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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: 14] [Impact Index Per Article: 14.0] [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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2
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Sorokin M, Rabushko E, Rozenberg JM, Mohammad T, Seryakov A, Sekacheva M, Buzdin A. Clinically relevant fusion oncogenes: detection and practical implications. Ther Adv Med Oncol 2022; 14:17588359221144108. [PMID: 36601633 PMCID: PMC9806411 DOI: 10.1177/17588359221144108] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 11/22/2022] [Indexed: 12/28/2022] Open
Abstract
Mechanistically, chimeric genes result from DNA rearrangements and include parts of preexisting normal genes combined at the genomic junction site. Some rearranged genes encode pathological proteins with altered molecular functions. Those which can aberrantly promote carcinogenesis are called fusion oncogenes. Their formation is not a rare event in human cancers, and many of them were documented in numerous study reports and in specific databases. They may have various molecular peculiarities like increased stability of an oncogenic part, self-activation of tyrosine kinase receptor moiety, and altered transcriptional regulation activities. Currently, tens of low molecular mass inhibitors are approved in cancers as the drugs targeting receptor tyrosine kinase (RTK) oncogenic fusion proteins, that is, including ALK, ABL, EGFR, FGFR1-3, NTRK1-3, MET, RET, ROS1 moieties. Therein, the presence of the respective RTK fusion in the cancer genome is the diagnostic biomarker for drug prescription. However, identification of such fusion oncogenes is challenging as the breakpoint may arise in multiple sites within the gene, and the exact fusion partner is generally unknown. There is no gold standard method for RTK fusion detection, and many alternative experimental techniques are employed nowadays to solve this issue. Among them, RNA-seq-based methods offer an advantage of unbiased high-throughput analysis of only transcribed RTK fusion genes, and of simultaneous finding both fusion partners in a single RNA-seq read. Here we focus on current knowledge of biology and clinical aspects of RTK fusion genes, related databases, and laboratory detection methods.
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Affiliation(s)
| | - Elizaveta Rabushko
- Moscow Institute of Physics and Technology,
Dolgoprudny, Moscow Region, Russia,I.M. Sechenov First Moscow State Medical
University, Moscow, Russia
| | | | - Tharaa Mohammad
- Moscow Institute of Physics and Technology,
Dolgoprudny, Moscow Region, Russia
| | | | - Marina Sekacheva
- I.M. Sechenov First Moscow State Medical
University, Moscow, Russia
| | - Anton Buzdin
- Moscow Institute of Physics and Technology,
Dolgoprudny, Moscow Region, Russia,I.M. Sechenov First Moscow State Medical
University, Moscow, Russia,Shemyakin-Ovchinnikov Institute of Bioorganic
Chemistry, Moscow, Russia,PathoBiology Group, European Organization for
Research and Treatment of Cancer (EORTC), Brussels, Belgium
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3
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Mirzaei G. GraphChrom: A Novel Graph-Based Framework for Cancer Classification Using Chromosomal Rearrangement Endpoints. Cancers (Basel) 2022; 14:cancers14133060. [PMID: 35804833 PMCID: PMC9265123 DOI: 10.3390/cancers14133060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 06/06/2022] [Accepted: 06/18/2022] [Indexed: 11/16/2022] Open
Abstract
Chromosomal rearrangements are generally a consequence of improperly repaired double-strand breaks in DNA. These genomic aberrations can be a driver of cancers. Here, we investigated the use of chromosomal rearrangements for classification of cancer tumors and the effect of inter- and intrachromosomal rearrangements in cancer classification. We used data from the Catalogue of Somatic Mutations in Cancer (COSMIC) for breast, pancreatic, and prostate cancers, for which the COSMIC dataset reports the highest number of chromosomal aberrations. We developed a framework known as GraphChrom for cancer classification. GraphChrom was developed using a graph neural network which models the complex structure of chromosomal aberrations (CA) and provides local connectivity between the aberrations. The proposed framework illustrates three important contributions to the field of cancers. Firstly, it successfully classifies cancer types and subtypes. Secondly, it evolved into a novel data extraction technique which can be used to extract more informative graphs (informative aberrations associated with a sample); and thirdly, it predicts that interCAs (rearrangements between two or more chromosomes) are more effective in cancer prediction than intraCAs (rearrangements within the same chromosome), although intraCAs are three times more likely to occur than intraCAs.
