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Guo Y, Yi M. THGNCDA: circRNA-disease association prediction based on triple heterogeneous graph network. Brief Funct Genomics 2024; 23:384-394. [PMID: 37738503 DOI: 10.1093/bfgp/elad042] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 08/21/2023] [Accepted: 09/04/2023] [Indexed: 09/24/2023] Open
Abstract
Circular RNAs (circRNAs) are a class of noncoding RNA molecules featuring a closed circular structure. They have been proved to play a significant role in the reduction of many diseases. Besides, many researches in clinical diagnosis and treatment of disease have revealed that circRNA can be considered as a potential biomarker. Therefore, understanding the association of circRNA and diseases can help to forecast some disorders of life activities. However, traditional biological experimental methods are time-consuming. The most common method for circRNA-disease association prediction on the basis of machine learning can avoid this, which relies on diverse data. Nevertheless, topological information of circRNA and disease usually is not involved in these methods. Moreover, circRNAs can be associated with diseases through miRNAs. With these considerations, we proposed a novel method, named THGNCDA, to predict the association between circRNAs and diseases. Specifically, for a certain pair of circRNA and disease, we employ a graph neural network with attention to learn the importance of its each neighbor. In addition, we use a multilayer convolutional neural network to explore the relationship of a circRNA-disease pair based on their attributes. When calculating embeddings, we introduce the information of miRNAs. The results of experiments show that THGNCDA outperformed the SOTA methods. In addition, it can be observed that our method gives a better recall rate. To confirm the significance of attention, we conducted extensive ablation studies. Case studies on Urinary Bladder and Prostatic Neoplasms further show THGNCDA's ability in discovering known relationships between circRNA candidates and diseases.
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Affiliation(s)
- Yuwei Guo
- School of Mathematics and Physics, China University of Geosciences, 388 Lumo Road, Hongshan District, 430074, Wuhan, Hubei, China
| | - Ming Yi
- School of Mathematics and Physics, China University of Geosciences, 388 Lumo Road, Hongshan District, 430074, Wuhan, Hubei, China
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2
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Chaudhary U, Banerjee S. Decoding the Non-coding: Tools and Databases Unveiling the Hidden World of "Junk" RNAs for Innovative Therapeutic Exploration. ACS Pharmacol Transl Sci 2024; 7:1901-1915. [PMID: 39022352 PMCID: PMC11249652 DOI: 10.1021/acsptsci.3c00388] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 05/15/2024] [Accepted: 05/27/2024] [Indexed: 07/20/2024]
Abstract
Non-coding RNAs are pivotal regulators of gene and protein expression, exerting crucial influences on diverse biological processes. Their dysregulation is frequently implicated in the onset and progression of diseases, notably cancer. A profound comprehension of the intricate mechanisms governing ncRNAs is imperative for devising innovative therapeutic interventions against these debilitating conditions. Significantly, nearly 80% of our genome comprises ncRNAs, underscoring their centrality in cellular processes. The elucidation of ncRNA functions is pivotal for grasping the complexities of gene regulation and its implications for human health. Modern genome sequencing techniques yield vast datasets, stored in specialized databases. To harness this wealth of information and to understand the crosstalk of non-coding RNAs, knowledge of available databases is required, and many new sophisticated computational tools have emerged. These tools play a pivotal role in the identification, prediction, and annotation of ncRNAs, thereby facilitating their experimental validation. This Review succinctly outlines the current understanding of ncRNAs, emphasizing their involvement in disease development. It also highlights the databases and tools instrumental in classifying, annotating, and evaluating ncRNAs. By extracting meaningful biological insights from seemingly "junk" data, these tools empower scientists to unravel the intricate roles of ncRNAs in shaping human health.
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Affiliation(s)
- Uma Chaudhary
- Department of Biotechnology,
School of Biosciences and Technology, Vellore
Institute of Technology (VIT), Vellore, Tamil Nadu 632014, India
| | - Satarupa Banerjee
- Department of Biotechnology,
School of Biosciences and Technology, Vellore
Institute of Technology (VIT), Vellore, Tamil Nadu 632014, India
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3
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Li S, Wang J, Ren G. CircRNA: An emerging star in plant research: A review. Int J Biol Macromol 2024; 272:132800. [PMID: 38825271 DOI: 10.1016/j.ijbiomac.2024.132800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 05/27/2024] [Accepted: 05/30/2024] [Indexed: 06/04/2024]
Abstract
CircRNAs are a class of covalently closed non-coding RNA formed by linking the 5' terminus and the 3' terminus after reverse splicing. CircRNAs are widely found in eukaryotes, and they are highly conserved, with spatio-temporal expression specificity and stability. CircRNAs can act as miRNA sponges to regulate the expression of downstream target genes, regulating the transcription of parental genes and some can even be translated into peptides or proteins. Research on circRNAs in plants is still in its infancy compared to that in animals. With the deepening of research, the results of a variety of plant circRNAs suggest that they play an important role in growth and development, and tolerance towards abiotic stresses such as salt, drought, low temperature, high temperature and other adverse environments. In this review paper, we elaborated the molecular characteristics, mechanism of action, function and bioinformatics databases of plant circRNAs, combined with the progress of circRNA research in animals, discussed the potential mechanism of action of plant circRNAs, and proposed the unsolved problems and prospects for future application of plant circRNAs.
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Affiliation(s)
- Simin Li
- Shandong Provincial Key Laboratory of Plant Stress, College of Life Sciences, Shandong Normal University, Jinan 250014, China
| | - Jingyi Wang
- Shandong Provincial Key Laboratory of Plant Stress, College of Life Sciences, Shandong Normal University, Jinan 250014, China
| | - Guocheng Ren
- Shandong Provincial Key Laboratory of Plant Stress, College of Life Sciences, Shandong Normal University, Jinan 250014, China; Dongying Institute, Shandong Normal University, Dongying 257000, China.
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4
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Wang Y, Lu P. GEHGAN: CircRNA-disease association prediction via graph embedding and heterogeneous graph attention network. Comput Biol Chem 2024; 110:108079. [PMID: 38704917 DOI: 10.1016/j.compbiolchem.2024.108079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 04/03/2024] [Accepted: 04/16/2024] [Indexed: 05/07/2024]
Abstract
There is growing proof suggested that circRNAs play a crucial function in diverse important biological reactions related to human diseases. Within the area of biochemistry, a massive range of wet experiments have been carried out to find out the connections of circRNA-disease in recent years. Since wet experiments are expensive and laborious, nowadays, calculation-based solutions have increasingly attracted the attention of researchers. However, the performance of these methods is restricted due to the inability to balance the distribution among various types of nodes. To remedy the problem, we present a novel computational method called GEHGAN to forecast the new relationships in this research, leveraging graph embedding and heterogeneous graph attention networks. Firstly, we calculate circRNA sequences similarity, circRNA RBP similarity, disease semantic similarity and corresponding GIP kernel similarity to construct heterogeneous graph. Secondly, a graph embedding method using random walks with jump and stay strategies is applied to obtain the preliminary embeddings of circRNAs and diseases, greatly improving the performance of the model. Thirdly, a multi-head graph attention network is employed to further update the embeddings, followed by the employment of the MLP as a predictor. As a result, the five-fold cross-validation indicates that GEHGAN achieves an outstanding AUC score of 0.9829 and an AUPR value of 0.9815 on the CircR2Diseasev2.0 database, and case studies on osteosarcoma, gastric and colorectal neoplasms further confirm the model's efficacy at identifying circRNA-disease correlations.
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Affiliation(s)
- Yuehao Wang
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China.
| | - Pengli Lu
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China.
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5
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Digby B, Finn S, Ó Broin P. Computational approaches and challenges in the analysis of circRNA data. BMC Genomics 2024; 25:527. [PMID: 38807085 PMCID: PMC11134749 DOI: 10.1186/s12864-024-10420-0] [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: 02/13/2024] [Accepted: 05/15/2024] [Indexed: 05/30/2024] Open
Abstract
Circular RNAs (circRNA) are a class of non-coding RNA, forming a single-stranded covalently closed loop structure generated via back-splicing. Advancements in sequencing methods and technologies in conjunction with algorithmic developments of bioinformatics tools have enabled researchers to characterise the origin and function of circRNAs, with practical applications as a biomarker of diseases becoming increasingly relevant. Computational methods developed for circRNA analysis are predicated on detecting the chimeric back-splice junction of circRNAs whilst mitigating false-positive sequencing artefacts. In this review, we discuss in detail the computational strategies developed for circRNA identification, highlighting a selection of tool strengths, weaknesses and assumptions. In addition to circRNA identification tools, we describe methods for characterising the role of circRNAs within the competing endogenous RNA (ceRNA) network, their interactions with RNA-binding proteins, and publicly available databases for rich circRNA annotation.
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Affiliation(s)
- Barry Digby
- School of Mathematical and Statistical Sciences, University of Galway, Galway, Ireland.
| | - Stephen Finn
- Discipline of Histopathology, School of Medicine, Trinity College Dublin and Cancer Molecular Diagnostic Laboratory, Dublin, Ireland
| | - Pilib Ó Broin
- School of Mathematical and Statistical Sciences, University of Galway, Galway, Ireland
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Yang J, Lei X, Zhang F. Identification of circRNA-disease associations via multi-model fusion and ensemble learning. J Cell Mol Med 2024; 28:e18180. [PMID: 38506066 PMCID: PMC10951890 DOI: 10.1111/jcmm.18180] [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: 12/18/2023] [Revised: 01/21/2024] [Accepted: 02/05/2024] [Indexed: 03/21/2024] Open
Abstract
Circular RNA (circRNA) is a common non-coding RNA and plays an important role in the diagnosis and therapy of human diseases, circRNA-disease associations prediction based on computational methods can provide a new way for better clinical diagnosis. In this article, we proposed a novel method for circRNA-disease associations prediction based on ensemble learning, named ELCDA. First, the association heterogeneous network was constructed via collecting multiple information of circRNAs and diseases, and multiple similarity measures are adopted here, then, we use metapath, matrix factorization and GraphSAGE-based models to extract features of nodes from different views, the final comprehensive features of circRNAs and diseases via ensemble learning, finally, a soft voting ensemble strategy is used to integrate the predicted results of all classifier. The performance of ELCDA is evaluated by fivefold cross-validation and compare with other state-of-the-art methods, the experimental results show that ELCDA is outperformance than others. Furthermore, three common diseases are used as case studies, which also demonstrate that ELCDA is an effective method for predicting circRNA-disease associations.
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Affiliation(s)
- Jing Yang
- School of Computer ScienceShaanxi Normal UniversityXi'anShaanxiChina
| | - Xiujuan Lei
- School of Computer ScienceShaanxi Normal UniversityXi'anShaanxiChina
| | - Fa Zhang
- School of Medical TechnologyBeijing Institute of TechnologyBeijingChina
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Turgut H, Turanli B, Boz B. DCDA: CircRNA-Disease Association Prediction with Feed-Forward Neural Network and Deep Autoencoder. Interdiscip Sci 2024; 16:91-103. [PMID: 37978116 DOI: 10.1007/s12539-023-00590-y] [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: 03/20/2023] [Revised: 10/13/2023] [Accepted: 10/15/2023] [Indexed: 11/19/2023]
Abstract
Circular RNA is a single-stranded RNA with a closed-loop structure. In recent years, academic research has revealed that circular RNAs play critical roles in biological processes and are related to human diseases. The discovery of potential circRNAs as disease biomarkers and drug targets is crucial since it can help diagnose diseases in the early stages and be used to treat people. However, in conventional experimental methods, conducting experiments to detect associations between circular RNAs and diseases is time-consuming and costly. To overcome this problem, various computational methodologies are proposed to extract essential features for both circular RNAs and diseases and predict the associations. Studies showed that computational methods successfully predicted performance and made it possible to detect possible highly related circular RNAs for diseases. This study proposes a deep learning-based circRNA-disease association predictor methodology called DCDA, which uses multiple data sources to create circRNA and disease features and reveal hidden feature codings of a circular RNA-disease pair with a deep autoencoder, then predict the relation score of the pair by a deep neural network. Fivefold cross-validation results on the benchmark dataset showed that our model outperforms state-of-the-art prediction methods in the literature with the AUC score of 0.9794.
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Affiliation(s)
- Hacer Turgut
- Computer Engineering Department, Marmara University, 34854, Istanbul, Türkiye.
| | - Beste Turanli
- Bioengineering Department, Marmara University, 34854, Istanbul, Türkiye
| | - Betül Boz
- Computer Engineering Department, Marmara University, 34854, Istanbul, Türkiye.
