Dokument: Entwicklung einer zirkulären RNA Detektionspipeline und dessen Applikation in Medulloblastoma
| Titel: | Entwicklung einer zirkulären RNA Detektionspipeline und dessen Applikation in Medulloblastoma | |||||||
| Weiterer Titel: | Development of a circular RNA detection pipeline and its application in medulloblastoma | |||||||
| URL für Lesezeichen: | https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=62929 | |||||||
| URN (NBN): | urn:nbn:de:hbz:061-20230627-130042-0 | |||||||
| Kollektion: | Dissertationen | |||||||
| Sprache: | Englisch | |||||||
| Dokumententyp: | Wissenschaftliche Abschlussarbeiten » Dissertation | |||||||
| Medientyp: | Text | |||||||
| Autor: | Rickert, Daniel [Autor] | |||||||
| Dateien: |
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| Beitragende: | Prof. Dr. Klau, Gunnar [Gutachter] Prof. Dr. Reifenberger, Guido [Gutachter] | |||||||
| Stichwörter: | circular RNA, RNA, circRNA, medulloblastoma, cancer, biomarker | |||||||
| Dewey Dezimal-Klassifikation: | 500 Naturwissenschaften und Mathematik » 570 Biowissenschaften; Biologie | |||||||
| Beschreibungen: | Zusammenfassung
Die großen Fortschritte in der Next-Generation-Sequenzierung führten zu umfangreichen RNA- Sequenzierungsdaten (RNA-Seq) von unterschiedlichen Krebsarten beim Menschen und unterstützen damit die Omics-basierte Krebsforschung und die Entdeckung von neuen molekularen Biomarkern. Die Instabilität der linearen mRNA schränkt jedoch die Verwendung von mRNA-basierten Signaturen für den robusten Biomarker-Nachweis im diagnostischen Alltag ein. Die vorliegende Dissertationsarbeit beschäftigt sich daher mit dem diesbezüglichen Potenzial einer stabileren RNA-Spezies, nämlich der zirkulären RNA (circRNA), die je nach Entwicklungsstadium und Differenzierung von Zellen und Geweben sehr spezifische Expressionsmuster zeigt. Zirkuläre RNA stellt eine geschlossene Schleife aus einzelsträngiger RNA dar und macht insgesamt nur etwa 1 % der in einem Zell- oder Gewebe-basierten Extrakt nachweisbaren Gesamt-RNA aus. Aktuell sind mehrere circRNA-Quantifizierungsmethoden öffentlich verfügbar. Um die circRNA- basierte Forschung weiter zu beschleunigen und bestehende Limitationen bei Verwendung einer einzelnen der verfügbaren Analysepipelines zu überwinden, wurde in dieser Dissertationsarbeit eine neuartige Multi-Pipeline-Detektionsmethode für circRNA namens „circ“ entwickelt. Diese Pipeline ist hochempfindlich für den circRNA-Nachweis und erreicht eine geringere Falsch-Positiv- Rate im Vergleich zu den bislang vorhandenen circRNA-Nachweispipelines. In dieser Arbeit wurde 'circ' erfolgreich auf veröffentlichte sowie unveröffentlichte RNA-Seq-Daten angewendet und ermöglichte die Quantifizierung der circRNA-Expression in RNAseq-Datensätzen aus zwei unabhängigen Kohorten von Medulloblastomen (MB), der häufigsten Art von bösartigen Hirntumoren im Kindesalter. Diese Kohorten bestanden aus RNAseq-Datensätzen von 38 und 35 MB Patientenproben, die jeweils Proben der vier großen MB-Gruppen in unterschiedlichen Anteilen enthielten. Diese MB-Gruppen werden Wingless (WNT), Sonic Hedgehog (SHH), Gruppe 3 und Gruppe 4 genannt. Sie zeichnen sich durch gruppenspezifische genomische Veränderungen aus, die zu einer aberranten Aktivierung unterschiedlicher Signalwege und einem unterschiedlichen klinischen Verhalten hinsichtlich der Wahrscheinlichkeit einer Metastasenbildung und der Prognose für die Patienten führen. 8 Die in dieser Dissertationsschrift zusammengefassten Arbeiten zeigen, dass die circRNA-Expressi- on in MB-Gewebeproben verwendet werden kann, um die Tumoren sehr präzise in die einzelnen MB-Gruppen einzuordnen. Tatsächlich war die Zuordnung der MB-Gruppen basierend auf circR- NA-Expressionsprofilen genauso präzise wie die Zuordnung basierend auf der Similarity Network Fusion (SNF) Analyse, die mehrere Omics-Datensätze für die molekulare Tumorklassifizierung in- tegriert. Insgesamt unterschieden sich die circRNA-Expressionsprofile signifikant zwischen den MB-Gruppen, was in den beiden unabhängigen Patientenkohorten validiert wurde. Die validierten circRNA-Profile ermöglichten nicht nur eine zuverlässige Unterscheidung der Gruppen sondern identifizierten auch einzelne circRNA-Spezies mit selektiver Expression in den einzelnen MB-Grup- pen. Dabei erwies sich circRMST in beiden Kohorten als stabil und stark exprimierter Biomarker für Medulloblastome der WNT-Gruppe. Im Gegensatz dazu war circISPD der wichtigste Biomarker für Medulloblastome der SHH-Gruppe, während circEXOC6B in den Gruppe 4 Medulloblastomen spe- zifisch hochreguliert war. Durch die Analyse zusätzlicher Tumordatenbanken konnten diese circR- NA-Biomarker in weiteren öffentlich