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Affiliation(s)
- Golrokh Mirzaei
- Department of Computer Science and Engineering, Ohio State University, Marion, OH 403302, USA
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Thind AS, Monga I, Thakur PK, Kumari P, Dindhoria K, Krzak M, Ranson M, Ashford B. Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology. Brief Bioinform 2021; 22:6330938. [PMID: 34329375 DOI: 10.1093/bib/bbab259] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 06/14/2021] [Accepted: 06/18/2021] [Indexed: 12/13/2022] Open
Abstract
Significant innovations in next-generation sequencing techniques and bioinformatics tools have impacted our appreciation and understanding of RNA. Practical RNA sequencing (RNA-Seq) applications have evolved in conjunction with sequence technology and bioinformatic tools advances. In most projects, bulk RNA-Seq data is used to measure gene expression patterns, isoform expression, alternative splicing and single-nucleotide polymorphisms. However, RNA-Seq holds far more hidden biological information including details of copy number alteration, microbial contamination, transposable elements, cell type (deconvolution) and the presence of neoantigens. Recent novel and advanced bioinformatic algorithms developed the capacity to retrieve this information from bulk RNA-Seq data, thus broadening its scope. The focus of this review is to comprehend the emerging bulk RNA-Seq-based analyses, emphasizing less familiar and underused applications. In doing so, we highlight the power of bulk RNA-Seq in providing biological insights.
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Affiliation(s)
- Amarinder Singh Thind
- University of Wollongong, Wollongong, Australia.,Illawarra Health and Medical Research Institute, Wollongong, Australia
| | - Isha Monga
- Columbia University, New York City, NY, USA
| | | | - Pallawi Kumari
- Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India
| | - Kiran Dindhoria
- Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India
| | | | - Marie Ranson
- University of Wollongong, Wollongong, Australia.,Illawarra Health and Medical Research Institute, Wollongong, Australia
| | - Bruce Ashford
- University of Wollongong, Wollongong, Australia.,Illawarra Health and Medical Research Institute, Wollongong, Australia
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Cai Z, Xue H, Xu Y, Köhler J, Cheng X, Dai Y, Zheng J, Wang H. Fcirc: A comprehensive pipeline for the exploration of fusion linear and circular RNAs. Gigascience 2020; 9:5848590. [PMID: 32470133 PMCID: PMC7259471 DOI: 10.1093/gigascience/giaa054] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 03/01/2020] [Accepted: 04/29/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND In cancer cells, fusion genes can produce linear and chimeric fusion-circular RNAs (f-circRNAs), which are functional in gene expression regulation and implicated in malignant transformation, cancer progression, and therapeutic resistance. For specific cancers, proteins encoded by fusion transcripts have been identified as innovative therapeutic targets (e.g., EML4-ALK). Even though RNA sequencing (RNA-Seq) technologies combined with existing bioinformatics approaches have enabled researchers to systematically identify fusion transcripts, specifically detecting f-circRNAs in cells remains challenging owing to their general sparsity and low abundance in cancer cells but also owing to imperfect computational methods. RESULTS We developed the Python-based workflow "Fcirc" to identify fusion linear and f-circRNAs from RNA-Seq data with high specificity. We applied Fcirc to 3 different types of RNA-Seq data scenarios: (i) actual synthetic spike-in RNA-Seq data, (ii) simulated RNA-Seq data, and (iii) actual cancer cell-derived RNA-Seq data. Fcirc showed significant advantages over existing methods regarding both detection accuracy (i.e., precision, recall, F-measure) and computing performance (i.e., lower runtimes). CONCLUSION Fcirc is a powerful and comprehensive Python-based pipeline to identify linear and circular RNA transcripts from known fusion events in RNA-Seq datasets with higher accuracy and shorter computing times compared with previously published algorithms. Fcirc empowers the research community to study the biology of fusion RNAs in cancer more effectively.