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Yuan LX, Luo M, Liu RY, Wang HX, Ju LL, Wang F, Cao YL, Wang ZC, Chen L. Hsa_circ_0005397 promotes hepatocellular carcinoma progression through EIF4A3. BMC Cancer 2024; 24:239. [PMID: 38383334 PMCID: PMC10882807 DOI: 10.1186/s12885-024-11984-6] [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: 09/25/2023] [Accepted: 02/08/2024] [Indexed: 02/23/2024] Open
Abstract
PURPOSE The purpose of this study was to explore the expression and potential mechanism of hsa_circ_0005397 in hepatocellular carcinoma progression. METHODS Quantitative reverse transcription-polymerase chain reaction(qRT-PCR) was used to measure the expression level of hsa_circ_0005397 and EIF4A3 from paired HCC tissues and cell lines. Western Blot (WB) and immunohistochemistry (IHC) were used to verify the protein level of EIF4A3. The specificity of primers was confirmed by agarose gel electrophoresis. Receiver Operating Characteristic (ROC) Curve was drawn to analyze diagnostic value. Actinomycin D and nuclear and cytoplasmic extraction assays were utilized to evaluate the characteristics of hsa_circ_0005397. Cell Counting kit-8 (CCK-8) and colony formation assays were performed to detect cell proliferation. Flow cytometry analysis was used to detect the cell cycle. Transwell assay was performed to determine migration and invasion ability. RNA-binding proteins (RBPs) of hsa_circ_0005397 in HCC were explored using bioinformatics websites. The relationship between hsa_circ_0005397 and Eukaryotic Translation Initiation Factor 4A3 (EIF4A3) was verified by RNA Binding Protein Immunoprecipitation (RIP) assays, correlation and rescue experiments. RESULTS In this study, hsa_circ_0005397 was found to be significantly upregulated in HCC, and the good diagnostic sensitivity and specificity shown a potential diagnostic capability. Upregulated expression of hsa_circ_0005397 was significantly related to tumor size and stage. Hsa_circ_0005397 was circular structure which more stable than liner mRNA, and mostly distributed in the cytoplasm. Upregulation of hsa_circ_0005397 generally resulted in stronger proliferative ability, clonality, and metastatic potency of HCC cells; its downregulation yielded the opposite results. EIF4A3 is an RNA-binding protein of hsa_circ_0005397, which overexpressed in paired HCC tissues and cell lines. In addition, expression of hsa_circ_0005397 decreased equally when EIF4A3 was depleted. RIP assays and correlation assay estimated that EIF4A3 could interacted with hsa_circ_0005397. Knockdown of EIF4A3 could reverse hsa_circ_0005397 function in HCC progression. CONCLUSIONS Hsa_circ_0005397 promotes progression of hepatocellular carcinoma through EIF4A3. These research findings may provide novel clinical value for hepatocellular carcinoma.
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Affiliation(s)
- Liu-Xia Yuan
- Institute of Liver Diseases, Nantong Third People's Hospital, Affiliated Nantong Hospital 3 of Nantong University, 226000, Nantong, Jiangsu, China
| | - Mei Luo
- Nantong Third People's Hospital, Medical School of Nantong University, 226000, Nantong, Jiangsu, China
| | - Ruo-Yu Liu
- Medical School of Nantong University, Affiliated Hospital of Nantong University, 226000, Nantong, Jiangsu, China
| | - Hui-Xuan Wang
- Institute of Liver Diseases, Nantong Third People's Hospital, Affiliated Nantong Hospital 3 of Nantong University, 226000, Nantong, Jiangsu, China
| | - Lin-Ling Ju
- Institute of Liver Diseases, Nantong Third People's Hospital, Affiliated Nantong Hospital 3 of Nantong University, 226000, Nantong, Jiangsu, China
| | - Feng Wang
- Medical School of Nantong University, Affiliated Hospital of Nantong University, 226000, Nantong, Jiangsu, China
| | - Ya-Li Cao
- Preventive Health Department, Nantong Third People's Hospital, Affiliated Nantong Hospital 3 of Nantong University, 226000, Nantong, Jiangsu, China
| | - Zhong-Cheng Wang
- Hepatology Department of integrated Chinese and Western Medicine, Nantong Third People's Hospital, Affiliated Nantong Hospital 3 of Nantong University, 226000, Nantong, Jiangsu, China.
| | - Lin Chen
- Institute of Liver Diseases, Nantong Third People's Hospital, Affiliated Nantong Hospital 3 of Nantong University, 226000, Nantong, Jiangsu, China.
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9
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Pietrzak J, Świechowski R, Wosiak A, Wcisło S, Balcerczak E. ADAMTS Gene-Derived circRNA Molecules in Non-Small-Cell Lung Cancer: Expression Profiling, Clinical Correlations and Survival Analysis. Int J Mol Sci 2024; 25:1897. [PMID: 38339175 PMCID: PMC10855670 DOI: 10.3390/ijms25031897] [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: 11/28/2023] [Revised: 01/26/2024] [Accepted: 02/03/2024] [Indexed: 02/12/2024] Open
Abstract
The present study examines the relationship between circular RNA (circRNA) derived from three genes of the family a disintegrin and metalloproteinase with thrombospondin motifs (ADAMTSs): ADAMTS6, ADAMTS9 and ADAMTS12 and the host gene expression in non-small-cell lung cancer (NSCLC) with regard to various clinical factors. Notably, an association was identified between ADAMTS12 expression and specific circRNA molecules, as well as certain expression patterns of ADAMTS6 and its derived circRNA that were specific to histopathological subtypes. The survival analysis demonstrated that a lower ADAMTS6 expression in squamous cell carcinoma was associated with extended survival. Furthermore, the higher ADAMTS9 expression was linked to prolonged survival, while the overexpression of ADAMTS12 was correlated with a shorter survival. These findings suggest that circRNA molecules may serve as potential diagnostic or prognostic biomarkers for NSCLC, highlighting the importance of considering molecular patterns in distinct cancer subtypes.
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Affiliation(s)
- Jacek Pietrzak
- Laboratory of Molecular Diagnostics and Pharmacogenomics, Department of Pharmaceutical Biochemistry and Molecular Diagnostics, Medical University of Lodz, Muszynskiego 1, 90-151 Lodz, Poland; (J.P.); (R.Ś.); (A.W.)
- BRaIn Laboratories, Medical University of Lodz, Czechoslowacka 4, 92-216 Lodz, Poland
| | - Rafał Świechowski
- Laboratory of Molecular Diagnostics and Pharmacogenomics, Department of Pharmaceutical Biochemistry and Molecular Diagnostics, Medical University of Lodz, Muszynskiego 1, 90-151 Lodz, Poland; (J.P.); (R.Ś.); (A.W.)
- BRaIn Laboratories, Medical University of Lodz, Czechoslowacka 4, 92-216 Lodz, Poland
| | - Agnieszka Wosiak
- Laboratory of Molecular Diagnostics and Pharmacogenomics, Department of Pharmaceutical Biochemistry and Molecular Diagnostics, Medical University of Lodz, Muszynskiego 1, 90-151 Lodz, Poland; (J.P.); (R.Ś.); (A.W.)
- BRaIn Laboratories, Medical University of Lodz, Czechoslowacka 4, 92-216 Lodz, Poland
| | - Szymon Wcisło
- Department of Thoracic, General and Oncological Surgery, Medical University of Lodz and Military Medical Academy Memorial Teaching Hospital of the Medical University of Lodz - Central Veteran Hospital, 90-542 Lodz, Poland;
| | - Ewa Balcerczak
- Laboratory of Molecular Diagnostics and Pharmacogenomics, Department of Pharmaceutical Biochemistry and Molecular Diagnostics, Medical University of Lodz, Muszynskiego 1, 90-151 Lodz, Poland; (J.P.); (R.Ś.); (A.W.)
- BRaIn Laboratories, Medical University of Lodz, Czechoslowacka 4, 92-216 Lodz, Poland
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10
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Niu M, Wang C, Zhang Z, Zou Q. A computational model of circRNA-associated diseases based on a graph neural network: prediction and case studies for follow-up experimental validation. BMC Biol 2024; 22:24. [PMID: 38281919 PMCID: PMC10823650 DOI: 10.1186/s12915-024-01826-z] [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: 07/20/2023] [Accepted: 01/11/2024] [Indexed: 01/30/2024] Open
Abstract
BACKGROUND Circular RNAs (circRNAs) have been confirmed to play a vital role in the occurrence and development of diseases. Exploring the relationship between circRNAs and diseases is of far-reaching significance for studying etiopathogenesis and treating diseases. To this end, based on the graph Markov neural network algorithm (GMNN) constructed in our previous work GMNN2CD, we further considered the multisource biological data that affects the association between circRNA and disease and developed an updated web server CircDA and based on the human hepatocellular carcinoma (HCC) tissue data to verify the prediction results of CircDA. RESULTS CircDA is built on a Tumarkov-based deep learning framework. The algorithm regards biomolecules as nodes and the interactions between molecules as edges, reasonably abstracts multiomics data, and models them as a heterogeneous biomolecular association network, which can reflect the complex relationship between different biomolecules. Case studies using literature data from HCC, cervical, and gastric cancers demonstrate that the CircDA predictor can identify missing associations between known circRNAs and diseases, and using the quantitative real-time PCR (RT-qPCR) experiment of HCC in human tissue samples, it was found that five circRNAs were significantly differentially expressed, which proved that CircDA can predict diseases related to new circRNAs. CONCLUSIONS This efficient computational prediction and case analysis with sufficient feedback allows us to identify circRNA-associated diseases and disease-associated circRNAs. Our work provides a method to predict circRNA-associated diseases and can provide guidance for the association of diseases with certain circRNAs. For ease of use, an online prediction server ( http://server.malab.cn/CircDA ) is provided, and the code is open-sourced ( https://github.com/nmt315320/CircDA.git ) for the convenience of algorithm improvement.
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Affiliation(s)
- Mengting Niu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic University, Shenzhen, 518055, China
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chunyu Wang
- Faculty of Computing, Harbin Institute of Technology, Harbin, 150000, Heilongjiang, China
| | - Zhanguo Zhang
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, China.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, No. 4 Block 2 North Jianshe Road, Chengdu, 610054, China.
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
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11
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Baulina NM, Kiselev IS, Chumakova OS, Favorova OO. Circular RNAs: Biogenesis, Functions, and Role in Myocardial Hypertrophy. BIOCHEMISTRY. BIOKHIMIIA 2024; 89:S1-S13. [PMID: 38621741 DOI: 10.1134/s0006297924140013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/14/2023] [Accepted: 07/16/2023] [Indexed: 04/17/2024]
Abstract
Circular RNAs (circRNAs) are a large class of endogenous single-stranded covalently closed RNA molecules. High-throughput RNA sequencing and bioinformatic algorithms have identified thousands of eukaryotic circRNAs characterized by high stability and tissue-specific expression pattern. Recent studies have shown that circRNAs play an important role in the regulation of physiological processes in the norm and in various diseases, including cardiovascular disorders. The review presents current concepts of circRNA biogenesis, structural features, and biological functions, describes the methods of circRNA analysis, and summarizes the results of studies on the role of circRNAs in the pathogenesis of hypertrophic cardiomyopathy, the most common inherited heart disease.
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Affiliation(s)
- Natalia M Baulina
- Chazov National Medical Research Centre of Cardiology, Ministry of Health of the Russian Federation, Moscow, 121552, Russia.
- Pirogov Russian National Research Medical University, Moscow, 117997, Russia
| | - Ivan S Kiselev
- Chazov National Medical Research Centre of Cardiology, Ministry of Health of the Russian Federation, Moscow, 121552, Russia
- Pirogov Russian National Research Medical University, Moscow, 117997, Russia
| | - Olga S Chumakova
- Chazov National Medical Research Centre of Cardiology, Ministry of Health of the Russian Federation, Moscow, 121552, Russia
| | - Olga O Favorova
- Chazov National Medical Research Centre of Cardiology, Ministry of Health of the Russian Federation, Moscow, 121552, Russia
- Pirogov Russian National Research Medical University, Moscow, 117997, Russia
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12
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Shen Q, Gong W, Pan X, Cai J, Jiang Y, He M, Zhao S, Li Y, Yuan X, Li J. Comprehensive Analysis of CircRNA Expression Profiles in Multiple Tissues of Pigs. Int J Mol Sci 2023; 24:16205. [PMID: 38003395 PMCID: PMC10671760 DOI: 10.3390/ijms242216205] [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: 09/28/2023] [Revised: 11/01/2023] [Accepted: 11/08/2023] [Indexed: 11/26/2023] Open
Abstract
Circular RNAs (circRNAs) are a class of non-coding RNAs with diverse functions, and previous studies have reported that circRNAs are involved in the growth and development of pigs. However, studies about porcine circRNAs over the past few years have focused on a limited number of tissues. Based on 215 publicly available RNA sequencing (RNA-seq) samples, we conducted a comprehensive analysis of circRNAs in nine pig tissues, namely, the gallbladder, heart, liver, longissimus dorsi, lung, ovary, pituitary, skeletal muscle, and spleen. Here, we identified a total of 82,528 circRNAs and discovered 3818 novel circRNAs that were not reported in the CircAtlas database. Moreover, we obtained 492 housekeeping circRNAs and 3489 tissue-specific circRNAs. The housekeeping circRNAs were enriched in signaling pathways regulating basic biological tissue activities, such as chromatin remodeling, nuclear-transcribed mRNA catabolic process, and protein methylation. The tissue-specific circRNAs were enriched in signaling pathways related to tissue-specific functions, such as muscle system process in skeletal muscle, cilium organization in pituitary, and cortical cytoskeleton in ovary. Through weighted gene co-expression network analysis, we identified 14 modules comprising 1377 hub circRNAs. Additionally, we explored circRNA-miRNA-mRNA networks to elucidate the interaction relationships between tissue-specific circRNAs and tissue-specific genes. Furthermore, our conservation analysis revealed that 19.29% of circRNAs in pigs shared homologous positions with their counterparts in humans. In summary, this extensive profiling of housekeeping, tissue-specific, and co-expressed circRNAs provides valuable insights into understanding the molecular mechanisms of pig transcriptional expression, ultimately deepening our understanding of genetic and biological processes.
