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Hasanabadi HE, Govahi A, Chaichian S, Mehdizadeh M, Haghighi L, Ajdary M. LnCRNAs in the Regulation of Endometrial Receptivity for Embryo Implantation. JBRA Assist Reprod 2024; 28:503-510. [PMID: 38875127 PMCID: PMC11349255 DOI: 10.5935/1518-0557.20240038] [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: 11/11/2023] [Accepted: 05/30/2024] [Indexed: 06/16/2024] Open
Abstract
The development of endometrial receptivity is crucial for successful embryo implantation and the initiation of pregnancy. Understanding the molecular regulatory processes that transform the endometrium into a receptive phase is essential for enhancing implantation rates in fertility treatments, such as in vitro fertilization (IVF). Long non-coding RNAs (lncRNAs) play a pivotal role as gene regulators and have been examined in the endometrium. This review offers current insights into the role of lncRNAs in regulating endometrial receptivity. Considering the significant variation in endometrial remodeling among species, we summarize the key events in the human endometrial cycle and discuss the identified lncRNAs in both humans and other species, which may play a crucial role in establishing receptivity. Notably, there are 742 lncRNAs in humans and 4438 lncRNAs that have the potential to modulate endometrial receptivity. Additionally, lncRNAs regulating matrix metalloproteinases (MMPs) and Let-7 have been observed in both species. Future investigations should explore the potential of lncRNAs as therapeutic targets and/or biomarkers for diagnosing and improving endometrial receptivity in human fertility therapy.
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Affiliation(s)
- Haniyeh Ebrahimnejad Hasanabadi
- Department of Pediatric Nursing and NICU, School of Nursing
& Midwifery, Tehran University of Medical Sciences, Tehran, Iran
| | - Azam Govahi
- Endometriosis Research Center, Iran University of Medical
Sciences, Tehran, Iran
| | - Shahla Chaichian
- Endometriosis Research Center, Iran University of Medical
Sciences, Tehran, Iran
| | - Mehdi Mehdizadeh
- Reproductive Sciences and Technology Research Center, Department
of Anatomy, Iran University of Medical Sciences, Tehran, Iran
| | - Ladan Haghighi
- Endometriosis Research Center, Iran University of Medical
Sciences, Tehran, Iran
| | - Marziyeh Ajdary
- Endometriosis Research Center, Iran University of Medical
Sciences, Tehran, Iran
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2
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Bonomo M, Rombo SE. Neighborhood based computational approaches for the prediction of lncRNA-disease associations. BMC Bioinformatics 2024; 25:187. [PMID: 38741200 DOI: 10.1186/s12859-024-05777-8] [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: 12/13/2023] [Accepted: 04/11/2024] [Indexed: 05/16/2024] Open
Abstract
MOTIVATION Long non-coding RNAs (lncRNAs) are a class of molecules involved in important biological processes. Extensive efforts have been provided to get deeper understanding of disease mechanisms at the lncRNA level, guiding towards the detection of biomarkers for disease diagnosis, treatment, prognosis and prevention. Unfortunately, due to costs and time complexity, the number of possible disease-related lncRNAs verified by traditional biological experiments is very limited. Computational approaches for the prediction of disease-lncRNA associations allow to identify the most promising candidates to be verified in laboratory, reducing costs and time consuming. RESULTS We propose novel approaches for the prediction of lncRNA-disease associations, all sharing the idea of exploring associations among lncRNAs, other intermediate molecules (e.g., miRNAs) and diseases, suitably represented by tripartite graphs. Indeed, while only a few lncRNA-disease associations are still known, plenty of interactions between lncRNAs and other molecules, as well as associations of the latters with diseases, are available. A first approach presented here, NGH, relies on neighborhood analysis performed on a tripartite graph, built upon lncRNAs, miRNAs and diseases. A second approach (CF) relies on collaborative filtering; a third approach (NGH-CF) is obtained boosting NGH by collaborative filtering. The proposed approaches have been validated on both synthetic and real data, and compared against other methods from the literature. It results that neighborhood analysis allows to outperform competitors, and when it is combined with collaborative filtering the prediction accuracy further improves, scoring a value of AUC equal to 0966. AVAILABILITY Source code and sample datasets are available at: https://github.com/marybonomo/LDAsPredictionApproaches.git.
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Affiliation(s)
| | - Simona E Rombo
- Kazaam Lab s.r.l., Palermo, Italy
- Department of Mathematics and Computer Science, University of Palermo, Palermo, Italy
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3
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Zhou L, Peng X, Zeng L, Peng L. Finding potential lncRNA-disease associations using a boosting-based ensemble learning model. Front Genet 2024; 15:1356205. [PMID: 38495672 PMCID: PMC10940470 DOI: 10.3389/fgene.2024.1356205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 02/01/2024] [Indexed: 03/19/2024] Open
Abstract
Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious. Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA-disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA-disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network. Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively. Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification.
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Affiliation(s)
- Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, Hunan, China
| | - Xinhuai Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, Hunan, China
| | - Lijun Zeng
- School of Computer Science, Hunan Institute of Technology, Hengyang, China
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, Hunan, China
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4
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Peng L, Yang Y, Yang C, Li Z, Cheong N. HRGCNLDA: Forecasting of lncRNA-disease association based on hierarchical refinement graph convolutional neural network. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:4814-4834. [PMID: 38872515 DOI: 10.3934/mbe.2024212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Long non-coding RNA (lncRNA) is considered to be a crucial regulator involved in various human biological processes, including the regulation of tumor immune checkpoint proteins. It has great potential as both a cancer biomolecular biomarker and therapeutic target. Nevertheless, conventional biological experimental techniques are both resource-intensive and laborious, making it essential to develop an accurate and efficient computational method to facilitate the discovery of potential links between lncRNAs and diseases. In this study, we proposed HRGCNLDA, a computational approach utilizing hierarchical refinement of graph convolutional neural networks for forecasting lncRNA-disease potential associations. This approach effectively addresses the over-smoothing problem that arises from stacking multiple layers of graph convolutional neural networks. Specifically, HRGCNLDA enhances the layer representation during message propagation and node updates, thereby amplifying the contribution of hidden layers that resemble the ego layer while reducing discrepancies. The results of the experiments showed that HRGCNLDA achieved the highest AUC-ROC (area under the receiver operating characteristic curve, AUC for short) and AUC-PR (area under the precision versus recall curve, AUPR for short) values compared to other methods. Finally, to further demonstrate the reliability and efficacy of our approach, we performed case studies on the case of three prevalent human diseases, namely, breast cancer, lung cancer and gastric cancer.
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Affiliation(s)
- Li Peng
- College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
- Hunan Key Laboratory for Service Computing and Novel Software Technology, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Yujie Yang
- College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Cheng Yang
- College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Zejun Li
- School of Computer Science and Engineering, Hunan Institute of Technology, Hengyang 421002, China
| | - Ngai Cheong
- Faculty of Applied Sciences, Macao Polytechnic University, Macau 999078, China
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5
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Rinaldi S, Moroni E, Rozza R, Magistrato A. Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation. J Chem Theory Comput 2024; 20:993-1018. [PMID: 38287883 DOI: 10.1021/acs.jctc.3c01239] [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: 01/31/2024]
Abstract
Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.
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Affiliation(s)
- Silvia Rinaldi
- National Research Council of Italy (CNR) - Institute of Chemistry of OrganoMetallic Compounds (ICCOM), c/o Area di Ricerca CNR di Firenze Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy
| | - Elisabetta Moroni
- National Research Council of Italy (CNR) - Institute of Chemical Sciences and Technologies (SCITEC), via Mario Bianco 9, 20131 Milano, Italy
| | - Riccardo Rozza
- National Research Council of Italy (CNR) - Institute of Material Foundry (IOM) c/o International School for Advanced Studies (SISSA), Via Bonomea, 265, 34136 Trieste, Italy
| | - Alessandra Magistrato
- National Research Council of Italy (CNR) - Institute of Material Foundry (IOM) c/o International School for Advanced Studies (SISSA), Via Bonomea, 265, 34136 Trieste, Italy
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6
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Yao D, Li B, Zhan X, Zhan X, Yu L. GCNFORMER: graph convolutional network and transformer for predicting lncRNA-disease associations. BMC Bioinformatics 2024; 25:5. [PMID: 38166659 PMCID: PMC10763317 DOI: 10.1186/s12859-023-05625-1] [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: 10/22/2023] [Accepted: 12/18/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND A growing body of researches indicate that the disrupted expression of long non-coding RNA (lncRNA) is linked to a range of human disorders. Therefore, the effective prediction of lncRNA-disease association (LDA) can not only suggest solutions to diagnose a condition but also save significant time and labor costs. METHOD In this work, we proposed a novel LDA predicting algorithm based on graph convolutional network and transformer, named GCNFORMER. Firstly, we integrated the intraclass similarity and interclass connections between miRNAs, lncRNAs and diseases, and built a graph adjacency matrix. Secondly, to completely obtain the features between various nodes, we employed a graph convolutional network for feature extraction. Finally, to obtain the global dependencies between inputs and outputs, we used a transformer encoder with a multiheaded attention mechanism to forecast lncRNA-disease associations. RESULTS The results of fivefold cross-validation experiment on the public dataset revealed that the AUC and AUPR of GCNFORMER achieved 0.9739 and 0.9812, respectively. We compared GCNFORMER with six advanced LDA prediction models, and the results indicated its superiority over the other six models. Furthermore, GCNFORMER's effectiveness in predicting potential LDAs is underscored by case studies on breast cancer, colon cancer and lung cancer. CONCLUSIONS The combination of graph convolutional network and transformer can effectively improve the performance of LDA prediction model and promote the in-depth development of this research filed.
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Affiliation(s)
- Dengju Yao
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China.
| | - Bailin Li
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China
| | - Xiaojuan Zhan
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China
- College of Computer Science and Technology, Heilongjiang Institute of Technology, Harbin, 150050, China
| | - Xiaorong Zhan
- Department of Endocrinology and Metabolism, Hospital of South, University of Science and Technology, Shenzhen, 518055, China
| | - Liyang Yu
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China
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7
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Lu Z, Zhong H, Tang L, Luo J, Zhou W, Liu L. Predicting lncRNA-disease associations based on heterogeneous graph convolutional generative adversarial network. PLoS Comput Biol 2023; 19:e1011634. [PMID: 38019786 PMCID: PMC10686445 DOI: 10.1371/journal.pcbi.1011634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
There is a growing body of evidence indicating the crucial roles that long non-coding RNAs (lncRNAs) play in the development and progression of various diseases, including cancers, cardiovascular diseases, and neurological disorders. However, accurately predicting potential lncRNA-disease associations remains a challenge, as existing methods have limitations in extracting heterogeneous association information and handling sparse and unbalanced data. To address these issues, we propose a novel computational method, called HGC-GAN, which combines heterogeneous graph convolutional neural networks (GCN) and generative adversarial networks (GAN) to predict potential lncRNA-disease associations. Specifically, we construct a lncRNA-miRNA-disease heterogeneous network by integrating multiple association data and sequence information. The GCN-based generator is then employed to aggregate neighbor information of nodes and obtain node embeddings, which are used to predict lncRNA-disease associations. Meanwhile, the GAN-based discriminator is trained to distinguish between real and fake lncRNA-disease associations generated by the generator, enabling the generator to improve its ability to generate accurate lncRNA-disease associations gradually. Our experimental results demonstrate that HGC-GAN performs better in predicting potential lncRNA-disease associations, with AUC and AUPR values of 0.9591 and 0.9606, respectively, under 10-fold cross-validation. Moreover, our case study further confirms the effectiveness of HGC-GAN in predicting potential lncRNA-disease associations, even for novel lncRNAs without any known lncRNA-disease associations. Overall, our proposed method HGC-GAN provides a promising approach to predict potential lncRNA-disease associations and may have important implications for disease diagnosis, treatment, and drug development.