verfügbaren Datensätzen bestätigt werden. Darüber hinaus wurde die Spezifität der circRMST-Hochregulation in WNT-Medulloblastomen anhand von circR- NA-Expressionsprofilen in >2000 Gewebeproben einschließlich diverser Krebsentitäten und Kon- trollgewebe belegt. Um weitere circRNA-Biomarkerkandidaten weiter zu validieren, wurde das Protokoll „circleseq“ mit isogenen MYC-überexprimierenden MB-Zelllinien verwendet. Diese Ergebnisse bestätigten nicht nur viele circRNAs, die in menschlichen MB-Gewebeproben mit „circs“ nachgewiesen wurden, sondern validierten auch einen zuvor beobachteten Trend, dass eine MYC-Überpression einen In- dikator für eine generell verminderte circRNA-Expression in darstellt. Die hier entwickelte Pipeline „circs“ ist frei verfügbar und öffentlich zugänglich, sodass andere Forscher*innen ihre RNA-Seq- Daten erneut analysieren können, um eine weitere Omics-Datenschicht mit viel versprechendem Biomarkerpotenzial untersuchen zu können.Recent advances in next generation sequencing have provided a rich resource of large- scale RNA sequencing (RNA-Seq) data from various types of human cancer, thereby fostering omics-based cancer research and biomarker discovery. However, the instability of linear mRNA limits its use for robust biomarker detection in routine diagnostic applications. This thesis therefore evaluates the potential of a more stable RNA species, namely circular RNA (circRNA), which shows specific expression patterns according to developmental stage and differentiation of cells and tissues. Circular RNA is a closed loop of single-stranded RNA and amounts to ~1% of total RNA detectable in a given sample. Currently, there are several circRNA quantification methods publicly available. To accelerate circRNA research and overcome certain single-pipeline-based limitations, a novel multi-pipeline circRNA detection method called 'circs' was developed. This method allows highly sensitive circRNA detection and achieves a lower false-positive rate compared to previous circRNA-detection pipelines. In this work, 'circ' was successfully applied to both published and unpublished RNA-Seq data and allowed to quantify circRNA expression in RNAseq data sets from two independent cohorts of medulloblastoma (MB), the most common type of malignant brain tumor in children. These cohorts consisted of RNAseq data sets of 38 and 35 MB patient samples, and included various proportions of the four major MB groups: wingless (WNT), sonic hedgehog (SHH), group 3 and group 4. They are characterized by group-specific recurrent genomic alterations leading to aberrant activation of distinct signaling pathways and divergent clinical behavior concerning likelihood of metastasis formation and patient outcome. The work summarized in this thesis shows that circRNA expression in MB tissue samples can be used to precisely classify tumors into these distinct MB groups without any additional data. In fact, MB group assignment based on circRNA expression profiles proved as precise as assignment based on Similarity Network Fusion (SNF), which integrates multiple omics layers for molecular tumor classification. CircRNA expression profiles differed significantly between the MB groups and were validated in the two 6 independent patient cohorts. The validated MB-group-specific circRNA profiles not only allowed reliable distinction between groups, but also identified individual circRNA species with selective expression in single MB groups. For example, circRMST was found to be a remarkably stable and highly expressed biomarker for WNT MB in both cohorts, circISPD was the top biomarker for SHH MB, and circEXOC6B was specifically upregulated in Group 4 MB. Employing additional online tumor databases, it was possible to confirm these circRNA biomarkers in further published datasets. Additionally, the specificity of circRMST upregulation for WNT MB was substantiated using circRNA expression profiles of >2000 tissue samples, including various other cancer entities and control tissues. To further validate several circRNA biomarker candidates, the ‘circleseq’ protocol was used with isogenic MYC-overexpressing MB cell lines. In addition to identifying many circRNAs detected in human MB tissue samples, these results also confirmed a previously observed trend of MYC overexpression being an indicator of globally decreased circRNA abundance in MB. The ‘circs’ pipeline developed in this thesis is freely available for public use, thus enabling other researchers to re-analyze their RNA-Seq data to uncover another omics data layer with highly promising biomarker potential. | |||||||
| Quelle: | 1. Hallmarks of Cancer: The Next Generation | Elsevier Enhanced Reader.