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Affiliation(s)
- Qingpeng Shen
- Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Q.S.); (W.G.); (X.P.); (J.C.); (Y.J.); (M.H.); (S.Z.); (Y.L.)
| | - Wentao Gong
- Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Q.S.); (W.G.); (X.P.); (J.C.); (Y.J.); (M.H.); (S.Z.); (Y.L.)
| | - Xiangchun Pan
- Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Q.S.); (W.G.); (X.P.); (J.C.); (Y.J.); (M.H.); (S.Z.); (Y.L.)
| | - Jiali Cai
- Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Q.S.); (W.G.); (X.P.); (J.C.); (Y.J.); (M.H.); (S.Z.); (Y.L.)
| | - Yao Jiang
- Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Q.S.); (W.G.); (X.P.); (J.C.); (Y.J.); (M.H.); (S.Z.); (Y.L.)
- School of Medical, Molecular and Forensic Sciences, Murdoch University, Murdoch, WA 6149, Australia
| | - Mingran He
- Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Q.S.); (W.G.); (X.P.); (J.C.); (Y.J.); (M.H.); (S.Z.); (Y.L.)
| | - Shanghui Zhao
- Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Q.S.); (W.G.); (X.P.); (J.C.); (Y.J.); (M.H.); (S.Z.); (Y.L.)
| | - Yipeng Li
- Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Q.S.); (W.G.); (X.P.); (J.C.); (Y.J.); (M.H.); (S.Z.); (Y.L.)
| | - Xiaolong Yuan
- Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Q.S.); (W.G.); (X.P.); (J.C.); (Y.J.); (M.H.); (S.Z.); (Y.L.)
| | - Jiaqi Li
- Guangdong Laboratory of Lingnan Modern Agriculture, National Engineering Research Center for Breeding Swine Industry, State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Q.S.); (W.G.); (X.P.); (J.C.); (Y.J.); (M.H.); (S.Z.); (Y.L.)
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13
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Feng XY, Zhu SX, Pu KJ, Huang HJ, Chen YQ, Wang WT. New insight into circRNAs: characterization, strategies, and biomedical applications. Exp Hematol Oncol 2023; 12:91. [PMID: 37828589 PMCID: PMC10568798 DOI: 10.1186/s40164-023-00451-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 09/23/2023] [Indexed: 10/14/2023] Open
Abstract
Circular RNAs (circRNAs) are a class of covalently closed, endogenous ncRNAs. Most circRNAs are derived from exonic or intronic sequences by precursor RNA back-splicing. Advanced high-throughput RNA sequencing and experimental technologies have enabled the extensive identification and characterization of circRNAs, such as novel types of biogenesis, tissue-specific and cell-specific expression patterns, epigenetic regulation, translation potential, localization and metabolism. Increasing evidence has revealed that circRNAs participate in diverse cellular processes, and their dysregulation is involved in the pathogenesis of various diseases, particularly cancer. In this review, we systematically discuss the characterization of circRNAs, databases, challenges for circRNA discovery, new insight into strategies used in circRNA studies and biomedical applications. Although recent studies have advanced the understanding of circRNAs, advanced knowledge and approaches for circRNA annotation, functional characterization and biomedical applications are continuously needed to provide new insights into circRNAs. The emergence of circRNA-based protein translation strategy will be a promising direction in the field of biomedicine.
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Affiliation(s)
- Xin-Yi Feng
- MOE Key Laboratory of Gene Function and Regulation, Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China
| | - Shun-Xin Zhu
- MOE Key Laboratory of Gene Function and Regulation, Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China
| | - Ke-Jia Pu
- MOE Key Laboratory of Gene Function and Regulation, Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China
| | - Heng-Jing Huang
- MOE Key Laboratory of Gene Function and Regulation, Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China
| | - Yue-Qin Chen
- MOE Key Laboratory of Gene Function and Regulation, Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China.
| | - Wen-Tao Wang
- MOE Key Laboratory of Gene Function and Regulation, Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China.
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14
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Yuan L, Zhao J, Shen Z, Zhang Q, Geng Y, Zheng CH, Huang DS. iCircDA-NEAE: Accelerated attribute network embedding and dynamic convolutional autoencoder for circRNA-disease associations prediction. PLoS Comput Biol 2023; 19:e1011344. [PMID: 37651321 PMCID: PMC10470932 DOI: 10.1371/journal.pcbi.1011344] [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: 05/11/2023] [Accepted: 07/10/2023] [Indexed: 09/02/2023] Open
Abstract
Accumulating evidence suggests that circRNAs play crucial roles in human diseases. CircRNA-disease association prediction is extremely helpful in understanding pathogenesis, diagnosis, and prevention, as well as identifying relevant biomarkers. During the past few years, a large number of deep learning (DL) based methods have been proposed for predicting circRNA-disease association and achieved impressive prediction performance. However, there are two main drawbacks to these methods. The first is these methods underutilize biometric information in the data. Second, the features extracted by these methods are not outstanding to represent association characteristics between circRNAs and diseases. In this study, we developed a novel deep learning model, named iCircDA-NEAE, to predict circRNA-disease associations. In particular, we use disease semantic similarity, Gaussian interaction profile kernel, circRNA expression profile similarity, and Jaccard similarity simultaneously for the first time, and extract hidden features based on accelerated attribute network embedding (AANE) and dynamic convolutional autoencoder (DCAE). Experimental results on the circR2Disease dataset show that iCircDA-NEAE outperforms other competing methods significantly. Besides, 16 of the top 20 circRNA-disease pairs with the highest prediction scores were validated by relevant literature. Furthermore, we observe that iCircDA-NEAE can effectively predict new potential circRNA-disease associations.
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Affiliation(s)
- Lin Yuan
- Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Engineering Research Center of Big Data Applied Technology, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China
| | - Jiawang Zhao
- Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Engineering Research Center of Big Data Applied Technology, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China
| | - Zhen Shen
- School of Computer and Software, Nanyang Institute of Technology, Nanyang, China
| | - Qinhu Zhang
- Eastern Institute for Advanced Study, Eastern Institute of Technology, Ningbo, China
| | - Yushui Geng
- Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Engineering Research Center of Big Data Applied Technology, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China
| | - Chun-Hou Zheng
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, China
| | - De-Shuang Huang
- Eastern Institute for Advanced Study, Eastern Institute of Technology, Ningbo, China
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15
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Malviya A, Bhuyan R. The recent advancements in circRNA research: From biogenesis to therapeutic interventions. Pathol Res Pract 2023; 248:154697. [PMID: 37506629 DOI: 10.1016/j.prp.2023.154697] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 07/14/2023] [Accepted: 07/14/2023] [Indexed: 07/30/2023]
Abstract
Circular RNAs (circRNAs) belong to the genre of long non-coding RNAs that are formed by special back-splicing events and are currently the molecule of interest for studies globally due their involvement in various ailments like diabetes, neurodegenerative disorders, cardio-vascular diseases and cancers. These class of highly stable RNAs participate in diverse cellular functionalities including microRNA (miRNA) sponging, ceRNA (competing endogenous RNA) activity or via exhibiting RNA binding protein (RBP) interactions. They are also known to regulate cancer progression both positively and negatively through various biological pathways such as, modulating the cell cycle and apoptotic pathways, epigenetic regulation, and translational and/or transcriptional regulations etc. Given its significance, a variety of computational tools and dedicated databases have been created for the identification, quantification, and differential expression of such RNAs in combination with sequencing approaches. In this review, we provide a comprehensive analysis of the numerous computational tools, pipelines, and online resources developed in recent years for the detection and annotation of circRNAs. We also summarise the most recent findings regarding the characteristics, functions, biological processes, and involvement of circRNAs in diseases. The review emphasises the significance of circRNAs as potential disease biomarkers and new treatment targets.
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Affiliation(s)
- Ayushi Malviya
- Department of Bioscience and Biotechnology, Banasthali Vidyapith, Banasthali, Tonk, Rajasthan 304022, India
| | - Rajabrata Bhuyan
- Department of Bioscience and Biotechnology, Banasthali Vidyapith, Banasthali, Tonk, Rajasthan 304022, India.
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16
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Fiscon G, Funari A, Paci P. Circular RNA mediated gene regulation in human breast cancer: A bioinformatics analysis. PLoS One 2023; 18:e0289051. [PMID: 37494404 PMCID: PMC10370684 DOI: 10.1371/journal.pone.0289051] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 07/11/2023] [Indexed: 07/28/2023] Open
Abstract
Circular RNAs (circRNAs) are a new acknowledged class of RNAs that has been shown to play a major role in several biological functions both in physiological and pathological conditions, operating as critical part of regulatory processes, like competing endogenous RNA (ceRNA) networks. The ceRNA hypothesis is a recently discovered molecular mechanism that adds a new key layer of post-transcriptional regulation, whereby various types of RNAs can reciprocally influence each other's expression competing for binding the same pool of microRNAs, even affecting disease development. In this study, we build a network of circRNA-miRNA-mRNA interactions in human breast cancer, called CERNOMA, that is a bipartite graph with one class of nodes corresponding to differentially expressed miRNAs (DEMs) and the other one corresponding to differentially expressed circRNAs (DEC) and mRNAs (DEGs). A link between a DEC (or DEG) and DEM is placed if it is predicted to be a target of the DEM and shows an opposite expression level trend with respect to the DEM. Within the CERNOMA, we highlighted an interesting deregulated circRNA-miRNA-mRNA triplet, including the up-regulated hsa_circRNA_102908 (BRCA1 associated RING domain 1), the down-regulated miR-410-3p, and the up-regulated ESM1, whose overexpression has been already shown to promote tumor dissemination and metastasis in breast cancer.
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Affiliation(s)
- Giulia Fiscon
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Roma, Italy
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Alessio Funari
- Department of Translational and Precision Medicine, Sapienza University of Rome, Roma, Italy
| | - Paola Paci
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Roma, Italy
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
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17
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Hu F, Peng Y, Fan X, Zhang X, Jin Z. Circular RNAs: implications of signaling pathways and bioinformatics in human cancer. Cancer Biol Med 2023; 20:j.issn.2095-3941.2022.0466. [PMID: 36861443 PMCID: PMC9978890 DOI: 10.20892/j.issn.2095-3941.2022.0466] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023] Open
Abstract
Circular RNAs (circRNAs) form a class of endogenous single-stranded RNA transcripts that are widely expressed in eukaryotic cells. These RNAs mediate post-transcriptional control of gene expression and have multiple functions in biological processes, such as transcriptional regulation and splicing. They serve predominantly as microRNA sponges, RNA-binding proteins, and templates for translation. More importantly, circRNAs are involved in cancer progression, and may serve as promising biomarkers for tumor diagnosis and therapy. Although traditional experimental methods are usually time-consuming and laborious, substantial progress has been made in exploring potential circRNA-disease associations by using computational models, summarized signaling pathway data, and other databases. Here, we review the biological characteristics and functions of circRNAs, including their roles in cancer. Specifically, we focus on the signaling pathways associated with carcinogenesis, and the status of circRNA-associated bioinformatics databases. Finally, we explore the potential roles of circRNAs as prognostic biomarkers in cancer.
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Affiliation(s)
- Fan Hu
- Guangdong Provincial Key Laboratory of Genome Stability and Disease Prevention and Regional Immunity and Diseases, Department of Pathology, School of Basic Medical Sciences, Medical School, Shenzhen University, Shenzhen 518060, China
| | - Yin Peng
- Guangdong Provincial Key Laboratory of Genome Stability and Disease Prevention and Regional Immunity and Diseases, Department of Pathology, School of Basic Medical Sciences, Medical School, Shenzhen University, Shenzhen 518060, China
| | - Xinmin Fan
- Guangdong Provincial Key Laboratory of Genome Stability and Disease Prevention and Regional Immunity and Diseases, Department of Pathology, School of Basic Medical Sciences, Medical School, Shenzhen University, Shenzhen 518060, China
| | - Xiaojing Zhang
- Guangdong Provincial Key Laboratory of Genome Stability and Disease Prevention and Regional Immunity and Diseases, Department of Pathology, School of Basic Medical Sciences, Medical School, Shenzhen University, Shenzhen 518060, China
- Correspondence to: Zhe Jin and Xiaojing Zhang, E-mail: and
| | - Zhe Jin
- Guangdong Provincial Key Laboratory of Genome Stability and Disease Prevention and Regional Immunity and Diseases, Department of Pathology, School of Basic Medical Sciences, Medical School, Shenzhen University, Shenzhen 518060, China
- Correspondence to: Zhe Jin and Xiaojing Zhang, E-mail: and
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18
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Circular RNAs-New Kids on the Block in Cancer Pathophysiology and Management. Cells 2023; 12:cells12040552. [PMID: 36831219 PMCID: PMC9953808 DOI: 10.3390/cells12040552] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/30/2023] [Accepted: 02/04/2023] [Indexed: 02/11/2023] Open
Abstract
The ever-increasing number of cancer cases and persistently high mortality underlines the urgent need to acquire new perspectives for developing innovative therapeutic approaches. As the research on protein-coding genes brought significant yet only incremental progress in the development of anticancer therapy, much attention is now devoted to understanding the role of non-coding RNAs (ncRNAs) in various types of cancer. Recent years have brought about the awareness that ncRNAs recognized previously as "dark matter" are, in fact, key players in shaping cancer development. Moreover, breakthrough discoveries concerning the role of a new group of ncRNAs, circular RNAs, have evidenced their high importance in many diseases, including malignancies. Therefore, in the following review, we focus on the role of circular RNAs in cancer, particularly in cancer stem-like cells, summarize their mechanisms of action, and provide an overview of the state-of-the-art toolkits to study them.