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Affiliation(s)
- Zhonghao Lu
- School of Information, Yunnan Normal University, Yunnan, People’s Republic of China
| | - Hua Zhong
- School of Information, Yunnan Normal University, Yunnan, People’s Republic of China
| | - Lin Tang
- Key Laboratory of Educational Information for Nationalities Ministry of Education, Yunnan Normal University, Yunnan, People’s Republic of China
| | - Jing Luo
- State Key Laboratory for Conservation and Utilization of Bio-resource in Yunnan, School of Life Sciences and School of Ecology and Environment, Yunnan University, Kunming, People’s Republic of China
| | - Wei Zhou
- School of Software, Yunnan University, Kunming, People’s Republic of China
| | - Lin Liu
- School of Information, Yunnan Normal University, Yunnan, People’s Republic of China
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8
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Wang J, Zhao J, Lin L, Peng X, Li W, Huang Y, Wang K, Li J. LncRNA-Anrel promotes the proliferation and migration of synovial fibroblasts through regulating miR-146a-mediated annexin A1 expression. AMERICAN JOURNAL OF CLINICAL AND EXPERIMENTAL IMMUNOLOGY 2023; 12:49-59. [PMID: 37736077 PMCID: PMC10509487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 08/18/2023] [Indexed: 09/23/2023]
Abstract
OBJECTIVE Increasing evidence demonstrates that long non-coding RNAs (lncRNAs) are closely related to multiple human autoimmune diseases, and their dysregulation is tightly linked to inflammation and disease progression. Nonetheless, little is known about the consequences of aberrant expression of lncRNAs during rheumatoid arthritis (RA) development. In this study, we screened for the expressions of lncRNAs in RA synovial fibroblasts (RA-SF) and investigated their functions in RA-SF proliferation and migration, and the relevant underlying mechanisms. METHODS The lncRNAs expression profiles were interrogated with microarrays. The expressions of key lncRNAs were confirmed in synovial fibroblasts from RA patients and MH7A cells using qRT-PCR. Proliferations and migrations of MH7A and HFL-1 cells were evaluated using CCK-8 assay and cell migration assay kits, respectively. The expression of inflammatory cytokines (IL-6, IL-1β, and TNF-α) and cell migration related proteins (MMP-1 and MMP-3) were evaluated using qRT-PCR and western blotting. Collagen type II-induced arthritis (CIA) in mice was used as an animal model of RA. RESULTS Nine lncRNAs were significantly altered in RA-SF, of which lncRNA-000239 showing the most significant upregulation. Overexpression of lncRNA-000239 significantly enhanced the proliferation and migration of human RS-SF cells (MH7A), while the opposite effect was observed with lncRNA-000239 silencing. Importantly, lncRNA-000239 enhanced annexin A1 expression by upregulating the expression of miR-146a. Moreover, locally enhanced expression of lncRNA-000239 promoted the onset of arthritis in CIA. CONCLUSION These data indicate that lncRNA-000239 upregulates annexin A1 expression via miR-146a and thus, promotes the proliferation and migration of RA-SF. This highlights a potential role of lncRNA-000239 as an inflammatory factor of RA.
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Affiliation(s)
- Juan Wang
- Department of Laboratory Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of MedicineShanghai 20080, China
| | - Jiangfeng Zhao
- Department of Rheumatology, Shanghai Renji Hospital, Shanghai Jiao Tong University School of MedicineShanghai 201112, China
| | - Lihui Lin
- Department of Laboratory Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of MedicineShanghai 20080, China
| | - Xia Peng
- Department of Laboratory Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of MedicineShanghai 20080, China
| | - Weize Li
- Department of Laboratory Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of MedicineShanghai 20080, China
| | - Yuji Huang
- Department of Laboratory Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of MedicineShanghai 20080, China
| | - Kaiwen Wang
- Department of Rheumatology, Shanghai Renji Hospital, Shanghai Jiao Tong University School of MedicineShanghai 201112, China
| | - Jia Li
- Department of Laboratory Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of MedicineShanghai 20080, China
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9
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Teng Z, Shi L, Yu H, Wu C, Tian Z. Measuring functional similarity of lncRNAs based on variable K-mer profiles of nucleotide sequences. Methods 2023; 212:21-30. [PMID: 36813016 DOI: 10.1016/j.ymeth.2023.02.009] [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: 12/30/2022] [Revised: 02/10/2023] [Accepted: 02/17/2023] [Indexed: 02/22/2023] Open
Abstract
Long non-coding RNAs are a class of essential non-coding RNAs with a length of more than 200 nts. Recent studies have indicated that lncRNAs have various complex regulatory functions, which play great impacts on many fundamental biological processes. However, measuring the functional similarity between lncRNAs by traditional wet-experiments is time-consuming and labor intensive, computational-based approaches have been an effective choice to tackle this problem. Meanwhile, most sequences-based computation methods measure the functional similarity of lncRNAs with their fixed length vector representations, which could not capture the features on larger k-mers. Therefore, it is urgent to improve the predict performance of the potential regulatory functions of lncRNAs. In this study, we propose a novel approach called MFSLNC to comprehensively measure functional similarity of lncRNAs based on variable k-mer profiles of nucleotide sequences. MFSLNC employs the dictionary tree storage, which could comprehensively represent lncRNAs with long k-mers. The functional similarity between lncRNAs is evaluated by the Jaccard similarity. MFSLNC verified the similarity between two lncRNAs with the same mechanism, detecting homologous sequence pairs between human and mouse. Besides, MFSLNC is also applied to lncRNA-disease associations, combined with the association prediction model WKNKN. Moreover, we also proved that our method can more effectively calculate the similarity of lncRNAs by comparing with the classical methods based on the lncRNA-mRNA association data. The detected AUC value of prediction is 0.867, which achieves good performance in the comparison of similar models.
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Affiliation(s)
- Zhixia Teng
- College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
| | - Linyue Shi
- College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
| | - Haihao Yu
- College of Computer Science and Technology, Heilongjiang Institute of Technology, Harbin 150040, China
| | - Chengyan Wu
- Baotou Teacher's College, Inner Mongolia University of Science and Technology, Baotou 014030, China
| | - Zhen Tian
- College of Information Engineering, Zhengzhou University, Zhengzhou 450001, China.
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Recent advances in predicting lncRNA-disease associations based on computational methods. Drug Discov Today 2023; 28:103432. [PMID: 36370992 DOI: 10.1016/j.drudis.2022.103432] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/19/2022] [Accepted: 11/03/2022] [Indexed: 11/11/2022]
Abstract
Mutations in and dysregulation of long non-coding RNAs (lncRNAs) are closely associated with the development of various human complex diseases, but only a few lncRNAs have been experimentally confirmed to be associated with human diseases. Predicting new potential lncRNA-disease associations (LDAs) will help us to understand the pathogenesis of human diseases and to detect disease markers, as well as in disease diagnosis, prevention and treatment. Computational methods can effectively narrow down the screening scope of biological experiments, thereby reducing the duration and cost of such experiments. In this review, we outline recent advances in computational methods for predicting LDAs, focusing on LDA databases, lncRNA/disease similarity calculations, and advanced computational models. In addition, we analyze the limitations of various computational models and discuss future challenges and directions for development.
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Zhao J, Sun J, Shuai SC, Zhao Q, Shuai J. Predicting potential interactions between lncRNAs and proteins via combined graph auto-encoder methods. Brief Bioinform 2023; 24:6896030. [PMID: 36515153 DOI: 10.1093/bib/bbac527] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/23/2022] [Accepted: 11/06/2022] [Indexed: 12/15/2022] Open
Abstract
Long noncoding RNA (lncRNA) is a kind of noncoding RNA with a length of more than 200 nucleotide units. Numerous research studies have proven that although lncRNAs cannot be directly translated into proteins, lncRNAs still play an important role in human growth processes by interacting with proteins. Since traditional biological experiments often require a lot of time and material costs to explore potential lncRNA-protein interactions (LPI), several computational models have been proposed for this task. In this study, we introduce a novel deep learning method known as combined graph auto-encoders (LPICGAE) to predict potential human LPIs. First, we apply a variational graph auto-encoder to learn the low dimensional representations from the high-dimensional features of lncRNAs and proteins. Then the graph auto-encoder is used to reconstruct the adjacency matrix for inferring potential interactions between lncRNAs and proteins. Finally, we minimize the loss of the two processes alternately to gain the final predicted interaction matrix. The result in 5-fold cross-validation experiments illustrates that our method achieves an average area under receiver operating characteristic curve of 0.974 and an average accuracy of 0.985, which is better than those of existing six state-of-the-art computational methods. We believe that LPICGAE can help researchers to gain more potential relationships between lncRNAs and proteins effectively.
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Affiliation(s)
- Jingxuan Zhao
- University of Science and Technology Liaoning, 66459, Anshan, China
| | | | - Stella C Shuai
- Northwestern University, 3270, Evanston, IllinoisUnited States
| | - Qi Zhao
- University of Science and Technology Liaoning, 66459, Anshan, China
| | - Jianwei Shuai
- Department of Physics, Xiamen University, Xiamen, China
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Li J, Wang D, Yang Z, Liu M. HEGANLDA: A Computational Model for Predicting Potential Lncrna-Disease Associations Based On Multiple Heterogeneous Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:388-398. [PMID: 34932483 DOI: 10.1109/tcbb.2021.3136886] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Long non-coding RNAs (lncRNAs) play vital regulatory roles in many human complex diseases, however, the number of validated lncRNA-disease associations is notable rare so far. How to predict potential lncRNA-disease associations precisely through computational methods remains challenging. In this study, we proposed a novel method, LDVCHN (LncRNA-Disease Vector Calculation Heterogeneous Networks), and also developed the corresponding model, HEGANLDA (Heterogeneous Embedding Generative Adversarial Networks LncRNA-Disease Association), for predicting potential lncRNA-disease associations. In HEGANLDA, the graph embedding algorithm (HeGAN) was introduced for mapping all nodes in the lncRNA-miRNA-disease heterogeneous network into the low-dimensional vectors which severed as the inputs of LDVCHN. HEGANLDA effectively adopted the XGBoost (eXtreme Gradient Boosting) classifier, which was trained by the low-dimensional vectors, to predict potential lncRNA-disease associations. The 10-fold cross-validation method was utilized to evaluate the performance of our model, our model finally achieved an area under the ROC curve of 0.983. According to the experiment results, HEGANLDA outperformed any one of five current state-of-the-art methods. To further evaluate the effectiveness of HEGANLDA in predicting potential lncRNA-disease associations, both case studies and robustness tests were performed and the results confirmed its effectiveness and robustness. The source code and data of HEGANLDA are available at https://github.com/HEGANLDA/HEGANLDA.
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Du XX, Liu Y, Wang B, Zhang JF. lncRNA-disease association prediction method based on the nearest neighbor matrix completion model. Sci Rep 2022; 12:21653. [PMID: 36522410 PMCID: PMC9755128 DOI: 10.1038/s41598-022-25730-0] [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: 08/20/2022] [Accepted: 12/05/2022] [Indexed: 12/23/2022] Open
Abstract
State-of-the-art medical studies proved that long noncoding ribonucleic acids (lncRNAs) are closely related to various diseases. However, their large-scale detection in biological experiments is problematic and expensive. To aid screening and improve the efficiency of biological experiments, this study introduced a prediction model based on the nearest neighbor concept for lncRNA-disease association prediction. We used a new similarity algorithm in the model that fused potential associations. The experimental validation of the proposed algorithm proved its superiority over the available Cosine, Pearson, and Jaccard similarity algorithms. Satisfactory results in the comparative leave-one-out cross-validation test (with AUC = 0.96) confirmed its excellent predictive performance. Finally, the proposed model's reliability was confirmed by performing predictions using a new dataset, yielding AUC = 0.92.