doi:10.1016/j.cell.2011.02.013 2. Pollack IF, Agnihotri S, Broniscer A. Childhood brain tumors: current management, biological insights, and future directions: JNSPG 75th Anniversary Invited Review Article. J Neurosurg Pediatr. 2019;23(3):261-273. doi:10.3171/2018.10.PEDS18377 3. Barnholtz-Sloan JS, Ostrom QT, Cote D. Epidemiology of Brain Tumors. Neurol Clin. 2018;36(3):395-419. doi:10.1016/j.ncl.2018.04.001 4. Louis DN, Perry A, Reifenberger G, et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol (Berl). 2016;131(6):803-820. doi:10.1007/s00401-016-1545-1 5. Arvanitis CD, Ferraro GB, Jain RK. The blood–brain barrier and blood–tumour barrier in brain tumours and metastases. Nat Rev Cancer. 2020;20(1):26-41. doi:10.1038/s41568-019-0205-x 6. Haumann R, Videira JC, Kaspers GJL, van Vuurden DG, Hulleman E. Overview of Current Drug Delivery Methods Across the Blood–Brain Barrier for the Treatment of Primary Brain Tumors. CNS Drugs. 2020;34(11):1121-1131. doi:10.1007/s40263-020- 00766-w 7. Mulhern RK, Merchant TE, Gajjar A, Reddick WE, Kun LE. Late neurocognitive sequelae in survivors of brain tumours in childhood. Lancet Oncol. 2004;5(7):399-408. doi:10.1016/S1470-2045(04)01507-4 8. Olesen J, Gustavsson A, Svensson M, et al. The economic cost of brain disorders in Europe: Economic cost of brain disorders in Europe. Eur J Neurol. 2012;19(1):155- 162. doi:10.1111/j.1468-1331.2011.03590.x 9. Northcott PA, Robinson GW, Kratz CP, et al. Medulloblastoma. Nat Rev Dis Primer. 2019;5(1). doi:10.1038/s41572-019-0063-6 10. Guerreiro Stucklin AS, Ramaswamy V, Daniels C, Taylor MD. Review of molecular classification and treatment implications of pediatric brain tumors: Curr Opin Pediatr. 2018;30(1):3-9. doi:10.1097/MOP.0000000000000562 11. Othman RT, Kimishi I, Bradshaw TD, et al. Overcoming multiple drug resistance mechanisms in medulloblastoma. Acta Neuropathol Commun. 2014;2(1):57. doi:10.1186/2051-5960-2-57 12. Kralik SF, Ho CY, Finke W, Buchsbaum JC, Haskins CP, Shih C-S. Radiation Necrosis in Pediatric Patients with Brain Tumors Treated with Proton Radiotherapy. Am J Neuroradiol. 2015;36(8):1572-1578. doi:10.3174/ajnr.A4333 13. Chapter 8 - Cerebellum: Development and Medulloblastoma | Elsevier Enhanced Reader. doi:10.1016/B978-0-12-380916-2.00008-5 14. Kool M, Koster J, Bunt J, et al. Integrated Genomics Identifies Five Medulloblastoma Subtypes with Distinct Genetic Profiles, Pathway Signatures and Clinicopathological Features. Hide W, ed. PLoS ONE. 2008;3(8):e3088. doi:10.1371/journal.pone.0003088 96 15. Remke M, Hielscher T, Northcott PA, et al. Adult Medulloblastoma Comprises Three Major Molecular Variants. J Clin Oncol. 2011;29(19):2717-2723. doi:10.1200/JCO.2011.34.9373 16. Remke M, Ramaswamy V, Taylor MD. Medulloblastoma molecular dissection: the way toward targeted therapy. Curr Opin Oncol. 2013;25(6):674-681. doi:10.1097/CCO.0000000000000008 17. Archer TC, Ehrenberger T, Mundt F, et al. Proteomics, Post-translational Modifications, and Integrative Analyses Reveal Molecular Heterogeneity within Medulloblastoma Subgroups. Cancer Cell. 2018;34(3):396-410.e8. doi:10.1016/j.ccell.2018.08.004 18. Cavalli FMG, Remke M, Rampasek L, et al. Intertumoral Heterogeneity within Medulloblastoma Subgroups. Cancer Cell. 2017;31(6):737-754.e6. doi:10.1016/j.ccell.2017.05.005 19. Remke M, Ramaswamy V. Infant medulloblastoma — learning new lessons from old strata. Nat Rev Clin Oncol. 2018;15(11):659-660. doi:10.1038/s41571-018-0071-6 20. Ellison DW, Kocak M, Dalton J, et al. Definition of Disease-Risk Stratification Groups in Childhood Medulloblastoma Using Combined Clinical, Pathologic, and Molecular Variables. J Clin Oncol. 2011;29(11):1400-1407. doi:10.1200/JCO.2010.30.2810 21. Mulhern RK, Palmer SL, Merchant TE, et al. Neurocognitive Consequences of Risk- Adapted Therapy for Childhood Medulloblastoma. J Clin Oncol. 2005;23(24):5511- 5519. doi:10.1200/JCO.2005.00.703 22. Manoranjan B, Venugopal C, Bakhshinyan D, et al. Wnt activation as a therapeutic strategy in medulloblastoma. Nat Commun. 2020;11(1):4323. doi:10.1038/s41467-020- 17953-4 23. Juraschka K, Taylor MD. Medulloblastoma in the age of molecular subgroups: a review. J Neurosurg Pediatr. 2019;24(4):353-363. doi:10.3171/2019.5.PEDS18381 24. Hovestadt V, Smith KS, Bihannic L, et al. Resolving medulloblastoma cellular architecture by single-cell genomics. Nature. 