Table Info

Table 1.

Summary of some of the most widely used databases for the investigation of miRNA sponge interactions

Database	Type of data	Description	Ref	URL
CeRDB	Predicted	One of the earliest miRNA sponge interaction databases, finds miRNA sponges for a specific target mRNA based on the homology of miRNA binding sites	[192]	https://www.oncomir.umn.edu/cefinder/
DIANA-TarBase v.8	Validated	Aims to provide hundreds of thousands of high-quality manually curated experimentally validated miRNA: gene interactions, enhanced with detailed meta-data	[193]	https://dianalab.e-ce.uth.gr/html/diana/web/index.php?r=tarbasev8
DIANA-microT	Predicted	Web server dedicated to miRNA target prediction/functional analysis	[194]	https://dianalab.e-ce.uth.gr/microt_webserver/#/
ENCORI	Validated	Previously known as starBase v2.0, identifies millions of RBP-RNA and RRIs by analyzing thousands of CLIP-seq and various high-throughput sequencing data, studying their functions and mechanisms in human diseases	[172]	https://rnasysu.com/encori/
LncACTdb v3.0	Predicted	A comprehensive database of experimentally supported interactions among ceRNAs and the corresponding personalized networks contributing to precision medicine	[195]	http://bio-bigdata.hrbmu.edu.cn/LncACTdb/
LnCeCell	Predicted and validated	Provides functional lncRNA-related miRNA sponge interaction networks in single cells and sub-cellular locations curated from the published literature and high-throughput datasets	[196]	http://bio-bigdata.hrbmu.edu.cn/LnCeCell/
LnCeVar	Predicted and validated	Database collecting genomic variations that disturb lncRNA-related miRNA sponge regulation curated from the published literature and high-throughput datasets across 33 human cancers	[197]	http://www.bio-bigdata.net/LnCeVar/
miRanda	Predicted	Algorithm for finding genomic targets for miRNAs, written in C and available as an open-source method	[168]	https://cbio.mskcc.org/miRNA2003/miranda.html
miRcode	Predicted	Provides “whole transcriptome” human microRNA target predictions based on the comprehensive GENCODE gene annotation	[198]	http://www.mircode.org/
miRTarBase	Validated	Database providing information about experimentally validated miRNA-target interactions	[199]	https://mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase_2022/php/index.php
miRTissue_ce	Predicted	Web service for the analysis and characterization of miRNA sponge interactions in several cancer tissue types	[200]	http://tblab.pa.icar.cnr.it/mirtissue.html
miRWalk	Validated	A comprehensive archive supplying the largest available collection of predicted and experimentally verified miRNA-target interactions	[201]	http://mirwalk.umm.uni-heidelberg.de/
Pan-ceRNADB	Predicted	Investigates mRNA-related miRNA sponge interactions across 20 major human cancers	[177]	http://bio-bigdata.hrbmu.edu.cn/pan-cernadb/
PicTar	Predicted	An algorithm for the identification of miRNA targets	[202]	https://pictar.mdc-berlin.de/
PITA	Predicted	An algorithm using standard settings to identify initial seeds for each miRNA in 3’ UTRs	[203]	https://genie.weizmann.ac.il/pubs/mir07/mir07_prediction.html
RNA22	Predicted	A method for identifying miRNA binding sites and their corresponding heteroduplexes	[204]	https://cm.jefferson.edu/rna22/
SomamiR	Predicted	Database of cancer somatic mutations that potentially alter the interactions between miRNAs and miRNA sponges	[205]	http://compbio.uthsc.edu/SomamiR/
TargetScan	Predicted	Prediction of miRNA target sites conserved among orthologous 3’-UTRs of vertebrates	[206]	https://www.targetscan.org/vert_80/
VECTOR	Predicted and validated	An interactive tool that identifies and visualizes the relationships among RNA molecules associated with ChiP-seq data and expression data of miRNAs, lncRNAs, and mRNAs both in UM and physiological tissues	[207]	https://vectordb.it/

CeRDB: Competitive Endogenous mRNA DataBase; Pan-ceRNADB: Pan-cancer associated ceRNA database; ChiP-seq: chromatin immunoprecipitation sequencing; VECTOR: uVeal mElanoma Correlation NeTwORk; UM: uveal melanoma

Declarations

Acknowledgments

The authors thank the Scientific Bureau of the University of Catania for language support.

Author contributions

CB: Conceptualization, Writing—original draft, Writing—review & editing. MS: Writing—original draft, Writing—review & editing. CF, AC, RB, DB, and CDP: Writing—review & editing. MR: Conceptualization, Supervision, Writing—review & editing.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

Not applicable.

Funding

This study was supported by “linea intervento 3 Open Access Piano PIACERI 2020‐2022” from the University of Catania. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright

References

Fields C, Adams MD, White O, Venter JC. How many genes in the human genome? Nat Genet. 1994;7:345–6. [DOI] [PubMed]

Liang F, Holt I, Pertea G, Karamycheva S, Salzberg SL, Quackenbush J. Gene index analysis of the human genome estimates approximately 120,000 genes. Nat Genet. 2000;25:239–40. [DOI] [PubMed]

Singh RS, Gupta BP. Genes and genomes and unnecessary complexity in precision medicine. NPJ Genom Med. 2020;5:21. [DOI] [PubMed] [PMC]

Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, et al.; International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature. 2001;409:860–921. [DOI] [PubMed]

Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, et al. The sequence of the human genome. Science. 2001;291:1304–51. [DOI] [PubMed]

International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome. Nature. 2004;431:931–45. [DOI] [PubMed]

Clamp M, Fry B, Kamal M, Xie X, Cuff J, Lin MF, et al. Distinguishing protein-coding and noncoding genes in the human genome. Proc Natl Acad Sci U S A. 2007;104:19428–33. [DOI] [PubMed] [PMC]

Ezkurdia I, Juan D, Rodriguez JM, Frankish A, Diekhans M, Harrow J, et al. Multiple evidence strands suggest that there may be as few as 19,000 human protein-coding genes. Hum Mol Genet. 2014;23:5866–78. [DOI] [PubMed] [PMC]

Nurk S, Koren S, Rhie A, Rautiainen M, Bzikadze AV, Mikheenko A, et al. The complete sequence of a human genome. Science. 2022;376:44–53. [DOI]

10.

Zhang C, Ötjengerdes RM, Roewe J, Mejias R, Marschall ALJ. Applying antibodies inside cells: principles and recent advances in neurobiology, virology and oncology. BioDrugs. 2020;34:435–62. [DOI] [PubMed] [PMC]

11.

The ENCODE Project Consortium. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature. 2007;447:799–816. [DOI] [PubMed] [PMC]

12.

