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Drula R, Braicu C, Neagoe IB. Current advances in circular RNA detection and investigation methods: Are we running in circles? WILEY INTERDISCIPLINARY REVIEWS. RNA 2024; 15:e1850. [PMID: 38702943 DOI: 10.1002/wrna.1850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 03/30/2024] [Accepted: 04/01/2024] [Indexed: 05/06/2024]
Abstract
Circular RNAs (circRNAs), characterized by their closed-loop structure, have emerged as significant transcriptomic regulators, with roles spanning from microRNA sponging to modulation of gene expression and potential peptide coding. The discovery and functional analysis of circRNAs have been propelled by advancements in both experimental and bioinformatics tools, yet the field grapples with challenges related to their detection, isoform diversity, and accurate quantification. This review navigates through the evolution of circRNA research methodologies, from early detection techniques to current state-of-the-art approaches that offer comprehensive insights into circRNA biology. We examine the limitations of existing methods, particularly the difficulty in differentiating circRNA isoforms and distinguishing circRNAs from their linear counterparts. A critical evaluation of various bioinformatics tools and novel experimental strategies is presented, emphasizing the need for integrated approaches to enhance our understanding and interpretation of circRNA functions. Our insights underscore the dynamic and rapidly advancing nature of circRNA research, highlighting the ongoing development of analytical frameworks designed to address the complexity of circRNAs and facilitate the assessment of their clinical utility. As such, this comprehensive overview aims to catalyze further advancements in circRNA study, fostering a deeper understanding of their roles in cellular processes and potential implications in disease. This article is categorized under: RNA Methods > RNA Nanotechnology RNA Methods > RNA Analyses in Cells RNA Methods > RNA Analyses In Vitro and In Silico.
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Affiliation(s)
- Rareș Drula
- Research Center for Functional Genomics, Biomedicine and Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Cornelia Braicu
- Research Center for Functional Genomics, Biomedicine and Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Ioana-Berindan Neagoe
- Research Center for Functional Genomics, Biomedicine and Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
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2
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Zhou Z, Zhang J, Zheng X, Pan Z, Zhao F, Gao Y. CIRI-Deep Enables Single-Cell and Spatial Transcriptomic Analysis of Circular RNAs with Deep Learning. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308115. [PMID: 38308181 PMCID: PMC11005702 DOI: 10.1002/advs.202308115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 01/03/2024] [Indexed: 02/04/2024]
Abstract
Circular RNAs (circRNAs) are a crucial yet relatively unexplored class of transcripts known for their tissue- and cell-type-specific expression patterns. Despite the advances in single-cell and spatial transcriptomics, these technologies face difficulties in effectively profiling circRNAs due to inherent limitations in circRNA sequencing efficiency. To address this gap, a deep learning model, CIRI-deep, is presented for comprehensive prediction of circRNA regulation on diverse types of RNA-seq data. CIRI-deep is trained on an extensive dataset of 25 million high-confidence circRNA regulation events and achieved high performances on both test and leave-out data, ensuring its accuracy in inferring differential events from RNA-seq data. It is demonstrated that CIRI-deep and its adapted version enable various circRNA analyses, including cluster- or region-specific circRNA detection, BSJ ratio map visualization, and trans and cis feature importance evaluation. Collectively, CIRI-deep's adaptability extends to all major types of RNA-seq datasets including single-cell and spatial transcriptomic data, which will undoubtedly broaden the horizons of circRNA research.