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19
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Wang K, Gao XQ, Wang T, Zhou LY. The Function and Therapeutic Potential of Circular RNA in Cardiovascular Diseases. Cardiovasc Drugs Ther 2023; 37:181-198. [PMID: 34269929 DOI: 10.1007/s10557-021-07228-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/06/2021] [Indexed: 01/14/2023]
Abstract
Circular RNA (circRNA) has a closed-loop structure, and its 3' and 5' ends are directly covalently connected by reverse splicing, which is more stable than linear RNA. CircRNAs usually possess microRNA (miRNA) binding sites, which can bind miRNAs and inhibit miRNA function. Many studies have shown that circRNAs are involved in the processes of cell senescence, proliferation and apoptosis and a series of signalling pathways, playing an important role in the prevention and treatment of diseases. CircRNAs are potential biological diagnostic markers and therapeutic targets for cardiovascular diseases (CVDs). To identify biomarkers and potential effective therapeutic targets without toxicity for heart disease, we summarize the biogenesis, biology, characterization and functions of circRNAs in CVDs, hoping that this information will shed new light on the prevention and treatment of CVDs.
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Affiliation(s)
- Kai Wang
- Institute of Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, 266021, Shandong, China
| | - Xiang-Qian Gao
- Institute of Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, 266021, Shandong, China
| | - Tao Wang
- Institute of Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, 266021, Shandong, China
| | - Lu-Yu Zhou
- Institute of Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, 266021, Shandong, China.
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20
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Lu C, Zhang L, Zeng M, Lan W, Duan G, Wang J. Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network. Brief Bioinform 2023; 24:6960978. [PMID: 36572658 DOI: 10.1093/bib/bbac549] [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: 08/19/2022] [Revised: 11/03/2022] [Accepted: 11/11/2022] [Indexed: 12/28/2022] Open
Abstract
Emerging evidence has proved that circular RNAs (circRNAs) are implicated in pathogenic processes. They are regarded as promising biomarkers for diagnosis due to covalently closed loop structures. As opposed to traditional experiments, computational approaches can identify circRNA-disease associations at a lower cost. Aggregating multi-source pathogenesis data helps to alleviate data sparsity and infer potential associations at the system level. The majority of computational approaches construct a homologous network using multi-source data, but they lose the heterogeneity of the data. Effective methods that use the features of multi-source data are considered as a matter of urgency. In this paper, we propose a model (CDHGNN) based on edge-weighted graph attention and heterogeneous graph neural networks for potential circRNA-disease association prediction. The circRNA network, micro RNA network, disease network and heterogeneous network are constructed based on multi-source data. To reflect association probabilities between nodes, an edge-weighted graph attention network model is designed for node features. To assign attention weights to different types of edges and learn contextual meta-path, CDHGNN infers potential circRNA-disease association based on heterogeneous neural networks. CDHGNN outperforms state-of-the-art algorithms in terms of accuracy. Edge-weighted graph attention networks and heterogeneous graph networks have both improved performance significantly. Furthermore, case studies suggest that CDHGNN is capable of identifying specific molecular associations and investigating biomolecular regulatory relationships in pathogenesis. The code of CDHGNN is freely available at https://github.com/BioinformaticsCSU/CDHGNN.
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Affiliation(s)
- Chengqian Lu
- School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China.,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, Hunan, China.,School of Computer Science, Xiangtan University, Xiangtan, 411105, Hunan, China
| | - Lishen Zhang
- School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China.,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, Hunan, China
| | - Min Zeng
- School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China.,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, Hunan, China
| | - Wei Lan
- School of Computer, Electronic and Information, Guangxi University, Nanning, 530004, Guangxi, China
| | - Guihua Duan
- School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China.,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, Hunan, China
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China.,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, Hunan, China
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21
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Lan W, Dong Y, Zhang H, Li C, Chen Q, Liu J, Wang J, Chen YPP. Benchmarking of computational methods for predicting circRNA-disease associations. Brief Bioinform 2023; 24:6972300. [PMID: 36611256 DOI: 10.1093/bib/bbac613] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 10/29/2022] [Accepted: 12/11/2022] [Indexed: 01/09/2023] Open
Abstract
Accumulating evidences demonstrate that circular RNA (circRNA) plays an important role in human diseases. Identification of circRNA-disease associations can help for the diagnosis of human diseases, while the traditional method based on biological experiments is time-consuming. In order to address the limitation, a series of computational methods have been proposed in recent years. However, few works have summarized these methods or compared the performance of them. In this paper, we divided the existing methods into three categories: information propagation, traditional machine learning and deep learning. Then, the baseline methods in each category are introduced in detail. Further, 5 different datasets are collected, and 14 representative methods of each category are selected and compared in the 5-fold, 10-fold cross-validation and the de novo experiment. In order to further evaluate the effectiveness of these methods, six common cancers are selected to compare the number of correctly identified circRNA-disease associations in the top-10, top-20, top-50, top-100 and top-200. In addition, according to the results, the observation about the robustness and the character of these methods are concluded. Finally, the future directions and challenges are discussed.
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Affiliation(s)
- Wei Lan
- School of Computer, Electronic and Information and Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning, Guangxi 530004, China
| | - Yi Dong
- School of Computer, Electronic and Information and Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning, Guangxi 530004, China
| | - Hongyu Zhang
- School of Computer, Electronic and Information and Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning, Guangxi 530004, China
| | - Chunling Li
- School of Computer, Electronic and Information and Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning, Guangxi 530004, China
| | - Qingfeng Chen
- School of Computer, Electronic and Information and State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangxi University, Nanning, Guangxi 530004, China
| | - Jin Liu
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Yi-Ping Phoebe Chen
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, Victoria 3086, Australia
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22
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Chen JW, Shrestha L, Green G, Leier A, Marquez-Lago TT. The hitchhikers' guide to RNA sequencing and functional analysis. Brief Bioinform 2023; 24:bbac529. [PMID: 36617463 PMCID: PMC9851315 DOI: 10.1093/bib/bbac529] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 10/18/2022] [Accepted: 11/07/2022] [Indexed: 01/10/2023] Open
Abstract
DNA and RNA sequencing technologies have revolutionized biology and biomedical sciences, sequencing full genomes and transcriptomes at very high speeds and reasonably low costs. RNA sequencing (RNA-Seq) enables transcript identification and quantification, but once sequencing has concluded researchers can be easily overwhelmed with questions such as how to go from raw data to differential expression (DE), pathway analysis and interpretation. Several pipelines and procedures have been developed to this effect. Even though there is no unique way to perform RNA-Seq analysis, it usually follows these steps: 1) raw reads quality check, 2) alignment of reads to a reference genome, 3) aligned reads' summarization according to an annotation file, 4) DE analysis and 5) gene set analysis and/or functional enrichment analysis. Each step requires researchers to make decisions, and the wide variety of options and resulting large volumes of data often lead to interpretation challenges. There also seems to be insufficient guidance on how best to obtain relevant information and derive actionable knowledge from transcription experiments. In this paper, we explain RNA-Seq steps in detail and outline differences and similarities of different popular options, as well as advantages and disadvantages. We also discuss non-coding RNA analysis, multi-omics, meta-transcriptomics and the use of artificial intelligence methods complementing the arsenal of tools available to researchers. Lastly, we perform a complete analysis from raw reads to DE and functional enrichment analysis, visually illustrating how results are not absolute truths and how algorithmic decisions can greatly impact results and interpretation.
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Affiliation(s)
- Jiung-Wen Chen
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Lisa Shrestha
- Department of Genetics, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
| | - George Green
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - André Leier
- Department of Genetics, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
- Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
| | - Tatiana T Marquez-Lago
- Department of Genetics, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
- Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
- Department of Microbiology, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
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23
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Liu R, Ma Y, Guo T, Li G. Identification, biogenesis, function, and mechanism of action of circular RNAs in plants. PLANT COMMUNICATIONS 2023; 4:100430. [PMID: 36081344 PMCID: PMC9860190 DOI: 10.1016/j.xplc.2022.100430] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/11/2022] [Accepted: 09/05/2022] [Indexed: 06/15/2023]
Abstract
Circular RNAs (circRNAs) are a class of single-stranded, closed RNA molecules with unique functions that are ubiquitously expressed in all eukaryotes. The biogenesis of circRNAs is regulated by specific cis-acting elements and trans-acting factors in humans and animals. circRNAs mainly exert their biological functions by acting as microRNA sponges, forming R-loops, interacting with RNA-binding proteins, or being translated into polypeptides or proteins in human and animal cells. Genome-wide identification of circRNAs has been performed in multiple plant species, and the results suggest that circRNAs are abundant and ubiquitously expressed in plants. There is emerging compelling evidence to suggest that circRNAs play essential roles during plant growth and development as well as in the responses to biotic and abiotic stress. However, compared with recent advances in human and animal systems, the roles of most circRNAs in plants are unclear at present. Here we review the identification, biogenesis, function, and mechanism of action of plant circRNAs, which will provide a fundamental understanding of the characteristics and complexity of circRNAs in plants.
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Affiliation(s)
- Ruiqi Liu
- Key Laboratory of Ministry of Education for Medicinal Plant Resource and Natural Pharmaceutical Chemistry, National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest China, College of Life Sciences, Shaanxi Normal University, Xi'an, Shaanxi 710119, China
| | - Yu Ma
- Key Laboratory of Ministry of Education for Medicinal Plant Resource and Natural Pharmaceutical Chemistry, National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest China, College of Life Sciences, Shaanxi Normal University, Xi'an, Shaanxi 710119, China
| | - Tao Guo
- State Key Laboratory of Crop Stress Biology for Arid Areas and Institute of Future Agriculture, Northwest A&F University, Yangling, Shaanxi 712100, China.
| | - Guanglin Li
- Key Laboratory of Ministry of Education for Medicinal Plant Resource and Natural Pharmaceutical Chemistry, National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest China, College of Life Sciences, Shaanxi Normal University, Xi'an, Shaanxi 710119, China.
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24
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Chen Y, Wang J, Wang C, Liu M, Zou Q. Deep learning models for disease-associated circRNA prediction: a review. Brief Bioinform 2022; 23:6696465. [PMID: 36130259 DOI: 10.1093/bib/bbac364] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/30/2022] [Accepted: 08/03/2022] [Indexed: 12/14/2022] Open
Abstract
Emerging evidence indicates that circular RNAs (circRNAs) can provide new insights and potential therapeutic targets for disease diagnosis and treatment. However, traditional biological experiments are expensive and time-consuming. Recently, deep learning with a more powerful ability for representation learning enables it to be a promising technology for predicting disease-associated circRNAs. In this review, we mainly introduce the most popular databases related to circRNA, and summarize three types of deep learning-based circRNA-disease associations prediction methods: feature-generation-based, type-discrimination and hybrid-based methods. We further evaluate seven representative models on benchmark with ground truth for both balance and imbalance classification tasks. In addition, we discuss the advantages and limitations of each type of method and highlight suggested applications for future research.
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Affiliation(s)
- Yaojia Chen
- College of Electronics and Information Engineering Guangdong Ocean University, Zhanjiang, China and the Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiacheng Wang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Chuyu Wang
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Mingxin Liu
- College of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, China
| | - Quan Zou
- University of Electronic Science and Technology of China, China
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25
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A Review and In Silico Analysis of Tissue and Exosomal Circular RNAs: Opportunities and Challenges in Thyroid Cancer. Cancers (Basel) 2022; 14:cancers14194728. [PMID: 36230649 PMCID: PMC9564022 DOI: 10.3390/cancers14194728] [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: 07/16/2022] [Revised: 09/22/2022] [Accepted: 09/26/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Thyroid cancer is the most common endocrine neoplasm. Recently, knowledge of the molecular genetic changes of thyroid cancer has dramatically improved. Understanding the roles of these molecular changes in thyroid cancer tumorigenesis and progression is essential in developing a successful treatment strategy and improving disease outcomes. As a family of non-coding RNAs, circular RNAs (circRNAs) have been involved in several aspects of the physiological and pathological processes of the cells. The roles of circRNAs in cancer development and progress are evident. In the current review, we aimed to explore the clinical potential of circRNAs as potential diagnostic, prognostic, and therapeutic targets in thyroid cancer. Furthermore, screening the genome-wide circRNAs and performing functional enrichment analyses for all associated dysregulated circRNAs in thyroid cancer have been done. Given the unique advantages circRNAs have, such as superior stability, higher abundance, and presence in different body fluids, this family of non-coding RNAs could be promising diagnostic and prognostic biomarkers and potential therapeutic targets for thyroid cancer. Abstract Thyroid cancer (TC) is the most common endocrine tumor. The genetic and epigenetic molecular alterations of TC have become more evident in recent years. However, a deeper understanding of the roles these molecular changes play in TC tumorigenesis and progression is essential in developing a successful treatment strategy and improving patients’ prognoses. Circular RNAs (circRNAs), a family of non-coding RNAs, have been implicated in several aspects of carcinogenesis in multiple cancers, including TC. In the current review, we aimed to explore the clinical potential of circRNAs as putative diagnostic, prognostic, and therapeutic targets in TC. The current analyses, including genome-wide circRNA screening and functional enrichment for all deregulated circRNA expression signatures, show that circRNAs display atypical contributions, such as sponging for microRNAs, regulating transcription and translation processes, and decoying for proteins. Given their exceptional clinical advantages, such as higher stability, wider abundance, and occurrence in several body fluids, circRNAs are promising prognostic and theranostic biomarkers for TC.