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Affiliation(s)
- Xiao-xin Du
- grid.412616.60000 0001 0002 2355College of Computer and Control, Qiqihar University, Qiqihar, 161006 China
| | - Yan Liu
- grid.412616.60000 0001 0002 2355College of Computer and Control, Qiqihar University, Qiqihar, 161006 China
| | - Bo Wang
- grid.412616.60000 0001 0002 2355College of Computer and Control, Qiqihar University, Qiqihar, 161006 China
| | - Jian-fei Zhang
- grid.412616.60000 0001 0002 2355College of Computer and Control, Qiqihar University, Qiqihar, 161006 China
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14
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Tan J, Li X, Zhang L, Du Z. Recent advances in machine learning methods for predicting LncRNA and disease associations. Front Cell Infect Microbiol 2022; 12:1071972. [PMID: 36530425 PMCID: PMC9748103 DOI: 10.3389/fcimb.2022.1071972] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 11/11/2022] [Indexed: 12/03/2022] Open
Abstract
Long non-coding RNAs (lncRNAs) are involved in almost the entire cell life cycle through different mechanisms and play an important role in many key biological processes. Mutations and dysregulation of lncRNAs have been implicated in many complex human diseases. Therefore, identifying the relationship between lncRNAs and diseases not only contributes to biologists' understanding of disease mechanisms, but also provides new ideas and solutions for disease diagnosis, treatment, prognosis and prevention. Since the existing experimental methods for predicting lncRNA-disease associations (LDAs) are expensive and time consuming, machine learning methods for predicting lncRNA-disease associations have become increasingly popular among researchers. In this review, we summarize some of the human diseases studied by LDAs prediction models, association and similarity features of LDAs prediction, performance evaluation methods of models and some advanced machine learning prediction models of LDAs. Finally, we discuss the potential limitations of machine learning-based methods for LDAs prediction and provide some ideas for designing new prediction models.
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15
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Bang D, Gu J, Park J, Jeong D, Koo B, Yi J, Shin J, Jung I, Kim S, Lee S. A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective. Int J Mol Sci 2022; 23:ijms231911498. [PMID: 36232792 PMCID: PMC9570358 DOI: 10.3390/ijms231911498] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 09/18/2022] [Accepted: 09/26/2022] [Indexed: 02/01/2023] Open
Abstract
Molecular and sequencing technologies have been successfully used in decoding biological mechanisms of various diseases. As revealed by many novel discoveries, the role of non-coding RNAs (ncRNAs) in understanding disease mechanisms is becoming increasingly important. Since ncRNAs primarily act as regulators of transcription, associating ncRNAs with diseases involves multiple inference steps. Leveraging the fast-accumulating high-throughput screening results, a number of computational models predicting ncRNA-disease associations have been developed. These tools suggest novel disease-related biomarkers or therapeutic targetable ncRNAs, contributing to the realization of precision medicine. In this survey, we first introduce the biological roles of different ncRNAs and summarize the databases containing ncRNA-disease associations. Then, we suggest a new trend in recent computational prediction of ncRNA-disease association, which is the mode of action (MoA) network perspective. This perspective includes integrating ncRNAs with mRNA, pathway and phenotype information. In the next section, we describe computational methodologies widely used in this research domain. Existing computational studies are then summarized in terms of their coverage of the MoA network. Lastly, we discuss the potential applications and future roles of the MoA network in terms of integrating biological mechanisms for ncRNA-disease associations.
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Affiliation(s)
- Dongmin Bang
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Jeonghyeon Gu
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul 08826, Korea
| | - Joonhyeong Park
- Department of Computer Science and Engineering, Seoul National University, Seoul 08826, Korea
| | - Dabin Jeong
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Bonil Koo
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Jungseob Yi
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul 08826, Korea
| | - Jihye Shin
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Inuk Jung
- Department of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Korea
| | - Sun Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul 08826, Korea
- Department of Computer Science and Engineering, Seoul National University, Seoul 08826, Korea
- MOGAM Institute for Biomedical Research, Yongin-si 16924, Korea
| | - Sunho Lee
- AIGENDRUG Co., Ltd., Seoul 08826, Korea
- Correspondence:
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Zhang Y, Ye F, Gao X. MCA-Net: Multi-Feature Coding and Attention Convolutional Neural Network for Predicting lncRNA-Disease Association. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2907-2919. [PMID: 34283719 DOI: 10.1109/tcbb.2021.3098126] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
With the advent of the era of big data, it is troublesome to accurately predict the associations between lncRNAs and diseases based on traditional biological experiments due to its time-consuming and subjective. In this paper, we propose a novel deep learning method for predicting lncRNA-disease associations using multi-feature coding and attention convolutional neural network (MCA-Net). We first calculate six similarity features to extract different types of lncRNA and disease feature information. Second, a multi-feature coding method is proposed to construct the feature vectors of lncRNA-disease association samples by integrating the six similarity features. Furthermore, an attention convolutional neural network is developed to identify lncRNA-disease associations under 10-fold cross-validation. Finally, we evaluate the performance of MCA-Net from different perspectives including the effects of the model parameters, distinct deep learning models, and the necessity of attention mechanism. We also compare MCA-Net with several state-of-the-art methods on three publicly available datasets, i.e., LncRNADisease, Lnc2Cancer, and LncRNADisease2.0. The results show that our MCA-Net outperforms the state-of-the-art methods on all three dataset. Besides, case studies on breast cancer and lung cancer further verify that MCA-Net is effective and accurate for the lncRNA-disease association prediction.
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17
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Yao D, Zhang T, Zhan X, Zhang S, Zhan X, Zhang C. Geometric complement heterogeneous information and random forest for predicting lncRNA-disease associations. Front Genet 2022; 13:995532. [PMID: 36092871 PMCID: PMC9448985 DOI: 10.3389/fgene.2022.995532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 08/01/2022] [Indexed: 11/20/2022] Open
Abstract
More and more evidences have showed that the unnatural expression of long non-coding RNA (lncRNA) is relevant to varieties of human diseases. Therefore, accurate identification of disease-related lncRNAs can help to understand lncRNA expression at the molecular level and to explore more effective treatments for diseases. Plenty of lncRNA-disease association prediction models have been raised but it is still a challenge to recognize unknown lncRNA-disease associations. In this work, we have proposed a computational model for predicting lncRNA-disease associations based on geometric complement heterogeneous information and random forest. Firstly, geometric complement heterogeneous information was used to integrate lncRNA-miRNA interactions and miRNA-disease associations verified by experiments. Secondly, lncRNA and disease features consisted of their respective similarity coefficients were fused into input feature space. Thirdly, an autoencoder was adopted to project raw high-dimensional features into low-dimension space to learn representation for lncRNAs and diseases. Finally, the low-dimensional lncRNA and disease features were fused into input feature space to train a random forest classifier for lncRNA-disease association prediction. Under five-fold cross-validation, the AUC (area under the receiver operating characteristic curve) is 0.9897 and the AUPR (area under the precision-recall curve) is 0.7040, indicating that the performance of our model is better than several state-of-the-art lncRNA-disease association prediction models. In addition, case studies on colon and stomach cancer indicate that our model has a good ability to predict disease-related lncRNAs.
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Affiliation(s)
- Dengju Yao
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
- *Correspondence: Dengju Yao,
| | - Tao Zhang
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Xiaojuan Zhan
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
- College of Computer Science and Technology, Heilongjiang Institute of Technology, Harbin, China
| | - Shuli Zhang
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Xiaorong Zhan
- Department of Endocrinology and Metabolism, Hospital of South University of Science and Technology, Shenzhen, China
| | - Chao Zhang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China
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18
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Wu H, Liang Q, Zhang W, Zou Q, El-Latif Hesham A, Liu B. iLncDA-LTR: Identification of lncRNA-disease associations by learning to rank. Comput Biol Med 2022; 146:105605. [PMID: 35594681 DOI: 10.1016/j.compbiomed.2022.105605] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/27/2022] [Accepted: 05/09/2022] [Indexed: 12/12/2022]
Abstract
Identifying the associations between lncRNAs and diseases is helpful for the treatment and diagnosis of complex diseases. The existing computational methods mainly focus on the identification of associations between known lncRNAs and known diseases. However, with the application of high-throughput sequencing in lncRNA research, more and more lncRNAs have been detected. Predicting diseases related with newly detected lncRNAs has not been fully explored. Therefore, there is an urgent need for developing powerful computational methods to predict diseases related with newly detected lncRNAs. In this paper, we propose a Learning to Rank (LTR)-based method called iLncDA-LTR to predict diseases related with newly detected lncRNAs. iLncDA-LTR treats this task as an information retrieval task. The newly detected lncRNAs and diseases are considered as queries and documents, respectively. For a given newly detected lncRNA (query), iLncDA-LTR integrates multiple relevant information into LTR for predicting candidate diseases associated with query lncRNA. Experimental results show that iLncDA-LTR outperforms the other exiting state-of-the-art predictors on independent dataset. The corresponding web server of iLncDA-LTR has been constructed as well (http://bliulab.net/iLncDA-LTR/).
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Affiliation(s)
- Hao Wu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Qi Liang
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Wenxiang Zhang
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
| | - Abd El-Latif Hesham
- Genetics Department, Faculty of Agriculture, Beni-Suef University, Beni-Suef, 62511, Egypt.
| | - Bin Liu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China; Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, China.
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19
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Lombardo SD, Wangsaputra IF, Menche J, Stevens A. Network Approaches for Charting the Transcriptomic and Epigenetic Landscape of the Developmental Origins of Health and Disease. Genes (Basel) 2022; 13:764. [PMID: 35627149 PMCID: PMC9141211 DOI: 10.3390/genes13050764] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 04/04/2022] [Accepted: 04/13/2022] [Indexed: 02/04/2023] Open
Abstract
The early developmental phase is of critical importance for human health and disease later in life. To decipher the molecular mechanisms at play, current biomedical research is increasingly relying on large quantities of diverse omics data. The integration and interpretation of the different datasets pose a critical challenge towards the holistic understanding of the complex biological processes that are involved in early development. In this review, we outline the major transcriptomic and epigenetic processes and the respective datasets that are most relevant for studying the periconceptional period. We cover both basic data processing and analysis steps, as well as more advanced data integration methods. A particular focus is given to network-based methods. Finally, we review the medical applications of such integrative analyses.
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Affiliation(s)
- Salvo Danilo Lombardo
- Max Perutz Labs, Department of Structural and Computational Biology, University of Vienna, 1030 Vienna, Austria;
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1030 Vienna, Austria
| | - Ivan Fernando Wangsaputra
- Maternal and Fetal Health Research Group, Division of Developmental Biology and Medicine, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9WL, UK;
| | - Jörg Menche
- Max Perutz Labs, Department of Structural and Computational Biology, University of Vienna, 1030 Vienna, Austria;
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1030 Vienna, Austria
- Faculty of Mathematics, University of Vienna, 1030 Vienna, Austria
| | - Adam Stevens
- Maternal and Fetal Health Research Group, Division of Developmental Biology and Medicine, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9WL, UK;
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20
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Yang X, Liu R. Long non-coding RNA HCG18 promotes gastric cancer progression by regulating miRNA-146a-5p/tumor necrosis factor receptor-associated factor 6 axis. Bioengineered 2022; 13:6781-6793. [PMID: 35240920 PMCID: PMC8973972 DOI: 10.1080/21655979.2022.2034565] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Although long non-coding RNAs (lncRNAs) have been demonstrated to be dysregulated in gastric cancer (GC), the function of lncRNA HCG18 (HCG18) in GC is elusive. Therefore, the study was designed to evaluate the underlying mechanism of HCG18 in GC. HCG18 and microRNA 146a-5p (miR-146a-5p) levels in GC were evaluated by RT-qPCR. The effects of miR-146a-5p and HCG18 on GC cell function were examined using Transwell assay, colony formation, and CCK-8 assays. Tumor necrosis factor receptor-associated factor 6 (TRAF6) and p65 expression levels were detected by Western blot. HCG18 and miR-146a-5p target genes were identified using luciferase reporter and bioinformatics assays. HCG18 expression was increased in GC. HCG18 overexpression significantly increased GC cell proliferation, invasion, and migration. Furthermore, HCG18 overexpression inhibited miR-146a-5p and upregulated TRAF6 and p65 expression. Finally, miR-146a-5p/TRAF6 was found to be involved in the role of HCG18 in GC progression in vivo. Altogether, HCG18 promotes GC progression via the miR-146a-5p/TRAF6 axis and could be a GC treatment target.
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Affiliation(s)
- Xianwu Yang
- Department of Gastroenterology, Shijiazhuang People's Hospital, Shijiazhuang City, P. R. China
| | - Run Liu
- Department of Gastroenterology, Shijiazhuang People's Hospital, Shijiazhuang City, P. R. China
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21
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Xie G, Jiang J, Sun Y. LDA-LNSUBRW: lncRNA-Disease Association Prediction Based on Linear Neighborhood Similarity and Unbalanced bi-Random Walk. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:989-997. [PMID: 32870798 DOI: 10.1109/tcbb.2020.3020595] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Increasing number of experiments show that lncRNAs are involved in many biological processes, and their mutations and disorders are associated with many diseases. However, verifying the relationships between lncRNAs and diseases is time consuming and laborio. Searching for effective computational methods will contribute to our understanding of the underlying mechanisms of disease and identifying biomarkers of diseases. Therefore, we proposed a method called lncRNA-disease association prediction based on linear neighborhood similarity and unbalanced bi-random walk (LDA-LNSUBRW). Given that the known lncRNA-disease associations are rare, a pretreatment step should be performed to obtain the interaction possibility of unknown cases, so as to help us predict the potential associations. In the framework of leave-one-out cross-validation (LOOCV)and fivefold cross-validation (5-fold CV), LDA-LNSUBRW achieved effective performance with AUC of 0.8874 and 0.8632 ± 0.0051, respectively. The experimental results in this paper show that the proposed method is superior to five other state-of-the-art methods. In addition, case studies of three diseases (lung cancer, breast cancer, and osteosarcoma)were carried out to illustrate that LDA-LNSUBRW could predict the relevant lncRNAs.
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22
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Yang L, Li LP, Yi HC. DeepWalk based method to predict lncRNA-miRNA associations via lncRNA-miRNA-disease-protein-drug graph. BMC Bioinformatics 2022; 22:621. [PMID: 35216549 PMCID: PMC8875942 DOI: 10.1186/s12859-022-04579-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 01/18/2022] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Long non-coding RNAs (lncRNAs) play a crucial role in diverse biological processes and have been confirmed to be concerned with various diseases. Largely uncharacterized of the physiological role and functions of lncRNA remains. MicroRNAs (miRNAs), which are usually 20-24 nucleotides, have several critical regulatory parts in cells. LncRNA can be regarded as a sponge to adsorb miRNA and indirectly regulate transcription and translation. Thus, the identification of lncRNA-miRNA associations is essential and valuable. RESULTS In our work, we present DWLMI to infer the potential associations between lncRNAs and miRNAs by representing them as vectors via a lncRNA-miRNA-disease-protein-drug graph. Specifically, DeepWalk can be used to learn the behavior representation of vertices. The methods of fingerprint, k-mer and MeSH descriptors were mainly used to learn the attribute representation of vertices. By combining the above two kinds of information, unknown lncRNA-miRNA associations can be predicted by the random forest classifier. Under the five-fold cross-validation, the proposed DWLMI model obtained an average prediction accuracy of 95.22% with a sensitivity of 94.35% at the AUC of 98.56%. CONCLUSIONS The experimental results demonstrated that DWLMI can effectively predict the potential lncRNA-miRNA associated pairs, and the results can provide a new insight for related non-coding RNA researchers in the field of combing biology big data with deep learning.
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Affiliation(s)
- Long Yang
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Li-Ping Li
- College of Grassland and Environmental Science, Xinjiang Agricultural University, Urumqi, 830052, China.
| | - Hai-Cheng Yi
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
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23
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Chen M, Deng Y, Li A, Tan Y. Inferring Latent Disease-lncRNA Associations by Label-Propagation Algorithm and Random Projection on a Heterogeneous Network. Front Genet 2022; 13:798632. [PMID: 35186029 PMCID: PMC8854791 DOI: 10.3389/fgene.2022.798632] [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: 12/07/2021] [Accepted: 01/18/2022] [Indexed: 11/13/2022] Open
Abstract
Long noncoding RNA (lncRNA), a type of more than 200 nucleotides non-coding RNA, is related to various complex diseases. To precisely identify the potential lncRNA–disease association is important to understand the disease pathogenesis, to develop new drugs, and to design individualized diagnosis and treatment methods for different human diseases. Compared with the complexity and high cost of biological experiments, computational methods can quickly and effectively predict potential lncRNA–disease associations. Thus, it is a promising avenue to develop computational methods for lncRNA-disease prediction. However, owing to the low prediction accuracy ofstate of the art methods, it is vastly challenging to accurately and effectively identify lncRNA-disease at present. This article proposed an integrated method called LPARP, which is based on label-propagation algorithm and random projection to address the issue. Specifically, the label-propagation algorithm is initially used to obtain the estimated scores of lncRNA–disease associations, and then random projections are used to accurately predict disease-related lncRNAs.The empirical experiments showed that LAPRP achieved good prediction on three golddatasets, which is superior to existing state-of-the-art prediction methods. It can also be used to predict isolated diseases and new lncRNAs. Case studies of bladder cancer, esophageal squamous-cell carcinoma, and colorectal cancer further prove the reliability of the method. The proposed LPARP algorithm can predict the potential lncRNA–disease interactions stably and effectively with fewer data. LPARP can be used as an effective and reliable tool for biomedical research.
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24
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Jiang H, Huang Y. An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network. BMC Bioinformatics 2022; 23:9. [PMID: 34983364 PMCID: PMC8726520 DOI: 10.1186/s12859-021-04553-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 12/29/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Drug-disease associations (DDAs) can provide important information for exploring the potential efficacy of drugs. However, up to now, there are still few DDAs verified by experiments. Previous evidence indicates that the combination of information would be conducive to the discovery of new DDAs. How to integrate different biological data sources and identify the most effective drugs for a certain disease based on drug-disease coupled mechanisms is still a challenging problem. RESULTS In this paper, we proposed a novel computation model for DDA predictions based on graph representation learning over multi-biomolecular network (GRLMN). More specifically, we firstly constructed a large-scale molecular association network (MAN) by integrating the associations among drugs, diseases, proteins, miRNAs, and lncRNAs. Then, a graph embedding model was used to learn vector representations for all drugs and diseases in MAN. Finally, the combined features were fed to a random forest (RF) model to predict new DDAs. The proposed model was evaluated on the SCMFDD-S data set using five-fold cross-validation. Experiment results showed that GRLMN model was very accurate with the area under the ROC curve (AUC) of 87.9%, which outperformed all previous works in terms of both accuracy and AUC in benchmark dataset. To further verify the high performance of GRLMN, we carried out two case studies for two common diseases. As a result, in the ranking of drugs that were predicted to be related to certain diseases (such as kidney disease and fever), 15 of the top 20 drugs have been experimentally confirmed. CONCLUSIONS The experimental results show that our model has good performance in the prediction of DDA. GRLMN is an effective prioritization tool for screening the reliable DDAs for follow-up studies concerning their participation in drug reposition.
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Affiliation(s)
- Hanjing Jiang
- Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, Institute of Artificial Intelligence, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yabing Huang
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, China.
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25
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26
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Gong Y, Zhu W, Sun M, Shi L. Bioinformatics Analysis of Long Non-coding RNA and Related Diseases: An Overview. Front Genet 2021; 12:813873. [PMID: 34956340 PMCID: PMC8692768 DOI: 10.3389/fgene.2021.813873] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 11/26/2021] [Indexed: 12/30/2022] Open
Abstract
Long non-coding RNAs (lncRNAs) are usually located in the nucleus and cytoplasm of cells. The transcripts of lncRNAs are >200 nucleotides in length and do not encode proteins. Compared with small RNAs, lncRNAs have longer sequences, more complex spatial structures, and more diverse and complex mechanisms involved in the regulation of gene expression. LncRNAs are widely involved in the biological processes of cells, and in the occurrence and development of many human diseases. Many studies have shown that lncRNAs can induce the occurrence of diseases, and some lncRNAs undergo specific changes in tumor cells. Research into the roles of lncRNAs has covered the diagnosis of, for example, cardiovascular, cerebrovascular, and central nervous system diseases. The bioinformatics of lncRNAs has gradually become a research hotspot and has led to the discovery of a large number of lncRNAs and associated biological functions, and lncRNA databases and recognition models have been developed. In this review, the research progress of lncRNAs is discussed, and lncRNA-related databases and the mechanisms and modes of action of lncRNAs are described. In addition, disease-related lncRNA methods and the relationships between lncRNAs and human lung adenocarcinoma, rectal cancer, colon cancer, heart disease, and diabetes are discussed. Finally, the significance and existing problems of lncRNA research are considered.
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Affiliation(s)
- Yuxin Gong
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China.,Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.,Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, China
| | - Wen Zhu
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Meili Sun
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Lei Shi
- Department of Spine Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
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27
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Bian W, Jiang XX, Wang Z, Zhu YR, Zhang H, Li X, Liu Z, Xiong J, Zhang DM. Comprehensive analysis of the ceRNA network in coronary artery disease. Sci Rep 2021; 11:24279. [PMID: 34930980 PMCID: PMC8688464 DOI: 10.1038/s41598-021-03688-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 12/08/2021] [Indexed: 12/14/2022] Open
Abstract
With the rapid aging of the population, coronary artery disease (CAD) has become one of the most fatal chronic diseases. However, the genetic mechanism of CAD is still unclear. The purpose of this study is to construct the lncRNA-miRNA-mRNA regulatory network for CAD diseases and systematically identify differentially expressed genes in patients with coronary heart disease. In this study, two lncRNA datasets (GSE69587 and GSE113079) and a microRNA dataset (GSE105449) which contained 393 and 38 CAD samples were selected. In addition, two mRNA datasets which named GSE113079 (98 CAD samples) and GSE9820 (8 CAD samples) were selected to search the differentially expressed genes (DEGs). By comparing the expression data between CAD and control samples, a total of 1111 lncRNAs, 2595 mRNAs and 22 miRNAs were identified. Based on the DEGs, a lncRNA-miRNA-mRNA ceRNA network was constructed to explore the hub nodes in CAD. In the ceRNA network, the lncRNAs KCNQ1OT1 and H19 showed high connectivity with the nine miRNAs. GO and KEGG results showed that genes in ceRNA networks were mainly involved in nitrogen compound metabolic process, PI3K-Akt signaling pathway and retrograde endocannabinoid signaling. These findings will improve the understanding of the occurrence and development mechanism of CAD.
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Affiliation(s)
- Weikang Bian
- Department of Cardiology, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing, 210006, Jiangsu, People's Republic of China
| | - Xiao-Xin Jiang
- Department of Cardiology, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing, 210006, Jiangsu, People's Republic of China
| | - Zhicheng Wang
- Department of Cardiology, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing, 210006, Jiangsu, People's Republic of China
| | - Yan-Rong Zhu
- Department of Cardiology, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing, 210006, Jiangsu, People's Republic of China
| | - Hongsong Zhang
- Department of Cardiology, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing, 210006, Jiangsu, People's Republic of China
| | - Xiaobo Li
- Department of Cardiology, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing, 210006, Jiangsu, People's Republic of China
| | - Zhizhong Liu
- Department of Cardiology, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing, 210006, Jiangsu, People's Republic of China
| | - Jing Xiong
- Department of Pharmacology, China Pharmaceutical University, Nanjing, 210009, Jiangsu, People's Republic of China
| | - Dai-Min Zhang
- Department of Cardiology, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing, 210006, Jiangsu, People's Republic of China.
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Zhang Y, Chen M, Huang L, Xie X, Li X, Jin H, Wang X, Wei H. Fusion of KATZ measure and space projection to fast probe potential lncRNA-disease associations in bipartite graphs. PLoS One 2021; 16:e0260329. [PMID: 34807960 PMCID: PMC8608294 DOI: 10.1371/journal.pone.0260329] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 11/06/2021] [Indexed: 11/19/2022] Open
Abstract
It is well known that numerous long noncoding RNAs (lncRNAs) closely relate to the physiological and pathological processes of human diseases and can serves as potential biomarkers. Therefore, lncRNA-disease associations that are identified by computational methods as the targeted candidates reduce the cost of biological experiments focusing on deep study furtherly. However, inaccurate construction of similarity networks and inadequate numbers of observed known lncRNA–disease associations, such inherent problems make many mature computational methods that have been developed for many years still exit some limitations. It motivates us to explore a new computational method that was fused with KATZ measure and space projection to fast probing potential lncRNA-disease associations (namely KATZSP). KATZSP is comprised of following key steps: combining all the global information with which to change Boolean network of known lncRNA–disease associations into the weighted networks; changing the similarities calculation into counting the number of walks that connect lncRNA nodes and disease nodes in bipartite graphs; obtaining the space projection scores to refine the primary prediction scores. The process to fuse KATZ measure and space projection was simplified and uncomplicated with needing only one attenuation factor. The leave-one-out cross validation (LOOCV) experimental results showed that, compared with other state-of-the-art methods (NCPLDA, LDAI-ISPS and IIRWR), KATZSP had a higher predictive accuracy shown with area-under-the-curve (AUC) value on the three datasets built, while KATZSP well worked on inferring potential associations related to new lncRNAs (or isolated diseases). The results from real cases study (such as pancreas cancer, lung cancer and colorectal cancer) further confirmed that KATZSP is capable of superior predictive ability to be applied as a guide for traditional biological experiments.
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Affiliation(s)
- Yi Zhang
- School of Information Science and Engineering, Guilin University of Technology, Guilin, China
- Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin, China
| | - Min Chen
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, China
| | - Li Huang
- Academy of Arts and Design, Tsinghua University, Beijing, China
- The Future Laboratory, Tsinghua University, Beijing, China
| | - Xiaolan Xie
- School of Information Science and Engineering, Guilin University of Technology, Guilin, China
| | - Xin Li
- School of Information Science and Engineering, Guilin University of Technology, Guilin, China
| | - Hong Jin
- School of Information Science and Engineering, Guilin University of Technology, Guilin, China
| | - Xiaohua Wang
- Pharmacy School, Guilin Medical University, Guilin, China
| | - Hanyan Wei
- Pharmacy School, Guilin Medical University, Guilin, China
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29
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Xuan P, Zhan L, Cui H, Zhang T, Nakaguchi T, Zhang W. Graph Triple-Attention Network for Disease-related LncRNA Prediction. IEEE J Biomed Health Inform 2021; 26:2839-2849. [PMID: 34813484 DOI: 10.1109/jbhi.2021.3130110] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Abnormal expressions of long non-coding RNAs (lncRNAs) are associated with various human diseases. Identifying disease-related lncRNAs can help clarify complex disease pathogeneses. The latest methods for lncRNA-disease association prediction rely on diverse data about lncRNAs and diseases. These methods, however, cannot adequately integrate the neighbour topological information of lncRNA and disease nodes. Moreover, more intrinsic features of lncRNA-disease node pairs can be explored to better predict the latent associations between lncRNAs and diseases. We developed a novel method, named GTAN, to predict the association propensities between lncRNAs and diseases. GTAN integrates various information about lncRNAs and diseases, including similarities, associations and interactions among lncRNAs, diseases and miRNAs, and exploits neighbour topology and attribute representations of a pair of lncRNA-disease nodes. We adopted in GTAN a graph neural network architecture with three attention mechanisms and multi-layer convolutional neural networks. First, a neighbour-level self-attention mechanism is constructed to learn the importance of each neighbour for an interested lncRNA or disease node. Second, topology-level attention is proposed to enhance contextual dependencies among multiple local topology representations of the lncRNA or disease node. An attention-enhanced graph neural network framework is then established to learn a topology representation of top-ranked neighbours for a pair of lncRNA-disease nodes. GTAN also has attribute-level attention to distinguish various contributions of attributes of the lncRNA-disease pair. Finally, attribute representation is learned by multi-layer CNN to integrate detailed features and representative features of the pair. Extensive experimental results demonstrated that GTAN outperformed state-of-the-art methods. The improved recall rates also showed GTANs capacity for retrieving more actual lncRNA-disease associations in the top-ranked candidates. The ablation studies confirmed the important contributions of three attention mechanisms. Case studies on lung cancer, prostate cancer and colon cancer further showed GTANs ability in discovering potential lncRNA candidates related to diseases.
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SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec. BMC Bioinformatics 2021; 22:538. [PMID: 34727886 PMCID: PMC8561941 DOI: 10.1186/s12859-021-04457-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 10/18/2021] [Indexed: 11/10/2022] Open
Abstract
Background Numerous studies on discovering the roles of long non-coding RNAs (lncRNAs) in the occurrence, development and prognosis progresses of various human diseases have drawn substantial attentions. Since only a tiny portion of lncRNA-disease associations have been properly annotated, an increasing number of computational methods have been proposed for predicting potential lncRNA-disease associations. However, traditional predicting models lack the ability to precisely extract features of biomolecules, it is urgent to find a model which can identify potential lncRNA-disease associations with both efficiency and accuracy. Results In this study, we proposed a novel model, SVDNVLDA, which gained the linear and non-linear features of lncRNAs and diseases with Singular Value Decomposition (SVD) and node2vec methods respectively. The integrated features were constructed from connecting the linear and non-linear features of each entity, which could effectively enhance the semantics contained in ultimate representations. And an XGBoost classifier was employed for identifying potential lncRNA-disease associations eventually. Conclusions We propose a novel model to predict lncRNA-disease associations. This model is expected to identify potential relationships between lncRNAs and diseases and further explore the disease mechanisms at the lncRNA molecular level. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04457-1.
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31
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Zeng M, Lu C, Fei Z, Wu FX, Li Y, Wang J, Li M. DMFLDA: A Deep Learning Framework for Predicting lncRNA-Disease Associations. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2353-2363. [PMID: 32248123 DOI: 10.1109/tcbb.2020.2983958] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
A growing amount of evidence suggests that long non-coding RNAs (lncRNAs) play important roles in the regulation of biological processes in many human diseases. However, the number of experimentally verified lncRNA-disease associations is very limited. Thus, various computational approaches are proposed to predict lncRNA-disease associations. Current matrix factorization-based methods cannot capture the complex non-linear relationship between lncRNAs and diseases, and traditional machine learning-based methods are not sufficiently powerful to learn the representation of lncRNAs and diseases. Considering these limitations in existing computational methods, we propose a deep matrix factorization model to predict lncRNA-disease associations (DMFLDA in short). DMFLDA uses a cascade of non-linear hidden layers to learn latent representation to represent lncRNAs and diseases. By using non-linear hidden layers, DMFLDA captures the more complex non-linear relationship between lncRNAs and diseases than traditional matrix factorization-based methods. In addition, DMFLDA learns features directly from the lncRNA-disease interaction matrix and thus can obtain more accurate representation learning for lncRNAs and diseases than traditional machine learning methods. The low dimensional representations of the lncRNAs and diseases are fused to estimate the new interaction value. To evaluate the performance of DMFLDA, we perform leave-one-out cross-validation and 5-fold cross-validation on known experimentally verified lncRNA-disease associations. The experimental results show that DMFLDA performs better than the existing methods. The case studies show that many predicted interactions of colorectal cancer, prostate cancer, and renal cancer have been verified by recent biomedical literature. The source code and datasets can be obtained from https://github.com/CSUBioGroup/DMFLDA.
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32
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Yi HC, You ZH, Guo ZH, Huang DS, Chan KCC. Learning Representation of Molecules in Association Network for Predicting Intermolecular Associations. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2546-2554. [PMID: 32070992 DOI: 10.1109/tcbb.2020.2973091] [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
A key aim of post-genomic biomedical research is to systematically understand molecules and their interactions in human cells. Multiple biomolecules coordinate to sustain life activities, and interactions between various biomolecules are interconnected. However, existing studies usually only focusing on associations between two or very limited types of molecules. In this study, we propose a network representation learning based computational framework MAN-SDNE to predict any intermolecular associations. More specifically, we constructed a large-scale molecular association network of multiple biomolecules in human by integrating associations among long non-coding RNA, microRNA, protein, drug, and disease, containing 6,528 molecular nodes, 9 kind of,105,546 associations. And then, the feature of each node is represented by its network proximity and attribute features. Furthermore, these features are used to train Random Forest classifier to predict intermolecular associations. MAN-SDNE achieves a remarkable performance with an AUC of 0.9552 and an AUPR of 0.9338 under five-fold cross-validation. To indicate the ability to predict specific types of interactions, a case study for predicting lncRNA-protein interactions using MAN-SDNE is also executed. Experimental results demonstrate this work offers a systematic insight for understanding the synergistic associations between molecules and complex diseases and provides a network-based computational tool to systematically explore intermolecular interactions.
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33
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Sajjadi RS, Modarressi MH, Akbarian F, Tabatabaiefar MA. A Computational Framework to Infer Prostate Cancer-Associated Long Noncoding RNAs and Analyses for Identifying a Competing Endogenous RNA Network. Genet Test Mol Biomarkers 2021; 25:582-589. [PMID: 34550779 DOI: 10.1089/gtmb.2021.0053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: Prostate cancer (PC) is the second leading cause of cancer death after lung cancer in men. Current biomarkers are ineffective for the treatment and management of the disease. Long noncoding RNAs (lncRNAs) are a heterogeneous group of transcripts that are involved in complex gene expression regulatory networks. Although lncRNAs have been suggested to be promising as future biomarkers, the connection between the majority of lncRNAs and human disease remains to be elucidated. One approach to elucidate the roles of lncRNAs in disease is through the development of computational models. For example, a novel computational model termed HyperGeometric distribution for LncRNA-Disease Association (HGLDA) has been developed. Such models need to be developed on a tumor-specific basis to better suit the particular problem. Methods: In this study, we constructed a potential pipeline through two models, HGLDA and pathway-based using data from several databases. To validate the obtained data, the expression levels of selected lncRNAs were investigated quantitatively in the DU-145, LNCaP, and PC3 PC cell lines using quantitative real-time PCR. Results: We obtained a number of lncRNAs from both models, many of which were filtered through several databases that ultimately resulted in identification of six high-value lncRNA targets. Their expression was correlated with one important component of the PI3K pathway, known to be related to PC. Conclusion: Through the assembly of a lncRNA-miRNAs-mRNA competing endogenous RNA network, we successfully predicted lncRNAs interfering with miRNAs and coding genes related to PC.
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Affiliation(s)
- Roshanak S Sajjadi
- Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | | | - Fahimeh Akbarian
- Department of Medical Genetics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Amin Tabatabaiefar
- Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.,Pediatric Inherited Diseases Research Center, Research Institute for Primordial Prevention of Noncommunicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
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34
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Yao Y, Ji B, Lv Y, Li L, Xiang J, Liao B, Gao W. Predicting LncRNA-Disease Association by a Random Walk With Restart on Multiplex and Heterogeneous Networks. Front Genet 2021; 12:712170. [PMID: 34490041 PMCID: PMC8417042 DOI: 10.3389/fgene.2021.712170] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 07/23/2021] [Indexed: 02/05/2023] Open
Abstract
Studies have found that long non-coding RNAs (lncRNAs) play important roles in many human biological processes, and it is critical to explore potential lncRNA–disease associations, especially cancer-associated lncRNAs. However, traditional biological experiments are costly and time-consuming, so it is of great significance to develop effective computational models. We developed a random walk algorithm with restart on multiplex and heterogeneous networks of lncRNAs and diseases to predict lncRNA–disease associations (MHRWRLDA). First, multiple disease similarity networks are constructed by using different approaches to calculate similarity scores between diseases, and multiple lncRNA similarity networks are also constructed by using different approaches to calculate similarity scores between lncRNAs. Then, a multiplex and heterogeneous network was constructed by integrating multiple disease similarity networks and multiple lncRNA similarity networks with the lncRNA–disease associations, and a random walk with restart on the multiplex and heterogeneous network was performed to predict lncRNA–disease associations. The results of Leave-One-Out cross-validation (LOOCV) showed that the value of Area under the curve (AUC) was 0.68736, which was improved compared with the classical algorithm in recent years. Finally, we confirmed a few novel predicted lncRNAs associated with specific diseases like colon cancer by literature mining. In summary, MHRWRLDA contributes to predict lncRNA–disease associations.
