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Knockdown of Long Non-Coding RNA HCP5 Increases Radiosensitivity Through Cellular Senescence by Regulating microRNA-128 in Gliomas

Wang, Cuihong, Yu, Guanying, Xu, Ying, Liu, Chengfei, Sun, Qian, Li, Wenqing, Sun, Junhua, Jiang, Yuhua, Ye, Lan·2021·Cancer Management and Research

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Figure 1 Expression of long non-coding RNA histocompatibility leukocyte antigen (HLA) complex P5 (HCP5) and miR-128 in glioma cell lines and human glioma tissues. The expression of (A) HCP5 and (B) miR-128 in human normal astrocytes and glioma cell lines. Error bars represent the mean ± SD (n = 5, each group). The expression of (C) HCP5 and (D) miR-128 was represented as the mean ± SD. The clinical specimens include normal brain tissues (NBTs, n=5), low-grade glioma (LGG, n = 5), Grade III (n=7) and glioblastoma (GBM, n=8). *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. (E) The typical pathological photograph of NBT and gliomas (Amplification: 100×).
Figure 1 Expression of long non-coding RNA histocompatibility leukocyte antigen (HLA) complex P5 (HCP5) and miR-128 in glioma cell lines and human glioma tissues. The expression of (A) HCP5 and (B) miR-128 in human normal astrocytes and glioma cell lines. Error bars represent the mean ± SD (n = 5, each group). The expression of (C) HCP5 and (D) miR-128 was represented as the mean ± SD. The clinical specimens include normal brain tissues (NBTs, n=5), low-grade glioma (LGG, n = 5), Grade III (n=7) and glioblastoma (GBM, n=8). *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. (E) The typical pathological photograph of NBT and gliomas (Amplification: 100×).CC BY-NC · PubMed Central
Figure 2 Expression level and prognostic value of HCP5 and miR-128 in glioma. The data were from the TCGA database obtained by online tools Starbase and Oncolnc. (A) The expression of HCP5 in low-grade glioma (LGG) and glioblastoma multiforme (GBM). Red bars correspond to tumor tissue and the gray ones are non-tumor tissues. (B) Overall survival of glioma patients associated with HCP5 expression level. (C) The level of HCP5 in LGG and GBM. (D) Overall survival of glioma patients associated with miR-128 expression level. (E)The level of miR-128 in LGG and GBM. *p < 0.05.
Figure 2 Expression level and prognostic value of HCP5 and miR-128 in glioma. The data were from the TCGA database obtained by online tools Starbase and Oncolnc. (A) The expression of HCP5 in low-grade glioma (LGG) and glioblastoma multiforme (GBM). Red bars correspond to tumor tissue and the gray ones are non-tumor tissues. (B) Overall survival of glioma patients associated with HCP5 expression level. (C) The level of HCP5 in LGG and GBM. (D) Overall survival of glioma patients associated with miR-128 expression level. (E)The level of miR-128 in LGG and GBM. *p < 0.05.CC BY-NC · PubMed Central
Figure 3 HCP5 knockdown increased radiosensitivity in human glioma cell lines. si-HCP5 was transfected into U87 and U251 cells. (A) The expression of HCP5 for 48h following si-HCP5 transfection. (B) The effect of HCP5 Knockdown on cell proliferation exposed following radiation. (C) Representative photographs for SA-β-Gal staining. (D) The effect of HCP5 Knockdown on cell senescence for 72h following radiation exposure. *p < 0.05, **p < 0.01, ##p < 0.01, ####p < 0.0001.
Figure 3 HCP5 knockdown increased radiosensitivity in human glioma cell lines. si-HCP5 was transfected into U87 and U251 cells. (A) The expression of HCP5 for 48h following si-HCP5 transfection. (B) The effect of HCP5 Knockdown on cell proliferation exposed following radiation. (C) Representative photographs for SA-β-Gal staining. (D) The effect of HCP5 Knockdown on cell senescence for 72h following radiation exposure. *p < 0.05, **p < 0.01, ##p < 0.01, ####p < 0.0001.CC BY-NC · PubMed Central