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26
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Samarfard S, Ghorbani A, Karbanowicz TP, Lim ZX, Saedi M, Fariborzi N, McTaggart AR, Izadpanah K. Regulatory non-coding RNA: The core defense mechanism against plant pathogens. J Biotechnol 2022; 359:82-94. [PMID: 36174794 DOI: 10.1016/j.jbiotec.2022.09.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 09/18/2022] [Accepted: 09/21/2022] [Indexed: 12/13/2022]
Abstract
Plant pathogens damage crops and threaten global food security. Plants have evolved complex defense networks against pathogens, using crosstalk among various signaling pathways. Key regulators conferring plant immunity through signaling pathways include protein-coding genes and non-coding RNAs (ncRNAs). The discovery of ncRNAs in plant transcriptomes was first considered "transcriptional noise". Recent reviews have highlighted the importance of non-coding RNAs. However, understanding interactions among different types of noncoding RNAs requires additional research. This review attempts to consider how long-ncRNAs, small-ncRNAs and circular RNAs interact in response to pathogenic diseases within different plant species. Developments within genomics and bioinformatics could lead to the further discovery of plant ncRNAs, knowledge of their biological roles, as well as an understanding of their importance in exploiting the recent molecular-based technologies for crop protection.
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Affiliation(s)
- Samira Samarfard
- Department of Primary Industries and Regional Development, DPIRD Diagnostic Laboratory Services, South Perth, WA, Australia
| | - Abozar Ghorbani
- Nuclear Agriculture Research School, Nuclear Science and Technology Research Institute (NSTRI), Karaj, the Islamic Republic of Iran.
| | | | - Zhi Xian Lim
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD 4072, Australia
| | - Mahshid Saedi
- Department of Plant Protection, Faculty of Agriculture, University of Kurdistan, Sanandaj, the Islamic Republic of Iran
| | - Niloofar Fariborzi
- Department of Medical Entomology and Vector Control, School of Health, Shiraz University of Medical Sciences, Shiraz, the Islamic Republic of Iran
| | - Alistair R McTaggart
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Ecosciences Precinct, Dutton Park, QLD 4102, Australia
| | - Keramatollah Izadpanah
- Plant Virology Research Center, College of Agriculture, Shiraz University, Shiraz, the Islamic Republic of Iran
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27
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Dai Q, Liu Z, Wang Z, Duan X, Guo M. GraphCDA: a hybrid graph representation learning framework based on GCN and GAT for predicting disease-associated circRNAs. Brief Bioinform 2022; 23:6692549. [PMID: 36070619 DOI: 10.1093/bib/bbac379] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/18/2022] [Accepted: 08/09/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION CircularRNA (circRNA) is a class of noncoding RNA with high conservation and stability, which is considered as an important disease biomarker and drug target. Accumulating pieces of evidence have indicated that circRNA plays a crucial role in the pathogenesis and progression of many complex diseases. As the biological experiments are time-consuming and labor-intensive, developing an accurate computational prediction method has become indispensable to identify disease-related circRNAs. RESULTS We presented a hybrid graph representation learning framework, named GraphCDA, for predicting the potential circRNA-disease associations. Firstly, the circRNA-circRNA similarity network and disease-disease similarity network were constructed to characterize the relationships of circRNAs and diseases, respectively. Secondly, a hybrid graph embedding model combining Graph Convolutional Networks and Graph Attention Networks was introduced to learn the feature representations of circRNAs and diseases simultaneously. Finally, the learned representations were concatenated and employed to build the prediction model for identifying the circRNA-disease associations. A series of experimental results demonstrated that GraphCDA outperformed other state-of-the-art methods on several public databases. Moreover, GraphCDA could achieve good performance when only using a small number of known circRNA-disease associations as the training set. Besides, case studies conducted on several human diseases further confirmed the prediction capability of GraphCDA for predicting potential disease-related circRNAs. In conclusion, extensive experimental results indicated that GraphCDA could serve as a reliable tool for exploring the regulatory role of circRNAs in complex diseases.
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Affiliation(s)
- Qiguo Dai
- School of Computer Science and Engineering, Dalian Minzu University, 116600, Dalian, China.,SEAC Key Laboratory of Big Data Applied Technology, Dalian Minzu University, 116600, Dalian, China
| | - Ziqiang Liu
- School of Computer Science and Engineering, Dalian Minzu University, 116600, Dalian, China.,SEAC Key Laboratory of Big Data Applied Technology, Dalian Minzu University, 116600, Dalian, China
| | - Zhaowei Wang
- SEAC Key Laboratory of Big Data Applied Technology, Dalian Minzu University, 116600, Dalian, China.,School of Computer Science and Technology, Dalian University of Technology, 116024, Dalian, China
| | - Xiaodong Duan
- SEAC Key Laboratory of Big Data Applied Technology, Dalian Minzu University, 116600, Dalian, China
| | - Maozu Guo
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, 100044, Beijing, China
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28
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Xu X, Du T, Mao W, Li X, Ye CY, Zhu QH, Fan L, Chu Q. PlantcircBase 7.0: Full-length transcripts and conservation of plant circRNAs. PLANT COMMUNICATIONS 2022; 3:100343. [PMID: 35637632 PMCID: PMC9284285 DOI: 10.1016/j.xplc.2022.100343] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/13/2022] [Accepted: 05/26/2022] [Indexed: 06/15/2023]
Abstract
Circular RNA (circRNA) is a special type of non-coding RNA that participates in diverse biological processes in both animals and plants. Five years ago, we developed a comprehensive plant circRNA database (PlantcircBase), which has attracted much attention from the plant circRNA community. Here, we report an updated PlantcircBase (v.7.0), which contains 171,118 circRNAs from 21 plant species. Over 31,000 of the circRNAs have full-length sequences constructed based on analysis of 749 bulk RNA sequencing (RNA-seq) datasets downloaded from the public domain and Nanopore long-read sequencing results of rice RNAs newly generated in this study. A plant multiple conservation score (PMCS), based on the conservation of both sequence and expression profiles, was calculated for each circRNA to quantify and compare the conservation of all circRNAs. A new parameter, plant circRNA confidence level (PCCL), is introduced to measure the identity reliability of each circRNA based on experimental validation results and the number of references that support the circRNA. All this information and other details of circRNAs can be browsed, searched, and downloaded from PlantcircBase 7.0, which also provides online bioinformatics tools for visualization and sequence alignment. PlantcircBase 7.0 is publicly and freely accessible at http://ibi.zju.edu.cn/plantcircbase/.
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Affiliation(s)
- Xiaoxu Xu
- Institute of Crop Science & Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China; Shandong (Linyi) Institute of Modern Agriculture of Zhejiang University, Linyi 310014, China
| | - Tianyu Du
- Institute of Crop Science & Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China; Shandong (Linyi) Institute of Modern Agriculture of Zhejiang University, Linyi 310014, China
| | - Weihua Mao
- Analysis Center of Agrobiology and Environmental Science, Zhejiang University, Hangzhou 310058, China
| | - Xiaohan Li
- Institute of Crop Science & Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China
| | - Chu-Yu Ye
- Institute of Crop Science & Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China
| | - Qian-Hao Zhu
- CSIRO Agriculture and Food, Black Mountain Laboratories, Canberra, ACT 2601, Australia
| | - Longjiang Fan
- Institute of Crop Science & Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China; Shandong (Linyi) Institute of Modern Agriculture of Zhejiang University, Linyi 310014, China
| | - Qinjie Chu
- Institute of Crop Science & Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China.
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29
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Cao R, He C, Wei P, Su Y, Xia J, Zheng C. Prediction of circRNA-Disease Associations Based on the Combination of Multi-Head Graph Attention Network and Graph Convolutional Network. Biomolecules 2022; 12:biom12070932. [PMID: 35883487 PMCID: PMC9313348 DOI: 10.3390/biom12070932] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/22/2022] [Accepted: 06/30/2022] [Indexed: 11/16/2022] Open
Abstract
Circular RNAs (circRNAs) are covalently closed single-stranded RNA molecules, which have many biological functions. Previous experiments have shown that circRNAs are involved in numerous biological processes, especially regulatory functions. It has also been found that circRNAs are associated with complex diseases of human beings. Therefore, predicting the associations of circRNA with disease (called circRNA-disease associations) is useful for disease prevention, diagnosis and treatment. In this work, we propose a novel computational approach called GGCDA based on the Graph Attention Network (GAT) and Graph Convolutional Network (GCN) to predict circRNA-disease associations. Firstly, GGCDA combines circRNA sequence similarity, disease semantic similarity and corresponding Gaussian interaction profile kernel similarity, and then a random walk with restart algorithm (RWR) is used to obtain the preliminary features of circRNA and disease. Secondly, a heterogeneous graph is constructed from the known circRNA-disease association network and the calculated similarity of circRNAs and diseases. Thirdly, the multi-head Graph Attention Network (GAT) is adopted to obtain different weights of circRNA and disease features, and then GCN is employed to aggregate the features of adjacent nodes in the network and the features of the nodes themselves, so as to obtain multi-view circRNA and disease features. Finally, we combined a multi-layer fully connected neural network to predict the associations of circRNAs with diseases. In comparison with state-of-the-art methods, GGCDA can achieve AUC values of 0.9625 and 0.9485 under the results of fivefold cross-validation on two datasets, and AUC of 0.8227 on the independent test set. Case studies further demonstrate that our approach is promising for discovering potential circRNA-disease associations.
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Affiliation(s)
- Ruifen Cao
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Computer Science and Technology, Anhui University, Hefei 230601, China;
- Correspondence: (R.C.); (C.Z.)
| | - Chuan He
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Computer Science and Technology, Anhui University, Hefei 230601, China;
| | - Pijing Wei
- Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China; (P.W.); (J.X.)
| | - Yansen Su
- School of Artificial Intelligence, Anhui University, Hefei 230601, China;
| | - Junfeng Xia
- Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China; (P.W.); (J.X.)
| | - Chunhou Zheng
- School of Artificial Intelligence, Anhui University, Hefei 230601, China;
- Correspondence: (R.C.); (C.Z.)
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30
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Liu Y, Wang L, Liu W. Roles of circRNAs in the Tumorigenesis and Metastasis of HCC: A Mini Review. Cancer Manag Res 2022; 14:1847-1856. [PMID: 35668744 PMCID: PMC9166687 DOI: 10.2147/cmar.s362594] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 05/20/2022] [Indexed: 12/21/2022] Open
Abstract
Circular RNAs (circRNAs) are a class of non-coding RNAs with loop structures that are stable and widely distributed in different tumor tissues. The development of high-throughput sequencing and in silico tools has enabled the discovery of numerous functional circRNAs. Hepatocellular carcinoma (HCC) is a malignant tumor, and the mechanism involved in its progression has remained unclear. In recent years, an increasing number of circRNAs have been identified in HCC, contributing to tumorigenesis and metastasis and with the potential role as biomarkers through competitive endogenous RNAs (ceRNAs) as miRNA sponges or by interacting with RNA binding proteins (RBPs). In this review, we summarize the regulatory roles of circRNAs in HCC development as well as the use of bioinformatics tools in the annotation and prioritization of circRNA and highlight the participation of exosomal circRNAs in HCC metastasis and drug resistance.
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Affiliation(s)
- Yichen Liu
- Fujian Provincial Key Laboratory of Innovative Drug Target Research, School of Pharmaceutical Sciences, Xiamen University, Xiamen, Fujian, 361102, People's Republic of China
| | - Lei Wang
- Fujian Provincial Key Laboratory of Innovative Drug Target Research, School of Pharmaceutical Sciences, Xiamen University, Xiamen, Fujian, 361102, People's Republic of China
| | - Wen Liu
- Fujian Provincial Key Laboratory of Innovative Drug Target Research, School of Pharmaceutical Sciences, Xiamen University, Xiamen, Fujian, 361102, People's Republic of China
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31
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Fan C, Lei X, Tie J, Zhang Y, Wu FX, Pan Y. CircR2Disease v2.0: An Updated Web Server for Experimentally Validated circRNA-disease Associations and Its Application. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:435-445. [PMID: 34856391 PMCID: PMC9801044 DOI: 10.1016/j.gpb.2021.10.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 10/24/2021] [Accepted: 11/24/2021] [Indexed: 01/26/2023]
Abstract
With accumulating dysregulated circular RNAs (circRNAs) in pathological processes, the regulatory functions of circRNAs, especially circRNAs as microRNA (miRNA) sponges and their interactions with RNA-binding proteins (RBPs), have been widely validated. However, the collected information on experimentally validated circRNA-disease associations is only preliminary. Therefore, an updated CircR2Disease database providing a comprehensive resource and web tool to clarify the relationships between circRNAs and diseases in diverse species is necessary. Here, we present an updated CircR2Disease v2.0 with the increased number of circRNA-disease associations and novel characteristics. CircR2Disease v2.0 provides more than 5-fold experimentally validated circRNA-disease associations compared to its previous version. This version includes 4201 entries between 3077 circRNAs and 312 disease subtypes. Secondly, the information of circRNA-miRNA, circRNA-miRNA-target, and circRNA-RBP interactions has been manually collected for various diseases. Thirdly, the gene symbols of circRNAs and disease name IDs can be linked with various nomenclature databases. Detailed descriptions such as samples and journals have also been integrated into the updated version. Thus, CircR2Disease v2.0 can serve as a platform for users to systematically investigate the roles of dysregulated circRNAs in various diseases and further explore the posttranscriptional regulatory function in diseases. Finally, we propose a computational method named circDis based on the graph convolutional network (GCN) and gradient boosting decision tree (GBDT) to illustrate the applications of the CircR2Disease v2.0 database. CircR2Disease v2.0 is available at http://bioinfo.snnu.edu.cn/CircR2Disease_v2.0 and https://github.com/bioinforlab/CircR2Disease-v2.0.