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Affiliation(s)
- Yuhua Yao
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China.,Key Laboratory of Data Science and Intelligence Education, Ministry of Education, Hainan Normal University, Haikou, China.,Key Laboratory of Computational Science and Application of Hainan Province, Hainan Normal University, Haikou, China
| | - Binbin Ji
- Geneis Beijing Co., Ltd., Beijing, China
| | - Yaping Lv
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Ling Li
- Basic Courses Department, Zhejiang Shuren University, Hangzhou, China
| | - Ju Xiang
- School of Computer Science and Engineering, Central South University, Changsha, China.,Department of Basic Medical Sciences, Changsha Medical University, Changsha, China.,Department of Computer Science, Changsha Medical University, Changsha, China
| | - Bo Liao
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Wei Gao
- Departments of Internal Medicine-Oncology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, China
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35
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Fan Y, Chen M, Pan X. GCRFLDA: scoring lncRNA-disease associations using graph convolution matrix completion with conditional random field. Brief Bioinform 2021; 23:6363052. [PMID: 34486019 DOI: 10.1093/bib/bbab361] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 07/19/2021] [Accepted: 08/16/2021] [Indexed: 12/12/2022] Open
Abstract
Long noncoding RNAs (lncRNAs) play important roles in various biological regulatory processes, and are closely related to the occurrence and development of diseases. Identifying lncRNA-disease associations is valuable for revealing the molecular mechanism of diseases and exploring treatment strategies. Thus, it is necessary to computationally predict lncRNA-disease associations as a complementary method for biological experiments. In this study, we proposed a novel prediction method GCRFLDA based on the graph convolutional matrix completion. GCRFLDA first constructed a graph using the available lncRNA-disease association information. Then, it constructed an encoder consisting of conditional random field and attention mechanism to learn efficient embeddings of nodes, and a decoder layer to score lncRNA-disease associations. In GCRFLDA, the Gaussian interaction profile kernels similarity and cosine similarity were fused as side information of lncRNA and disease nodes. Experimental results on four benchmark datasets show that GCRFLDA is superior to other existing methods. Moreover, we conducted case studies on four diseases and observed that 70 of 80 predicted associated lncRNAs were confirmed by the literature.
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Affiliation(s)
- Yongxian Fan
- School of Computer Science and Information Security, Guilin University of Electronic Technology
| | - Meijun Chen
- Guilin University of Electronic Technology, Guilin 541004, China
| | - Xiaoyong Pan
- Department of Automation of Shanghai Jiao Tong University
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36
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Sun Y, Zhao H, Zhou G, Guan T, Wang Y, Gao J. Random distributed logistic regression framework for predicting potential lncRNA‒disease association. J Mol Cell Biol 2021; 13:386-388. [PMID: 33493268 PMCID: PMC8373264 DOI: 10.1093/jmcb/mjab005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Affiliation(s)
- Yichen Sun
- School of Science, Jiangnan University, Wuxi 214122, China
| | - Hongqian Zhao
- School of Science, Jiangnan University, Wuxi 214122, China
| | - Gang Zhou
- School of Science, Jiangnan University, Wuxi 214122, China
| | - Tianhao Guan
- School of Science, Jiangnan University, Wuxi 214122, China
| | - Yujie Wang
- School of Science, Jiangnan University, Wuxi 214122, China
| | - Jie Gao
- School of Science, Jiangnan University, Wuxi 214122, China
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37
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Li J, Liu M, Li X, Shi H, Sun S. Long noncoding RNA ZFAS1 suppresses chondrocytes apoptosis via miR-302d-3p/SMAD2 in osteoarthritis. Biosci Biotechnol Biochem 2021; 85:842-850. [PMID: 33686420 DOI: 10.1093/bbb/zbab008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 12/15/2020] [Indexed: 12/17/2022]
Abstract
Osteoarthritis (OA) seriously affects people's quality of life due to joint pain, stiffness, disability, and dyskinesia worldwide. Long noncoding RNA zinc finger antisense 1 (ZFAS1) is downregulated and tightly associated with proliferation, migration, apoptosis, and matrix synthesis of chondrocyte in OA. However, the molecular mechanisms of ZFAS1 in OA remain unknown. The expression correlation between ZFAS1, miR-302d-3p, and SMAD2 in OA tissues was analyzed by Pearson correlation analysis. ZFAS1 was a lower expression, and expedited proliferation and repressed apoptosis of chondrocytes. MiR-302d-3p was a direct target of ZFAS1. MiR-302d-3p hindered proliferation and facilitated apoptosis of chondrocytes. MiR-302d-3p partially reversed the effect of ZFAS1 on proliferation and apoptosis of chondrocytes. SMAD2 was positively regulated by the ZFAS1/miR-302d-3p. MiR-302d-3p-mediated proliferation and apoptosis were partly abrogated by targeting SMAD2. ZFAS1 promoted chondrocytes proliferation and repressed apoptosis possibly by regulating miR-302d-3p/SMAD2 axis, providing a potential target for OA treatment.
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Affiliation(s)
- Jian Li
- Department of Joint Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Shandong, China.,Department of Joint Surgery, Binzhou Medical University Hospital, Shandong, China
| | - Mingting Liu
- Department of Joint Surgery, Binzhou Medical University Hospital, Shandong, China
| | - Xianrang Li
- Department of Joint Surgery, Binzhou Medical University Hospital, Shandong, China
| | - Hui Shi
- Department of Joint Surgery, Binzhou Medical University Hospital, Shandong, China
| | - Shui Sun
- Department of Joint Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Shandong, China
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38
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Zhang L, Yang P, Feng H, Zhao Q, Liu H. Using Network Distance Analysis to Predict lncRNA-miRNA Interactions. Interdiscip Sci 2021; 13:535-545. [PMID: 34232474 DOI: 10.1007/s12539-021-00458-z] [Citation(s) in RCA: 141] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 06/26/2021] [Accepted: 06/29/2021] [Indexed: 01/08/2023]
Abstract
LncRNA-miRNA interactions contribute to the regulation of therapeutic targets and diagnostic biomarkers in multifarious human diseases. However, it remains difficult to experimentally identify lncRNA-miRNA associations at large scale, and computational prediction methods are limited. In this study, we developed a network distance analysis model for lncRNA-miRNA association prediction (NDALMA). Similarity networks for lncRNAs and miRNAs were calculated and integrated with Gaussian interaction profile (GIP) kernel similarity. Then, network distance analysis was applied to the integrated similarity networks, and final scores were obtained after confidence calculation and score conversion. Our model obtained satisfactory results in fivefold cross validation, achieving an AUC of 0.8810 and an AUPR of 0.8315. Moreover, NDALMA showed superior prediction performance over several other network algorithms, and we tested the suitability and flexibility of the model by comparing different types of similarity. In addition, case studies of the relationships between lncRNAs and miRNAs were conducted, which verified the reliability of our method in predicting lncRNA-miRNA associations. The datasets and source code used in this study are available at https://github.com/Liu-Lab-Lnu/NDALMA .
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Affiliation(s)
- Li Zhang
- School of Life Science, Liaoning University, Shenyang, 110036, China
- Research Center for Computer Simulating and Information Processing of Bio-Macromolecules of Shenyang, Liaoning University, Shenyang, 110036, China
- Technology Innovation Center for Computer Simulating and Information Processing of Bio-Macromolecules of Shenyang, Shenyang, 110036, China
| | - Pengyu Yang
- School of Information, Liaoning University, Shenyang, 110036, China
| | - Huawei Feng
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
| | - Hongsheng Liu
- Research Center for Computer Simulating and Information Processing of Bio-Macromolecules of Shenyang, Liaoning University, Shenyang, 110036, China.
- Technology Innovation Center for Computer Simulating and Information Processing of Bio-Macromolecules of Shenyang, Shenyang, 110036, China.
- School of Pharmacy, Liaoning University, Shenyang, 110036, China.
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39
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Chowdhary A, Satagopam V, Schneider R. Long Non-coding RNAs: Mechanisms, Experimental, and Computational Approaches in Identification, Characterization, and Their Biomarker Potential in Cancer. Front Genet 2021; 12:649619. [PMID: 34276764 PMCID: PMC8281131 DOI: 10.3389/fgene.2021.649619] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 04/20/2021] [Indexed: 01/09/2023] Open
Abstract
Long non-coding RNAs are diverse class of non-coding RNA molecules >200 base pairs of length having various functions like gene regulation, dosage compensation, epigenetic regulation. Dysregulation and genomic variations of several lncRNAs have been implicated in several diseases. Their tissue and developmental specific expression are contributing factors for them to be viable indicators of physiological states of the cells. Here we present an comprehensive review the molecular mechanisms and functions, state of the art experimental and computational pipelines and challenges involved in the identification and functional annotation of lncRNAs and their prospects as biomarkers. We also illustrate the application of co-expression networks on the TCGA-LIHC dataset for putative functional predictions of lncRNAs having a therapeutic potential in Hepatocellular carcinoma (HCC).
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Affiliation(s)
- Anshika Chowdhary
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Venkata Satagopam
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
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40
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Li HY, Chen HY, Wang L, Song SJ, You ZH, Yan X, Yu JQ. A structural deep network embedding model for predicting associations between miRNA and disease based on molecular association network. Sci Rep 2021; 11:12640. [PMID: 34135401 PMCID: PMC8209151 DOI: 10.1038/s41598-021-91991-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 04/30/2021] [Indexed: 02/05/2023] Open
Abstract
Previous studies indicated that miRNA plays an important role in human biological processes especially in the field of diseases. However, constrained by biotechnology, only a small part of the miRNA-disease associations has been verified by biological experiment. This impel that more and more researchers pay attention to develop efficient and high-precision computational methods for predicting the potential miRNA-disease associations. Based on the assumption that molecules are related to each other in human physiological processes, we developed a novel structural deep network embedding model (SDNE-MDA) for predicting miRNA-disease association using molecular associations network. Specifically, the SDNE-MDA model first integrating miRNA attribute information by Chao Game Representation (CGR) algorithm and disease attribute information by disease semantic similarity. Secondly, we extract feature by structural deep network embedding from the heterogeneous molecular associations network. Then, a comprehensive feature descriptor is constructed by combining attribute information and behavior information. Finally, Convolutional Neural Network (CNN) is adopted to train and classify these feature descriptors. In the five-fold cross validation experiment, SDNE-MDA achieved AUC of 0.9447 with the prediction accuracy of 87.38% on the HMDD v3.0 dataset. To further verify the performance of SDNE-MDA, we contrasted it with different feature extraction models and classifier models. Moreover, the case studies with three important human diseases, including Breast Neoplasms, Kidney Neoplasms, Lymphoma were implemented by the proposed model. As a result, 47, 46 and 46 out of top-50 predicted disease-related miRNAs have been confirmed by independent databases. These results anticipate that SDNE-MDA would be a reliable computational tool for predicting potential miRNA-disease associations.