Bernstein BE, Stamatoyannopoulos JA, Costello JF, Ren B, Milosavljevic A, Meissner A, et al. The NIH roadmap epigenomics mapping consortium. Nat Biotechnol. 2010;28:1045–8. [DOI] [PubMed] [PMC]

13.

Sana J, Faltejskova P, Svoboda M, Slaby O. Novel classes of non-coding RNAs and cancer. J Transl Med. 2012;10:103. [DOI] [PubMed] [PMC]

14.

Ragusa M, Barbagallo C, Brex D, Caponnetto A, Cirnigliaro M, Battaglia R, et al. Molecular crosstalking among noncoding RNAs: a new network layer of genome regulation in cancer. Int J Genomics. 2017;2017:4723193. [DOI] [PubMed] [PMC]

15.

Caponnetto A, Battaglia R, Ferrara C, Vento ME, Borzì P, Paradiso M, et al.; Italian Society of Embryology, Reproduction, Research (SIERR). Down-regulation of long non-coding RNAs in reproductive aging and analysis of the lncRNA-miRNA-mRNA networks in human cumulus cells. J Assist Reprod Genet. 2022;39:919–31. [DOI] [PubMed] [PMC]

16.

Ganser LR, Kelly ML, Herschlag D, Al-Hashimi HM. The roles of structural dynamics in the cellular functions of RNAs. Nat Rev Mol Cell Biol. 2019;20:474–89. [DOI] [PubMed] [PMC]

17.

Ragusa M, Barbagallo C, Statello L, Condorelli AG, Battaglia R, Tamburello L, et al. Non-coding landscapes of colorectal cancer. World J Gastroenterol. 2015;21:11709–39. [DOI] [PubMed] [PMC]

18.

Zhang P, Wu W, Chen Q, Chen M. Non-coding RNAs and their integrated networks. J Integr Bioinform. 2019;16:20190027. [DOI] [PubMed] [PMC]

19.

Karreth FA, Pandolfi PP. ceRNA cross-talk in cancer: when ce-bling rivalries go awry. Cancer Discov. 2013;3:1113–21. [DOI] [PubMed] [PMC]

20.

Ala U. Competing endogenous RNAs, non-coding RNAs and diseases: an intertwined story. Cells. 2020;9:1574. [DOI] [PubMed] [PMC]

21.

Wheeler EC, Van Nostrand EL, Yeo GW. Advances and challenges in the detection of transcriptome-wide protein-RNA interactions. Wiley Interdiscip Rev RNA. 2018;9:e1436. [DOI] [PubMed] [PMC]

22.

Cooper TA, Wan L, Dreyfuss G. RNA and disease. Cell. 2009;136:777–93. [DOI]

23.

Chan JJ, Tay Y. Noncoding RNA:RNA regulatory networks in cancer. Int J Mol Sci. 2018;19:1310. [DOI] [PubMed] [PMC]

24.

Tay Y, Rinn J, Pandolfi PP. The multilayered complexity of ceRNA crosstalk and competition. Nature. 2014;505:344–52. [DOI] [PubMed] [PMC]

25.

Salmena L, Poliseno L, Tay Y, Kats L, Pandolfi PP. A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language? Cell. 2011;146:353–8. [DOI] [PubMed] [PMC]

26.

Seitz H. Redefining microRNA targets. Curr Biol. 2009;19:870–3. [DOI] [PubMed]

27.

Marques TM, Gama-Carvalho M. Network approaches to study endogenous RNA competition and its impact on tissue-specific microRNA functions. Biomolecules. 2022;12:332. [DOI] [PubMed] [PMC]

28.

Wang GQ, Wang Y, Xiong Y, Chen XC, Ma ML, Cai R, et al. Sirt1 AS lncRNA interacts with its mRNA to inhibit muscle formation by attenuating function of miR-34a. Sci Rep. 2016;6:21865. [DOI] [PubMed] [PMC]

29.

Faghihi MA, Zhang M, Huang J, Modarresi F, Van der Brug MP, Nalls MA, et al. Evidence for natural antisense transcript-mediated inhibition of microRNA function. Genome Biol. 2010;11:R56. [DOI] [PubMed] [PMC]

30.

Beltran M, Puig I, Peña C, García JM, Alvarez AB, Peña R, et al. A natural antisense transcript regulates Zeb2/Sip1 gene expression during Snail1-induced epithelial-mesenchymal transition. Genes Dev. 2008;22:756–69. [DOI] [PubMed] [PMC]

31.

Battaglia R, Alonzo R, Pennisi C, Caponnetto A, Ferrara C, Stella M, et al. MicroRNA-mediated regulation of the virus cycle and pathogenesis in the SARS-CoV-2 disease. Int J Mol Sci. 2021;22:13192. [DOI] [PubMed] [PMC]

32.

Pratt AJ, MacRae IJ. The RNA-induced silencing complex: a versatile gene-silencing machine. J Biol Chem. 2009;284:17897–901. [DOI] [PubMed] [PMC]

33.

Martin HC, Wani S, Steptoe AL, Krishnan K, Nones K, Nourbakhsh E, et al. Imperfect centered miRNA binding sites are common and can mediate repression of target mRNAs. Genome Biol. 2014;15:R51. [DOI] [PubMed] [PMC]

34.

Qi X, Lin Y, Chen J, Shen B. Decoding competing endogenous RNA networks for cancer biomarker discovery. Brief Bioinform. 2020;21:441–57. [DOI] [PubMed]

35.

Riolo G, Cantara S, Marzocchi C, Ricci C. miRNA targets: from prediction tools to experimental validation. Methods Protoc. 2020;4:1. [DOI] [PubMed] [PMC]

36.

Ameres SL, Horwich MD, Hung JH, Xu J, Ghildiyal M, Weng Z, et al. Target RNA-directed trimming and tailing of small silencing RNAs. Science. 2010;328:1534–9. [DOI] [PubMed] [PMC]

37.

Zhu S, Wang J, He Y, Meng N, Yan GR. Peptides/Proteins encoded by non-coding RNA: a novel resource bank for drug targets and biomarkers. Front Pharmacol. 2018;9:1295. [DOI] [PubMed] [PMC]

38.

O’Brien J, Hayder H, Zayed Y, Peng C. Overview of microRNA biogenesis, mechanisms of actions, and circulation. Front Endocrinol (Lausanne). 2018;9:402. [DOI] [PubMed] [PMC]

39.

Su X, Xing J, Wang Z, Chen L, Cui M, Jiang B. microRNAs and ceRNAs: RNA networks in pathogenesis of cancer. Chin J Cancer Res. 2013;25:235–9. [DOI] [PubMed] [PMC]

40.