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Affiliation(s)
- Chunyan Fan
- School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xi’an 710119, China,Corresponding authors.
| | - Jiaojiao Tie
- School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
| | - Yuchen Zhang
- School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada,Corresponding authors.
| | - Yi Pan
- Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA,Corresponding authors.
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32
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Bin Li, Yan R, Liu X, Meng Z, Meng P, Wang Y, Huang Y. CircRNAs Biogenesis, Functions, and Its Research Progress in Aquaculture. RUSSIAN JOURNAL OF BIOORGANIC CHEMISTRY 2022. [DOI: 10.1134/s1068162022020042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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33
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Chiang TW, Mai TL, Chuang TJ. CircMiMi: a stand-alone software for constructing circular RNA-microRNA-mRNA interactions across species. BMC Bioinformatics 2022; 23:164. [PMID: 35524165 PMCID: PMC9074202 DOI: 10.1186/s12859-022-04692-0] [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] [Received: 08/26/2021] [Accepted: 04/17/2022] [Indexed: 01/22/2023] Open
Abstract
Background Circular RNAs (circRNAs) are a class of non-coding RNAs formed by pre-mRNA back-splicing, which are widely expressed in animal/plant cells and often play an important role in regulating microRNA (miRNA) activities. While numerous databases have collected a large amount of predicted circRNA candidates and provided the corresponding circRNA-regulated interactions, a stand-alone package for constructing circRNA-miRNA-mRNA interactions based on user-identified circRNAs across species is lacking. Results We present CircMiMi (circRNA-miRNA-mRNA interactions), a modular, Python-based software to identify circRNA-miRNA-mRNA interactions across 18 species (including 16 animals and 2 plants) with the given coordinates of circRNA junctions. The CircMiMi-constructed circRNA-miRNA-mRNA interactions are derived from circRNA-miRNA and miRNA-mRNA axes with the support of computational predictions and/or experimental data. CircMiMi also allows users to examine alignment ambiguity of back-splice junctions for checking circRNA reliability and examine reverse complementary sequences residing in the sequences flanking the circularized exons for investigating circRNA formation. We further employ CircMiMi to identify circRNA-miRNA-mRNA interactions based on the circRNAs collected in NeuroCirc, a large-scale database of circRNAs in the human brain. We construct circRNA-miRNA-mRNA interactions comprising differentially expressed circRNAs, and miRNAs in autism spectrum disorder (ASD) and cross-species analyze the relevance of the targets to ASD. We thus provide a rich set of ASD-associated circRNA-miRNA-mRNA axes and a useful starting point for investigation of regulatory mechanisms in ASD pathophysiology. Conclusions CircMiMi allows users to identify circRNA-mediated interactions in multiple species, shedding light on regulatory roles of circRNAs. The software package and web interface are freely available at https://github.com/TreesLab/CircMiMi and http://circmimi.genomics.sinica.edu.tw/, respectively. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04692-0.
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Affiliation(s)
- Tai-Wei Chiang
- Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - Te-Lun Mai
- Department of Life Science, National Taiwan University, Taipei, Taiwan
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34
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Niu M, Zou Q, Wang C. GMNN2CD: identification of circRNA-disease associations based on variational inference and graph Markov neural networks. Bioinformatics 2022; 38:2246-2253. [PMID: 35157027 DOI: 10.1093/bioinformatics/btac079] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 12/05/2021] [Accepted: 02/09/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION With the analysis of the characteristic and function of circular RNAs (circRNAs), people have realized that they play a critical role in the diseases. Exploring the relationship between circRNAs and diseases is of far-reaching significance for searching the etiopathogenesis and treatment of diseases. Nevertheless, it is inefficient to learn new associations only through biotechnology. RESULTS Consequently, we present a computational method, GMNN2CD, which employs a graph Markov neural network (GMNN) algorithm to predict unknown circRNA-disease associations. First, used verified associations, we calculate semantic similarity and Gaussian interactive profile kernel similarity (GIPs) of the disease and the GIPs of circRNA and then merge them to form a unified descriptor. After that, GMNN2CD uses a fusion feature variational map autoencoder to learn deep features and uses a label propagation map autoencoder to propagate tags based on known associations. Based on variational inference, GMNN alternate training enhances the ability of GMNN2CD to obtain high-efficiency high-dimensional features from low-dimensional representations. Finally, 5-fold cross-validation of five benchmark datasets shows that GMNN2CD is superior to the state-of-the-art methods. Furthermore, case studies have shown that GMNN2CD can detect potential associations. AVAILABILITY AND IMPLEMENTATION The source code and data are available at https://github.com/nmt315320/GMNN2CD.git.
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Affiliation(s)
- Mengting Niu
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 610000, China.,Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang 324000, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 610000, China.,Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang 324000, China
| | - Chunyu Wang
- Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150000, China
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35
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Yu W, Gu Q, Wu D, Zhang W, Li G, Lin L, Lowe JM, Hu S, Li TW, Zhou Z, Miao MZ, Gong Y, Zhao Y, Lu E. Identification of potentially functional circRNAs and prediction of circRNA-miRNA-mRNA regulatory network in periodontitis: Bridging the gap between bioinformatics and clinical needs. J Periodontal Res 2022; 57:594-614. [PMID: 35388494 PMCID: PMC9325354 DOI: 10.1111/jre.12989] [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] [Received: 07/22/2021] [Revised: 03/19/2022] [Accepted: 03/23/2022] [Indexed: 02/06/2023]
Abstract
Background and Objective Periodontitis is a multifactorial chronic inflammatory disease that can lead to the irreversible destruction of dental support tissues. As an epigenetic factor, the expression of circRNA is tissue‐dependent and disease‐dependent. This study aimed to identify novel periodontitis‐associated circRNAs and predict relevant circRNA‐periodontitis regulatory network by using recently developed bioinformatic tools and integrating sequencing profiling with clinical information for getting a better and more thorough image of periodontitis pathogenesis, from gene to clinic. Material and Methods High‐throughput sequencing and RT‐qPCR were conducted to identify differentially expressed circRNAs in gingival tissues from periodontitis patients. The relationship between upregulated circRNAs expression and probing depth (PD) was performed using Spearman's correlation analysis. Bioinformatic analyses including GO analysis, circRNA‐disease association prediction, and circRNA‐miRNA‐mRNA network prediction were performed to clarify potential regulatory functions of identified circRNAs in periodontitis. A receiver‐operating characteristic (ROC) curve was established to assess the diagnostic significance of identified circRNAs. Results High‐throughput sequencing identified 70 differentially expressed circRNAs (68 upregulated and 2 downregulated circRNAs) in human periodontitis (fold change >2.0 and p < .05). The top five upregulated circRNAs were validated by RT‐qPCR that had strong associations with multiple human diseases, including periodontitis. The upregulation of circRNAs were positively correlated with PD (R = .40–.69, p < .05, moderate). A circRNA‐miRNA‐mRNA network with the top five upregulated circRNAs, differentially expressed mRNAs, and overlapped predicted miRNAs indicated potential roles of circRNAs in immune response, cell apoptosis, migration, adhesion, and reaction to oxidative stress. The ROC curve showed that circRNAs had potential value in periodontitis diagnosis (AUC = 0.7321–0.8667, p < .05). Conclusion CircRNA‐disease associations were predicted by online bioinformatic tools. Positive correlation between upregulated circRNAs, circPTP4A2, chr22:23101560‐23135351+, circARHGEF28, circBARD1 and circRASA2, and PD suggested function of circRNAs in periodontitis. Network prediction further focused on downstream targets regulated by circRNAs during periodontitis pathogenesis.
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Affiliation(s)
- Weijun Yu
- Department of Stomatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.,College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
| | - Qisheng Gu
- Key Laboratory of Molecular Virology and Immunology, Institut Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai, China.,Department of Immunology, Bio Sorbonne Paris Cité, University of Paris, Paris, France
| | - Di Wu
- Division of Oral and Craniofacial Biomedicine, University of North Carolina Adams School of Dentistry, Chapel Hill, North Carolina, USA.,Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Weiqi Zhang
- Department of Stomatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Gang Li
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Lu Lin
- Department of Stomatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jared M Lowe
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Shucheng Hu
- Department of Stomatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Tia Wenjun Li
- Division of Oral and Craniofacial Biomedicine, University of North Carolina Adams School of Dentistry, Chapel Hill, North Carolina, USA.,Gene Therapy Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Zhen Zhou
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Michael Z Miao
- Division of Oral and Craniofacial Biomedicine, University of North Carolina Adams School of Dentistry, Chapel Hill, North Carolina, USA
| | - Yuhua Gong
- Department of Stomatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yifei Zhao
- Department of Stomatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Eryi Lu
- Department of Stomatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.,College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
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Patterns of Differentially Expressed circRNAs in Human Thymocytes. Noncoding RNA 2022; 8:ncrna8020026. [PMID: 35447889 PMCID: PMC9030292 DOI: 10.3390/ncrna8020026] [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: 02/25/2022] [Revised: 03/24/2022] [Accepted: 03/29/2022] [Indexed: 12/05/2022] Open
Abstract
Circular RNAs (circRNAs) are suggested to play a discriminative role between some stages of thymocyte differentiation. However, differential aspects of the stage of mature single-positive thymocytes remain to be explored. The purpose of this study is to investigate the differential expression pattern of circRNAs in three different development stages of human thymocytes, including mature single-positive cells, and perform predictions in silico regarding the ability of specific circRNAs when controlling the expression of genes involved in thymocyte differentiation. We isolate human thymocytes at three different stages of intrathymic differentiation and determine the expression of circRNAs and mRNA by RNASeq. We show that the differential expression pattern of 50 specific circRNAs serves to discriminate between the three human thymocyte populations. Interestingly, the downregulation of RAG2, a gene involved in T-cell differentiation in the thymus, could be simultaneously controlled by the downregulation of two circRNASs (hsa_circ_0031584 and hsa_circ_0019079) through the hypothetical liberation of hsa-miR-609. Our study provides, for the first time, significant insights into the usefulness of circRNAs in discriminating between different stages of thymocyte differentiation and provides new potential circRNA–miRNA–mRNA networks capable of controlling the expression of genes involved in T-cell differentiation in the thymus.
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Chao H, Hu Y, Zhao L, Xin S, Ni Q, Zhang P, Chen M. Biogenesis, Functions, Interactions, and Resources of Non-Coding RNAs in Plants. Int J Mol Sci 2022; 23:ijms23073695. [PMID: 35409060 PMCID: PMC8998614 DOI: 10.3390/ijms23073695] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/19/2022] [Accepted: 03/23/2022] [Indexed: 12/14/2022] Open
Abstract
Plant transcriptomes encompass a large number of functional non-coding RNAs (ncRNAs), only some of which have protein-coding capacity. Since their initial discovery, ncRNAs have been classified into two broad categories based on their biogenesis and mechanisms of action, housekeeping ncRNAs and regulatory ncRNAs. With advances in RNA sequencing technology and computational methods, bioinformatics resources continue to emerge and update rapidly, including workflow for in silico ncRNA analysis, up-to-date platforms, databases, and tools dedicated to ncRNA identification and functional annotation. In this review, we aim to describe the biogenesis, biological functions, and interactions with DNA, RNA, protein, and microorganism of five major regulatory ncRNAs (miRNA, siRNA, tsRNA, circRNA, lncRNA) in plants. Then, we systematically summarize tools for analysis and prediction of plant ncRNAs, as well as databases. Furthermore, we discuss the silico analysis process of these ncRNAs and present a protocol for step-by-step computational analysis of ncRNAs. In general, this review will help researchers better understand the world of ncRNAs at multiple levels.
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Affiliation(s)
| | | | | | | | | | - Peijing Zhang
- Correspondence: (P.Z.); (M.C.); Tel./Fax: +86-(0)571-88206612 (M.C.)
| | - Ming Chen
- Correspondence: (P.Z.); (M.C.); Tel./Fax: +86-(0)571-88206612 (M.C.)