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Affiliation(s)
- Zhaoqing Cai
- School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China
| | - Hongzhang Xue
- School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China.,School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Yue Xu
- School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China
| | - Jens Köhler
- Department of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA
| | - Xiaojie Cheng
- School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China
| | - Yao Dai
- School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China
| | - Jie Zheng
- School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China
| | - Haiyun Wang
- School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, China
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Zhang Z, Ma F, Zhao S, Yang X, Liu F, Xue C, Liu L, Gu J, Piao H. Effects of somatic alterations at pathway level are more mechanism-explanatory and clinically applicable to quantity of liver metastases of colorectal cancer. Cancer Med 2019; 8:4732-4742. [PMID: 31219228 PMCID: PMC6712451 DOI: 10.1002/cam4.2368] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 05/13/2019] [Accepted: 06/06/2019] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND The quantity of metastases lesions is an important reference when it comes to making a more informed treatment decision for patients with colorectal cancer liver metastases. However, the molecular alterations in patients with different numbers of lesions have not been systematically studied. METHODS We investigated somatic alterations and microsatellite instability (MSI) of liver metastases from patients with single, multiple or diffuse metastasis lesions. A new algorithm "Pathway Damage Score" was developed to comprehensively assess the functional impact of somatic alterations at the pathway level. Pathogenic pathways of different metastasis were identified and their prognosis effects were evaluated. Furthermore, the subnetworks and affected phenotypes of the altered genes in each pathogenic pathway were analyzed. RESULTS Somatic alterations and altered genes occurred sporadically as well as in MSI state in different metastasis types, although MSS patients had more metastatic lesions than that of the MSI patients. Every metastasis group has their own pathogenic pathways and damaged "Cargo recognition for clathrin-mediated endocytosis" is significantly associated with poor prognosis (P < 0.001). Further pathway subnetwork analysis showed that except conventional drivers, other genes could also contribute to metastasis formation. CONCLUSIONS Progression of liver metastasis could be driven by the coefficient of all altered genes belonging to the pathways. Thus, compared to somatic alterations and genes, pathway level analysis is more reasonable for functional interpretations of molecular alterations in clinical samples.
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Affiliation(s)
- Zhong‐guo Zhang
- Large‐scale Data Analysis Center of Cancer Precision MedicineCancer Hospital of Chinese Medical University, Liaoning Provincial Cancer Institute and HospitalShenyangChina
| | - Fei Ma
- Wankangyuan Tianjin Gene Technology, IncTianjinChina
| | - Shuang Zhao
- Wankangyuan Tianjin Gene Technology, IncTianjinChina
| | - Xiaoyu Yang
- Large‐scale Data Analysis Center of Cancer Precision MedicineCancer Hospital of Chinese Medical University, Liaoning Provincial Cancer Institute and HospitalShenyangChina
| | - Fang Liu
- Large‐scale Data Analysis Center of Cancer Precision MedicineCancer Hospital of Chinese Medical University, Liaoning Provincial Cancer Institute and HospitalShenyangChina
| | - Chenghai Xue
- Large‐scale Data Analysis Center of Cancer Precision MedicineCancer Hospital of Chinese Medical University, Liaoning Provincial Cancer Institute and HospitalShenyangChina
- Wankangyuan Tianjin Gene Technology, IncTianjinChina
| | - Liren Liu
- Department of Gastrointestinal Cancer BiologyNational Clinical Research Center of Cancer, Tianjin Medical University Cancer Institute and HospitaslTianjinChina
| | - Jin Gu