Londin E, Loher P, Telonis AG, Quann K, Clark P, Jing Y, et al. Analysis of 13 cell types reveals evidence for the expression of numerous novel primate- and tissue-specific microRNAs. Proc Natl Acad Sci U S A. 2015;112:E1106–15. [DOI] [PubMed] [PMC]

41.

Tomasello L, Distefano R, Nigita G, Croce CM. The microRNA family gets wider: the isomirs classification and role. Front Cell Dev Biol. 2021;9:668648. [DOI] [PubMed] [PMC]

42.

Park HJ, Ji P, Kim S, Xia Z, Rodriguez B, Li L, et al. 3’ UTR shortening represses tumor-suppressor genes in trans by disrupting ceRNA crosstalk. Nat Genet. 2018;50:783–9. [DOI] [PubMed] [PMC]

43.

Vitsios DM, Davis MP, van Dongen S, Enright AJ. Large-scale analysis of microRNA expression, epi-transcriptomic features and biogenesis. Nucleic Acids Res. 2017;45:1079–90. [DOI] [PubMed] [PMC]

44.

Brown BD, Gentner B, Cantore A, Colleoni S, Amendola M, Zingale A, et al. Endogenous microRNA can be broadly exploited to regulate transgene expression according to tissue, lineage and differentiation state. Nat Biotechnol. 2007;25:1457–67. [DOI] [PubMed]

45.

Carè A, Catalucci D, Felicetti F, Bonci D, Addario A, Gallo P. MicroRNA-133 controls cardiac hypertrophy. Nat Med. 2007;13:613–8. [DOI] [PubMed]

46.

Ebert MS, Neilson JR, Sharp PA. MicroRNA sponges: competitive inhibitors of small RNAs in mammalian cells. Nat Methods. 2007;4:721–6. [DOI] [PubMed] [PMC]

47.

Ebert MS, Sharp PA. MicroRNA sponges: progress and possibilities. RNA. 2010;16:2043–50. [DOI] [PubMed] [PMC]

48.

Kluiver J, Slezak-Prochazka I, Smigielska-Czepiel K, Halsema N, Kroesen BJ, van den Berg A. Generation of miRNA sponge constructs. Methods. 2012;58:113–7. [DOI] [PubMed]

49.

Gentner B, Schira G, Giustacchini A, Amendola M, Brown BD, Ponzoni M, et al. Stable knockdown of microRNA in vivo by lentiviral vectors. Nat Methods. 2009;6:63–6. [DOI] [PubMed]

50.

Valastyan S, Reinhardt F, Benaich N, Calogrias D, Szász AM, Wang ZC, et al. A pleiotropically acting microRNA, miR-31, inhibits breast cancer metastasis. Cell. 2009;137:1032–46. Retraction in: Cell. 2015;161:417. [DOI] [PubMed] [PMC]

51.

Brown BD, Naldini L. Exploiting and antagonizing microRNA regulation for therapeutic and experimental applications. Nat Rev Genet. 2009;10:578–85. [DOI] [PubMed]

52.

Haraguchi T, Ozaki Y, Iba H. Vectors expressing efficient RNA decoys achieve the long-term suppression of specific microRNA activity in mammalian cells. Nucleic Acids Res. 2009;37:e43. [DOI] [PubMed] [PMC]

53.

Franco-Zorrilla JM, Valli A, Todesco M, Mateos I, Puga MI, Rubio-Somoza I, et al. Target mimicry provides a new mechanism for regulation of microRNA activity. Nat Genet. 2007;39:1033–7. [DOI] [PubMed]

54.

Helwak A, Kudla G, Dudnakova T, Tollervey D. Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell. 2013;153:654–65. [DOI] [PubMed] [PMC]

55.

Cazalla D, Yario T, Steitz JA. Down-regulation of a host microRNA by a Herpesvirus saimiri noncoding RNA. Science. 2010;328:1563–6. [DOI] [PubMed] [PMC]

56.

Waters LS, Storz G. Regulatory RNAs in bacteria. Cell. 2009;136:615–28. [DOI] [PubMed] [PMC]

57.

Tay Y, Kats L, Salmena L, Weiss D, Tan SM, Ala U, et al. Coding-independent regulation of the tumor suppressor PTEN by competing endogenous mRNAs. Cell. 2011;147:344–57. [DOI] [PubMed] [PMC]

58.

Karreth FA, Tay Y, Perna D, Ala U, Tan SM, Rust AG, et al. In vivo identification of tumor- suppressive PTEN ceRNAs in an oncogenic BRAF-induced mouse model of melanoma. Cell. 2011;147:382–95. [DOI] [PubMed] [PMC]

59.

Sumazin P, Yang X, Chiu HS, Chung WJ, Iyer A, Llobet-Navas D, et al. An extensive microRNA-mediated network of RNA-RNA interactions regulates established oncogenic pathways in glioblastoma. Cell. 2011;147:370–81. [DOI] [PubMed] [PMC]

60.

Gilot D, Migault M, Bachelot L, Journé F, Rogiers A, Donnou-Fournet E, et al. A non-coding function of TYRP1 mRNA promotes melanoma growth. Nat Cell Biol. 2017;19:1348–57. [DOI] [PubMed]

61.

Lee DY, Jeyapalan Z, Fang L, Yang J, Zhang Y, Yee AY, et al. Expression of versican 3’-untranslated region modulates endogenous microRNA functions. PLoS One. 2010;5:e13599. [DOI] [PubMed] [PMC]

62.

Papadas A, Arauz G, Cicala A, Wiesner J, Asimakopoulos F. Versican and versican-matrikines in cancer progression, inflammation, and immunity. J Histochem Cytochem. 2020;68:871–85. [DOI] [PubMed] [PMC]

63.

Fang L, Du WW, Yang X, Chen K, Ghanekar A, Levy G, et al. Versican 3’-untranslated region (3’-UTR) functions as a ceRNA in inducing the development of hepatocellular carcinoma by regulating miRNA activity. FASEB J. 2013;27:907–19. [DOI] [PubMed]

64.

Jeyapalan Z, Deng Z, Shatseva T, Fang L, He C, Yang BB. Expression of CD44 3’-untranslated region regulates endogenous microRNA functions in tumorigenesis and angiogenesis. Nucleic Acids Res. 2011;39:3026–41. [DOI] [PubMed] [PMC]

65.

Rutnam ZJ, Yang BB. The non-coding 3’ UTR of CD44 induces metastasis by regulating extracellular matrix functions. J Cell Sci. 2012;125:2075–85. [DOI] [PubMed]

66.

Mayr C, Bartel DP. Widespread shortening of 3’UTRs by alternative cleavage and polyadenylation activates oncogenes in cancer cells. Cell. 2009;138:673–84. [DOI] [PubMed] [PMC]

67.