2019;572(7767):74-79. doi:10.1038/s41586-019-1434-6 25. Taylor MD, Northcott PA, Korshunov A, et al. Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathol (Berl). 2012;123(4):465- 472. doi:10.1007/s00401-011-0922-z 26. Rathi KS, Arif S, Koptyra M, et al. A transcriptome-based classifier to determine molecular subtypes in medulloblastoma. PLOS Comput Biol.:15. 27. Gendoo DMA, Smirnov P, Lupien M, Haibe-Kains B. Personalized diagnosis of medulloblastoma subtypes across patients and model systems. Genomics. 2015 Aug;106(2):96-106. doi: 10.1016/j.ygeno.2015.05.002. 28. Schwalbe EC. Novel molecular subgroups for clinical classification and outcome prediction in childhood medulloblastoma: a cohort study. Lancet Oncol. 2017 Jul;18(7):958-971. doi: 10.1016/S1470-2045(17)30243-7. 2017;18:14. 29. Remke M, Hielscher T, Korshunov A, et al. FSTL5 Is a Marker of Poor Prognosis in Non-WNT/Non-SHH Medulloblastoma. J Clin Oncol. 2011;29(29):3852-3861. doi:10.1200/JCO.2011.36.2798 97 30. Massimino M, Biassoni V, Gandola L. Childhood medulloblastoma. Crit Rev Oncol Hematol. 2016 ;105:35-51. doi: 10.1016/j.critrevonc.2016.05.012. 31. Zhukova N, Ramaswamy V, Remke M, et al. Subgroup-Specific Prognostic Implications of TP53 Mutation in Medulloblastoma. J Clin Oncol. 2013;31(23):2927- 2935. doi:10.1200/JCO.2012.48.5052 32. Khanna V, Achey RL, Ostrom QT, et al. Incidence and survival trends for medulloblastomas in the United States from 2001 to 2013. J Neurooncol. 2017;135(3):433-441. doi:10.1007/s11060-017-2594-6 33. Forget A, Martignetti L, Puget S, et al. Aberrant ERBB4-SRC Signaling as a Hallmark of Group 4 Medulloblastoma Revealed by Integrative Phosphoproteomic Profiling. Cancer Cell. 2018;34(3):379-395.e7. doi:10.1016/j.ccell.2018.08.002 34. Hovestadt V, Remke M, Kool M, et al. Robust molecular subgrouping and copy- number profiling of medulloblastoma from small amounts of archival tumour material using high-density DNA methylation arrays. Acta Neuropathol (Berl). 2013;125(6):913- 916. doi:10.1007/s00401-013-1126-5 35. Ramaswamy V, Samuel N, Remke M. Can miRNA-based real-time PCR be used to classify medulloblastomas? CNS Oncol. 2014;3(3):173-175. doi:10.2217/cns.14.14 36. Capper D, Jones DTW, Sill M, et al. DNA methylation-based classification of central nervous system tumours. Nature. 2018;555(7697):469-474. doi:10.1038/nature26000 37. Ramaswamy V, Remke M, Bouffet E, et al. Risk stratification of childhood medulloblastoma in the molecular era: the current consensus. Acta Neuropathol (Berl). 2016;131(6):821-831. doi:10.1007/s00401-016-1569-6 38. Eberhart CG, Tihan T, Burger PC. Nuclear localization and mutation of beta-catenin in medulloblastomas. J Neuropathol Exp Neurol. 2000 ;59(4):333-7. doi: 10.1093/jnen/59.4.333. 39. Orr BA, Clay MR, Pinto EM, Kesserwan C. An update on the central nervous system manifestations of Li–Fraumeni syndrome. Acta Neuropathol (Berl). 2020;139(4):669-687. doi:10.1007/s00401-019-02055-3 40. Polkinghorn WR, Tarbell NJ. Medulloblastoma: tumorigenesis, current clinical paradigm, and efforts to improve risk stratification. Nat Clin Pract Oncol. 2007;4(5):295-304. doi:10.1038/ncponc0794 41. Northcott PA, Shih DJH, Peacock J, et al. Subgroup-specific structural variation across 1,000 medulloblastoma genomes. Nature. 2012;488(7409):49-56. doi:10.1038/nature11327 42. Kumari A, Folk W, Sakamuro D. The Dual Roles of MYC in Genomic Instability and Cancer Chemoresistance. Genes. 2017;8(6):158. doi:10.3390/genes8060158 43. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10(1):57-63. doi:10.1038/nrg2484 44. Yang H-P. Reference Based RNA-Seq Data Analysis. Available at https://bioinformatics.uconn.edu/reference-based-rna-seq-data-analysis/, 21. April 2021 :66. 45. Scholes AN, Lewis JA. Comparison of RNA isolation methods on RNA-Seq: implications for differential expression and meta-analyses. BMC Genomics. 98 2020;21(1):249. doi:10.1186/s12864-020-6673-2 46. HiSeq 2500 System Guide. Available at https://support.illumina.com/content/dam/illumina-support/documents/documentation/ system_documentation/hiseq2500/hiseq-2500-system-guide-15035786-03.pdf, 21. April 2021:96. 47. Baruzzo G, Hayer KE, Kim EJ, Di Camillo B, FitzGerald GA, Grant GR. Simulation- based comprehensive benchmarking of RNA-seq aligners. Nat Methods. 