- MOE Key Laboratory of Bioinformatics, Beijing National Research Center for Information Science and Technology, Department of AutomationTsinghua UniversityBeijingChina
| | - Haozhe Piao
- Large‐scale Data Analysis Center of Cancer Precision MedicineCancer Hospital of Chinese Medical University, Liaoning Provincial Cancer Institute and HospitalShenyangChina
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7
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Latysheva NS, Babu MM. Molecular Signatures of Fusion Proteins in Cancer. ACS Pharmacol Transl Sci 2019; 2:122-133. [PMID: 32219217 PMCID: PMC7088938 DOI: 10.1021/acsptsci.9b00019] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Indexed: 01/07/2023]
Abstract
![]()
Although gene fusions
are recognized as driver mutations in a wide
variety of cancers, the general molecular mechanisms underlying oncogenic
fusion proteins are insufficiently understood. Here, we employ large-scale
data integration and machine learning and (1) identify three functionally
distinct subgroups of gene fusions and their molecular signatures;
(2) characterize the cellular pathways rewired by fusion events across
different cancers; and (3) analyze the relative importance of over
100 structural, functional, and regulatory features of ∼2200
gene fusions. We report subgroups of fusions that likely act as driver
mutations and find that gene fusions disproportionately affect pathways
regulating cellular shape and movement. Although fusion proteins are
similar across different cancer types, they affect cancer type-specific
pathways. Key indicators of fusion-forming proteins include high and
nontissue specific expression, numerous splice sites, and higher centrality
in protein-interaction networks. Together, these findings provide
unifying and cancer type-specific trends across diverse oncogenic
fusion proteins.
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Affiliation(s)
- Natasha S Latysheva
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom
| | - M Madan Babu
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom
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He H, Li W, Yan P, Bundschuh R, Killian JA, Labanowska J, Brock P, Shen R, Heerema NA, de la Chapelle A. Identification of a Recurrent LMO7-BRAF Fusion in Papillary Thyroid Carcinoma. Thyroid 2018; 28:748-754. [PMID: 29768105 PMCID: PMC5994666 DOI: 10.1089/thy.2017.0258] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND The BRAFV600E mutation is the most common driver in papillary thyroid carcinoma (PTC) tumors. In recent years, gene fusions have also been recognized as important drivers of cancer in PTC. Previous studies have suggested that thyroid tumors with fusion genes frequently display an aggressive course. These observations prompted further exploration of gene fusions in PTC tumors. The aim was to search for previously unrecognized gene fusions using thyroid tissue samples from PTC patients. METHODS Gene fusions were analyzed in RNA sequencing data obtained from 12 PTC tumors and paired unaffected thyroid tissue samples. Candidate fusions were further filtered and validated using reverse transcriptase polymerase chain reaction, Sanger sequencing, and fluorescence in situ hybridization. An Ohio cohort of 148 PTC tumor samples was screened for a LMO7-BRAF fusion and the BRAFV600E mutation. Functional assays were performed to assess the LMO7-BRAF fusion. RESULTS Two coding fusions (CCDC6-RET and LMO7-BRAF) were found in one tumor sample each. The novel LMO7-BRAF fusion was validated by reverse transcriptase polymerase chain reaction and fluorescence in situ hybridization. The LMO7-BRAF fusion was a recurrent somatic alteration with a frequency of 2.0% (3/148) in PTC tumors, while the BRAFV600E point mutation was found in 63.5% (94/148) of tumors. Enforced expression of LMO7-BRAF fusion protein stimulated endogenous ERK1/2 phosphorylation and promoted anchorage independent cell growth to an extent similar to BRAFV600E. CONCLUSIONS A novel fusion gene, LMO7-BRAF, was identified in PTC tumors. The results indicate that the LMO7-BRAF fusion behaves as an oncogenic alteration. This observation expands the spectrum of fusion genes involving kinases in thyroid cancer.