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Montico B, Giurato G, Pecoraro G, Salvati A, Covre A, Colizzi F, Steffan A, Weisz A, Maio M, Sigalotti L, Fratta E. The pleiotropic roles of circular and long noncoding RNAs in cutaneous melanoma. Mol Oncol 2022; 16:565-593. [PMID: 34080276 PMCID: PMC8807361 DOI: 10.1002/1878-0261.13034] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 04/30/2021] [Accepted: 05/17/2021] [Indexed: 12/14/2022] Open
Abstract
Cutaneous melanoma (CM) is a very aggressive disease, often characterized by unresponsiveness to conventional therapies and high mortality rates worldwide. The identification of the activating BRAFV600 mutations in approximately 50% of CM patients has recently fueled the development of novel small-molecule inhibitors that specifically target BRAFV600 -mutant CM. In addition, a major progress in CM treatment has been made by monoclonal antibodies that regulate the immune checkpoint inhibitors. However, although target-based therapies and immunotherapeutic strategies have yielded promising results, CM treatment remains a major challenge. In the last decade, accumulating evidence points to the aberrant expression of different types of noncoding RNAs (ncRNAs) in CM. While studies on microRNAs have grown exponentially leading to significant insights on CM biology, the role of circular RNAs (circRNAs) and long noncoding RNAs (lncRNAs) in this tumor is less understood, and much remains to be discovered. Here, we summarize and critically review the available evidence on the molecular functions of circRNAs and lncRNAs in BRAFV600 -mutant CM and CM immunogenicity, providing recent updates on their functional role in targeted therapy and immunotherapy resistance. In addition, we also include an evaluation of several algorithms and databases for prediction and validation of circRNA and lncRNA functional interactions.
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Affiliation(s)
- Barbara Montico
- Immunopathology and Cancer BiomarkersCentro di Riferimento Oncologico di Aviano (CRO)IRCCSAvianoItaly
| | - Giorgio Giurato
- Laboratory of Molecular Medicine and GenomicsDepartment of Medicine, Surgery and Dentistry 'Scuola Medica Salernitana'University of SalernoBaronissiItaly
- Genome Research Center for Health – CRGSUniversity of Salerno Campus of MedicineBaronissiItaly
| | - Giovanni Pecoraro
- Laboratory of Molecular Medicine and GenomicsDepartment of Medicine, Surgery and Dentistry 'Scuola Medica Salernitana'University of SalernoBaronissiItaly
- Genome Research Center for Health – CRGSUniversity of Salerno Campus of MedicineBaronissiItaly
| | - Annamaria Salvati
- Laboratory of Molecular Medicine and GenomicsDepartment of Medicine, Surgery and Dentistry 'Scuola Medica Salernitana'University of SalernoBaronissiItaly
| | - Alessia Covre
- Center for Immuno‐OncologyUniversity Hospital of SienaItaly
- University of SienaItaly
| | - Francesca Colizzi
- Immunopathology and Cancer BiomarkersCentro di Riferimento Oncologico di Aviano (CRO)IRCCSAvianoItaly
| | - Agostino Steffan
- Immunopathology and Cancer BiomarkersCentro di Riferimento Oncologico di Aviano (CRO)IRCCSAvianoItaly
| | - Alessandro Weisz
- Laboratory of Molecular Medicine and GenomicsDepartment of Medicine, Surgery and Dentistry 'Scuola Medica Salernitana'University of SalernoBaronissiItaly
- Genome Research Center for Health – CRGSUniversity of Salerno Campus of MedicineBaronissiItaly
| | - Michele Maio
- Center for Immuno‐OncologyUniversity Hospital of SienaItaly
- University of SienaItaly
- NIBIT Foundation OnlusSienaItaly
| | - Luca Sigalotti
- Oncogenetics and Functional Oncogenomics UnitCentro di Riferimento Oncologico di Aviano (CRO)IRCCSAvianoItaly
| | - Elisabetta Fratta
- Immunopathology and Cancer BiomarkersCentro di Riferimento Oncologico di Aviano (CRO)IRCCSAvianoItaly
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Oliveira LS, Patera AC, Domingues DS, Sanches DS, Lopes FM, Bugatti PH, Saito PTM, Maracaja-Coutinho V, Durham AM, Paschoal AR. Computational Analysis of Transposable Elements and CircRNAs in Plants. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2362:147-172. [PMID: 34195962 DOI: 10.1007/978-1-0716-1645-1_9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
This chapter provides two main contributions: (1) a description of computational tools and databases used to identify and analyze transposable elements (TEs) and circRNAs in plants; and (2) data analysis on public TE and circRNA data. Our goal is to highlight the primary information available in the literature on circular noncoding RNAs and transposable elements in plants. The exploratory analysis performed on publicly available circRNA and TEs data help discuss four sequence features. Finally, we investigate the association on circRNAs:TE in plants in the model organism Arabidopsis thaliana.
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Affiliation(s)
- Liliane Santana Oliveira
- Department of Computer Science, Federal University of Technology-Paraná (UTFPR), Cornélio Procópio, PR, Brazil. .,Embrapa Soja, Londrina, Paraná, Brazil.
| | - Andressa Caroline Patera
- Department of Computer Science, Federal University of Technology-Paraná (UTFPR), Cornélio Procópio, PR, Brazil
| | - Douglas Silva Domingues
- Department of Computer Science, Federal University of Technology-Paraná (UTFPR), Cornélio Procópio, PR, Brazil.,Group of Genomics and Transcriptomes in Plants, Instituto de Biociências de Rio Claro, Universidade Estadual Paulista (UNESP), Rio Claro, SP, Brazil
| | - Danilo Sipoli Sanches
- Department of Computer Science, Federal University of Technology-Paraná (UTFPR), Cornélio Procópio, PR, Brazil
| | - Fabricio Martins Lopes
- Department of Computer Science, Federal University of Technology-Paraná (UTFPR), Cornélio Procópio, PR, Brazil
| | - Pedro Henrique Bugatti
- Department of Computer Science, Federal University of Technology-Paraná (UTFPR), Cornélio Procópio, PR, Brazil
| | - Priscila Tiemi Maeda Saito
- Department of Computer Science, Federal University of Technology-Paraná (UTFPR), Cornélio Procópio, PR, Brazil
| | - Vinicius Maracaja-Coutinho
- Centro de Modelamiento Molecular, Biofísica y Bioinformática-CM2B2, Facultad de Ciencias Quimicas y Farmaceuticas, Universidad de Chile, Santiago, Chile
| | - Alan Mitchell Durham
- Department of Computer Science, Instituto de Matemática e Estatística, Universidade de São Paulo (USP), Cidade Universitária, SP, Brazil
| | - Alexandre Rossi Paschoal
- Department of Computer Science, Federal University of Technology-Paraná (UTFPR), Cornélio Procópio, PR, Brazil.
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Ma Z, Kuang Z, Deng L. CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network. BMC Bioinformatics 2021; 22:551. [PMID: 34772332 PMCID: PMC8588735 DOI: 10.1186/s12859-021-04467-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 11/01/2021] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND The existing studies show that circRNAs can be used as a biomarker of diseases and play a prominent role in the treatment and diagnosis of diseases. However, the relationships between the vast majority of circRNAs and diseases are still unclear, and more experiments are needed to study the mechanism of circRNAs. Nowadays, some scholars use the attributes between circRNAs and diseases to study and predict their associations. Nonetheless, most of the existing experimental methods use less information about the attributes of circRNAs, which has a certain impact on the accuracy of the final prediction results. On the other hand, some scholars also apply experimental methods to predict the associations between circRNAs and diseases. But such methods are usually expensive and time-consuming. Based on the above shortcomings, follow-up research is needed to propose a more efficient calculation-based method to predict the associations between circRNAs and diseases. RESULTS In this study, a novel algorithm (method) is proposed, which is based on the Graph Convolutional Network (GCN) constructed with Random Walk with Restart (RWR) and Principal Component Analysis (PCA) to predict the associations between circRNAs and diseases (CRPGCN). In the construction of CRPGCN, the RWR algorithm is used to improve the similarity associations of the computed nodes with their neighbours. After that, the PCA method is used to dimensionality reduction and extract features, it makes the connection between circRNAs with higher similarity and diseases closer. Finally, The GCN algorithm is used to learn the features between circRNAs and diseases and calculate the final similarity scores, and the learning datas are constructed from the adjacency matrix, similarity matrix and feature matrix as a heterogeneous adjacency matrix and a heterogeneous feature matrix. CONCLUSIONS After 2-fold cross-validation, 5-fold cross-validation and 10-fold cross-validation, the area under the ROC curve of the CRPGCN is 0.9490, 0.9720 and 0.9722, respectively. The CRPGCN method has a valuable effect in predict the associations between circRNAs and diseases.
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Affiliation(s)
- Zhihao Ma
- School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China
| | - Zhufang Kuang
- School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha, China
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Babin L, Andraos E, Fuchs S, Pyronnet S, Brunet E, Meggetto F. From circRNAs to fusion circRNAs in hematological malignancies. JCI Insight 2021; 6:151513. [PMID: 34747369 PMCID: PMC8663548 DOI: 10.1172/jci.insight.151513] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Circular RNAs (circRNAs) represent a type of endogenous noncoding RNA generated by back-splicing events. Unlike the majority of RNAs, circRNAs are covalently closed, without a 5' end or a 3' poly(A) tail. A few circRNAs can be associated with polysomes, suggesting a protein-coding potential. CircRNAs are not degraded by RNA exonucleases or ribonuclease R and are enriched in exosomes. Recent developments in experimental methods coupled with evolving bioinformatic approaches have accelerated functional investigation of circRNAs, which exhibit a stable structure, a long half-life, and tumor specificity and can be extracted from body fluids and used as potential biological markers for tumors. Moreover, circRNAs may regulate the occurrence and development of cancers and contribute to drug resistance through a variety of molecular mechanisms. Despite the identification of a growing number of circRNAs, their effects in hematological cancers remain largely unknown. Recent studies indicate that circRNAs could also originate from fusion genes (fusion circRNAs, f-circRNAs) next to chromosomal translocations, which are considered the primary cause of various cancers, notably hematological malignancies. This Review will focus on circRNAs and f-circRNAs in hematological cancers.
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Affiliation(s)
- Loelia Babin
- CRCT INSERM, UMR1037, Toulouse, France.,Toulouse III University-Paul Sabatier, UMR1037 INSERM, UMR5071 CNRS, Toulouse, France.,The Toulouse Cancer Laboratory of Excellence (TOUCAN), Toulouse, France
| | - Elissa Andraos
- CRCT INSERM, UMR1037, Toulouse, France.,Toulouse III University-Paul Sabatier, UMR1037 INSERM, UMR5071 CNRS, Toulouse, France.,The Toulouse Cancer Laboratory of Excellence (TOUCAN), Toulouse, France
| | - Steffen Fuchs
- CRCT INSERM, UMR1037, Toulouse, France.,Toulouse III University-Paul Sabatier, UMR1037 INSERM, UMR5071 CNRS, Toulouse, France.,The Toulouse Cancer Laboratory of Excellence (TOUCAN), Toulouse, France.,Department of Pediatric Oncology, Charité University Berlin, Berlin, Germany.,Berlin Institute of Health (BIH), Berlin, Germany.,German Cancer Consortium (DKTK), Partner Site Berlin, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Stéphane Pyronnet
- CRCT INSERM, UMR1037, Toulouse, France.,Toulouse III University-Paul Sabatier, UMR1037 INSERM, UMR5071 CNRS, Toulouse, France.,The Toulouse Cancer Laboratory of Excellence (TOUCAN), Toulouse, France
| | - Erika Brunet
- Imagine Institute INSERM Joint Research Unit 1163, Laboratory of Genome Dynamics in the Immune System, Paris, France.,Paris Descartes-Sorbonne University, Imagine Institute, Paris, France
| | - Fabienne Meggetto
- CRCT INSERM, UMR1037, Toulouse, France.,Toulouse III University-Paul Sabatier, UMR1037 INSERM, UMR5071 CNRS, Toulouse, France.,The Toulouse Cancer Laboratory of Excellence (TOUCAN), Toulouse, France
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Wang CC, Han CD, Zhao Q, Chen X. Circular RNAs and complex diseases: from experimental results to computational models. Brief Bioinform 2021; 22:bbab286. [PMID: 34329377 PMCID: PMC8575014 DOI: 10.1093/bib/bbab286] [Citation(s) in RCA: 104] [Impact Index Per Article: 34.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 06/23/2021] [Accepted: 07/03/2021] [Indexed: 12/13/2022] Open
Abstract
Circular RNAs (circRNAs) are a class of single-stranded, covalently closed RNA molecules with a variety of biological functions. Studies have shown that circRNAs are involved in a variety of biological processes and play an important role in the development of various complex diseases, so the identification of circRNA-disease associations would contribute to the diagnosis and treatment of diseases. In this review, we summarize the discovery, classifications and functions of circRNAs and introduce four important diseases associated with circRNAs. Then, we list some significant and publicly accessible databases containing comprehensive annotation resources of circRNAs and experimentally validated circRNA-disease associations. Next, we introduce some state-of-the-art computational models for predicting novel circRNA-disease associations and divide them into two categories, namely network algorithm-based and machine learning-based models. Subsequently, several evaluation methods of prediction performance of these computational models are summarized. Finally, we analyze the advantages and disadvantages of different types of computational models and provide some suggestions to promote the development of circRNA-disease association identification from the perspective of the construction of new computational models and the accumulation of circRNA-related data.