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Affiliation(s)
- Zihan Zhou
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information Beijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- University of Chinese Academy of SciencesBeijing100101China
| | - Jinyang Zhang
- Beijing Institutes of Life ScienceChinese Academy of SciencesBeijing100101China
- University of Chinese Academy of SciencesBeijing100101China
| | - Xin Zheng
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information Beijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- University of Chinese Academy of SciencesBeijing100101China
| | - Zhicheng Pan
- Center for Computational Biology Flatiron InstituteNew York10010USA
| | - Fangqing Zhao
- Beijing Institutes of Life ScienceChinese Academy of SciencesBeijing100101China
- University of Chinese Academy of SciencesBeijing100101China
| | - Yuan Gao
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information Beijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- University of Chinese Academy of SciencesBeijing100101China
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3
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Shi H, Zhou Y, Jia E, Liu Z, Pan M, Bai Y, Zhao X, Ge Q. Comparative analysis of circular RNA enrichment methods. RNA Biol 2021; 19:55-67. [PMID: 34895057 PMCID: PMC8786342 DOI: 10.1080/15476286.2021.2012632] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 09/22/2021] [Accepted: 11/27/2021] [Indexed: 11/12/2022] Open
Abstract
The circRNAs sequencing results vary due to the different enrichment methods and their performance is needed to systematic comparison. This study investigated the effects of different circRNA enrichment methods on sequencing results, including abundance and species of circRNAs, as well as the sensitivity and precision. This experiment was carried out by following four common circRNA enrichment methods: including ribosomal RNA depletion (rRNA-), polyadenylation and poly (A+) RNA depletion followed by RNase R treatment (polyA+RNase R), rRNA-+polyA+RNase R and polyA+RNase R+ rRNA-. The results showed that polyA+RNase R+ rRNA - enrichment method obtained more circRNA number, higher sensitivity and abundance among them; polyA+RNase R method obtained higher precision. The linear RNAs can be thoroughly removed in all enrichment methods except rRNA depletion method. Overall, our results helps researchers to quickly selection a circRNA enrichment of suitable for own study among many enrichment methods, and it provides a benchmark framework for future improvements circRNA enrichment methods.[Figure: see text].
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Affiliation(s)
- Huajuan Shi
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Ying Zhou
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Erteng Jia
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Zhiyu Liu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Min Pan
- School of Medicine, Southeast University, Nanjing, China
| | - Yunfei Bai
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Xiangwei Zhao
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Qinyu Ge
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
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4
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Wang L, You ZH, Li JQ, Huang YA. IMS-CDA: Prediction of CircRNA-Disease Associations From the Integration of Multisource Similarity Information With Deep Stacked Autoencoder Model. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:5522-5531. [PMID: 33027025 DOI: 10.1109/tcyb.2020.3022852] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Emerging evidence indicates that circular RNA (circRNA) has been an indispensable role in the pathogenesis of human complex diseases and many critical biological processes. Using circRNA as a molecular marker or therapeutic target opens up a new avenue for our treatment and detection of human complex diseases. The traditional biological experiments, however, are usually limited to small scale and are time consuming, so the development of an effective and feasible computational-based approach for predicting circRNA-disease associations is increasingly favored. In this study, we propose a new computational-based method, called IMS-CDA, to predict potential circRNA-disease associations based on multisource biological information. More specifically, IMS-CDA combines the information from the disease semantic similarity, the Jaccard and Gaussian interaction profile kernel similarity of disease and circRNA, and extracts the hidden features using the stacked autoencoder (SAE) algorithm of deep learning. After training in the rotation forest (RF) classifier, IMS-CDA achieves 88.08% area under the ROC curve with 88.36% accuracy at the sensitivity of 91.38% on the CIRCR2Disease dataset. Compared with the state-of-the-art support vector machine and K -nearest neighbor models and different descriptor models, IMS-CDA achieves the best overall performance. In the case studies, eight of the top 15 circRNA-disease associations with the highest prediction score were confirmed by recent literature. These results indicated that IMS-CDA has an outstanding ability to predict new circRNA-disease associations and can provide reliable candidates for biological experiments.
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5
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Gaffo E, Buratin A, Dal Molin A, Bortoluzzi S. Bioinformatic Analysis of Circular RNA Expression. Methods Mol Biol 2021; 2348:343-370. [PMID: 34160817 DOI: 10.1007/978-1-0716-1581-2_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Circular RNAs (circRNAs) are stable RNA molecules generated by backsplicing that play regulatory functions through interaction with other RNA and proteins, as well as by encoding peptides. Dysregulation of circRNA expression can drive cancer development and progression with different mechanisms. CircRNAs are currently regarded as extremely attractive molecules in cancer research for the identification of new and possibly targetable disease regulatory networks and for the development of biomarkers for cancer diagnosis, prognosis definition, and monitoring. Using specific experimental and computational protocols, circRNAs can be identified through RNA-seq by spotting the reads spanning backsplice junctions, which are specific to circular molecules. In this chapter, we report a state-of-the-art computational protocol for a genome-wide analysis of circRNAs from RNA-seq data, which considers circRNA detection, quantification, and differential expression testing. Finally, we indicate how to determine circular transcript sequences and the resources for an in silico functional characterization of circRNAs.