Navarro E, Mallén A, Hueso M. Dynamic variations of 3’UTR length reprogram the mRNA regulatory landscape. Biomedicines. 2021;9:1560. [DOI] [PubMed] [PMC]

68.

Sun M, Wang Y, Zheng C, Wei Y, Hou J, Zhang P, et al. Systematic functional interrogation of human pseudogenes using CRISPRi. Genome Biol. 2021;22:240. [DOI] [PubMed] [PMC]

69.

Kim MS, Pinto SM, Getnet D, Nirujogi RS, Manda SS, Chaerkady R, et al. A draft map of the human proteome. Nature. 2014;509:575–81. [DOI] [PubMed] [PMC]

70.

Pink RC, Wicks K, Caley DP, Punch EK, Jacobs L, Carter DR. Pseudogenes: pseudo-functional or key regulators in health and disease? RNA. 2011;17:792–8. [DOI] [PubMed] [PMC]

71.

Poliseno L, Salmena L, Zhang J, Carver B, Haveman WJ, Pandolfi PP. A coding-independent function of gene and pseudogene mRNAs regulates tumour biology. Nature. 2010;465:1033–8. [DOI] [PubMed] [PMC]

72.

Johnsson P, Ackley A, Vidarsdottir L, Lui WO, Corcoran M, Grandér D, et al. A pseudogene long-noncoding-RNA network regulates PTEN transcription and translation in human cells. Nat Struct Mol Biol. 2013;20:440–6. [DOI] [PubMed] [PMC]

73.

Chen X, Zhu H, Wu X, Xie X, Huang G, Xu X, et al. Downregulated pseudogene CTNNAP1 promote tumor growth in human cancer by downregulating its cognate gene CTNNA1 expression. Oncotarget. 2016;7:55518–28. [DOI] [PubMed] [PMC]

74.

Rutnam ZJ, Du WW, Yang W, Yang X, Yang BB. The pseudogene TUSC2P promotes TUSC2 function by binding multiple microRNAs. Nat Commun. 2014;5:2914. [DOI] [PubMed] [PMC]

75.

Peng H, Ishida M, Li L, Saito A, Kamiya A, Hamilton JP, et al. Pseudogene INTS6P1 regulates its cognate gene INTS6 through competitive binding of miR-17-5p in hepatocellular carcinoma. Oncotarget. 2015;6:5666–77. [DOI] [PubMed] [PMC]

76.

Novikova IV, Hennelly SP, Sanbonmatsu KY. Sizing up long non-coding RNAs: do lncRNAs have secondary and tertiary structure? Bioarchitecture. 2012;2:189–99. [DOI] [PubMed] [PMC]

77.

Statello L, Guo CJ, Chen LL, Huarte M. Gene regulation by long non-coding RNAs and its biological functions. Nat Rev Mol Cell Biol. 2021;22:96–118. [DOI] [PubMed] [PMC]

78.

Jarroux J, Morillon A, Pinskaya M. History, discovery, and classification of lncRNAs. Adv Exp Med Biol. 2017;1008:1–46. [DOI] [PubMed]

79.

Bridges MC, Daulagala AC, Kourtidis A. LNCcation: lncRNA localization and function. J Cell Biol. 2021;220:e202009045. [DOI] [PubMed] [PMC]

80.

Zhang X, Wang W, Zhu W, Dong J, Cheng Y, Yin Z, et al. Mechanisms and functions of long non-coding RNAs at multiple regulatory levels. Int J Mol Sci. 2019;20:5573. [DOI] [PubMed] [PMC]

81.

Brex D, Barbagallo C, Mirabella F, Caponnetto A, Battaglia R, Barbagallo D, et al. LINC00483 has a potential tumor-suppressor role in colorectal cancer through multiple molecular axes. Front Oncol. 2021;10:614455. [DOI] [PubMed] [PMC]

82.

Barbagallo C, Caltabiano R, Broggi G, Russo A, Puzzo L, Avitabile T, et al. LncRNA LINC00518 acts as an oncogene in uveal melanoma by regulating an RNA-based network. Cancers (Basel). 2020;12:3867. [DOI] [PubMed] [PMC]

83.

Barbagallo C, Brex D, Caponnetto A, Cirnigliaro M, Scalia M, Magnano A, et al. LncRNA UCA1, upregulated in CRC biopsies and downregulated in serum exosomes, controls mRNA expression by RNA-RNA interactions. Mol Ther Nucleic Acids. 2018;12:229–41. [DOI] [PubMed] [PMC]

84.

Wang J, Liu X, Wu H, Ni P, Gu Z, Qiao Y, et al. CREB up-regulates long non-coding RNA, HULC expression through interaction with microRNA-372 in liver cancer. Nucleic Acids Res. 2010;38:5366–83. [DOI] [PubMed] [PMC]

85.

Cesana M, Cacchiarelli D, Legnini I, Santini T, Sthandier O, Chinappi M, et al. A long noncoding RNA controls muscle differentiation by functioning as a competing endogenous RNA. Cell. 2011;147:358–69. [DOI] [PubMed] [PMC]

86.

Hajjari M, Salavaty A. HOTAIR: an oncogenic long non-coding RNA in different cancers. Cancer Biol Med. 2015;12:1–9. [DOI] [PubMed] [PMC]

87.

Liu XH, Sun M, Nie FQ, Ge YB, Zhang EB, Yin DD, et al. Lnc RNA HOTAIR functions as a competing endogenous RNA to regulate HER2 expression by sponging miR-331-3p in gastric cancer. Mol Cancer. 2014;13:92. [DOI] [PubMed] [PMC]

88.

Hampel H, Vassar R, De Strooper B, Hardy J, Willem M, Singh N, et al. The β-secretase BACE1 in Alzheimer’s disease. Biol Psychiatry. 2021;89:745–56. [DOI] [PubMed]

89.

Gong C, Maquat LE. lncRNAs transactivate STAU1-mediated mRNA decay by duplexing with 3’ UTRs via Alu elements. Nature. 2011;470:284–8. [DOI] [PubMed] [PMC]

90.

Kim YK, Furic L, Parisien M, Major F, DesGroseillers L, Maquat LE. Staufen1 regulates diverse classes of mammalian transcripts. EMBO J. 2007;26:2670–81. [DOI] [PubMed] [PMC]

91.

Eger N, Schoppe L, Schuster S, Laufs U, Boeckel JN. Circular RNA splicing. Adv Exp Med Biol. 2018;1087:41–52. [DOI] [PubMed]

92.

Holdt LM, Kohlmaier A, Teupser D. Circular RNAs as therapeutic agents and targets. Front Physiol. 2018;9:1262. [DOI] [PubMed] [PMC]

93.