Figure 4 MiR-128 was down-regulated by HCP5 in glioma cell lines. (A) The predicted binding sites between HCP5 and miR-128. (B) The relative expression of miR-128 after knockdown HCP5 in glioma cells. Error bars were represented as the mean ± SD (n =5, each group). *p <0.05. (C) The relative luciferase activity was performed by dual-luciferase reporter assay. Error bars were represented as the mean ± SD (n = 5, each group). **p < 0.01. (D) qRT-PCR was performed to detect HCP5 and miR-128 in the same RNA-induced silencing complex using Ago2 antibody. (n = 5, each group). *** p < 0.001. (E) The correlation of MiR-128 expression and HCP5 expression in normal brain tissues (NBTs, n=5), LGG (n=5), Grade III (n=7), and GBM (n=8) tissues. (F) The correlation of MiR-128 expression and HCP5 expression in Glioma from the TCGA database. (G) The correlation of MiR-128 expression and HCP5 expression in LGG or GBM from the TCGA database. LGG: n=506; GBM: n=149.
Figure 4 MiR-128 was down-regulated by HCP5 in glioma cell lines. (A) The predicted binding sites between HCP5 and miR-128. (B) The relative expression of miR-128 after knockdown HCP5 in glioma cells. Error bars were represented as the mean ± SD (n =5, each group). *p <0.05. (C) The relative luciferase activity was performed by dual-luciferase reporter assay. Error bars were represented as the mean ± SD (n = 5, each group). **p < 0.01. (D) qRT-PCR was performed to detect HCP5 and miR-128 in the same RNA-induced silencing complex using Ago2 antibody. (n = 5, each group). *** p < 0.001. (E) The correlation of MiR-128 expression and HCP5 expression in normal brain tissues (NBTs, n=5), LGG (n=5), Grade III (n=7), and GBM (n=8) tissues. (F) The correlation of MiR-128 expression and HCP5 expression in Glioma from the TCGA database. (G) The correlation of MiR-128 expression and HCP5 expression in LGG or GBM from the TCGA database. LGG: n=506; GBM: n=149.CC BY-NC · PubMed Central
Figure 5 MiR-128 mediated the radio-sensitization effects of HCP5 knockdown on glioma cell lines. Glioma cells were co-transfected with si-HCP5/con-siRNA and anti-miR-128/con-anti-miRNA for 48h. The expression of (A) HCP5 and (B) miR-128 for 48h following co-transfection. (C) The effect of co-transfection on cell proliferation exposed following radiation. (D) The effect of co-transfection on cell senescence for 72h following radiation. *p < 0.05, **p < 0.01, ####p < 0.0001.
Figure 5 MiR-128 mediated the radio-sensitization effects of HCP5 knockdown on glioma cell lines. Glioma cells were co-transfected with si-HCP5/con-siRNA and anti-miR-128/con-anti-miRNA for 48h. The expression of (A) HCP5 and (B) miR-128 for 48h following co-transfection. (C) The effect of co-transfection on cell proliferation exposed following radiation. (D) The effect of co-transfection on cell senescence for 72h following radiation. *p < 0.05, **p < 0.01, ####p < 0.0001.CC BY-NC · PubMed Central
Figure 6 The gene expression profile of HCP5 across all tumor samples and paired normal tissues. (A) The expression profile of HCP5 from Starbase online tools. Each dot represent the expression of samples. Tumor name was highlighted by red means HCP5 up-regulation in tumor tissue compared to paired normal tissue, as well as green means HCP5 down-regulation. (B) The fold change of HCP5 expression in tumor tissue compared to paired normal tissue. Data were obtained from the TCGA database using Starbase online tools.
Figure 6 The gene expression profile of HCP5 across all tumor samples and paired normal tissues. (A) The expression profile of HCP5 from Starbase online tools. Each dot represent the expression of samples. Tumor name was highlighted by red means HCP5 up-regulation in tumor tissue compared to paired normal tissue, as well as green means HCP5 down-regulation. (B) The fold change of HCP5 expression in tumor tissue compared to paired normal tissue. Data were obtained from the TCGA database using Starbase online tools.CC BY-NC · PubMed Central