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Affiliation(s)
- Enrico Gaffo
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | - Alessia Buratin
- Department of Molecular Medicine, University of Padova, Padova, Italy.,Department of Biology, University of Padova, Padova, Italy
| | - Anna Dal Molin
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | - Stefania Bortoluzzi
- Department of Molecular Medicine, University of Padova, Padova, Italy. .,Interdepartmental Research Center for Innovative Biotechnologies (CRIBI), University of Padova, Padova, Italy.
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Cai Z, Lu C, He J, Liu L, Zou Y, Zhang Z, Zhu Z, Ge X, Wu A, Jiang T, Zheng H, Peng Y. Identification and characterization of circRNAs encoded by MERS-CoV, SARS-CoV-1 and SARS-CoV-2. Brief Bioinform 2021; 22:1297-1308. [PMID: 33757279 PMCID: PMC7799257 DOI: 10.1093/bib/bbaa334] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 09/28/2020] [Accepted: 10/23/2020] [Indexed: 12/17/2022] Open
Abstract
The life-threatening coronaviruses MERS-CoV, SARS-CoV-1 and SARS-CoV-2 (SARS-CoV-1/2) have caused and will continue to cause enormous morbidity and mortality to humans. Virus-encoded noncoding RNAs are poorly understood in coronaviruses. Data mining of viral-infection-related RNA-sequencing data has resulted in the identification of 28 754, 720 and 3437 circRNAs encoded by MERS-CoV, SARS-CoV-1 and SARS-CoV-2, respectively. MERS-CoV exhibits much more prominent ability to encode circRNAs in all genomic regions than those of SARS-CoV-1/2. Viral circRNAs typically exhibit low expression levels. Moreover, majority of the viral circRNAs exhibit expressions only in the late stage of viral infection. Analysis of the competitive interactions of viral circRNAs, human miRNAs and mRNAs in MERS-CoV infections reveals that viral circRNAs up-regulated genes related to mRNA splicing and processing in the early stage of viral infection, and regulated genes involved in diverse functions including cancer, metabolism, autophagy, viral infection in the late stage of viral infection. Similar analysis in SARS-CoV-2 infections reveals that its viral circRNAs down-regulated genes associated with metabolic processes of cholesterol, alcohol, fatty acid and up-regulated genes associated with cellular responses to oxidative stress in the late stage of viral infection. A few genes regulated by viral circRNAs from both MERS-CoV and SARS-CoV-2 were enriched in several biological processes such as response to reactive oxygen and centrosome localization. This study provides the first glimpse into viral circRNAs in three deadly coronaviruses and would serve as a valuable resource for further studies of circRNAs in coronaviruses.
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Affiliation(s)
- Zena Cai
- College of Biology, Hunan University
| | - Congyu Lu
- College of Biology, Hunan University
| | | | - Li Liu
- Hunan Yuelu mountain data science and Technology Research Institute Co., Ltd, Changsha, China
| | | | | | | | - Xingyi Ge
- College of Biology, Hunan University
| | - Aiping Wu
- Suzhou Institute of Systems Medicine
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7
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Vivek AT, Kumar S. Computational methods for annotation of plant regulatory non-coding RNAs using RNA-seq. Brief Bioinform 2020; 22:6041165. [PMID: 33333550 DOI: 10.1093/bib/bbaa322] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 10/19/2020] [Accepted: 10/20/2020] [Indexed: 12/19/2022] Open
Abstract
Plant transcriptome encompasses numerous endogenous, regulatory non-coding RNAs (ncRNAs) that play a major biological role in regulating key physiological mechanisms. While studies have shown that ncRNAs are extremely diverse and ubiquitous, the functions of the vast majority of ncRNAs are still unknown. With ever-increasing ncRNAs under study, it is essential to identify, categorize and annotate these ncRNAs on a genome-wide scale. The use of high-throughput RNA sequencing (RNA-seq) technologies provides a broader picture of the non-coding component of transcriptome, enabling the comprehensive identification and annotation of all major ncRNAs across samples. However, the detection of known and emerging class of ncRNAs from RNA-seq data demands complex computational methods owing to their unique as well as similar characteristics. Here, we discuss major plant endogenous, regulatory ncRNAs in an RNA sample followed by computational strategies applied to discover each class of ncRNAs using RNA-seq. We also provide a collection of relevant software packages and databases to present a comprehensive bioinformatics toolbox for plant ncRNA researchers. We assume that the discussions in this review will provide a rationale for the discovery of all major categories of plant ncRNAs.
