Nucleic Acids Res 2021; 50:D326-D332. [PMID: 34718726 PMCID: PMC8728132 DOI: 10.1093/nar/gkab997] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/07/2021] [Accepted: 10/09/2021] [Indexed: 12/18/2022] Open
Abstract
Establishing an RNA-associated interaction repository facilitates the system-level understanding of RNA functions. However, as these interactions are distributed throughout various resources, an essential prerequisite for effectively applying these data requires that they are deposited together and annotated with confidence scores. Hence, we have updated the RNA-associated interaction database RNAInter (RNA Interactome Database) to version 4.0, which is freely accessible at http://www.rnainter.org or http://www.rna-society.org/rnainter/. Compared with previous versions, the current RNAInter not only contains an enlarged data set, but also an updated confidence scoring system. The merits of this 4.0 version can be summarized in the following points: (i) a redefined confidence scoring system as achieved by integrating the trust of experimental evidence, the trust of the scientific community and the types of tissues/cells, (ii) a redesigned fully functional database that enables for a more rapid retrieval and browsing of interactions via an upgraded user-friendly interface and (iii) an update of entries to >47 million by manually mining the literature and integrating six database resources with evidence from experimental and computational sources. Overall, RNAInter will provide a more comprehensive and readily accessible RNA interactome platform to investigate the regulatory landscape of cellular RNAs.
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Affiliation(s)
- Juanjuan Kang
- Affiliated Foshan Maternity & Child Healthcare Hospital, Southern Medical University (Foshan Maternity & Child Healthcare Hospital), Foshan 528000, China.,Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Qiang Tang
- School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, China
| | - Jun He
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou 350122, China
| | - Le Li
- The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People's Hospital, Qingyuan, 511518, China
| | - Nianling Yang
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou 350122, China
| | - Shuiyan Yu
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou 350122, China
| | - Mengyao Wang
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou 350122, China
| | - Yuchen Zhang
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou 350122, China
| | - Jiahao Lin
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Tianyu Cui
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Yongfei Hu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Puwen Tan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Jun Cheng
- Affiliated Foshan Maternity & Child Healthcare Hospital, Southern Medical University (Foshan Maternity & Child Healthcare Hospital), Foshan 528000, China.,Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Hailong Zheng
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Dong Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Xi Su
- Affiliated Foshan Maternity & Child Healthcare Hospital, Southern Medical University (Foshan Maternity & Child Healthcare Hospital), Foshan 528000, China
| | - Wei Chen
- School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, China
| | - Yan Huang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.,Cancer Research Institute, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.,Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan 528308, China
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15
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Li Y, Sun H, Feng S, Zhang Q, Han S, Du W. Capsule-LPI: a LncRNA-protein interaction predicting tool based on a capsule network. BMC Bioinformatics 2021; 22:246. [PMID: 33985444 PMCID: PMC8120853 DOI: 10.1186/s12859-021-04171-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 05/05/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Long noncoding RNAs (lncRNAs) play important roles in multiple biological processes. Identifying LncRNA-protein interactions (LPIs) is key to understanding lncRNA functions. Although some LPIs computational methods have been developed, the LPIs prediction problem remains challenging. How to integrate multimodal features from more perspectives and build deep learning architectures with better recognition performance have always been the focus of research on LPIs. RESULTS We present a novel multichannel capsule network framework to integrate multimodal features for LPI prediction, Capsule-LPI. Capsule-LPI integrates four groups of multimodal features, including sequence features, motif information, physicochemical properties and secondary structure features. Capsule-LPI is composed of four feature-learning subnetworks and one capsule subnetwork. Through comprehensive experimental comparisons and evaluations, we demonstrate that both multimodal features and the architecture of the multichannel capsule network can significantly improve the performance of LPI prediction. The experimental results show that Capsule-LPI performs better than the existing state-of-the-art tools. The precision of Capsule-LPI is 87.3%, which represents a 1.7% improvement. The F-value of Capsule-LPI is 92.2%, which represents a 1.4% improvement. CONCLUSIONS This study provides a novel and feasible LPI prediction tool based on the integration of multimodal features and a capsule network. A webserver ( http://csbg-jlu.site/lpc/predict ) is developed to be convenient for users.
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Affiliation(s)
- Ying Li
- Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Qianjin Street, 130012, Changchun, China
| | - Hang Sun
- Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Qianjin Street, 130012, Changchun, China
| | - Shiyao Feng
- Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Qianjin Street, 130012, Changchun, China
| | - Qi Zhang
- Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Qianjin Street, 130012, Changchun, China
| | - Siyu Han
- Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Qianjin Street, 130012, Changchun, China
- Department of Computer Science, Faculty of Engineering, University of Bristol, Bristol, BS8 1UB, UK
| | - Wei Du
- Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, College of Computer Science and Technology, Jilin University, Qianjin Street, 130012, Changchun, China.
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16
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Zhang Y, Long Y, Kwoh CK. Deep learning based DNA:RNA triplex forming potential prediction. BMC Bioinformatics 2020; 21:522. [PMID: 33183242 PMCID: PMC7663897 DOI: 10.1186/s12859-020-03864-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 11/09/2020] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND Long non-coding RNAs (lncRNAs) can exert functions via forming triplex with DNA. The current methods in predicting the triplex formation mainly rely on mathematic statistic according to the base paring rules. However, these methods have two main limitations: (1) they identify a large number of triplex-forming lncRNAs, but the limited number of experimentally verified triplex-forming lncRNA indicates that maybe not all of them can form triplex in practice, and (2) their predictions only consider the theoretical relationship while lacking the features from the experimentally verified data. RESULTS In this work, we develop an integrated program named TriplexFPP (Triplex Forming Potential Prediction), which is the first machine learning model in DNA:RNA triplex prediction. TriplexFPP predicts the most likely triplex-forming lncRNAs and DNA sites based on the experimentally verified data, where the high-level features are learned by the convolutional neural networks. In the fivefold cross validation, the average values of Area Under the ROC curves and PRC curves for removed redundancy triplex-forming lncRNA dataset with threshold 0.8 are 0.9649 and 0.9996, and these two values for triplex DNA sites prediction are 0.8705 and 0.9671, respectively. Besides, we also briefly summarize the cis and trans targeting of triplexes lncRNAs. CONCLUSIONS The TriplexFPP is able to predict the most likely triplex-forming lncRNAs from all the lncRNAs with computationally defined triplex forming capacities and the potential of a DNA site to become a triplex. It may provide insights to the exploration of lncRNA functions.
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Affiliation(s)
- Yu Zhang
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Yahui Long
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410000, China
| | - Chee Keong Kwoh
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore.
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