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Research works printed but not linked: 62

  1. Chen, 2017, Neurotherapeutics, 14, 284, 10.1007/s13311-017-0519-x · DOI · Paper page
  2. Cancer Genome Atlas Research N. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008;455(7216):1061–1068. doi:10.1038/nature07385 · DOI · Paper page
  3. Homma, 2006, J Neuropathol Exp Neurol, 65, 846, 10.1097/01.jnen.0000235118.75182.94 · DOI
  4. Khasraw, 2014, Cochrane Database Syst Rev, CD008218, 10.1002/14651858.CD008218.pub3 · DOI
  5. Deng, 2013, Int J Mol Sci, 14, 18319, 10.3390/ijms140918319 · DOI
  6. Ponting, 2009, Cell, 136, 629, 10.1016/j.cell.2009.02.006 · DOI · Paper page
  7. Teppan, 2020, Noncoding RNA, 6, 52
  8. Egranov, 2020, RNA Biol, 17, 1625, 10.1080/15476286.2020.1767455 · DOI
  9. Lu, 2019, Front Oncol, 9, 1099, 10.3389/fonc.2019.01099 · DOI
  10. Fu, 2018, Neurotherapeutics, 15, 1139, 10.1007/s13311-018-0649-9 · DOI · Paper page
  11. Li, 2019, Neoplasma, 66, 118, 10.4149/neo_2018_180613N391 · DOI
  12. Wang, 2015, Cell Signal, 27, 275, 10.1016/j.cellsig.2014.11.011 · DOI
  13. Cai, 2015, Oncotarget, 6, 19759, 10.18632/oncotarget.4331 · DOI
  14. Hu, 2018, J Cell Mol Med, 22, 497, 10.1111/jcmm.13338 · DOI
  15. Yao, 2015, Cancer Lett, 359, 75, 10.1016/j.canlet.2014.12.051 · DOI
  16. Wang, 2019, J Exp Clin Cancer Res, 38, 371, 10.1186/s13046-019-1361-2 · DOI
  17. Liu, 2008, PLoS Genet, 4, e1000041, 10.1371/journal.pgen.1000041 · DOI
  18. Kulski, 2019, Cells, 8, 10.3390/cells8050480 · DOI
  19. Yu, 2018, Eur Rev Med Pharmacol Sci, 22, 4812, 10.26355/eurrev_201808_15616 · DOI
  20. Hu, 2020, Oncol Rep, 43, 328, 10.3892/or.2019.7404 · DOI
  21. Zhao, 2020, Onco Targets Ther, 13, 11827, 10.2147/OTT.S249770 · DOI
  22. Liang, 2018, Cell Death Dis, 9, 372, 10.1038/s41419-018-0382-7 · DOI
  23. Chen, 2019, Biomed Pharmacother, 116, 108966, 10.1016/j.biopha.2019.108966 · DOI
  24. Wu, 2020, Biomed Pharmacother, 122, 109789, 10.1016/j.biopha.2019.109789 · DOI
  25. Liu, 2013, Oncol Lett, 6, 393, 10.3892/ol.2013.1380 · DOI
  26. Lange, 2013, J Hepatol, 59, 504, 10.1016/j.jhep.2013.04.032 · DOI
  27. Teng, 2016, Mol Ther, 24, 1806, 10.1038/mt.2016.103 · DOI
  28. Bartel, 2004, Cell, 116, 281, 10.1016/S0092-8674(04)00045-5 · DOI
  29. Tumilson, 2014, Mol Neurobiol, 50, 545, 10.1007/s12035-014-8679-8 · DOI
  30. Persengiev, 2012, Curr Gerontol Geriatr Res, 2012, 359369, 10.1155/2012/359369 · DOI
  31. Sun, 2018, Int J Clin Exp Pathol, 11, 1423
  32. Ye, 2015, Mol Med Rep, 12, 6247, 10.3892/mmr.2015.4175 · DOI
  33. Ye, 2014, Oncol Lett, 8, 2601, 10.3892/ol.2014.2606 · DOI