2017;14(2):135-139. doi:10.1038/nmeth.4106 48. Dobin A, Davis CA, Schlesinger F, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15-21. doi:10.1093/bioinformatics/bts635 49. Szabo L, Salzman J. Detecting circular RNAs: bioinformatic and experimental challenges. Nat Rev Genet. 2016;17(11):679-692. doi:10.1038/nrg.2016.114 50. Bullard JH, Purdom E, Hansen KD, Dudoit S. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics. 2010;11(1):94. doi:10.1186/1471-2105-11-94 51. Garber M, Grabherr MG, Guttman M, Trapnell C. Computational methods for transcriptome annotation and quantification using RNA-seq. Nat Methods. 2011;8(6):469-477. doi:10.1038/nmeth.1613 52. Salzman J, Chen RE, Olsen MN, Wang PL, Brown PO. Cell-Type Specific Features of Circular RNA Expression. Moran JV, ed. PLoS Genet. 2013;9(9):e1003777. doi:10.1371/journal.pgen.1003777 53. Memczak S, Jens M, Elefsinioti A, et al. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature. 2013;495(7441):333-338. doi:10.1038/nature11928 54. Salzman J, Gawad C, Wang PL, Lacayo N, Brown PO. Circular RNAs Are the Predominant Transcript Isoform from Hundreds of Human Genes in Diverse Cell Types. Preiss T, ed. PLoS ONE. 2012;7(2):e30733. doi:10.1371/journal.pone.0030733 55. Wilusz JE. Circular RNAs: Unexpected outputs of many protein-coding genes. RNA Biol. 2017;14(8):1007-1017. doi:10.1080/15476286.2016.1227905 56. Jakobi T, Dieterich C. Computational approaches for circular RNA analysis. Wiley Interdiscip Rev RNA. 2019;10(3):e1528. doi:10.1002/wrna.1528 57. Rybak-Wolf A, Stottmeister C, Glažar P, et al. Circular RNAs in the Mammalian Brain Are Highly Abundant, Conserved, and Dynamically Expressed. Mol Cell. 2015;58(5):870-885. doi:10.1016/j.molcel.2015.03.027 58. Dube U, Del-Aguila JL, Li Z, et al. An atlas of cortical circular RNA expression in Alzheimer disease brains demonstrates clinical and pathological associations. Nat Neurosci. 2019;22(11):1903-1912. doi:10.1038/s41593-019-0501-5 59. Gokool A, Anwar F, Voineagu I. The Landscape of Circular RNA Expression in the Human Brain. Biol Psychiatry. 2020;87(3):294-304. doi:10.1016/j.biopsych.2019.07.029 60. Hanan M, Soreq H, Kadener S. CircRNAs in the brain. RNA Biol. 2017;14(8):1028- 1034. doi:10.1080/15476286.2016.1255398 99 61. Venø MT, Hansen TB, Venø ST, et al. Spatio-temporal regulation of circular RNA expression during porcine embryonic brain development. Genome Biol. 2015;16(1). doi:10.1186/s13059-015-0801-3 62. Wang PL, Bao Y, Yee M-C, et al. Circular RNA Is Expressed across the Eukaryotic Tree of Life. Preiss T, ed. PLoS ONE. 2014;9(3):e90859. doi:10.1371/journal.pone.0090859 63. Demongeot J, Seligmann H. Spontaneous evolution of circular codes in theoretical minimal RNA rings. Gene. 2019;705:95-102. doi:10.1016/j.gene.2019.03.069 64. Hassanin, A. (2020). The SARS-CoV-2-like virus found in captive pangolins from Guangdong should be better sequenced. BioRxiv 2021. 65. Toptan T, Abere B, Nalesnik MA, et al. Circular DNA tumor viruses make circular RNAs. Proc Natl Acad Sci. 2018;115(37):E8737-E8745. doi:10.1073/pnas.1811728115 66. Ungerleider NA, Jain V, Wang Y, et al. Comparative Analysis of Gammaherpesvirus Circular RNA Repertoires: Conserved and Unique Viral Circular RNAs. Longnecker RM, ed. J Virol. 2018;93(6):e01952-18, /jvi/93/6/JVI.01952-18.atom. doi:10.1128/JVI.01952-18 67. Huang JT, Chen JN, Gong LP, et al. Identification of virus-encoded circular RNA. Virology. 2019;529:144-151. doi:10.1016/j.virol.2019.01.014 68. Salzman J. Circular RNA Expression: Its Potential Regulation and Function. Trends Genet. 2016;32(5):309-316. doi:10.1016/j.tig.2016.03.002 69. Robic A, Demars J, Kühn C. In-Depth Analysis Reveals Production of Circular RNAs from Non-Coding Sequences. Cells. 2020;9(8):1806. doi:10.3390/cells9081806 70. Panda AC, De S, Grammatikakis I, et al. High-purity circular RNA isolation method (RPAD) reveals vast collection of intronic circRNAs. Nucleic Acids Res. 2017;45(12):e116-e116. doi:10.1093/nar/gkx297 71. Stagsted LV, Nielsen KM, Daugaard I, Hansen TB. Noncoding AUG circRNAs constitute an abundant and conserved subclass of circles. Life Sci Alliance. 