Skip to main content

A Method for Constructing an Integrative Network of Competing Endogenous RNAs

  • Conference paper
  • First Online:
Intelligent Computing Theories and Application (ICIC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12838))

Included in the following conference series:

Abstract

Since the new gene regulation involving competing endogenous RNA (ceRNA) interactions targeted by common microRNAs (miRNAs) was found, several computational methods have been proposed to derive ceRNA networks. However, most of the ceRNA networks are restricted to represent either miRNA-target RNA interactions or lncRNA-miRNA-mRNA interactions. From the extensive data analysis, we found that competition for miRNA-binding occurs not only between lncRNAs and mRNAs but also between lncRNAs or between mRNAs. Furthermore, a large number of pseudogenes also act as ceRNAs, thereby regulate other genes. In this study, we considered all lncRNAs, mRNAs and pseudogenes as potential ceRNAs and developed a method for constructing an integrative ceRNA network which includes all possible interactions of ceRNAs mediated by miRNAs: lncRNA-miRNA-mRNA, lncRNA-miRNA-lncRNA, lncRNA-miRNA-pseudogene, mRNA-miRNA-mRNA, mRNA-miRNA-pseudogene, and pseudogene-miRNA-pseudogene. We constructed integrative ceRNA networks for breast cancer, liver cancer and lung cancer, and derived several triplets of ceRNAs which can be used as potential prognostic biomarkers. The potential prognostic triplets could not be found when only lncRNA-miRNA-mRNA interactions were considered. Although preliminary, our approach to constructing integrative ceRNA networks is applicable to multiple types of cancer and will help us find potential prognostic biomarkers in cancer.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wilusz, J.E., Sunwoo, H., Spector, D.L.: Long noncoding RNAs: functional surprises from the RNA world. Genes Dev. 23, 1494–1504 (2009)

    Article  Google Scholar 

  2. Bartel, D.P.: Metazoan microRNAs. Cell 173, 20–51 (2018)

    Article  Google Scholar 

  3. Lü, M.H., et al.: Long noncoding RNA BC032469, a novel competing endogenous RNA, upregulates hTERT expression by sponging miR-1207-5p and promotes proliferation in gastric cancer. Oncogene 35, 3524–3534 (2016)

    Article  Google Scholar 

  4. Salmena, L., Poliseno, L., Tay, Y., Kats, L., Pandolfi, P.P.: A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language? Cell 146, 353–358 (2011)

    Article  Google Scholar 

  5. Jiang, R., Zhao, C., Gao, B., Xu, J., Song, W., Shi, P.: Mixomics analysis of breast cancer: long non-coding RNA linc01561 acts as ceRNA involved in the progression of breast cancer. Int. J. Biochem. Cell Biol. 102, 1–9 (2018)

    Article  Google Scholar 

  6. Zhu, Y., Bian, Y., Zhang, Q., et al.: Construction and analysis of dysregulated lncRNA-associated ceRNA network in colorectal cancer. J. Cell Biochem. 120, 9250–9263 (2019)

    Article  Google Scholar 

  7. Poliseno, L., Salmena, L., Zhang, J., Carver, B., Haveman, W.J., Pandolfi, P.P.: A coding-independent function of gene and pseudogene mRNAs regulates tumour biology. Nature 465, 1033–1038 (2010)

    Article  Google Scholar 

  8. Robinson, M.D., McCarthy, D.J., Smyth, G.K.: edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010)

    Article  Google Scholar 

  9. Huang, H.Y., et al.: miRTarBase 2020: updates to the experimentally validated microRNA-target interaction database. Nucleic Acids Res. 48, D148–D154 (2020)

    Google Scholar 

  10. Jeggari, A., Marks, D.S., Larsson, E.: miRcode: a map of putative microRNA target sites in the long non-coding transcriptome. Bioinformatics 28, 2062–2063 (2012)

    Article  Google Scholar 

  11. Agarwal, V., Bell, G.W., Nam, J.-W., Bartel, D.P.: Predicting effective microRNA target sites in mammalian mRNAs. Elife 4, e05005 (2015)

    Article  Google Scholar 

  12. Zhou, M., et al.: Characterization of long non-coding RNA-associated ceRNA network to reveal potential prognostic lncRNA biomarkers in human ovarian cancer. Oncotarget 7, 12598–12611 (2016)