Hanan M, Soreq H, Kadener S. CircRNAs in the brain. RNA Biol. 2017;14:1028–34. [DOI] [PubMed] [PMC]

94.

Xu T, Wu J, Han P, Zhao Z, Song X. Circular RNA expression profiles and features in human tissues: a study using RNA-seq data. BMC Genomics. 2017;18:680. [DOI] [PubMed] [PMC]

95.

Barbagallo D, Palermo CI, Barbagallo C, Battaglia R, Caponnetto A, Spina V, et al. Competing endogenous RNA network mediated by circ_3205 in SARS-CoV-2 infected cells. Cell Mol Life Sci. 2022;79:75. [DOI] [PubMed] [PMC]

96.

Yang L, Fu J, Zhou Y. Circular RNAs and their emerging roles in immune regulation. Front Immunol. 2018;9:2977. [DOI] [PubMed] [PMC]

97.

Zhao X, Cai Y, Xu J. Circular RNAs: biogenesis, mechanism, and function in human cancers. Int J Mol Sci. 2019;20:3926. [DOI] [PubMed] [PMC]

98.

Ragusa M, Barbagallo D, Chioccarelli T, Manfrevola F, Cobellis G, Di Pietro C, et al. CircNAPEPLD is expressed in human and murine spermatozoa and physically interacts with oocyte miRNAs. RNA Biol. 2019;16:1237–48. [DOI] [PubMed] [PMC]

99.

Das A, Sinha T, Shyamal S, Panda AC. Emerging role of circular RNA-protein interactions. Noncoding RNA. 2021;7:48. [DOI] [PubMed] [PMC]

100.

Barbagallo D, Caponnetto A, Brex D, Mirabella F, Barbagallo C, Lauretta G, et al. CircSMARCA5 regulates VEGFA mRNA splicing and angiogenesis in glioblastoma multiforme through the binding of SRSF1. Cancers (Basel). 2019;11:194. [DOI] [PubMed] [PMC]

101.

Liu M, Wang Q, Shen J, Yang BB, Ding X. Circbank: a comprehensive database for circRNA with standard nomenclature. RNA Biol. 2019;16:899–905. [DOI] [PubMed] [PMC]

102.

Hansen TB, Jensen TI, Clausen BH, Bramsen JB, Finsen B, Damgaard CK, et al. Natural RNA circles function as efficient microRNA sponges. Nature. 2013;495:384–8. [DOI] [PubMed]

103.

Memczak S, Jens M, Elefsinioti A, Torti F, Krueger J, Rybak A, et al. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature. 2013;495:333–8. [DOI] [PubMed]

104.

Jian F, Yangyang R, Wei X, Jiadan X, Na L, Peng Y, et al. The prognostic and predictive significance of circRNA CDR1as in tumor progression. Front Oncol. 2021;10:549982. [DOI] [PubMed] [PMC]

105.

Li F, Zhang L, Li W, Deng J, Zheng J, An M, et al. Circular RNA ITCH has inhibitory effect on ESCC by suppressing the Wnt/β-catenin pathway. Oncotarget. 2015;6:6001–13. [DOI] [PubMed] [PMC]

106.

Huang G, Zhu H, Shi Y, Wu W, Cai H, Chen X. cir-ITCH plays an inhibitory role in colorectal cancer by regulating the Wnt/β-catenin pathway. PLoS One. 2015;10:e0131225. [DOI] [PubMed] [PMC]

107.

Yang C, Yuan W, Yang X, Li P, Wang J, Han J, et al. Circular RNA circ-ITCH inhibits bladder cancer progression by sponging miR-17/miR-224 and regulating p21, PTEN expression. Mol Cancer. 2018;17:19. [DOI] [PubMed] [PMC]

108.

Rossi F, Beltran M, Damizia M, Grelloni C, Colantoni A, Setti A, et al. Circular RNA ZNF609/CKAP5 mRNA interaction regulates microtubule dynamics and tumorigenicity. Mol Cell. 2022;82:75–89.e9. [DOI] [PubMed] [PMC]

109.

Denzler R, Agarwal V, Stefano J, Bartel DP, Stoffel M. Assessing the ceRNA hypothesis with quantitative measurements of miRNA and target abundance. Mol Cell. 2014;54:766–76. [DOI] [PubMed] [PMC]

110.

Ala U, Karreth FA, Bosia C, Pagnani A, Taulli R, Léopold V, et al. Integrated transcriptional and competitive endogenous RNA networks are cross-regulated in permissive molecular environments. Proc Natl Acad Sci U S A. 2013;110:7154–9. [DOI] [PubMed] [PMC]

111.

Denzler R, McGeary SE, Title AC, Agarwal V, Bartel DP, Stoffel M. Impact of microRNA levels, target-site complementarity, and cooperativity on competing endogenous RNA-regulated gene expression. Mol Cell. 2016;64:565–79. [DOI] [PubMed] [PMC]

112.

Leung AKL. The whereabouts of microRNA actions: cytoplasm and beyond. Trends Cell Biol. 2015;25:601–10. [DOI] [PubMed] [PMC]

113.

Turunen TA, Roberts TC, Laitinen P, Väänänen MA, Korhonen P, Malm T, et al. Changes in nuclear and cytoplasmic microRNA distribution in response to hypoxic stress. Sci Rep. 2019;9:10332. [DOI] [PubMed] [PMC]

114.

Carrieri C, Cimatti L, Biagioli M, Beugnet A, Zucchelli S, Fedele S, et al. Long non-coding antisense RNA controls Uchl1 translation through an embedded SINEB2 repeat. Nature. 2012;491:454–7. [DOI] [PubMed]

115.

Gott JM, Emeson RB. Functions and mechanisms of RNA editing. Annu Rev Genet. 2000;34:499–531. [DOI] [PubMed]

116.

Tang W, Fei Y, Page M. Biological significance of RNA editing in cells. Mol Biotechnol. 2012;52:91–100. [DOI] [PubMed]

117.

Wang Y, Hou J, He D, Sun M, Zhang P, Yu Y, et al. The emerging function and mechanism of ceRNAs in cancer. Trends Genet. 2016;32:211–24. [DOI] [PubMed] [PMC]

118.

Chan JJ, Kwok ZH, Chew XH, Zhang B, Liu C, Soong TW, et al. A FTH1 gene:pseudogene:microRNA network regulates tumorigenesis in prostate cancer. Nucleic Acids Res. 2018;46:1998–2011. [DOI] [PubMed] [PMC]

119.

Chi SW, Zang JB, Mele A, Darnell RB. Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature. 2009;460:479–86. [DOI] [PubMed] [PMC]

120.

Nguyen TC, Cao X, Yu P, Xiao S, Lu J, Biase FH, et al. Mapping RNA-RNA interactome and RNA structure in vivo by MARIO. Nat Commun. 2016;7:12023. [DOI] [PubMed] [PMC]

121.