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Affiliation(s)
- Huiling He
- Department of Cancer Biology and Genetics, The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
| | - Wei Li
- Department of Cancer Biology and Genetics, The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
| | - Pearlly Yan
- Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
| | - Ralf Bundschuh
- Department of Physics, The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
- Department of Chemistry and Biochemistry, Division of Hematology, Center for RNA Biology, The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
| | - Jackson A. Killian
- Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
| | - Jadwiga Labanowska
- Department of Pathology, The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
| | - Pamela Brock
- Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
| | - Rulong Shen
- Department of Pathology, The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
| | - Nyla A. Heerema
- Department of Pathology, The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
| | - Albert de la Chapelle
- Department of Cancer Biology and Genetics, The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
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Panigrahi P, Jere A, Anamika K. FusionHub: A unified web platform for annotation and visualization of gene fusion events in human cancer. PLoS One 2018; 13:e0196588. [PMID: 29715310 PMCID: PMC5929557 DOI: 10.1371/journal.pone.0196588] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Accepted: 04/16/2018] [Indexed: 12/15/2022] Open
Abstract
Gene fusion is a chromosomal rearrangement event which plays a significant role in cancer due to the oncogenic potential of the chimeric protein generated through fusions. At present many databases are available in public domain which provides detailed information about known gene fusion events and their functional role. Existing gene fusion detection tools, based on analysis of transcriptomics data usually report a large number of fusion genes as potential candidates, which could be either known or novel or false positives. Manual annotation of these putative genes is indeed time-consuming. We have developed a web platform FusionHub, which acts as integrated search engine interfacing various fusion gene databases and simplifies large scale annotation of fusion genes in a seamless way. In addition, FusionHub provides three ways of visualizing fusion events: circular view, domain architecture view and network view. Design of potential siRNA molecules through ensemble method is another utility integrated in FusionHub that could aid in siRNA-based targeted therapy. FusionHub is freely available at https://fusionhub.persistent.co.in.
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Affiliation(s)
| | - Abhay Jere
- LABS, Persistent Systems, Pingala-Aryabhata, Erandwane, Pune, India
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Latysheva NS, Babu MM. Discovering and understanding oncogenic gene fusions through data intensive computational approaches. Nucleic Acids Res 2016; 44:4487-503. [PMID: 27105842 PMCID: PMC4889949 DOI: 10.1093/nar/gkw282] [Citation(s) in RCA: 109] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2016] [Accepted: 03/24/2016] [Indexed: 12/21/2022] Open
Abstract
Although gene fusions have been recognized as important drivers of cancer for decades, our understanding of the prevalence and function of gene fusions has been revolutionized by the rise of next-generation sequencing, advances in bioinformatics theory and an increasing capacity for large-scale computational biology. The computational work on gene fusions has been vastly diverse, and the present state of the literature is fragmented. It will be fruitful to merge three camps of gene fusion bioinformatics that appear to rarely cross over: (i) data-intensive computational work characterizing the molecular biology of gene fusions; (ii) development research on fusion detection tools, candidate fusion prioritization algorithms and dedicated fusion databases and (iii) clinical research that seeks to either therapeutically target fusion transcripts and proteins or leverages advances in detection tools to perform large-scale surveys of gene fusion landscapes in specific cancer types. In this review, we unify these different-yet highly complementary and symbiotic-approaches with the view that increased synergy will catalyze advancements in gene fusion identification, characterization and significance evaluation.
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Affiliation(s)
- Natasha S Latysheva
- MRC Laboratory of Molecular Biology, Francis Crick Ave, Cambridge CB2 0QH, United Kingdom
| | - M Madan Babu
- MRC Laboratory of Molecular Biology, Francis Crick Ave, Cambridge CB2 0QH, United Kingdom
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