1
|
Miao S, Zhang Q. Circulating circRNA: a social butterfly in tumors. Front Oncol 2023; 13:1203696. [PMID: 37546422 PMCID: PMC10401440 DOI: 10.3389/fonc.2023.1203696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 06/20/2023] [Indexed: 08/08/2023] Open
Abstract
Circular RNAs (circRNAs) are a class of single-stranded non-coding RNAs that form circular structures through irregular splicing or post-splicing events. CircRNAs are abnormally expressed in many cancers and regulate the occurrence and development of tumors. Circulating circRNAs are cell-free circRNAs present in peripheral blood, they are considered promising biomarkers due to their high stability. In recent years, more and more studies have revealed that circulating circRNAs participate in various cellular communication and regulate the occurrence and development of tumors, which involve many pathological processes such as tumorigenesis, tumor-related immunity, tumor angiogenesis, and tumor metastasis. Understanding the role of cell communication mediated by circulating circRNAs in tumor will further reveal the value and significance behind their use as biomarkers and potential therapeutic targets. In this review, we summarize the recent findings and provide an overview of the cell-cell communication mediated by circulating circRNAs, aiming to explore the role and application value of circulating circRNAs in tumors.
Collapse
Affiliation(s)
- Shuo Miao
- Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China
- School of Basic Medicine, Qingdao University, Qingdao, China
| | - Qingsong Zhang
- Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China
| |
Collapse
|
2
|
Ferrero G, Licheri N, De Bortoli M, Calogero RA, Beccuti M, Cordero F. Computational Analysis of circRNA Expression Data. Methods Mol Biol 2021; 2284:181-192. [PMID: 33835443 DOI: 10.1007/978-1-0716-1307-8_10] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Analysis of circular RNA (circRNA) expression from RNA-Seq data can be performed with different algorithms and analysis pipelines, tools allowing the extraction of heterogeneous information on the expression of this novel class of RNAs. Computational pipelines were developed to facilitate the analysis of circRNA expression by leveraging different public tools in easy-to-use pipelines. This chapter describes the complete workflow for a computationally reproducible analysis of circRNA expression starting for a public RNA-Seq experiment. The main steps of circRNA prediction, annotation, classification, sequence reconstruction, quantification, and differential expression are illustrated.
Collapse
Affiliation(s)
- Giulio Ferrero
- Department of Computer Science, University of Turin, Turin, Italy.,Department of Clinical and Biological Sciences, University of Turin, Orbassano, Italy
| | - Nicola Licheri
- Department of Computer Science, University of Turin, Turin, Italy
| | - Michele De Bortoli
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, Italy
| | - Raffaele A Calogero
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
| | - Marco Beccuti
- Department of Computer Science, University of Turin, Turin, Italy
| | | |
Collapse
|
3
|
Nisar S, Bhat AA, Singh M, Karedath T, Rizwan A, Hashem S, Bagga P, Reddy R, Jamal F, Uddin S, Chand G, Bedognetti D, El-Rifai W, Frenneaux MP, Macha MA, Ahmed I, Haris M. Insights Into the Role of CircRNAs: Biogenesis, Characterization, Functional, and Clinical Impact in Human Malignancies. Front Cell Dev Biol 2021; 9:617281. [PMID: 33614648 PMCID: PMC7894079 DOI: 10.3389/fcell.2021.617281] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 01/04/2021] [Indexed: 01/17/2023] Open
Abstract
Circular RNAs (circRNAs) are an evolutionarily conserved novel class of non-coding endogenous RNAs (ncRNAs) found in the eukaryotic transcriptome, originally believed to be aberrant RNA splicing by-products with decreased functionality. However, recent advances in high-throughput genomic technology have allowed circRNAs to be characterized in detail and revealed their role in controlling various biological and molecular processes, the most essential being gene regulation. Because of the structural stability, high expression, availability of microRNA (miRNA) binding sites and tissue-specific expression, circRNAs have become hot topic of research in RNA biology. Compared to the linear RNA, circRNAs are produced differentially by backsplicing exons or lariat introns from a pre-messenger RNA (mRNA) forming a covalently closed loop structure missing 3′ poly-(A) tail or 5′ cap, rendering them immune to exonuclease-mediated degradation. Emerging research has identified multifaceted roles of circRNAs as miRNA and RNA binding protein (RBP) sponges and transcription, translation, and splicing event regulators. CircRNAs have been involved in many human illnesses, including cancer and neurodegenerative disorders such as Alzheimer’s and Parkinson’s disease, due to their aberrant expression in different pathological conditions. The functional versatility exhibited by circRNAs enables them to serve as potential diagnostic or predictive biomarkers for various diseases. This review discusses the properties, characterization, profiling, and the diverse molecular mechanisms of circRNAs and their use as potential therapeutic targets in different human malignancies.