Bak G, Han K, Kim KS, Lee Y. Electrophoretic mobility shift assay of RNA-RNA complexes. Methods Mol Biol. 2015;1240:153–63. [DOI] [PubMed]

122.

Hellman LM, Fried MG. Electrophoretic mobility shift assay (EMSA) for detecting protein-nucleic acid interactions. Nat Protoc. 2007;2:1849–61. [DOI] [PubMed] [PMC]

123.

Li Y, Jiang Z, Chen H, Ma WJ. A modified quantitative EMSA and its application in the study of RNA–protein interactions. J Biochem Biophys Methods. 2004;60:85–96. [DOI] [PubMed]

124.

Di Primo C, Dausse E, Toulmé JJ. Surface plasmon resonance investigation of RNA aptamer-RNA ligand interactions. Methods Mol Biol. 2011;764:279–300. [DOI] [PubMed]

125.

Yang Y, Wang Q, Guo D. A novel strategy for analyzing RNA-protein interactions by surface plasmon resonance biosensor. Mol Biotechnol. 2008;40:87–93. [DOI] [PubMed] [PMC]

126.

Helm M, Kobitski AY, Nienhaus GU. Single-molecule Förster resonance energy transfer studies of RNA structure, dynamics and function. Biophys Rev. 2009;1:161–76. [DOI] [PubMed] [PMC]

127.

Miyawaki A. Development of probes for cellular functions using fluorescent proteins and fluorescence resonance energy transfer. Annu Rev Biochem. 2011;80:357–73. [DOI] [PubMed]

128.

Yu D, Qin P, Cornish PV. Single molecule studies of RNA–RNA interactions. Methods Mol Biol. 2015;1240:97–112. [DOI] [PubMed]

129.

Singh S, Shyamal S, Panda AC. Detecting RNA-RNA interactome. Wiley Interdiscip Rev RNA. 2022;13:e1715. [DOI] [PubMed]

130.

Haimovich G, Gerst JE. Single-molecule fluorescence in situ hybridization (smFISH) for RNA detection in adherent animal cells. Bio Protoc. 2018;8:e3070. [DOI] [PubMed] [PMC]

131.

West JA, Davis CP, Sunwoo H, Simon MD, Sadreyev RI, Wang PI, et al. The long noncoding RNAs NEAT1 and MALAT1 bind active chromatin sites. Mol Cell. 2014;55:791–802. [DOI] [PubMed] [PMC]

132.

Smale ST. Luciferase assay. Cold Spring Harb Protoc. 2010;2010:pdb.prot5421. [DOI] [PubMed] [PMC]

133.

Jin Y, Chen Z, Liu X, Zhou X. Evaluating the microRNA targeting sites by luciferase reporter gene assay. Methods Mol Biol. 2013;936:117–27. [DOI] [PubMed] [PMC]

134.

Piganeau N, Schauer UE, Schroeder R. A yeast RNA-hybrid system for the detection of RNA-RNA interactions in vivo. RNA. 2006;12:177–84. [DOI] [PubMed] [PMC]

135.

Piganeau N, Schroeder R. Identification and detection of RNA-RNA interactions using the yeast RNA hybrid system. Nat Protoc. 2006;1:689–94. [DOI] [PubMed]

136.

Morozova O, Hirst M, Marra MA. Applications of new sequencing technologies for transcriptome analysis. Annu Rev Genomics Hum Genet. 2009;10:135–51. [DOI] [PubMed]

137.

Kudla G, Granneman S, Hahn D, Beggs JD, Tollervey D. Cross-linking, ligation, and sequencing of hybrids reveals RNA-RNA interactions in yeast. Proc Natl Acad Sci U S A. 2011;108:10010–5. [DOI] [PubMed] [PMC]

138.

Helwak A, Tollervey D. Mapping the miRNA interactome by cross-linking ligation and sequencing of hybrids (CLASH). Nat Protoc. 2014;9:711–28. [DOI] [PubMed] [PMC]

139.

Kozar I, Philippidou D, Margue C, Gay LA, Renne R, Kreis S. Cross-linking ligation and sequencing of hybrids (qCLASH) reveals an unpredicted miRNA targetome in melanoma cells. Cancers (Basel). 2021;13:1096. [DOI] [PubMed] [PMC]

140.

Gerber AP. RNA-centric approaches to profile the RNA-protein interaction landscape on selected RNAs. Noncoding RNA. 2021;7:11. [DOI] [PubMed] [PMC]

141.

Engreitz JM, Sirokman K, McDonel P, Shishkin AA, Surka C, Russell P, et al. RNA-RNA interactions enable specific targeting of noncoding RNAs to nascent pre-mRNAs and chromatin sites. Cell. 2014;159:188–99. [DOI] [PubMed] [PMC]

142.

Panda AC, Grammatikakis I, Kim KM, De S, Martindale JL, Munk R, et al. Identification of senescence-associated circular RNAs (SAC-RNAs) reveals senescence suppressor CircPVT1. Nucleic Acids Res. 2017;45:4021–35. [DOI] [PubMed] [PMC]

143.

Jaskiewicz L, Bilen B, Hausser J, Zavolan M. Argonaute CLIP – a method to identify in vivo targets of miRNAs. Methods. 2012;58:106–12. [DOI] [PubMed]

144.

Gu D, Ahn SH, Eom S, Lee HS, Ham J, Lee DH, et al. AGO-accessible anticancer siRNAs designed with synergistic miRNA-like activity. Mol Ther Nucleic Acids. 2021;23:1172–90. [DOI] [PubMed] [PMC]

145.

Sugimoto Y, Chakrabarti AM, Luscombe NM, Ule J. Using hiCLIP to identify RNA duplexes that interact with a specific RNA-binding protein. Nat Protoc. 2017;12:611–37. [DOI] [PubMed]

146.

Sugimoto Y, Vigilante A, Darbo E, Zirra A, Militti C, D’Ambrogio A, et al. hiCLIP reveals the in vivo atlas of mRNA secondary structures recognized by Staufen 1. Nature. 2015;519:491–4. [DOI] [PubMed] [PMC]

147.

Lustig Y, Wachtel C, Safro M, Liu L, Michaeli S. ‘RNA walk’ a novel approach to study RNA-RNA interactions between a small RNA and its target. Nucleic Acids Res. 2010;38:e5. [DOI] [PubMed] [PMC]

148.

Aw JG, Shen Y, Wilm A, Sun M, Lim XN, Boon KL, et al. In vivo mapping of eukaryotic RNA interactomes reveals principles of higher-order organization and regulation. Mol Cell. 2016;62:603–17. [DOI] [PubMed]

149.

Lu Z, Zhang QC, Lee B, Flynn RA, Smith MA, Robinson JT, et al. RNA duplex map in living cells reveals higher-order transcriptome structure. Cell. 2016;165:1267–79. [DOI] [PubMed] [PMC]

150.

Zhang M, Li K, Bai J, Velema WA, Yu C, van Damme R, et al. Optimized photochemistry enables efficient analysis of dynamic RNA structuromes and interactomes in genetic and infectious diseases. Nat Commun. 2021;12:2344. [DOI] [PubMed] [PMC]

151.

Sharma E, Sterne-Weiler T, O’Hanlon D, Blencowe BJ. Global mapping of human RNA-RNA interactions. Mol Cell. 2016;62:618–26. [DOI] [PubMed]

152.

Wan Y, Qu K, Ouyang Z, Chang HY. Genome-wide mapping of RNA structure using nuclease digestion and high-throughput sequencing. Nat Protoc. 2013;8:849–69. [DOI] [PubMed]

153.

Wan Y, Kertesz M, Spitale RC, Segal E, Chang HY. Understanding the transcriptome through RNA structure. Nat Rev Genet. 2011;12:641–55. [DOI] [PubMed] [PMC]

154.

Wan Y, Qu K, Ouyang Z, Kertesz M, Li J, Tibshirani R, et al. Genome-wide measurement of RNA folding energies. Mol Cell. 2012;48:169–81. [DOI] [PubMed] [PMC]

155.

Mortimer SA, Kidwell MA, Doudna JA. Insights into RNA structure and function from genome-wide studies. Nat Rev Genet. 2014;15:469–79. [DOI] [PubMed]

156.

Uzilov AV, Underwood JG. High-throughput nuclease probing of RNA structures using FragSeq. Methods Mol Biol. 2016;1490:105–34. [DOI] [PubMed]

157.

Loughrey D, Watters KE, Settle AH, Lucks JB. SHAPE-Seq 2.0: systematic optimization and extension of high-throughput chemical probing of RNA secondary structure with next generation sequencing. Nucleic Acids Res. 2014;42:e165. [DOI] [PubMed] [PMC]

158.

Lucks JB, Mortimer SA, Trapnell C, Luo S, Aviran S, Schroth GP, et al. Multiplexed RNA structure characterization with selective 2’-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq). Proc Natl Acad Sci U S A. 2011;108:11063–8. [DOI] [PubMed] [PMC]

159.

Watters KE, Lucks JB. Mapping RNA structure in vitro with SHAPE chemistry and next-generation sequencing (SHAPE-Seq). Methods Mol Biol. 2016;1490:135–62. [DOI] [PubMed]

160.

Chan D, Feng C, Spitale RC. Measuring RNA structure transcriptome-wide with icSHAPE. Methods. 2017;120:85–90. [DOI] [PubMed] [PMC]

161.

Wang P, Ning S, Zhang Y, Li R, Ye J, Zhao Z, et al. Identification of lncRNA-associated competing triplets reveals global patterns and prognostic markers for cancer. Nucleic Acids Res. 2015;43:3478–89. [DOI] [PubMed] [PMC]

162.

Cao Y, Wang P, Ning S, Xiao W, Xiao B, Li X. Identification of prognostic biomarkers in glioblastoma using a long non-coding RNA-mediated, competitive endogenous RNA network. Oncotarget. 2016;7:41737–47. [DOI] [PubMed] [PMC]

163.

Hutter C, Zenklusen JC. The cancer genome atlas: creating lasting value beyond its data. Cell. 2018;173:283–5. [DOI] [PubMed]

164.

Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, et al. NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res. 2009;37:D885–90. [DOI] [PubMed] [PMC]

165.

Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, Kokocinski F, et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 2012;22:1760–74. [DOI] [PubMed] [PMC]

166.

Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014;42:D68–73. [DOI] [PubMed] [PMC]

167.

Glažar P, Papavasileiou P, Rajewsky N. circBase: a database for circular RNAs. RNA. 2014;20:1666–70. [DOI] [PubMed] [PMC]

168.

John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS. Human MicroRNA targets. PLoS Biol. 2004;2:e363. [DOI] [PubMed] [PMC]

169.

Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. Elife. 2015;4:e05005. [DOI] [PubMed] [PMC]

170.

Krüger J, Rehmsmeier M. RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res. 2006;34:W451–4. [DOI] [PubMed] [PMC]

171.

Steinkraus BR, Toegel M, Fulga TA. Tiny giants of gene regulation: experimental strategies for microRNA functional studies. Wiley Interdiscip Rev Dev Biol. 2016;5:311–62. [DOI] [PubMed] [PMC]

172.

Li JH, Liu S, Zhou H, Qu LH, Yang JH. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 2014;42:D92–7. [DOI] [PubMed] [PMC]

173.

Karagkouni D, Paraskevopoulou MD, Chatzopoulos S, Vlachos IS, Tastsoglou S, Kanellos I, et al. DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA–gene interactions. Nucleic Acids Res. 2018;46:D239–45. [DOI] [PubMed] [PMC]

174.

Chou CH, Shrestha S, Yang CD, Chang NW, Lin YL, Liao KW, et al. miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res. 2018;46:D296–302. [DOI] [PubMed] [PMC]

175.

Le TD, Zhang J, Liu L, Li J. Computational methods for identifying miRNA sponge interactions. Brief Bioinform. 2017;18:577–90. [DOI] [PubMed]

176.

Zhou X, Liu J, Wang W. Construction and investigation of breast-cancer-specific ceRNA network based on the mRNA and miRNA expression data. IET Syst Biol. 2014;8:96–103. [DOI] [PubMed] [PMC]

177.

Xu J, Li Y, Lu J, Pan T, Ding N, Wang Z, et al. The mRNA related ceRNA-ceRNA landscape and significance across 20 major cancer types. Nucleic Acids Res. 2015;43:8169–82. [DOI] [PubMed] [PMC]

178.

Shao T, Wu A, Chen J, Chen H, Lu J, Bai J, et al. Identification of module biomarkers from the dysregulated ceRNA-ceRNA interaction network in lung adenocarcinoma. Mol Biosyst. 2015;11:3048–58. [DOI] [PubMed]

179.

Chiu YC, Hsiao TH, Chen Y, Chuang EY. Parameter optimization for constructing competing endogenous RNA regulatory network in glioblastoma multiforme and other cancers. BMC Genomics. 2015;16 Suppl 4:S1. [DOI] [PubMed] [PMC]

180.

Zhang X, Zhao XM, He K, Lu L, Cao Y, Liu J, et al. Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information. Bioinformatics. 2012;28:98–104. [DOI] [PubMed]

181.