2019;2(3):e201900398. doi:10.26508/lsa.201900398 72. Jeck WR, Sorrentino JA, Wang K, et al. Circular RNAs are abundant, conserved, and associated with ALU repeats. RNA. 2013;19(2):141-157. doi:10.1261/rna.035667.112 73. Kristensen LS, Hansen TB, Venø MT, Kjems J. Circular RNAs in cancer: opportunities and challenges in the field. Oncogene. 2018;37(5):555-565. doi:10.1038/onc.2017.361 74. Stagsted LVW. Title: The RNA-binding protein SFPQ preserves long-intron splicing and regulates circRNA biogenesis. :33. 75. Conn SJ, Pillman KA, Toubia J, et al. The RNA Binding Protein Quaking Regulates Formation of circRNAs. Cell. 2015;160(6):1125-1134. doi:10.1016/j.cell.2015.02.014 76. Knupp D, Cooper DA, Saito Y, Darnell RB, Miura P. NOVA2 regulates neural circRNA biogenesis. bioRxiv 2021:2021.05.02.442201. doi:10.1101/2021.05.02.442201 77. Starke S, Jost I, Rossbach O, et al. Exon Circularization Requires Canonical Splice Signals. Cell Rep. 2015;10(1):103-111. doi:10.1016/j.celrep.2014.12.002 100 78. Wilusz JE. A 360° view of circular RNAs: From biogenesis to functions. Wiley Interdiscip Rev RNA. 2018;9(4):e1478. doi:10.1002/wrna.1478 79. Zhang X-O, Dong R, Zhang Y, et al. Diverse alternative back-splicing and alternative splicing landscape of circular RNAs. Genome Res. 2016;26(9):1277-1287. doi:10.1101/gr.202895.115 80. Liang D, Tatomer DC, Luo Z, et al. The Output of Protein-Coding Genes Shifts to Circular RNAs When the Pre-mRNA Processing Machinery Is Limiting. Mol Cell. 2017;68(5):940-954.e3. doi:10.1016/j.molcel.2017.10.034 81. Hansen TB, Jensen TI, Clausen BH, et al. Natural RNA circles function as efficient microRNA sponges. Nature. 2013;495(7441):384-388. doi:10.1038/nature11993 82. Hao Z, Hu S, Liu Z, Song W, Zhao Y, Li M. Circular RNAs: Functions and Prospects in Glioma. J Mol Neurosci. 2019;67(1):72-81. doi:10.1007/s12031-018-1211-2 83. Pamudurti NR, Bartok O, Jens M, et al. Translation of CircRNAs. Mol Cell. 2017;66(1):9-21.e7. doi:10.1016/j.molcel.2017.02.021 84. Chekulaeva M, Rajewsky N. Roles of Long Noncoding RNAs and Circular RNAs in Translation. Cold Spring Harb Perspect Biol. 2019;11(6):a032680. doi:10.1101/cshperspect.a032680 85. Li HM, Ma XL, Li HG. Intriguing circles: Conflicts and controversies in circular RNA research. Wiley Interdiscip Rev RNA. 2019;10(5):e1538. doi:10.1002/wrna.1538 86. Arnaiz E, Sole C, Manterola L, Iparraguirre L, Otaegui D, Lawrie CH. CircRNAs and cancer: Biomarkers and master regulators. Semin Cancer Biol. 2019;58:90-99. doi:10.1016/j.semcancer.2018.12.002 87. Brown JR, Chinnaiyan AM. The Potential of Circular RNAs as Cancer Biomarkers. Cancer Epidemiol Biomarkers Prev. 2020;29(12):2541-2555. doi:10.1158/1055- 9965.EPI-20-0796 88. Vo JN, Cieslik M, Zhang Y, et al. The Landscape of Circular RNA in Cancer. Cell. 2019;176(4):869-881.e13. doi:10.1016/j.cell.2018.12.021 89. Bonizzato A, Gaffo E, te Kronnie G, Bortoluzzi S. CircRNAs in hematopoiesis and hematological malignancies. Blood Cancer J. 2016;6(10):e483-e483. doi:10.1038/bcj.2016.81 90. Lv T, Miao Y-F, Jin K, et al. Dysregulated circular RNAs in medulloblastoma regulate proliferation and growth of tumor cells via host genes. Cancer Med. 2018;7(12):6147- 6157. doi:10.1002/cam4.1613 91. Okholm TLH, Nielsen MM, Hamilton MP, et al. Circular RNA expression is abundant and correlated to aggressiveness in early-stage bladder cancer. Npj Genomic Med. 2017;2(1). doi:10.1038/s41525-017-0038-z 92. Li Y, Zheng Q, Bao C, et al. Circular RNA is enriched and stable in exosomes: a promising biomarker for cancer diagnosis. Cell Res. 2015;25(8):981-984. doi:10.1038/cr.2015.82 93. Wang Y, Liu J, Ma J, et al. Exosomal circRNAs: biogenesis, effect and application in human diseases. Mol Cancer. 2019;18(1). doi:10.1186/s12943-019-1041-z 94. Seimiya T, Otsuka M, Iwata T, et al. Emerging Roles of Exosomal Circular RNAs in 101 Cancer. Front Cell Dev Biol. 2020;8:568366. doi:10.3389/fcell.2020.568366 95. Hulstaert E, Morlion A, Avila Cobos F, et al. Charting Extracellular Transcriptomes in The Human Biofluid RNA Atlas. Cell Rep. 2020;33(13):108552. doi:10.1016/j.celrep.2020.108552 96. Chen L, Wang C, Sun H, et al. The bioinformatics toolbox for circRNA discovery and analysis. Brief Bioinform. 2021;22(2):1706-1728. doi:10.1093/bib/bbaa001 97. Dahl M, Daugaard I, Andersen MS, et al. Enzyme-free digital counting of endogenous circular RNA molecules in B-cell malignancies. Lab Invest. 