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Affiliation(s)
- Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining and Technology
| | - Chen-Di Han
- School of Information and Control Engineering, China University of Mining and Technology
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning
| | - Xing Chen
- China University of Mining and Technology
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Circulating expression levels of CircHIPK3 and CDR1as circular-RNAs in type 2 diabetes patients. Mol Biol Rep 2021; 49:131-138. [PMID: 34731367 DOI: 10.1007/s11033-021-06850-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 10/15/2021] [Indexed: 10/25/2022]
Abstract
BACKGROUND Recent investigations suggested that deregulated levels of Circular RNAs (circRNAs) could be associated with type 2 diabetes mellitus (T2DM) pathogenesis. Accordingly, this study aimed to determine the expression levels of circulating CircHIPK3, CDR1as and their correlation with biochemical parameters in patients with T2DM, pre-diabetes and control subjects. METHODS AND RESULTS The expression of circRNAs in peripheral blood was determined using QRT-PCR in 70 patients with T2DM, 60 pre-diabetes and in 69 age and sex matched healthy controls. Moreover, bioinformatics tools were applied to explore and predict the potential interactions between circRNAs and other non-coding RNAs (ncRNAs). Our analysis revealed that the expression level of CircHIPK3 was significantly elevated in T2DM patients compared to healthy participants (P < 0.001) and pre-diabetes subjects (P = 0.018). In addition, ROC analysis suggested that at the cutoff value of 0.24 and the sensitivity and specificity of 50% and 88.4%, respectively, CircHIPK3 could distinguish between T2DM patients and control subjects. Furthermore, it was observed that the expression level of CDR1as is higher in pre-diabetic individuals than healthy individuals (P = 0.004). Finally, Spearman correlation analysis showed that there was a significant correlation between CircHIPK3 and CDR1as expression levels and clinical and anthropometrical parameters such as BMI, systolic and diastolic blood pressure, HbA1c and fasting blood glucose (P < 0.005). CONCLUSIONS The data of this study provided evidence that the expression levels of CircHIPK3, CDR1as increased in T2DM and pre-diabetes subjects, respectively.
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Micheel J, Safrastyan A, Wollny D. Advances in Non-Coding RNA Sequencing. Noncoding RNA 2021; 7:70. [PMID: 34842804 PMCID: PMC8628893 DOI: 10.3390/ncrna7040070] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/22/2021] [Accepted: 10/26/2021] [Indexed: 12/11/2022] Open
Abstract
Non-coding RNAs (ncRNAs) comprise a set of abundant and functionally diverse RNA molecules. Since the discovery of the first ncRNA in the 1960s, ncRNAs have been shown to be involved in nearly all steps of the central dogma of molecular biology. In recent years, the pace of discovery of novel ncRNAs and their cellular roles has been greatly accelerated by high-throughput sequencing. Advances in sequencing technology, library preparation protocols as well as computational biology helped to greatly expand our knowledge of which ncRNAs exist throughout the kingdoms of life. Moreover, RNA sequencing revealed crucial roles of many ncRNAs in human health and disease. In this review, we discuss the most recent methodological advancements in the rapidly evolving field of high-throughput sequencing and how it has greatly expanded our understanding of ncRNA biology across a large number of different organisms.
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Affiliation(s)
| | | | - Damian Wollny
- RNA Bioinformatics/High Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University, 07743 Jena, Germany; (J.M.); (A.S.)
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Hsa_circ_0004296 inhibits metastasis of prostate cancer by interacting with EIF4A3 to prevent nuclear export of ETS1 mRNA. J Exp Clin Cancer Res 2021; 40:336. [PMID: 34696782 PMCID: PMC8543852 DOI: 10.1186/s13046-021-02138-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 10/10/2021] [Indexed: 02/08/2023] Open
Abstract
Background Circular RNAs (circRNAs) have been shown to play vital biological functions in various tumors, including prostate cancer (PCa). However, the roles of circRNAs in the metastasis of PCa remain unclear. In the present study, differentially expressed circRNAs associated with PCa metastasis were screened using high-throughput RNA sequencing, from which hsa_circ_0004296 was identified. Methods Quantitative real-time PCR (qRT-PCR) was used to detect the expression of circ_0004296 in PCa tissues and adjacent normal tissues as well as in blood and urine. Gain and loss of function experiments were performed to investigate the function of circ_0004296 in PCa. Bioinformatics analyses, RNA pull-down assay, and mass spectrometry were conducted to identify RNA-binding proteins. RNA immunoprecipitation and RNA and protein nuclear-cytoplasmic fractionation were performed to investigate the underlying mechanism. A xenograft mouse model was used to analyze the effect of circ_0004296 on PCa growth and metastasis in vivo. Results The expression of circ_0004296 was decreased in PCa tissues, blood, and urine, which was negatively associated with metastasis. Furthermore, gain and loss of function experiments in vitro and in vivo showed that circ_0004296 inhibited the proliferation, migration, invasion, and epithelial-mesenchymal transition of PCa cells. Mechanistically, circ_0004296 regulated host gene ETS1 expression at the post-transcriptional level. EIF4A3 was identified and confirmed as the downstream binding protein of circ_0004296. EIF4A3 expression was significantly upregulated in PCa tissues and associated with PCa metastasis. Silencing EIF4A3 suppressed PCa cell proliferation, migration, invasion, and EMT. Conclusions Circ_0004296 overexpression efficiently inhibited ETS1 mRNA nuclear export by promoting EIF4A3 retention in the nucleus, leading to the downregulation of ETS1 expression and suppression of PCa metastasis; thus, circ_0004296 might be a potential biomarker and therapeutic target for patients with PCa. Supplementary Information The online version contains supplementary material available at 10.1186/s13046-021-02138-8.
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Xiao Q, Dai J, Luo J. A survey of circular RNAs in complex diseases: databases, tools and computational methods. Brief Bioinform 2021; 23:6407737. [PMID: 34676391 DOI: 10.1093/bib/bbab444] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 09/21/2021] [Accepted: 09/28/2021] [Indexed: 01/22/2023] Open
Abstract
Circular RNAs (circRNAs) are a category of novelty discovered competing endogenous non-coding RNAs that have been proved to implicate many human complex diseases. A large number of circRNAs have been confirmed to be involved in cancer progression and are expected to become promising biomarkers for tumor diagnosis and targeted therapy. Deciphering the underlying relationships between circRNAs and diseases may provide new insights for us to understand the pathogenesis of complex diseases and further characterize the biological functions of circRNAs. As traditional experimental methods are usually time-consuming and laborious, computational models have made significant progress in systematically exploring potential circRNA-disease associations, which not only creates new opportunities for investigating pathogenic mechanisms at the level of circRNAs, but also helps to significantly improve the efficiency of clinical trials. In this review, we first summarize the functions and characteristics of circRNAs and introduce some representative circRNAs related to tumorigenesis. Then, we mainly investigate the available databases and tools dedicated to circRNA and disease studies. Next, we present a comprehensive review of computational methods for predicting circRNA-disease associations and classify them into five categories, including network propagating-based, path-based, matrix factorization-based, deep learning-based and other machine learning methods. Finally, we further discuss the challenges and future researches in this field.
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Affiliation(s)
- Qiu Xiao
- Hunan Normal University and Hunan Xiangjiang Artificial Intelligence Academy, Changsha, China
| | - Jianhua Dai
- Hunan Normal University and Hunan Xiangjiang Artificial Intelligence Academy, Changsha, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
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Mei X, Chen SY. Circular RNAs in cardiovascular diseases. Pharmacol Ther 2021; 232:107991. [PMID: 34592203 DOI: 10.1016/j.pharmthera.2021.107991] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 08/08/2021] [Accepted: 09/01/2021] [Indexed: 10/20/2022]
Abstract
In eukaryotes, precursor mRNAs (pre-mRNAs) produce a unique class of biologically active molecules namely circular RNAs (circRNAs) with a covalently closed-loop structure via back-splicing. Because of this unconventional circular form, circRNAs exhibit much higher stability than linear RNAs due to the resistance to exonuclease degradation and thereby play exclusive cellular regulatory roles. Recent studies have shown that circRNAs are widely expressed in eukaryotes and display tissue- and disease-specific expression patterns, including in the cardiovascular system. Although numerous circRNAs are discovered by in silico methods, a limited number of circRNAs have been studied. This review intends to summarize the current understanding of the characteristics, biogenesis, and functions of circRNAs and delineate the practical approaches for circRNAs investigation. Moreover, we discuss the emerging roles of circRNAs in cardiovascular diseases.
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Affiliation(s)
- Xiaohan Mei
- Departments of Surgery, University of Missouri School of Medicine, Columbia, MO, United States of America
| | - Shi-You Chen
- Departments of Surgery, University of Missouri School of Medicine, Columbia, MO, United States of America; Department of Medical Pharmacology & Physiology, University of Missouri School of Medicine, Columbia, MO, United States of America.
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Rabin A, Zaffagni M, Ashwal-Fluss R, Patop IL, Jajoo A, Shenzis S, Carmel L, Kadener S. SRCP: a comprehensive pipeline for accurate annotation and quantification of circRNAs. Genome Biol 2021; 22:277. [PMID: 34556162 PMCID: PMC8459468 DOI: 10.1186/s13059-021-02497-7] [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: 12/03/2019] [Accepted: 09/13/2021] [Indexed: 12/20/2022] Open
Abstract
Here we describe a new integrative approach for accurate annotation and quantification of circRNAs named Short Read circRNA Pipeline (SRCP). Our strategy involves two steps: annotation of validated circRNAs followed by a quantification step. We show that SRCP is more sensitive than other individual pipelines and allows for more comprehensive quantification of a larger number of differentially expressed circRNAs. To facilitate the use of SRCP, we generate a comprehensive collection of validated circRNAs in five different organisms, including humans. We then utilize our approach and identify a subset of circRNAs bound to the miRNA-effector protein AGO2 in human brain samples.
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Affiliation(s)
- Avigayel Rabin
- Biological Chemistry Department, Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, 91904, Jerusalem, Israel
| | - Michela Zaffagni
- Biology Department, Brandeis University, Waltham, MA, 02454, USA
| | - Reut Ashwal-Fluss
- Biological Chemistry Department, Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, 91904, Jerusalem, Israel
| | - Ines Lucia Patop
- Biology Department, Brandeis University, Waltham, MA, 02454, USA
| | - Aarti Jajoo
- Biology Department, Brandeis University, Waltham, MA, 02454, USA
| | - Shlomo Shenzis
- Biological Chemistry Department, Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, 91904, Jerusalem, Israel
| | - Liran Carmel
- Department of Genetics, Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, 91904, Jerusalem, Israel
| | - Sebastian Kadener
- Biological Chemistry Department, Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, 91904, Jerusalem, Israel.
- Biology Department, Brandeis University, Waltham, MA, 02454, USA.
- Department of Genetics, Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, 91904, Jerusalem, Israel.
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Sharma D, Sehgal P, Sivasubbu S, Scaria V. A genome-wide circular RNA transcriptome in rat. Biol Methods Protoc 2021; 6:bpab016. [PMID: 34527809 PMCID: PMC8435660 DOI: 10.1093/biomethods/bpab016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 08/11/2021] [Accepted: 08/12/2021] [Indexed: 11/26/2022] Open
Abstract
Circular RNAs (circRNAs) are a novel class of noncoding RNAs that back-splice from 5ʹ donor site and 3ʹ acceptor sites to form a circular structure. A number of circRNAs have been discovered in model organisms including human, mouse, Drosophila, among other organisms. There are a few candidate-based studies on circRNAs in rat, a well-studied model organism as well. A number of pipelines have been published to identify the back splice junctions for the discovery of circRNAs but studies comparing these tools have suggested that a combination of tools would be a better approach to identify high-confidence circRNAs. The availability of a recent dataset of transcriptomes encompassing 11 tissues, 4 developmental stages, and 2 genders motivated us to explore the landscape of circRNAs in the organism in this context. In order to understand the difference among different pipelines, we employed five different combinations of tools to identify circular RNAs from the dataset. We compared the results of the different combination of tools/pipelines with respect to alignment, total number of circRNAs identified and read-coverage. In addition, we identified tissue-specific, development-stage specific and gender-specific circRNAs and further independently validated 16 circRNA junctions out of 24 selected candidates in 5 tissue samples and estimated the quantitative expression of five circRNA candidates using real-time polymerase chain reaction and our analysis suggests three candidates as tissue-enriched. This study is one of the most comprehensive studies which provides a map of circRNAs transcriptome as well as to understand the difference among different computational pipelines in rat.
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Affiliation(s)
- Disha Sharma
- GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Mathura Road, Delhi 110025, India.,Academy of Scientific & Innovative Research (AcSIR), CSIR-IGIB South Campus, Mathura Road, Delhi 110025, India
| | - Paras Sehgal
- Genomics and Molecular Medicine, CSIR Institute of Genomics and Integrative Biology, Mathura Road (CSIR-IGIB), Delhi 110025, India.,Academy of Scientific & Innovative Research (AcSIR), CSIR-IGIB South Campus, Mathura Road, Delhi 110025, India
| | - Sridhar Sivasubbu
- Genomics and Molecular Medicine, CSIR Institute of Genomics and Integrative Biology, Mathura Road (CSIR-IGIB), Delhi 110025, India.,Academy of Scientific & Innovative Research (AcSIR), CSIR-IGIB South Campus, Mathura Road, Delhi 110025, India
| | - Vinod Scaria
- GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Mathura Road, Delhi 110025, India.,Academy of Scientific & Innovative Research (AcSIR), CSIR-IGIB South Campus, Mathura Road, Delhi 110025, India
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