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Affiliation(s)
- Hao-Yuan Li
- grid.411510.00000 0000 9030 231XSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116 China
| | - Hai-Yan Chen
- Xinjiang Autonomous Region tax Service, State Taxation Administration, Urumqi, 830011 China
| | - Lei Wang
- grid.9227.e0000000119573309Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011 China
| | - Shen-Jian Song
- Science & Technology Department of Xinjiang Uygur Autonomous Region, Urumqi, 830011 China
| | - Zhu-Hong You
- grid.9227.e0000000119573309Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011 China
| | - Xin Yan
- grid.411510.00000 0000 9030 231XSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116 China
| | - Jin-Qian Yu
- grid.411510.00000 0000 9030 231XSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116 China
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41
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Wang L, Wang L, Wang Q. Constitutive activation of the NEAT1/miR-22-3p/Ltb4r1 signaling pathway in mice with myocardial injury following acute myocardial infarction. Aging (Albany NY) 2021; 13:15307-15319. [PMID: 34081624 PMCID: PMC8221362 DOI: 10.18632/aging.203089] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 12/03/2020] [Indexed: 01/01/2023]
Abstract
Coronary heart disease (CHD) with myocardial infarction (MI) being the manifestation of its advanced manifestation, remains the primary cause of mortality and disability worldwide. Aberrant expression of long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) can affect the occurrence of MI in CHD. The present study aimed to explore whether NEAT1 sponging with miR-22-3p affected MI in CHD and its related mechanism. We established that the NEAT1 and Ltb4r1 expressions were increased, while miR-22-3p expression was down-regulated in MI mice following CHD. NEAT1 could competitively bind to miR-22-3p and positively regulate Ltb4r1 expression. Ltb4r1 was the downstream target of miR-22-3p. Moreover, silencing NEAT1 or downregulating Ltb4rl expression resulted in improved cardiac function, reduced infarct size, and declined levels of IL-1β, IL-6, and IL-18. Furthermore, silencing of NEAT1 also inhibited apoptosis by decreasing levels of Cleaved caspase-3 and Bax, and increasing Bcl-2 level through sponging miR-22-3p, resulting in reduced myocardial injury in CHD. Altogether, the activation of the NEAT1/miR-22-3p/Ltb4r1 signaling pathway appears to aggravate myocardial injury following a MI, which suggested that this signaling may be a useful target for improved and more individualized treatments for MI.
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Affiliation(s)
- Lijie Wang
- Department of Cardiology, The Fourth Affiliated Hospital of China Medical University, Shenyang 110032, P.R. China
| | - Lu Wang
- Department of Ultrasound, The Fourth Affiliated Hospital of China Medical University, Shenyang 110032, P.R. China
| | - Qi Wang
- Department of Cardiology, The Fourth Affiliated Hospital of China Medical University, Shenyang 110032, P.R. China
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Long Noncoding RNA LINC01518 Modulates Proliferation and Migration in TGF-β1-Treated Human Tenon Capsule Fibroblast Cells Through the Regulation of hsa-miR-216b-5p. Neuromolecular Med 2021; 24:88-96. [PMID: 33993456 DOI: 10.1007/s12017-021-08662-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 04/15/2021] [Indexed: 01/18/2023]
Abstract
In this study, we investigated the expression and functions of long noncoding RNAs (LncRNAs) of LINC01518 in an in vitro model of TGF-β1-treated human Tenon capsule fibroblast (HTF) cells. qRT-PCR was used to examine LINC01518 expression in in situ human glaucoma tissues, and in vitro HTF cells treated with TGF-β1. Lentivirus-mediated LINC01518 knockdown was performed in HTF cells to investigate its effect on TGF-β1-induced cell proliferation, migration and autophagy signaling pathway. The potential ceRNA candidate of LINC01518, hsa-miR-216b-5p, was probed by dual-luciferase assay and qRT-PCR. Hsa-miR-216b-5p was also knocked down in LINC01518-downregulated HTF cells to investigate the function of this lncRNA-miRNA epigenetic axis in TGF-β1-treated HTF cells. LINC01518 was upregulated in human glaucoma tissues and cultured HTF cells. LINC01518 downregulation significantly suppressed TGF-β1-induced cell proliferation, migration and autophagy signaling pathway in HTF cells. Hsa-miR-216b-5p was confirmed to be a ceRNA target of LINC01518. Knocking down hsa-miR-216b-5p reversed the suppressing effects of LINC01518 downregulation in TGF-β1-treated HTF cells. Our study demonstrated that LINC01518 is a functional factor in regulating proliferation and migration in TGF-β1-treated HTF cells, and hsa-miR-216b -5p may also be involved. Targeting the epigenetic axis of LINC01518/hsa-miR-216b-5p may provide new insight into the pathological development of human glaucoma.
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Fan Y, Zhang J, Zhuang X, Geng F, Jiang G, Yang X. Epigenetic transcripts of LINC01311 and hsa-miR-146a-5p regulate neural development in a cellular model of Alzheimer's disease. IUBMB Life 2021; 73:916-926. [PMID: 33830627 DOI: 10.1002/iub.2472] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/17/2021] [Accepted: 02/12/2021] [Indexed: 01/13/2023]
Abstract
Emerging evidence has shown that Long noncoding RNAs (LncRNAs) are aberrantly expressed and functionally involved in the development of neurodegenerative disorders. In this work, we investigated the regulatory effects of lncRNA of LINC01311 and its competing endogenous RNA target of hsa-miR-146a-5p in a cellular model of Alzheimer's disease (AD). SH-SY5Y cells were treated with synthetic Βeta-Amyloid Peptide (1-42) (AB1-42) in vitro to induce AD-like neural injuries. Expressions of LINC01311 and hsa-miR-146a-5p were monitored by qRT-PCR. LINC01311 was upregulated and hsa-miR-146a-5p downregulated to examine their functional regulations on AB1-42-induced apoptosis, proliferation slowdown, autophagy, and amyloid precursor protein (APP) accumulations. Hsa-miR-146a-5p was also overexpressed in LINC01311-upregulated SH-SY5Y cells to examine their correlated regulations on AB1-42-induced neural injuries. LINC01311 was downregulated whereas hsa-miR-146a-5p upregulated in AB1-42 treated SH-SY5Y cells. LINC01311 upregulation and hsa-miR-146a-5p downregulation protected AB1-42-induced apoptosis, proliferation slowdown, autophagy, and APP accumulations in SH-SY5Y cells. Hsa-miR-146a-5p overexpression reversed the protection of LINC01311 on AB1-42-induced neural injuries. Our work demonstrated that the epigenetic axis of LINC01311/hsa-miR-146a-5p was involved in the functional regulation of human-lineage neurons in a cellular model of AD, thus suggesting a clinical potential of exploring epigenetic network for treating AD patients.
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Affiliation(s)
- Yan Fan
- Department of Neurology, Liaocheng People's Hospital, Liaocheng City, Shandong Province, China
| | - Jingjing Zhang
- Department of Neurology, Liaocheng People's Hospital, Liaocheng City, Shandong Province, China
| | - Xianbo Zhuang
- Department of Neurology, Liaocheng People's Hospital, Liaocheng City, Shandong Province, China
| | - Fengyang Geng
- Department of Neurology, Liaocheng People's Hospital, Liaocheng City, Shandong Province, China
| | - Guisheng Jiang
- Department of Neurology, Liaocheng People's Hospital, Liaocheng City, Shandong Province, China
| | - Xiafeng Yang
- Department of Neurology, Liaocheng People's Hospital, Liaocheng City, Shandong Province, China
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Wang M, Liu Y, Zhang Y, Zhang L. LncRNA LOC729178 acts as a sponge of miR-144-3p to mitigate cigarette smoke extract-induced inflammatory injury via regulating PHLPP2 in 16HBE cells. J Mol Histol 2021; 52:437-447. [PMID: 33847879 DOI: 10.1007/s10735-021-09972-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 03/30/2021] [Indexed: 12/19/2022]
Abstract
Chronic obstructive pulmonary disease (COPD) is an inflammatory respiratory disease. Long non-coding RNAs (lncRNAs) have been implicated in the pathogenesis of COPD. In the present study, we set to investigate the role and mechanism of LOC729178 on cigarette smoke extract (CSE)-induced inflammatory damage in 16HBE cells. The expression levels of LOC729178, miR-144-3p, and PH domain leucine-rich repeat protein phosphatase 2 (PHLPP2) were detected by quantitative real-time polymerase chain reaction (qRT-PCR) and western blot. Cell viability and apoptosis were assessed by Cell Counting Kit-8 (CCK-8) and flow cytometry, respectively. Enzyme-linked immunosorbent assay (ELISA) assay was performed to evaluate the levels of interleukin-1β (IL-1β), IL-6, tumor necrosis factor-alpha (TNF-α), and IL-8. Targeted relationships among LOC729178, miR-144-3p, and PHLPP2 were verified by dual-luciferase reporter and RNA immunoprecipitation (RIP) assays. Our data indicated that LOC729178 was underexpressed in COPD tissues and CSE-treated 16HBE cells. Exogenous expression of LOC729178 alleviated CSE-induced inflammatory injury in 16HBE cells. LOC729178 targeted miR-144-3p by directly binding to miR-144-3p. miR-144-3p was a downstream effector of LOC729178 function. PHLPP2 was identified as a direct and functional target of miR-144-3p. Furthermore, LOC729178 operated as a post-transcriptional regulator of PHLPP2 expression through miR-144-3p. Our current study suggested that LOC729178 overexpression alleviated CSE-induced inflammatory injury in 16HBE cells at least in part by up-regulating PHLPP2 via sponging miR-144-3p, providing a rationale for developing LOC729178 as a potential therapeutic agent against COPD.
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Affiliation(s)
- Meihong Wang
- Department of Critical Care Medicine, First People's Hospital of Jining City, Jining City, Shandong Province, China
- First People's Hospital of Jining City Affiliated to Jining Medical College, Jining City, Shandong Province, China
| | - Yufang Liu
- Department of Critical Care Medicine, Dongying District People's Hospital, Dongying City, Shandong Province, China
| | - Yufen Zhang
- Department of Intensive Care Unit, Liaocheng Third People's Hospital, Liaocheng City, Shandong Province, China
| | - Luchang Zhang
- First People's Hospital of Jining City Affiliated to Jining Medical College, Jining City, Shandong Province, China.
- Department of Thoracic Surgery, First People's Hospital of Jining City, First People's Hospital of Jining City affiliated to Jining Medical College, No. 6, Health Road, Rencheng District, Jining City, 272011, Shandong Province, China.
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Zhu R, Wang Y, Liu JX, Dai LY. IPCARF: improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier. BMC Bioinformatics 2021; 22:175. [PMID: 33794766 PMCID: PMC8017839 DOI: 10.1186/s12859-021-04104-9] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 03/24/2021] [Indexed: 12/23/2022] Open
Abstract
Background Identifying lncRNA-disease associations not only helps to better comprehend the underlying mechanisms of various human diseases at the lncRNA level but also speeds up the identification of potential biomarkers for disease diagnoses, treatments, prognoses, and drug response predictions. However, as the amount of archived biological data continues to grow, it has become increasingly difficult to detect potential human lncRNA-disease associations from these enormous biological datasets using traditional biological experimental methods. Consequently, developing new and effective computational methods to predict potential human lncRNA diseases is essential. Results Using a combination of incremental principal component analysis (IPCA) and random forest (RF) algorithms and by integrating multiple similarity matrices, we propose a new algorithm (IPCARF) based on integrated machine learning technology for predicting lncRNA-disease associations. First, we used two different models to compute a semantic similarity matrix of diseases from a directed acyclic graph of diseases. Second, a characteristic vector for each lncRNA-disease pair is obtained by integrating disease similarity, lncRNA similarity, and Gaussian nuclear similarity. Then, the best feature subspace is obtained by applying IPCA to decrease the dimension of the original feature set. Finally, we train an RF model to predict potential lncRNA-disease associations. The experimental results show that the IPCARF algorithm effectively improves the AUC metric when predicting potential lncRNA-disease associations. Before the parameter optimization procedure, the AUC value predicted by the IPCARF algorithm under 10-fold cross-validation reached 0.8529; after selecting the optimal parameters using the grid search algorithm, the predicted AUC of the IPCARF algorithm reached 0.8611. Conclusions We compared IPCARF with the existing LRLSLDA, LRLSLDA-LNCSIM, TPGLDA, NPCMF, and ncPred prediction methods, which have shown excellent performance in predicting lncRNA-disease associations. The compared results of 10-fold cross-validation procedures show that the predictions of the IPCARF method are better than those of the other compared methods. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04104-9.