2018;98(12):1657-1669. doi:10.1038/s41374-018-0108-6 98. Ma XK, Wang MR, Liu CX, et al. CIRCexplorer3: A CLEAR Pipeline for Direct Comparison of Circular and Linear RNA Expression. Genomics Proteomics Bioinformatics. 2019;17(5):511-521. doi:10.1016/j.gpb.2019.11.004 99. Zhang X-O, Wang H-B, Zhang Y, Lu X, Chen L-L, Yang L. Complementary Sequence-Mediated Exon Circularization. Cell. 2014;159(1):134-147. doi:10.1016/j.cell.2014.09.001 100. Hansen TB, Venø MT, Damgaard CK, Kjems J. Comparison of circular RNA prediction tools. Nucleic Acids Res. 2016;44(6):e58-e58. doi:10.1093/nar/gkv1458 101. Barrett SP, Salzman J. Circular RNAs: analysis, expression and potential functions. Development. 2016;143(11):1838-1847. doi:10.1242/dev.128074 102. Hansen TB. Improved circRNA Identification by Combining Prediction Algorithms. Front Cell Dev Biol. 2018;6. doi:10.3389/fcell.2018.00020 103. Gaffo E, Bonizzato A, Kronnie G, Bortoluzzi S. CirComPara: A Multi‐Method Comparative Bioinformatics Pipeline to Detect and Study circRNAs from RNA‐seq Data. Non-Coding RNA. 2017;3(1):8. doi:10.3390/ncrna3010008 104. Ahmadov U, Bendikas MM, Ebbesen KK, et al. Distinct circular RNA expression profiles in pediatric ependymomas. Brain Pathol. 2021;31(2):387-392. doi:10.1111/bpa.12922 105. Jakobi T, Uvarovskii A, Dieterich C. circtools-a one-stop software solution for circular RNA research. Bioinformatics. 2019;35(13):2326-2328. doi:10.1093/bioinformatics/bty948 106. Boss M, Arenz C. A Fast and Easy Method for Specific Detection of Circular RNA by Rolling‐Circle Amplification. ChemBioChem. 2020;21(6):793-796. doi:10.1002/cbic.201900514 107. Zhu J, Ye J, Zhang L, et al. Differential Expression of Circular RNAs in Glioblastoma Multiforme and Its Correlation with Prognosis. Transl Oncol. 2017;10(2):271-279. doi:10.1016/j.tranon.2016.12.006 108. Liu M, Wang Q, Shen J, Yang BB, Ding X. Circbank: a comprehensive database for circRNA with standard nomenclature. RNA Biol. 2019;16(7):899-905. doi:10.1080/15476286.2019.1600395 109. Vromman M, Vandesompele J, Volders PJ. Closing the circle: current state and perspectives of circular RNA databases. Brief Bioinform. 2021;22(1):288-297. doi:10.1093/bib/bbz175 102 110. Glažar P, Papavasileiou P, Rajewsky N. circBase: a database for circular RNAs. RNA. 2014;20(11):1666-1670. doi:10.1261/rna.043687.113 111. Lyu Y, Caudron-Herger M, Diederichs S. circ2GO: A Database Linking Circular RNAs to Gene Function. Cancers. 2020;12(10):2975. doi:10.3390/cancers12102975 112. Metge F, Czaja-Hasse LF, Reinhardt R, Dieterich C. FUCHS—towards full circular RNA characterization using RNAseq. PeerJ. 2017;5:e2934. doi:10.7717/peerj.2934 113. Chen L, Wang F, Bruggeman EC, Li C, Yao B. circMeta: a unified computational framework for genomic feature annotation and differential expression analysis of circular RNAs. Bioinformatics. 2020;36(2):539-545. doi:10.1093/bioinformatics/btz606 114. Aufiero S, Reckman YJ, Tijsen AJ, Pinto YM, Creemers EE. circRNAprofiler: an R- based computational framework for the downstream analysis of circular RNAs. BMC Bioinformatics. 2020;21(1):164. doi:10.1186/s12859-020-3500-3 115. Ghosal S, Das S, Sen R, Basak P, Chakrabarti J. Circ2Traits: a comprehensive database for circular RNA potentially associated with disease and traits. Front Genet. 2013;4:283. Published 2013 Dec 10. doi:10.3389/fgene.2013.00283 116. Ungerleider N, Flemington E. SpliceV: analysis and publication quality printing of linear and circular RNA splicing, expression and regulation. BMC Bioinformatics. 2019;20(1). doi:10.1186/s12859-019-2865-7 117. Feng J, Xiang Y, Xia S, et al. CircView: a visualization and exploration tool for circular RNAs. Brief Bioinform. 2019;20(3):745-751. doi:10.1093/bib/bbx070 118. Ferrero G, Licheri N, Coscujuela Tarrero L, et al. Docker4Circ: A Framework for the Reproducible Characterization of circRNAs from RNA-Seq Data. Int J Mol Sci. 2019;21(1):293. doi:10.3390/ijms21010293 119. Cheng J, Metge F, Dieterich C. Specific identification and quantification of circular RNAs from sequencing data. Bioinformatics. 2016;32(7):1094-1096. doi:10.1093/bioinformatics/btv656 120. Akers NK, Schadt EE, Losic B. STAR Chimeric Post for rapid detection of circular RNA and fusion transcripts. Valencia A, ed. Bioinformatics. 