  34. Li, 2014, Nucleic Acids Res, 42, D92, 10.1093/nar/gkt1248 · DOI · Paper page
  35. Tang, 2017, Nucleic Acids Res, 45, W98, 10.1093/nar/gkx247 · DOI · Paper page
  36. Dimri, 1995, Proc Natl Acad Sci U S A, 92, 9363, 10.1073/pnas.92.20.9363 · DOI · Paper page
  37. Huo, 2019, Exp Ther Med, 17, 2921, 10.3892/etm.2019.7284 · DOI
  38. Van Meir, 2010, CA Cancer J Clin, 60, 166, 10.3322/caac.20069 · DOI · Paper page
  39. Prensner, 2011, Cancer Discov, 1, 391, 10.1158/2159-8290.CD-11-0209 · DOI
  40. Renganathan, 2017, Adv Exp Med Biol, 1008, 199, 10.1007/978-981-10-5203-3_7 · DOI
  41. Fu, 2020, Mol Ther Nucleic Acids, 21, 712, 10.1016/j.omtn.2020.07.005 · DOI
  42. Choudhari, 2020, Adv Clin Chem, 95, 105, 10.1016/bs.acc.2019.08.003 · DOI
  43. Peng, 2018, Mol Cancer, 17, 61, 10.1186/s12943-018-0812-2 · DOI
  44. Chen, 2020, Med Sci Monit, 26, e921383
  45. Qi, 2015, J Med Genet, 52, 710, 10.1136/jmedgenet-2015-103334 · DOI
  46. Ergun, 2015, Tumour Biol, 36, 3129, 10.1007/s13277-015-3346-x · DOI
  47. Tay, 2014, Nature, 505, 344, 10.1038/nature12986 · DOI · Paper page
  48. Wu, 2019, Discov Med, 28, 129
  49. Zhu, 2020, J Pediatr Surg, 10.1016/j.jpedsurg.2020.10.011 · DOI
  50. Zhao, 2019, Cancer Manag Res, 11, 10455, 10.2147/CMAR.S230324 · DOI
  51. Wang, 2019, Cancer Med, 8, 4389, 10.1002/cam4.2335 · DOI
  52. Liu, 2019, Onco Targets Ther, 12, 8207, 10.2147/OTT.S222703 · DOI
  53. Chen, 2019, J Pediatr Endocrinol Metab, 32, 1327, 10.1515/jpem-2018-0432 · DOI
  54. Ciafre, 2005, Biochem Biophys Res Commun, 334, 1351, 10.1016/j.bbrc.2005.07.030 · DOI · Paper page
  55. Godlewski, 2008, Cancer Res, 68, 9125, 10.1158/0008-5472.CAN-08-2629 · DOI · Paper page
  56. Zhang, 2009, J Mol Med, 87, 43, 10.1007/s00109-008-0403-6 · DOI · Paper page
  57. Ye, 2018, Eur Rev Med Pharmacol Sci, 22, 8781, 10.26355/eurrev_201812_16645 · DOI
  58. Shang, 2016, Oncol Lett, 11, 904, 10.3892/ol.2015.3927 · DOI
  59. She, 2014, Oncol Rep, 32, 957, 10.3892/or.2014.3318 · DOI
  60. Shan, 2016, Oncotarget, 7, 78813, 10.18632/oncotarget.12385 · DOI · Paper page
  61. Tu, 2013, Cancer Res, 73, 6046, 10.1158/0008-5472.CAN-13-0358 · DOI
  62. Peruzzi, 2013, Neuro Oncol, 15, 1212, 10.1093/neuonc/not055 · DOI · Paper page

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Metadata

Authors
Wang, C., Yu, G., Xu, Y., Liu, C., Sun, Q., Li, W., Sun, J., Jiang, Y., & Ye, L.
ORCID
Ye, Lan
Venue
Cancer Management and Research
Published
2021-05
Volume
Volume 13
Pages
3723-3737
ISSN
1179-1322
Cited by
13
References
1
Language
en

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