Paci P, Colombo T, Farina L. Computational analysis identifies a sponge interaction network between long non-coding RNAs and messenger RNAs in human breast cancer. BMC Syst Biol. 2014;8:83. [DOI] [PubMed] [PMC]

182.

Chiu HS, Llobet-Navas D, Yang X, Chung WJ, Ambesi-Impiombato A, Iyer A, et al. Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks. Genome Res. 2015;25:257–67. [DOI] [PubMed] [PMC]

183.

Bosia C, Pagnani A, Zecchina R. Modelling competing endogenous RNA networks. PLoS One. 2013;8:e66609.

184.

Figliuzzi M, Marinari E, De Martino A. MicroRNAs as a selective channel of communication between competing RNAs: a steady-state theory. Biophys J. 2013;104:1203–13. [DOI] [PubMed] [PMC]

185.

Swain AC, Mallick B. miRNA-mediated ‘tug-of-war’ model reveals ceRNA propensity of genes in cancers. Mol Oncol. 2018;12:855–68. [DOI] [PubMed] [PMC]

186.

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504. [DOI] [PubMed] [PMC]

187.

Piro RM, Ala U, Molineris I, Grassi E, Bracco C, Perego GP, et al. An atlas of tissue-specific conserved coexpression for functional annotation and disease gene prediction. Eur J Hum Genet. 2011;19:1173–80. [DOI] [PubMed] [PMC]

188.

Yuan Y, Liu B, Xie P, Zhang MQ, Li Y, Xie Z, et al. Model-guided quantitative analysis of microRNA-mediated regulation on competing endogenous RNAs using a synthetic gene circuit. Proc Natl Acad Sci U S A. 2015;112:3158–63. [DOI] [PubMed] [PMC]

189.

Zhang J, Liu L, Xu T, Zhang W, Li J, Rao N, et al. Time to infer miRNA sponge modules. Wiley Interdiscip Rev RNA. 2022;13:e1686. [DOI] [PubMed]

190.

Zhu Q, Wong AK, Krishnan A, Aure MR, Tadych A, Zhang R, et al. Targeted exploration and analysis of large cross-platform human transcriptomic compendia. Nat Methods. 2015;12:211–4. [DOI] [PubMed] [PMC]

191.

Alsina L, Israelsson E, Altman MC, Dang KK, Ghandil P, Israel L, et al. A narrow repertoire of transcriptional modules responsive to pyogenic bacteria is impaired in patients carrying loss-of-function mutations in MYD88 or IRAK4. Nat Immunol. 2014;15:1134–42. [DOI] [PMC]

192.

Sarver AL, Subramanian S. Competing endogenous RNA database. Bioinformation. 2012;8:731–3. [DOI] [PubMed] [PMC]

193.

Vlachos IS, Paraskevopoulou MD, Karagkouni D, Georgakilas G, Vergoulis T, Kanellos I, et al. DIANA-TarBase v7.0: indexing more than half a million experimentally supported miRNA:mRNA interactions. Nucleic Acids Res. 2015;43:D153–9. [DOI] [PubMed] [PMC]

194.

Paraskevopoulou MD, Georgakilas G, Kostoulas N, Vlachos IS, Vergoulis T, Reczko M, et al. DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows. Nucleic Acids Res. 2013;41:W169–73. [DOI] [PubMed] [PMC]

195.

Wang P, Guo Q, Qi Y, Hao Y, Gao Y, Zhi H, et al. LncACTdb 3.0: an updated database of experimentally supported ceRNA interactions and personalized networks contributing to precision medicine. Nucleic Acids Res. 2022;50:D183–9. [DOI] [PubMed] [PMC]

196.

Wang P, Guo Q, Hao Y, Liu Q, Gao Y, Zhi H, et al. LnCeCell: a comprehensive database of predicted lncRNA-associated ceRNA networks at single-cell resolution. Nucleic Acids Res. 2021;49:D125–33. [DOI] [PubMed] [PMC]

197.

Wang P, Li X, Gao Y, Guo Q, Ning S, Zhang Y, et al. LnCeVar: a comprehensive database of genomic variations that disturb ceRNA network regulation. Nucleic Acids Res. 2020;48:D111–7. [DOI] [PubMed] [PMC]

198.

Jeggari A, Marks DS, Larsson E. miRcode: a map of putative microRNA target sites in the long non-coding transcriptome. Bioinformatics. 2012;28:2062–3. [DOI] [PubMed] [PMC]

199.

Chou CH, Chang NW, Shrestha S, Hsu SD, Lin YL, Lee WH, et al. miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database. Nucleic Acids Res. 2016;44:D239–47. [DOI] [PubMed] [PMC]

200.

Fiannaca A, Paglia L, Rosa M, Rizzo R, Urso A. miRTissue _ce: extending miRTissue web service with the analysis of ceRNA-ceRNA interactions. BMC Bioinformatics. 2020;21:199. [DOI] [PubMed] [PMC]

201.

Dweep H, Gretz N. miRWalk2.0: a comprehensive atlas of microRNA-target interactions. Nat Methods. 2015;12:697. [DOI] [PubMed]

202.

Krek A, Grün D, Poy MN, Wolf R, Rosenberg L, Epstein EJ, et al. Combinatorial microRNA target predictions. Nat Genet. 2005;37:495–500. [DOI] [PubMed]

203.

Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E. The role of site accessibility in microRNA target recognition. Nat Genet. 2007;39:1278–84. [DOI] [PubMed]

204.

Miranda KC, Huynh T, Tay Y, Ang YS, Tam WL, Thomson AM, et al. A pattern-based method for the identification of microRNA binding sites and their corresponding heteroduplexes. Cell. 2006;126:1203–17. [DOI] [PubMed]

205.

Bhattacharya A, Cui Y. SomamiR 2.0: a database of cancer somatic mutations altering microRNA-ceRNA interactions. Nucleic Acids Res. 2016;44:D1005–10. [DOI] [PubMed] [PMC]

206.

Lewis BP, Burge CB, Bartel DP. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 2005;120:15–20. [DOI] [PubMed]

207.

Barbagallo C, Di Maria A, Alecci A, Barbagallo D, Alaimo S, Colarossi L, et al. VECTOR: an integrated correlation network database for the identification of ceRNA axes in uveal melanoma. Genes (Basel). 2021;12:1004. [DOI] [PubMed] [PMC]

208.

Winkle M, El-Daly SM, Fabbri M, Calin GA. Noncoding RNA therapeutics — challenges and potential solutions. Nat Rev Drug Discov. 2021;20:629–51. [DOI] [PubMed] [PMC]

209.

Marschall ALJ. Targeting the inside of cells with biologicals: chemicals as a delivery strategy. BioDrugs. 2021;35:643–71. [DOI] [PubMed] [PMC]