2018;34(14):2364-2370. doi:10.1093/bioinformatics/bty091 121. Chaabane M, Williams RM, Stephens AT, Park JW. circDeep: deep learning approach for circular RNA classification from other long non-coding RNA. Bioinformatics. 2020;36(1):73-80. doi:10.1093/bioinformatics/btz537 122. Song X, Zhang N, Han P, et al. Circular RNA profile in gliomas revealed by identification tool UROBORUS. Nucleic Acids Res. 2016;44(9):e87-e87. doi:10.1093/nar/gkw075 123. Wang J, Wang L. Deep learning of the back-splicing code for circular RNA formation. Bioinformatics. 2019;35(24):5235-5242. doi:10.1093/bioinformatics/btz382 124. Yoshimoto R, Rahimi K, Hansen TB, Kjems J, Mayeda A. Biosynthesis of Circular RNA ciRS-7/CDR1as Is Mediated by Mammalian-wide Interspersed Repeats. iScience. 2020;23(7):101345. doi:10.1016/j.isci.2020.101345 125. Mölder F, Jablonski KP, Letcher B, et al. Sustainable data analysis with Snakemake. F1000Research. 2021;10:33. doi:10.12688/f1000research.29032.1 103 126. Wang B, Mezlini AM, Demir F, et al. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods. 2014;11(3):333-337. doi:10.1038/nmeth.2810 127. Ng S-Y, Bogu GK, Soh BS, Stanton LW. The Long Noncoding RNA RMST Interacts with SOX2 to Regulate Neurogenesis. Mol Cell. 2013;51(3):349-359. doi:10.1016/j.molcel.2013.07.017 128. Pajtler KW, Witt H, Sill M, et al. Molecular Classification of Ependymal Tumors across All CNS Compartments, Histopathological Grades, and Age Groups. Cancer Cell. 2015;27(5):728-743. doi:10.1016/j.ccell.2015.04.002 129. Wang L, Liu D, Wu X, et al. Long non-coding RNA (LncRNA) RMST in triple- negative breast cancer (TNBC): Expression analysis and biological roles research. J Cell Physiol. 2018;233(10):6603-6612. doi:https://doi.org/10.1002/jcp.26311 130. Uhde CW, Vives J, Jaeger I, Li M. Rmst Is a Novel Marker for the Mouse Ventral Mesencephalic Floor Plate and the Anterior Dorsal Midline Cells. Riley B, ed. PLoS ONE. 2010;5(1):e8641. doi:10.1371/journal.pone.0008641 131. Izuogu OG, Alhasan AA, Mellough C, et al. Analysis of human ES cell differentiation establishes that the dominant isoforms of the lncRNAs RMST and FIRRE are circular. BMC Genomics. 2018;19(1). doi:10.1186/s12864-018-4660-7 132. Yu C-Y, Kuo H-C. The Trans-Spliced Long Noncoding RNA tsRMST Impedes Human Embryonic Stem Cell Differentiation Through WNT5A-Mediated Inhibition of the Epithelial-to-Mesenchymal Transition. Stem Cells Dayt Ohio. 2016;34(8):2052-2062. doi:10.1002/stem.2386 133. Cirak S, Foley AR, Herrmann R, et al. ISPD gene mutations are a common cause of congenital and limb-girdle muscular dystrophies. Brain. 2013;136(1):269-281. doi:10.1093/brain/aws312 134. Magri F, Colombo I, Del Bo R, et al. ISPD mutations account for a small proportion of Italian Limb Girdle Muscular Dystrophy cases. BMC Neurol. 2015;15(1):172. doi:10.1186/s12883-015-0428-8 135. Mahmoudi E, Kiltschewskij D, Fitzsimmons C, Cairns MJ. Depolarization- Associated CircRNA Regulate Neural Gene Expression and in Some Cases May Function as Templates for Translation. Cells. 2019;9(1):25. doi:10.3390/cells9010025 136. Evers C, Maas B, Koch KA, et al. Mosaic deletion of EXOC6B: Further evidence for an important role of the exocyst complex in the pathogenesis of intellectual disability. Am J Med Genet A. 2014;164(12):3088-3094. doi:https://doi.org/10.1002/ajmg.a.36770 137. Girisha KM, Kortüm F, Shah H, et al. A novel multiple joint dislocation syndrome associated with a homozygous nonsense variant in the EXOC6B gene. Eur J Hum Genet. 2016;24(8):1206-1210. doi:10.1038/ejhg.2015.261 138. Frühmesser A, Blake J, Haberlandt E, et al. Disruption of EXOC6B in a patient with developmental delay, epilepsy, and a de novo balanced t(2;8) translocation. Eur J Hum Genet. 2013;21(10):1177-1180. doi:10.1038/ejhg.2013.18 139. Strimbu K, Tavel JA. What are biomarkers?. Curr Opin HIV AIDS. 2010;5(6):463- 466. doi:10.1097/COH.0b013e32833ed177 | |||||||
| Lizenz: |  Dieses Werk ist lizenziert unter einer Creative Commons Namensnennung 4.0 International Lizenz | |||||||
| Fachbereich / Einrichtung: | Mathematisch- Naturwissenschaftliche Fakultät » WE Informatik » Bioinformatik | |||||||
| Dokument erstellt am: | 27.06.2023 | |||||||
| Dateien geändert am: | 27.06.2023 | |||||||
| Promotionsantrag am: | 22.12.2022 | |||||||
| Datum der Promotion: | 15.06.2023 |