    Article  Google Scholar 

  13. Liu, H., et al.: Integrative analysis of dysregulated lncRNA-associated ceRNA network reveals functional lncRNAs in gastric cancer. Genes 9, 303 (2018)

    Article  Google Scholar 

  14. Wasserman, S., Faust, K., Iacobucci, D., Granovetter, M.: Social Network Analysis: Methods and Applications. Cambridge University Press (1994)

    Google Scholar 

  15. Chen, E.Y., et al.: Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinf. 14, 128 (2013)

    Article  Google Scholar 

  16. Zhou, W., Liu, T., Saren, G., Liao, L., Fang, W., Zhao, H.: Comprehensive analysis of differentially expressed long non-coding RNAs in non-small cell lung cancer. Oncol. Lett. 18, 1145–1156 (2019)

    Google Scholar 

  17. Wang, S.M., Ooi, L.L., Hui, K.M.: Upregulation of Rac GTPase-activating protein 1 is significantly associated with the early recurrence of human hepatocellular carcinoma. Clin. Cancer Res. 17, 6040–6051 (2011)

    Article  Google Scholar 

  18. Saigusa, S., et al.: Clinical significance of RacGAP1 expression at the invasive front of gastric cancer. Gastric Cancer 18, 84–92 (2015)

    Article  Google Scholar 

  19. Imaoka, H., et al.: RacGAP1 expression, increasing tumor malignant potential, as a predictive biomarker for lymph node metastasis and poor prognosis in colorectal cancer. Carcinogenesis 36, 346–354 (2015)

    Article  Google Scholar 

  20. Zhang, X., Ma, N., Yao, W., Li, S., Ren, Z.: RAD51 is a potential marker for prognosis and regulates cell proliferation in pancreatic cancer. Cancer Cell Int. 19, 356 (2019)

    Article  Google Scholar 

  21. Nowacka-Zawisza, M., et al.: RAD51 and XRCC3 polymorphisms are associated with increased risk of prostate cancer. J. Oncol. 2019, 2976373 (2019)

    Article  Google Scholar 

  22. Du, S., et al.: Long non-coding RNA MAGI2-AS3 inhibits breast cancer cell migration and invasion via sponging microRNA-374a. Cancer Biomarkers Sect. A Dis. markers 24, 269–277 (2019)

    Article  Google Scholar 

  23. Tang, H., et al.: miR-200b and miR-200c as prognostic factors and mediators of gastric cancer cell progression. Clin. Cancer Res. Official J. Am. Assoc. Cancer Res. 19, 5602–5612 (2013)

    Article  Google Scholar 

  24. Liu, X.G., et al.: High expression of serum miR-21 and tumor miR-200c associated with poor prognosis in patients with lung cancer. Med. Oncol. (Northwood, London, England) 29, 618–626 (2012)

    Article  Google Scholar 

  25. Liu, C., et al.: The microRNA miR-34a inhibits prostate cancer stem cells and metastasis by directly repressing CD44. Nat. Med. 17, 211–215 (2011)

    Article  Google Scholar 

  26. Li, N., et al.: miR-34a inhibits migration and invasion by down-regulation of c-Met expression in human hepatocellular carcinoma cells. Cancer Lett. 275, 44–53 (2009)

    Article  Google Scholar 

  27. Mishan, M.A., Tabari, M.A.K., Parnian, J., Fallahi, J., Mahrooz, A., Bagheri, A.: Functional mechanisms of miR-192 family in cancer. Genes Chromosom. Cancer 59(12), 722–735 (2020)

    Article  Google Scholar 

  28. Hoeijmakers, J.H.: DNA damage, aging, and cancer. N. Engl. J. Med. 361, 1475–1485 (2009)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (NRF-2020R1A2B5B01096299, NRF-2018K2A9A2A11080914).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kyungsook Han .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lee, S., Lee, W., Ren, S., Han, K. (2021). A Method for Constructing an Integrative Network of Competing Endogenous RNAs. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12838. Springer, Cham. https://doi.org/10.1007/978-3-030-84532-2_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-84532-2_37

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-84531-5

  • Online ISBN: 978-3-030-84532-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics