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Screening of Key Genes in Retinoblastoma and Construction of ceRNA Regulatory Network

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Biomedical and Computational Biology (BECB 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13637))

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Abstract

Retinoblastoma (RB) is an intraocular malignancy with a high incidence and very severe symptoms in children and is a rare life-threatening ophthalmic disease. Screening for key genes in retinoblastoma to identify the main molecular mechanisms of RB pathogenesis. The high-throughput sequencing data GSE111168, gene expression microarray data sets GSE24673 and GSE41321 were obtained from the GEO database. Differential genes were screened using the “limma” package, the threshold was set “padj < 0.05 & |log2FoldChange| ≥ 2”, and the differential gene volcanic map and clustering heat map were drawn. The protein-protein interaction (PPI) network of differentially expressed genes (DEG) was constructed using the STRING database, and followed by functional enrichment analysis to predict biological function. The co-expression analysis obtained the DELs-DEGs relationship pairs, the miRcode website obtained the DEMs-DELs relationship pairs, and the miRDB and miRWalk websites obtained the DEMs-DEGs relationship pairs. According to the ceRNA theory, the DELs-DEMs-DEGs network was obtained by the intersection of pairwise representation of the Venn diagram and imported into Cytoscape software for visualization. PPI network results showed that 20 key genes out of 475 DEGs were likely to serve as new biomarkers to indicate the occurrence, development, and disease staging of retinoblastoma. The ceRNA regulatory network is composed of DELs-DEMs-DEGs consisting of one DEL (LINC00518), one DEM (hsa-miR-129-5p), and three DEGs (FBXO32, MEF2C, WLS). The components of the ceRNA regulatory network have been reported to have abnormal expressions in a variety of cancers, which can affect the growth and metastasis of some cancer cells. These results provide a theoretical basis for the research on the mechanism of retinoblastoma.

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References

  1. Mendoza, P.R., Grossniklaus, H.E.: The biology of retinoblastoma. Progress Mol. Biol. Transl. Sci. 134, 503–516 (2015)

    Article  Google Scholar 

  2. Rao, R., Honavar, S.G.: Retinoblastoma. Indian J. Pediatr. 84(12), 937–944 (2017)

    Article  PubMed  Google Scholar 

  3. Soliman, S.E., Racher, H., Zhang, C., MacDonald, H., Gallie, B.L.: Genetics and molecular diagnostics in retinoblastoma - an update. Asia-Pac. J. Ophthalmol. 6(2), 197–207 (2017)

    CAS  Google Scholar 

  4. Rodriguez-Galindo, C., Orbach, D.B., VanderVeen, D.: Retinoblastoma. Pediatr. Clin. North America 62(1), 201–223 (2015)

    Article  Google Scholar 

  5. Dimaras, H., Corson, T.W., Cobrinik, D., et al.: Retinoblastoma. Nat. Rev. Dis. Primers 15021 (2015)

    Google Scholar 

  6. Dimaras, H., Kimani, K., Dimba, E.A.O., et al.: Retinoblastoma. Lancet 379(9824), 1436–1446 (2012)

    Article  PubMed  Google Scholar 

  7. Eagle, R.C.: The pathology of ocular cancer. Eye (Basingstoke) 27(2), 128–136 (2013)

    CAS  Google Scholar 

  8. Esteller, M.: Non-coding RNAs in human disease. Nat. Rev. Genet. 12(12), 861–874 (2011)

    Article  CAS  PubMed  Google Scholar 

  9. Han, J., Lee, Y., Yeom, K.H., Kim, Y.K., Jin, H., Kim, V.N.: The Drosha-DGCR8 complex in primary microRNA processing. Genes Dev. 18(24), 3016–3027 (2004)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Hutvágner, G., Zamore, P.D.: A microRNA in a multiple-turnover RNAi enzyme complex. Science (1979) 297, 5589 (2002)

    Google Scholar 

  11. Bartel, D.P.: Metazoan MicroRNAs. Cell 173(1), 20–51 (2018)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. di Leva, G., Garofalo, M., Croce, C.M.: MicroRNAs in cancer. Annu. Rev. Pathol. 9, 287–314 (2014)

    Article  PubMed  Google Scholar 

  13. Salmena, L., Poliseno, L., Tay, Y., Kats, L., Pandolfi, P.P.: A ceRNA hypothesis: the rosetta stone of a hidden RNA language? Cell 146(3), 353–358 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Karreth, F.A., Pandolfi, P.P.: CeRNA cross-talk in cancer: when ce-bling rivalries go awry. Cancer Discov. 3(10), 1113–1121 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Barrett, T., Wilhite, S.E., Ledoux, P., et al.: NCBI GEO: archive for functional genomics data sets - update. Nucleic Acids Res. D991-5 (2013)

    Google Scholar 

  16. Sean, D., Meltzer, P.S.: GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics 23(14), 1846–1847 (2007)

    Article  CAS  Google Scholar 

  17. Szklarczyk, D., Gable, A.L., Lyon, D., et al.: STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47(D1), D607–D613 (2019)

    Article  CAS  PubMed  Google Scholar 

  18. Szklarczyk, D., Gable, A.L., Nastou, K.C., et al.: The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 49(D1), D605–D612 (2021)

    Article  CAS  PubMed  Google Scholar 

  19. Wu, T., Hu, E., Xu, S., et al.: clusterProfiler 4.0: a universal enrichment tool for interpreting OMICS data. Innovation 2(3), 100141 (2021)

    Google Scholar 

  20. Yu, G., Wang, L.G., Han, Y., He, Q.Y.: ClusterProfiler: an R package for comparing biological themes among gene clusters. OMICS J. Integr. Biol. 16(5), 284–287 (2012)

    Article  CAS  Google Scholar 

  21. Yu, G., Wang, L.G., Yan, G.R., He, Q.Y.: DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics 31(4), 608–609 (2015)

    Article  CAS  PubMed  Google Scholar 

  22. Jeggari, A., Marks, D.S., Larsson, E.: miRcode: a map of putative microrna target sites in the long non-coding transcriptome. Bioinformatics 28(15), 2062–2063 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Chen, Y., Wang, X.: MiRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res. 48(D1), D127–D131 (2020)

    Article  CAS  PubMed  Google Scholar 

  24. Sticht, C., de La Torre, C., Parveen, A., Gretz, N.: Mirwalk: an online resource for prediction of microrna binding sites. PLoS ONE 13(10), e0206239 (2018)

    Article  PubMed  PubMed Central  Google Scholar 

  25. Shannon, P., Markiel, A., Ozier, O., et al.: Cytoscape: a software Environment for integrated models of biomolecular interaction networks. Genome Res. 13(11), 2498–2504 (2003)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. He, X., Zhang, J.: Why do hubs tend to be essential in protein networks? PLoS Genet. 2(6), e88 (2006)

    Article  PubMed  PubMed Central  Google Scholar 

  27. Liu, Y., He, D., Xiao, M., Zhu, Y., Zhou, J., Cao, K.: Long noncoding RNA LINC00518 induces radioresistance by regulating glycolysis through an miR-33a-3p/HIF-1α negative feedback loop in melanoma. Cell Death Dis. 12(3), 245 (2021)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Luan, W., Ding, Y., Ma, S., Ruan, H., Wang, J., Lu, F.: Long noncoding RNA LINC00518 acts as a competing endogenous RNA to promote the metastasis of malignant melanoma via miR-204-5p/AP1S2 axis. Cell Death Dis. 10(11), 855 (2019)

    Article  PubMed  PubMed Central  Google Scholar 

  29. He, J., Sun, M., Geng, H., Tian, S.: Long non-coding RNA Linc00518 promotes paclitaxel resistance of the human prostate cancer by sequestering miR-216b-5p. Biol. Cell 111(2), 39–50 (2019)

    Article  CAS  PubMed  Google Scholar 

  30. Wang, D.W., You, D., Dong, J., Liu, T.F.: Knockdown of long non-coding RNA LINC00518 inhibits cervical cancer proliferation and metastasis by modulating JAK/STAT3 signaling. Eur. Rev. Med. Pharmacol. Sci. 23(2), 496–506 (2019)

    PubMed  Google Scholar 

  31. Barbagallo, C., Caltabiano, R., Broggi, G., et al.: Lncrna Linc00518 acts as an oncogene in uveal melanoma by regulating an RNA-based network. Cancers (Basel) 12(12), 3867 (2020)

    Article  CAS  PubMed  Google Scholar 

  32. Yu, X., Song, H., Xia, T., et al.: Growth inhibitory effects of three miR-129 family members on gastric cancer. Gene 532(1), 87–93 (2013)

    Article  CAS  PubMed  Google Scholar 

  33. Xu, S., Yi, X.M., Zhang, Z.Y., Ge, J.P., Zhou, W.Q.: MiR-129 predicts prognosis and inhibits cell growth in human prostate carcinoma. Mol. Med. Rep. 14(6), 5025–5032 (2016)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Tang, X., Tang, J., Liu, X., et al.: Downregulation of MIR-129-2 by promoter hypermethylation regulates breast cancer cell proliferation and apoptosis. Oncol. Rep. 35(5), 2963–2969 (2016)

    Article  CAS  PubMed  Google Scholar 

  35. Zheng, L., Qi, Y.X., Liu, S., Shi, M.L., Yang, W.P.: mir-129b suppresses cell proliferation in the human lung cancer cell lines A549 and H1299. Genet. Mol. Res. 15(4), 1–8 (2016)

    Article  Google Scholar 

  36. Wang, S., Chen, Y., Yu, X., et al.: miR-129-5p attenuates cell proliferation and epithelial mesenchymal transition via HMGB1 in gastric cancer. Pathol. Res. Pract. 215(4), 676–682 (2019)

    Article  CAS  PubMed  Google Scholar 

  37. Wu, Q., Meng, W.Y., Jie, Y., Zhao, H.: LncRNA MALAT1 induces colon cancer development by regulating miR-129-5p/HMGB1 axis. J. Cell. Physiol. 233(9), 6750–6757 (2018)

    Article  CAS  PubMed  Google Scholar 

  38. Han, H., Li, W., Shen, H., Zhang, J., Zhu, Y., Li, Y.: MicroRNA-129-5p, a c-Myc negative target, affects hepatocellular carcinoma progression by blocking the Warburg effect. J. Mol. Cell Biol. 8(5), 400–410 (2016)

    Article  CAS  PubMed  Google Scholar 

  39. Mei, Z., Zhang, D., Hu, B., Wang, J., Shen, X., Xiao, W.: FBXO32 targets c-Myc for proteasomal degradation and inhibits c-Myc activity. J. Biol. Chem. 290(26), 16202–16214 (2015)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Zhou, H., Liu, Y., Zhu, R., et al.: FBXO32 suppresses breast cancer tumorigenesis through targeting KLF4 to proteasomal degradation. Oncogene 36(23), 3312–3321 (2017)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Canté-Barrett, K., Pieters, R., Meijerink, J.P.P.: Myocyte enhancer factor 2C in hematopoiesis and leukemia. Oncogene33(4), 403–410 (2014)

    Google Scholar 

  42. Laszlo, G.S., Alonzo, T.A., Gudgeon, C.J., et al.: High expression of myocyte enhancer factor 2C (MEF2C) is associated with adverse-risk features and poor outcome in pediatric acute myeloid leukemia: a report from the Children’s Oncology Group. J. Hematol. Oncol. 8(1), 115 (2015)

    Article  PubMed  PubMed Central  Google Scholar 

  43. Zhang, H., Liu, W., Wang, Z., et al.: MEF2C promotes gefitinib resistance in hepatic cancer cells through regulating MIG6 transcription. Tumori 104(3), 221–231 (2018)

    Article  CAS  PubMed  Google Scholar 

  44. Zhou, C., Sun, Y., Guo, S., Chen, X., Bao, G., Wang, J.: Wls expression correlates with tumor differentiation and TNM stage in hepatocellular carcinoma. Dig. Dis. Sci. 63(1), 166–172 (2017). https://doi.org/10.1007/s10620-017-4823-4

    Article  CAS  PubMed  Google Scholar 

  45. Zhang, W., Tao, H., Chen, X., Sugimura, H., Wang, J., Zhou, P.: High expression of Wls is associated with lymph node metastasis and advanced TNM stage in gastric carcinomas. Pathol. Int. 67(3), 141–146 (2017)

    Article  CAS  PubMed  Google Scholar 

  46. Shi, Y., Bai, J., Guo, S., Wang, J.: Wntless is highly expressed in advanced-stage intrahepatic cholangiocarcinoma. Tohoku J. Exp. Med. 244(3), 195–199 (2018)

    Article  CAS  PubMed  Google Scholar 

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Guo, J. et al. (2023). Screening of Key Genes in Retinoblastoma and Construction of ceRNA Regulatory Network. In: Wen, S., Yang, C. (eds) Biomedical and Computational Biology. BECB 2022. Lecture Notes in Computer Science(), vol 13637. Springer, Cham. https://doi.org/10.1007/978-3-031-25191-7_12

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  • DOI: https://doi.org/10.1007/978-3-031-25191-7_12

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