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Affiliation(s)
- Rong Zhu
- School of Computer Science, Qufu Normal University, Rizhao, China.,Department of Internet of Things Engineering, Wuxi Taihu University, Wuxi, China
| | - Yong Wang
- Experimental Teaching Center, Qufu Normal University, Rizhao, China
| | - Jin-Xing Liu
- School of Computer Science, Qufu Normal University, Rizhao, China
| | - Ling-Yun Dai
- School of Computer Science, Qufu Normal University, Rizhao, China.
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Wang Y, Li Y, Ma C, Zhou T, Lu C, Ding L, Li L. LncRNA XIST Promoted OGD-Induced Neuronal Injury Through Modulating/miR-455-3p/TIPARP Axis. Neurochem Res 2021; 46:1447-1456. [PMID: 33738662 DOI: 10.1007/s11064-021-03286-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 02/04/2021] [Accepted: 02/23/2021] [Indexed: 12/09/2022]
Abstract
In recent years, the incidence of ischemic stroke has gradually increased, but its pathogenesis has not been fully elucidated. lncRNAs played an important role in the occurrence and regulation of disease, but the research on ischemic stroke is very limited. Therefore, the role of lncRNA in ischemic stroke needs further exploration. The mice model was built to obtain OGD-induced neuronal cells for the following experiments. The protein expression of TCDD inducible poly [ADP-ribose] polymerase (TIPARP), B-cell lymphoma-2 (Bcl-2) and Cleaved Caspase-3 (Cleaved-cas3) were detected with western blot. qRT-PCR was used to analyze expression of XIST, miR-455-3p and TIPARP. CCK-8 assay indicated the capacity of cell proliferation. Flow cytometry was applied to assess cell apoptosis rate. Moreover, dual-luciferase reporter assay and RIP assay were used to determine that the relationship among XIST, miR-455-3p and TIPARP. In this study, we found that oxygen-glucose deprivation (OGD) induced XIST expression, inhibited miR-455-3p expression and promoted TIPARP mRNA and protein expression in neurons. Furthermore, XIST could affect cell growth of OGD-induced neuronal cells. Further analysis showed that XIST could regulate TIPARP by binding to miR-455-3p, and overexpression of miR-455-3p or inhibition of TIPARP could reverse the effects of high XIST expression on OGD-induced neuronal cells. On the contrary, suppression of miR-455-3p or promotion of TIPARP could reverse the effects of low XIST expression on OGD-induced neuronal cells. XIST could affect cell proliferation and apoptosis through miR-455-3p/TIPARP axis in OGD-induced neuronal cells, providing a new regulatory network to understand the pathogenesis of hypoxia-induced neuronal injury.
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Affiliation(s)
- Ying Wang
- Department of Rehabilitation Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, 26 Shengli Street, Wuhan, 430014, Hubei, China
| | - Yunfei Li
- Department of Rehabilitation Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, 26 Shengli Street, Wuhan, 430014, Hubei, China
| | - Chaoyang Ma
- Department of Rehabilitation Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, 26 Shengli Street, Wuhan, 430014, Hubei, China
| | - Ting Zhou
- Department of Rehabilitation Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, 26 Shengli Street, Wuhan, 430014, Hubei, China
| | - Chi Lu
- Department of Oncology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Lin Ding
- Department of Rehabilitation Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, 26 Shengli Street, Wuhan, 430014, Hubei, China
| | - Lei Li
- Department of Rehabilitation Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, 26 Shengli Street, Wuhan, 430014, Hubei, China.
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The potency of lncRNA MALAT1/miR-155/CTLA4 axis in altering Th1/Th2 balance of asthma. Biosci Rep 2021; 40:221794. [PMID: 31909418 PMCID: PMC7024843 DOI: 10.1042/bsr20190397] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 11/14/2019] [Accepted: 11/15/2019] [Indexed: 12/14/2022] Open
Abstract
Objectives: The present study examined if the metastasis-associated lung adenocarcinoma transcript 1 (MALAT1)/miR-155/CTLA-4 axis was involved in modifying Th1/Th2 balance, a critical indicator for asthma progression. Methods: Altogether 772 asthma patients and 441 healthy controls were recruited, and their blood samples were collected to determine expressional levels of MALAT1, miR-155, CTLA-4, T-bet, GATA3, Th1-type cytokines and Th2-type cytokines. The CD4+ T cells were administered with pcDNA3.1-MALAT1, si-MALAT1, miR-155 mimic and miR-155 inhibitor to assess their effects on cytokine release. The luciferase reporter gene assay was also adopted to evaluate the sponging relationships between MALAT1 and miR-155, as well as between miR-155 and CTLA-4. Results: Over-expressed MALAT1 and under-expressed miR-155 were more frequently detected among asthma patients who showed traits of reduced forced expiratory failure volume in 1 s (FEV1), FEV1/forced vital capacity (FVC) and FEV1% of predicted (P<0.05). Moreover, MALAT1 expression was negatively expressed with the Th1/Th2 and T-bet/GATA3 ratios, yet miR-155 expression displayed a positively correlation with the ratios (P<0.05). Additionally, the IFN-γ, IL-2 and T-bet levels were reduced under the influence of pcDNA3.1-MALAT1 and miR-155 inhibitor, while levels of IL-4, IL-10 and GATA3 were raised under identical settings (P<0.05). Furthermore, MALAT1 constrained expression of miR-155 within CD4+ T cells by sponging it, and CTLA-4 could interfere with the effects of MALAT1 and miR-155 on Th1/Th2 balance and T-bet/Gata3 ratio (P<0.05). Conclusion: MALAT1 sponging miR-155 was involved with regulation of Th1/Th2 balance within CD4+ T cells, which might aid to develop therapies for amelioration of asthmatic inflammation.
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Jung J, Lee YJ, Kim CH, Ahn S. Landscape of epigenetically regulated lncRNAs and DNA methylation in smokers with lung adenocarcinoma. PLoS One 2021; 16:e0247928. [PMID: 33684161 PMCID: PMC7939300 DOI: 10.1371/journal.pone.0247928] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 02/16/2021] [Indexed: 12/31/2022] Open
Abstract
In this study, we identified long non-coding RNAs (lncRNAs) associated with DNA methylation in lung adenocarcinoma (LUAD) using clinical and methylation/expression data from 184 qualified LUAD tissue samples and 21 normal lung-tissue samples from The Cancer Genome Atlas (TCGA). We identified 1865 differentially expressed genes that correlated negatively with the methylation profiles of normal lung tissues, never-smoker LUAD tissues and smoker LUAD tissues, while 1079 differentially expressed lncRNAs were identified using the same criteria. These transcripts were integrated using ingenuity pathway analysis to determine significant pathways directly related to cancer, suggesting that lncRNAs play a crucial role in carcinogenesis. When comparing normal lung tissues and smoker LUAD tissues, 86 candidate genes were identified, including six lncRNAs. Of the 43 candidate genes revealed by comparing never-smoker LUAD tissues and smoker LUAD tissues, 13 were also different when compared to normal lung tissues. We then investigated the expression of these genes using the Gene Expression of Normal and Tumor Tissues (GENT) and Methylation and Expression Database of Normal and Tumor Tissues (MENT) databases. We observed an inverse correlation between the expression of 13 genes in normal lung tissues and smoker LUAD tissues, and the expression of five genes between the never-smoker and smoker LUAD tissues. These findings were further validated in clinical specimens using bisulfite sequencing, revealing that AGR2, AURKB, FOXP3, and HMGA1 displayed borderline differences in methylation. Finally, we explored the functional connections between DNA methylation, lncRNAs, and gene expression to identify possible targets that may contribute toward the pathogenesis of cigarette smoking-associated LUAD. Together, our findings suggested that differentially expressed lncRNAs and their target transcripts could serve as potential biomarkers for LUAD.
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Affiliation(s)
- Jiyoon Jung
- Department of Pathology, International St. Mary’s Hospital, Catholic Kwandong University College of Medicine, Incheon, Republic of Korea
| | - Yoo Jin Lee
- Department of Pathology, Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Chul Hwan Kim
- Department of Pathology, Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
- * E-mail: (CHK); (SA)
| | - Sangjeong Ahn
- Department of Pathology, International St. Mary’s Hospital, Catholic Kwandong University College of Medicine, Incheon, Republic of Korea
- * E-mail: (CHK); (SA)
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Zhou JR, You ZH, Cheng L, Ji BY. Prediction of lncRNA-disease associations via an embedding learning HOPE in heterogeneous information networks. MOLECULAR THERAPY. NUCLEIC ACIDS 2021; 23:277-285. [PMID: 33425486 PMCID: PMC7773765 DOI: 10.1016/j.omtn.2020.10.040] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Accepted: 10/28/2020] [Indexed: 12/30/2022]
Abstract
Uncovering additional long non-coding RNA (lncRNA)-disease associations has become increasingly important for developing treatments for complex human diseases. Identification of lncRNA biomarkers and lncRNA-disease associations is central to diagnoses and treatment. However, traditional experimental methods are expensive and time-consuming. Enormous amounts of data present in public biological databases are available for computational methods used to predict lncRNA-disease associations. In this study, we propose a novel computational method to predict lncRNA-disease associations. More specifically, a heterogeneous network is first constructed by integrating the associations among microRNA (miRNA), lncRNA, protein, drug, and disease, Second, high-order proximity preserved embedding (HOPE) was used to embed nodes into a network. Finally, the rotation forest classifier was adopted to train the prediction model. In the 5-fold cross-validation experiment, the area under the curve (AUC) of our method achieved 0.8328 ± 0.0236. We compare it with the other four classifiers, in which the proposed method remarkably outperformed other comparison methods. Otherwise, we constructed three case studies for three excess death rate cancers, respectively. The results show that 9 (lung cancer, gastric cancer, and hepatocellular carcinomas) out of the top 15 predicted disease-related lncRNAs were confirmed by our method. In conclusion, our method could predict the unknown lncRNA-disease associations effectively.
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Affiliation(s)
- Ji-Ren Zhou
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
| | - Zhu-Hong You
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
| | - Li Cheng
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
| | - Bo-Ya Ji
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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50
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Xie G, Huang B, Sun Y, Wu C, Han Y. RWSF-BLP: a novel lncRNA-disease association prediction model using random walk-based multi-similarity fusion and bidirectional label propagation. Mol Genet Genomics 2021; 296:473-483. [PMID: 33590345 DOI: 10.1007/s00438-021-01764-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Accepted: 01/28/2021] [Indexed: 12/13/2022]
Abstract
An increasing number of studies and experiments have demonstrated that long noncoding RNAs (lncRNAs) have a massive impact on various biological processes. Predicting potential associations between lncRNAs and diseases not only can improve our understanding of the molecular mechanisms of human diseases but also can facilitate the identification of biomarkers for disease diagnosis, treatment, and prevention. However, identifying such associations through experiments is costly and demanding, thereby prompting researchers to develop computational methods to complement these experiments. In this paper, we constructed a novel model called RWSF-BLP (a novel lncRNA-disease association prediction model using Random Walk-based multi-Similarity Fusion and Bidirectional Label Propagation), which applies an efficient random walk-based multi-similarity fusion (RWSF) method to fuse different similarity matrices and utilizes bidirectional label propagation to predict potential lncRNA-disease associations. Leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold-CV) were implemented in the evaluation RWSF-BLP performance. Results showed that, RWSF-BLP has reliable AUCs of 0.9086 and 0.9115 ± 0.0044 under the framework of LOOCV and 5-fold-CV and outperformed other four canonical methods. Case studies on lung cancer and leukemia demonstrated that potential lncRNA-disease associations can be predicted through our method. Therefore, our method can accurately infer potential lncRNA-disease associations and may be a good choice in future biomedical research.
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Affiliation(s)
- Guobo Xie
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Bin Huang
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Yuping Sun
- School of Computer Science, Guangdong University of Technology, Guangzhou, China.
| | - Changhai Wu
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Yuqiong Han
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
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