Collapse
Affiliation(s)
- Sabah Nisar
- Functional and Molecular Imaging Laboratory, Cancer Research Department, Sidra Medicine, Doha, Qatar
| | - Ajaz A Bhat
- Functional and Molecular Imaging Laboratory, Cancer Research Department, Sidra Medicine, Doha, Qatar
| | - Mayank Singh
- Dr. B. R. Ambedkar Institute Rotary Cancer Hospital (BRAIRCH), All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | | | - Arshi Rizwan
- Department of Nephrology, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Sheema Hashem
- Functional and Molecular Imaging Laboratory, Cancer Research Department, Sidra Medicine, Doha, Qatar
| | - Puneet Bagga
- Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN, United States
| | - Ravinder Reddy
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States
| | - Farrukh Jamal
- Dr. Rammanohar Lohia Avadh University, Ayodhya, India
| | - Shahab Uddin
- Translational Research Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
| | - Gyan Chand
- Department of Endocrine Surgery, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India
| | - Davide Bedognetti
- Laboratory of Cancer Immunogenomics, Cancer Research Department, Sidra Medicine, Doha, Qatar.,Department of Internal Medicine and Medical Specialties, University of Genoa, Genoa, Italy.,College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Wael El-Rifai
- Department of Surgery, University of Miami Miller School of Medicine, Miami, FL, United States
| | | | - Muzafar A Macha
- Watson-Crick Centre for Molecular Medicine, Islamic University of Science and Technology (IUST), Pulwama, India
| | - Ikhlak Ahmed
- Research Branch, Sidra Medicine, Doha, Qatar.,Research Branch, Sidra Medicine, Doha, Qatar
| | - Mohammad Haris
- Functional and Molecular Imaging Laboratory, Cancer Research Department, Sidra Medicine, Doha, Qatar.,Laboratory Animal Research Center, Qatar University, Doha, Qatar
| |
Collapse
|
4
|
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.
Collapse
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
| |
Collapse
|
5
|
Disease-Associated Circular RNAs: From Biology to Computational Identification. BIOMED RESEARCH INTERNATIONAL 2020; 2020:6798590. [PMID: 32908906 PMCID: PMC7450300 DOI: 10.1155/2020/6798590] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 07/10/2020] [Indexed: 02/07/2023]
Abstract
Circular RNAs (circRNAs) are endogenous RNAs with a covalently closed continuous loop, generated through various backsplicing events of pre-mRNA. An accumulating number of studies have shown that circRNAs are potential biomarkers for major human diseases such as cancer and Alzheimer's disease. Thus, identification and prediction of human disease-associated circRNAs are of significant importance. To this end, a computational analysis-assisted strategy is indispensable to detect, verify, and quantify circRNAs for downstream applications. In this review, we briefly introduce the biology of circRNAs, including the biogenesis, characteristics, and biological functions. In addition, we outline about 30 recent bioinformatic analysis tools that are publicly available for circRNA study. Principles for applying these computational strategies and considerations will be briefly discussed. Lastly, we give a complete survey on more than 20 key computational databases that are frequently used. To our knowledge, this is the most complete and updated summary on publicly available circRNA resources. In conclusion, this review summarizes key aspects of circRNA biology and outlines key computational strategies that will facilitate the genome-wide identification and prediction of circRNAs.
Collapse
|
6
|
Wang S, Zhang Y, Hu C, Zhang N, Gribskov M, Yang H. Shiny-DEG: A Web Application to Analyze and Visualize Differentially Expressed Genes in RNA-seq. Interdiscip Sci 2020; 12:349-354. [PMID: 32666343 DOI: 10.1007/s12539-020-00383-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 06/08/2020] [Accepted: 07/01/2020] [Indexed: 11/26/2022]
Abstract
RNA-seq analysis has become one of the most widely used methods for biological and medical experiments, aiming to identify differentially expressed genes at a large scale. However, due to lack of programming skills and statistical background, it is difficult for biologists including faculty and students to fully understand what the RNA-seq results are and how to interpret them. In recent years, even though, there are several programs or websites that assist researchers to analyze and visualize NGS results, they have several limitations. Therefore, Shiny-DEG, a web application that facilitates the exploration and visualization of differentially expressed genes from RNA-seq, was developed. It integrates multi-factor design experiments, allows users to modify the parameters interactively according to experiments purpose and all analysis results can be downloaded directly, aiming to further assisting the interpretation and explanation of the biological questions. Therefore, it serves better for biologists without programming skills. Overall, this project is of great significance to reveal the mechanism of transcriptome differences.
Collapse
Affiliation(s)
- Sufang Wang
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China.
| | - Yu Zhang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129, China.
- School of Computer Science and IT, RMIT University, Melbourne, VIC, 3000, Australia.
| | - Congzhan Hu
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China
| | - Nu Zhang
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China
| | - Michael Gribskov
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
- Department of Computer Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Hui Yang
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China
| |
Collapse
|