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Affiliation(s)
- A T Vivek
- National Institute of Plant Genome Research in New Delhi, India
| | - Shailesh Kumar
- National Institute of Plant Genome Research in New Delhi
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8
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Zhang W, Qin P, Gong X, Huang L, Wang C, Chen G, Chen J, Wang L, Lv Z. Identification of circRNAs in the Liver of Whitespotted Bamboo Shark ( Chiloscyllium plagiosum). Front Genet 2020; 11:596308. [PMID: 33362857 PMCID: PMC7759564 DOI: 10.3389/fgene.2020.596308] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 11/24/2020] [Indexed: 12/17/2022] Open
Abstract
Whitespotted bamboo shark (Chiloscyllium plagiosum), a member of the cartilaginous fish family, has an extremely large liver and demonstrates a strong regeneration ability and immune regulation. Circular RNAs (circRNAs) is an important class of non-coding RNAs. Increasing evidences suggest that circRNAs are a kind of potential regulators. Recently, researchers have isolated and identified different circRNAs from various species, while few reports were on the circRNAs of C. plagiosum. In this study, we have identified a total of 4,558 circRNAs in the liver of C. plagiosum. This finding suggests that circRNAs are not evenly distributed in the chromosomes and follow the GT-AG rule during cyclization. Alternative back-splicing might exist in shark circRNAs as shown by the authenticity identification of predicted circRNAs. The binding strength of circRNAs (<2,000 bp) and the detected miRNAs in shark liver were simultaneously analyzed to construct an mRNA–miRNA–circRNA network for the Glutathione S-transferase P1 gene, and the circRNA authenticity was simultaneously verified. Our data provide not only novel insights into the rich existence of circRNAs in marine animals, but also a basis for characterizing functions of identified circRNAs in the liver homeostasis of C. plagiosum.
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Affiliation(s)
- Wenjie Zhang
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou, China.,Zhejiang Provincial Key Laboratory of Silkworm Bioreactor and Biomedicine, Zhejiang Sci-Tech University, Hangzhou, China
| | - Ping Qin
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou, China.,Zhejiang Provincial Key Laboratory of Silkworm Bioreactor and Biomedicine, Zhejiang Sci-Tech University, Hangzhou, China
| | - Xiaoxia Gong
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou, China.,Zhejiang Provincial Key Laboratory of Silkworm Bioreactor and Biomedicine, Zhejiang Sci-Tech University, Hangzhou, China
| | - Lei Huang
- Hangzhou Hongqiao Sino-Science Gene Technology Co., Ltd., Hangzhou, China
| | - Chan Wang
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou, China.,Zhejiang Provincial Key Laboratory of Silkworm Bioreactor and Biomedicine, Zhejiang Sci-Tech University, Hangzhou, China
| | - Guiqian Chen
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou, China.,Zhejiang Provincial Key Laboratory of Silkworm Bioreactor and Biomedicine, Zhejiang Sci-Tech University, Hangzhou, China
| | - Jianqing Chen
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou, China.,Zhejiang Provincial Key Laboratory of Silkworm Bioreactor and Biomedicine, Zhejiang Sci-Tech University, Hangzhou, China
| | - Lei Wang
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou, China.,Zhejiang Provincial Key Laboratory of Silkworm Bioreactor and Biomedicine, Zhejiang Sci-Tech University, Hangzhou, China
| | - Zhengbing Lv
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou, China.,Zhejiang Provincial Key Laboratory of Silkworm Bioreactor and Biomedicine, Zhejiang Sci-Tech University, Hangzhou, China
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9
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Wang Y, Xu R, Zhang D, Lu T, Yu W, Wo Y, Liu A, Sui T, Cui J, Qin Y, Dong Y, Leng X, Kong D, Du W, Huang Z, Su W, Yuan T, Sun X, Wang J, Jiao W. Circ-ZKSCAN1 regulates FAM83A expression and inactivates MAPK signaling by targeting miR-330-5p to promote non-small cell lung cancer progression. Transl Lung Cancer Res 2019; 8:862-875. [PMID: 32010565 DOI: 10.21037/tlcr.2019.11.04] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Background Circular RNAs (circRNAs) belong to a new type of endogenous non-coding RNA and plays a key role in carcinogenesis. Circ-ZKSCAN1 (hsa_circ_0001727) has been proven to be a tumor-dependent circRNA. However, its role in non-small cell lung cancer (NSCLC) has been underreported. Methods The expression patterns of circ-ZKSCAN1 were determined using qRT-PCR in NSCLC samples and cell lines. Cell proliferation was examined utilizing the CCK-8 assay. Cell migration and invasion were evaluated using the Transwell assay. The combination of circ-ZKSCAN1 and miR-330-5p in NSCLC cells was analyzed by RNA pull-down and luciferase reporter assay. We used the bioinformatics software circbank, CircInteractome, TargetScan and Miranda to predict circRNA-miRNA and miRNA-mRNA interactions. Results Our results showed that circ-ZKSCAN1 was significantly up-regulated in NSCLC, closely related to malignant characteristics and poor prognosis, and clinically related to tumor size and clinical stage. Subsequent experiments showed that circ-ZKSCAN1 could inhibit the growth of NSCLC cells in vitro and in vivo. Importantly, circ-ZKSCAN1 can act as a sponge of carcinogenic miR-330-5p to increase the expression of FAM83A, resulting in the inhibition of MAPK signal transduction pathway, thus promoting the progress of NSCLC. Interestingly, the increase in FAM83A expression caused by circ-ZKSCAN1 overexpression could in turn promote the expression of circ-ZKSCAN1. Conclusions Circ-ZKSCAN1 is a key positive regulator of NSCLC, and clarifies the potential molecular mechanism of the new circ-ZKSCAN1/miR-330-5p/FAM83A feedback loop in promoting the progress of NSCLC.
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Affiliation(s)
- Yuanyong Wang
- Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao 266000, China.,Center for Regenerative Medicine, Institute for Translational Medicine, College of Medicine, Qingdao University, Qingdao 266000, China
| | - Rongjian Xu
- Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - Dongyang Zhang
- Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - Tong Lu
- Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao 266000, China.,Center for Regenerative Medicine, Institute for Translational Medicine, College of Medicine, Qingdao University, Qingdao 266000, China
| | - Wanpeng Yu
- Center for Regenerative Medicine, Institute for Translational Medicine, College of Medicine, Qingdao University, Qingdao 266000, China.,School of Basic Medical Sciences, Qingdao University, Qingdao 266000, China
| | - Yang Wo
- Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - Ao Liu
- Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - Tianyi Sui
- Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - Jian Cui
- Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - Yi Qin
- Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - Yanting Dong
- Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao 266000, China.,Center for Regenerative Medicine, Institute for Translational Medicine, College of Medicine, Qingdao University, Qingdao 266000, China
| | - Xiaoliang Leng
- Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - Dezhi Kong
- Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - Wenxing Du
- Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - Zhangfeng Huang
- Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - Wenhao Su
- Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - Tianxiang Yuan
- Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - Xiao Sun
- Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - Jianxun Wang
- Center for Regenerative Medicine, Institute for Translational Medicine, College of Medicine, Qingdao University, Qingdao 266000, China.,School of Basic Medical Sciences, Qingdao University, Qingdao 266000, China
| | - Wenjie Jiao
- Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao 266000, China
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10
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Hernández-Romero IA, Guerra-Calderas L, Salgado-Albarrán M, Maldonado-Huerta T, Soto-Reyes E. The Regulatory Roles of Non-coding RNAs in Angiogenesis and Neovascularization From an Epigenetic Perspective. Front Oncol 2019; 9:1091. [PMID: 31709179 PMCID: PMC6821677 DOI: 10.3389/fonc.2019.01091] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 10/03/2019] [Indexed: 12/13/2022] Open
Abstract
Angiogenesis is a crucial process for organ morphogenesis and growth during development, and it is especially relevant during the repair of wounded tissue in adults. It is coordinated by an equilibrium of pro- and anti-angiogenic factors; nevertheless, when affected, it promotes several diseases. Lately, a growing body of evidence is indicating that non-coding RNAs (ncRNAs), such as miRNAs, circRNAs, and lncRNAs, play critical roles in angiogenesis. These ncRNAs can act in cis or trans and alter gene transcription by several mechanisms including epigenetic processes. In the following pages, we will discuss the functions of ncRNAs in the regulation of angiogenesis and neovascularization, both in normal and disease contexts, from an epigenetic perspective. Additionally, we will describe the contribution of Next-Generation Sequencing (NGS) techniques to the discovery and understanding of the role of ncRNAs in angiogenesis.
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Affiliation(s)
| | | | | | | | - Ernesto Soto-Reyes
- Natural Sciences Department, Universidad Autónoma Metropolitana-Cuajimalpa, Mexico City, Mexico
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11
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Iparraguirre L, Prada-Luengo I, Regenberg B, Otaegui D. To Be or Not to Be: Circular RNAs or mRNAs From Circular DNAs? Front Genet 2019; 10:940. [PMID: 31681407 PMCID: PMC6797608 DOI: 10.3389/fgene.2019.00940] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 09/05/2019] [Indexed: 12/22/2022] Open
Abstract
In recent years, there has been a growing interest in circular RNAs (circRNAs) since they are involved in a wide spectrum of cellular functions that might have a large impact on phenotype and disease. CircRNAs are mainly recorded by RNA-Seq and computational methods focused on the detection of back-splicing junction sequences considered the diagnostic feature of circRNAs. While some protocols remove linear RNA prior to sequencing, many have characterized circRNAs by sorting through total RNA sequencing data without excluding the possibility that some linear RNA can provide the same signal as a circRNA. Recent studies have revealed that circular DNAs of chromosomal origin are common in eukaryotic genomes and that they can be transcribed. Transcription events across the junction of circular DNAs would result in a transcript with a junction similar to those present in circRNAs. Therefore, in this report, we want to draw attention to transcripts from such circular DNAs both as an interesting new player in the transcriptome and also as a confounding factor that must be taken into account when studying circRNAs.
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Affiliation(s)
- Leire Iparraguirre
- Neurosciences Area, Biodonostia Health Research Institute, San Sebastián, Spain
| | | | | | - David Otaegui
- Neurosciences Area, Biodonostia Health Research Institute, San Sebastián, Spain
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12
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Zheng Y, Ji P, Chen S, Hou L, Zhao F. Reconstruction of full-length circular RNAs enables isoform-level quantification. Genome Med 2019; 11:2. [PMID: 30660194 PMCID: PMC6339429 DOI: 10.1186/s13073-019-0614-1] [Citation(s) in RCA: 100] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 01/10/2019] [Indexed: 12/24/2022] Open
Abstract
Currently, circRNA studies are shifting from the identification of circular transcripts to understanding their biological functions. However, such endeavors have been limited by large-scale determination of their full-length sequences and also by the inability of accurate quantification at the isoform level. Here, we propose a new feature, reverse overlap (RO), for circRNA detection, which outperforms back-splice junction (BSJ)-based methods in identifying low-abundance circRNAs. By combining RO and BSJ features, we present a novel approach for effective reconstruction of full-length circRNAs and isoform-level quantification from the transcriptome. We systematically compared the difference between the BSJ-level and isoform-level differential expression analyses using human liver tumor and normal tissues and highlight the necessity of deepening circRNA studies to the isoform-level resolution. The CIRI-full software can be accessed at https://sourceforge.net/projects/ciri.
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Affiliation(s)
- Yi Zheng
- Computational Genomics Lab, Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Peifeng Ji
- Computational Genomics Lab, Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China
| | - Shuai Chen
- Computational Genomics Lab, Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Lingling Hou
- Computational Genomics Lab, Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China
| | - Fangqing Zhao
- Computational Genomics Lab, Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China. .,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
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